Historical transaction data was used to predict fraudulent credit card transactions, followed by calibration of predicted probabilities to better approximate real world values. The motivation is to use calibrated probabilities to make classification decisions that minimize the loss incurred by fraudulent purchases.
Analysis: The models used in analysis include Gaussian Naive Bayes (NB), feed forward Neural Networks (NN) and Random Forests (RF). NB was used as a baseline, while the other models were used in actual prediction and calibration. Performance was measured using precision, recall, F1, and precision-recall area under curve (PR-AUC).
Results and Conclusions: For the data used in this analysis, the best RF model shows a better calibrated reliability curve and achieves a higher PR-AUC value — 0.88 vs. 0.78 for the NN. The performance differences between the two models were small when evaluated using risk minimization.
Cost analysis shows that the the choice of model depends on the internal cost in dollars (Cin) of acting on potential fraud. Except for intermediate Cin values (appx. 1.5-5), the RF appears to be the better model, but the NN offers better performance within that range. Using these models, total loss can be reduced by as much as 83%, varying according to Cin.
Recommendations:
This report outlines the process of modeling historical transaction data (approximately 285,000 transactions) to predict credit card fraud, using Naive Bayes (NB), neural networks (NN), and random forest classifiers (RF). Modeling was followed by calibration of predicted probabilities, to better approximate real world values. The motivation behind the calibration is to make classification decisions that minimize the loss incurred by fraudulent purchases; in order to compute minimized risk correctly, it is necessary to have predicted probabilities that are as close as possible to real-world values.
The questions that motivated this study are:
The dataset used in this analysis was obtained from Kaggle datasets and is structured as follows:
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
| 1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | ... | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
| 2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | ... | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
| 3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | ... | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
| 4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | ... | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
Time is a generic integer value that conveys little more than the order in which the transactions occur. The anonymized features are mean-centered numerical floats, each denoted by Vi, where i is the feature number (from 1-28). Amount refers to the amount of the transaction in dollars. Class has two possible values, zero or one, where a value of one indicates fraud.
This step was nothing more than splitting the data into three sets: training, validation, and test, followed by standardizing all features in the training set as ~N(0, 1) — then transforming the validation and test sets accordingly. This split was the only method of cross validation used for this project.
The anonymized features were grouped by class, and individually plotted as scatter and kernel density estimates (see figures 1 and 2), such that each plot contained one feature with both classes for comparison. The motivation behind this was to gain insights for feature selection: features with some separation between class distributions are likely to perform better for classification, while those with little or no separation are likely to add noise to the models. The latter is less of a problem for NN and RF, but eliminating features can cut down on training time. Also, distributions that are highly irregular should typically be excluded from NB models.
Many features are approximately normal, though some look like Skellam distributions and some range from slightly to extremely irregular. Those that would likely be excluded from NB models due to irregularity have a lot of overlap between classes, and will already be removed when selecting by separation of distributions.
Based on the findings in the last section, only one method of feature selection was used: filter out those with too much overlap between classes. This was done by computing summary statistics for each feature by class, and selecting those where the medians of each class had a minimum separation of max(std class0, std class1). The features selected using this method were V[4, 11, 12, 14].
Because the data is extremely unbalanced (only a very small proportion of transactions are labeled as fraud), a few resampling methods were used to help offset this, and these methods were tested for each classifier. The methods used were random under sampling and generative over sampling.
For random under sampling, the set was separated by class, and a random sample of 50,000 was selected from the portion labeled as legitimate transactions. This sample was recombined with the fraudulent transactions to form a complete, resampled training set. Reducing the number of legitimate transactions to below 50,000 begins to cause large decreases in precision, and so smaller values were not used.
The generative oversampling selected only the fraudulent transactions from the original training set, and computed estimated distributions for all features. Fraudulent transactions were then generated by randomly sampling from these estimates, and class labels were added. This synthesized sample was recombined with the legitimate transactions. Four sets were generated using this method: 500, 1,000, 2,000, and 5,000 generated transactions.
For all models, including NB, performance was measured using precision, recall, and F1. For the final models, precision-recall area under curve (PR-AUC) was also measured. Accuracy was recorded, but because of the imbalance in the data, this value has no importance: predicting everything as being legitimate transactions will get only a fraction of one percent of the predictions wrong, giving a minimum accuracy greater than 99%.
To get a baseline for comparison against the NN and RF models, a Gaussian Naive Bayes classifier was chosen. Using both the raw data (all features) and only the features selected above, NB models were fitted to the original and resampled training sets, and scored on the validation set. The scores are tabulated in table 2 below.
| Accuracy | Precision | Recall | F1 | |
|---|---|---|---|---|
| raw | 0.996357 | 0.210526 | 0.410256 | 0.278260 |
| median | 0.998947 | 0.670455 | 0.756410 | 0.710843 |
| rand_under_50K | 0.998903 | 0.642857 | 0.807692 | 0.715909 |
| gen_over_500 | 0.998903 | 0.642857 | 0.807692 | 0.715909 |
| gen_over_1K | 0.998903 | 0.640000 | 0.820513 | 0.719101 |
| gen_over_5K | 0.998420 | 0.523810 | 0.846154 | 0.647058 |
The table above shows that the chosen feature selection method greatly improves performance over the raw data for the NB classifier, and the generative resampling method works well for smaller resampling sizes, but loses precision as the number of samples generated increases. The generative resampling with 1,000 additional samples was chosen as the resampling method to be tested with the NN and RF models.
To select the best models among NN and RF classifiers, the algorithms for each were run numerous times — with different hyper-parameters — on four training sets (raw, feature selected, and both of these resampled), and evaluated using the validation set. This was done with automated processes described in the next few paragraphs, where the details of modeling are discussed for each classifier.
The program used for constructing, training, and evaluating neural networks can be found on my GitHub. Three network architectures were tested on the raw set: one with no hidden layer, and two with a single hidden layer of 14 and 29 nodes. Two architectures on the feature-selected training set without resampling: one with no hidden layer, and one with a single hidden layer of 4 nodes. The output layer for each was a softmax layer with two nodes that output probabilities for each class.
These models were evaluated using an automated hyper-parameter (hypers) search algorithm built into the program, using a random search approach. This iteratively trained on 150 combinations of hypers for each model, at 40 epochs per combination. Results from printouts and plots showing epoch scores were used to choose final model architecture.
The two best performers used a single hidden layer with with 14 nodes (no feature selection), both resampled (gen 1K) and original sample. The resampled set gave slightly better recall scores on the validation set (0.778 vs. 0.741) at the expense of precision (0.764 vs 0.842), what appears to be an overall reduction in performance for the gen 1K set. However, the no-resample model achieved an approximate 0.03 bump in precision after what appeared to be some over-training. Because of this and a slight preference for higher recall, the final model used the gen 1K resampled data.
The final NN model used the concatenation of the resampled training set and validation set. Performance was tracked against this combined data for fine-tuning, and not the test data (see table 3); scoring predictions on the test set were saved until after tuning.
The scikit-learn random forest classifier was used for these models, using the standard Gini index. Several random forests were evaluated for each training set (no-resample, and gen 1K), using different numbers of estimators (forest size). The models were evaluated by computing PR_AUC. The best model resulted from the no-resmaple set, using 1000 estimators. The difference in scores between the two sets was marginal: a PR-AUC using 1,000 estimators of 0.70 vs. 0.69.
The final RF model was trained on the concatenated version of the original training and validation sets, and scored using precision, recall, and F1. See Results for details.
Because the predicted probabilities from different machine learning models are not always true to real-life values, something that is important to have for risk minimization, predicted values should be analyzed and likely calibrated.
Analysis of the probabilities had two parts: comparing the mean predicted value from each model to the proportion of positives in the test set, and visual inspection of reliability curves. The former is done simply by computing the mean of the predicted values from each model, while the latter is beyond the scope of this report. The comparisons can be found in table 3 and figures 4 and 5.
Predicted probabilities were calibrated using Platt’s scaling, which trains a logistic regression (LR) model on probabilities predicted from the training set. Test set predictions from the models of interest (NN and RF) were then used as inputs to the LR models, producing outputs as calibrated probabilities. Outputs from neural networks are typically well calibrated on their own, but this particular dataset poses some challenges in this regard (see Limitations). Mean predicted values (MPV) and reliability diagrams from before and after calibration are shown below. For the reliability diagrams, only 5 bins were used because of the limited number of positives.
| NN before | NN after | RF before | RF after | Truth | |
|---|---|---|---|---|---|
| MPV | 0.00344 | 0.00215 | 0.00185 | 0.00164 | 0.00165 |
The RF shows a notable improvement in the diagrams and MPV. The NN almost appears worse, but the large dip in the diagram is due to a lack of predicted probabilities in that range; the shape is otherwise better aligned with the diagonal, and there was a marked improvement in MPV.
While the trained classifiers can do a good job of correctly predicting fraudulent transactions, they are not perfect. Instead of making a decision based solely on the class with the highest predicted probability, risk minimization uses Bayesian decision making — involving conditional probabilities and the losses associated with possible decisions to minimize total loss. This can be described more completely and efficiently with a mathematical definition of risk and decision making:
$$R(\omega_i | x) = \sum_{j=0}^c\lambda(\omega_i | \omega_j)P(\omega_j | x)$$Where \(\lambda(\omega_i | \omega_j)\) is the loss incurred from deciding \(\omega_i\) when the true state is \(\omega_j\). More compacty, \(\lambda(\omega_i | \omega_j)\) will be referred to as \(\lambda_{ij}\).
Now, in the case of predicting fraud, we have two class conditional risk: $$R(\omega_0 | x) = \lambda_{00}P(\omega_0 | x) + \lambda_{01}P(\omega_1 | x)$$ $$R(\omega_1 | x) = \lambda_{10}P(\omega_0 | x) + \lambda_{11}P(\omega_1 | x)$$ Decision making: if \(R(\omega_0 | x) < R(\omega_1 | x)\) choose \(\omega_0\), else choose \(\omega_1\).
The conxtual definitions for \(\lambda_{ij}\) are:
\(\lambda_{00}\) = Zero ($): the cost of deciding the transaction is legitimated when it is.
\(\lambda_{01}\) = Amount of transaction ($): the cost of deciding a transaction is legitimate when it is not.
\(\lambda_{10}\) = Cin ($): the cost of deciding the transaction is fraudulent when it is not.
\(\lambda_{11}\) = Cin ($): the cost of deciding the transaction is fraudulent when it is.
The value Cin is some internal (administrative) cost associated with acting on the decision that a transaction is fraudulent. Because the internal cost is both unknown, and may not always be the same, the first goal of cost analysis was to compare the losses of the top two models as Cin varies, relative to the total loss if no fraud detection is used. The second goal was to evaluate the overall predictive performance of the two models under the same varying conditions, but without minimum risk decision making (i.e. making decisions based solely on model predictions).
This analysis was conducted in multiple parts:
In pure predictive performance, the RF outperformed the NN when using 1000 estimators. The test scores and are tabulated in table 4, and the PR curves with AUC can be seen in figure 5:
| Test scores | Precision | Recall | F1 |
|---|---|---|---|
| NN | 0.712 | 0.894 | 0.792 |
| RF | 0.933 | 0.894 | 0.913 |
While the two models can achieve the same level of recall, there is a large difference in precision, meaning the NN is generating many false-positives in order to achieve high recall. The performance difference is also reflected in the AUC values. The PR-AUC was unchanged after calibrating probabilities.
Two figures are presented for evaluation of the performance of the calibrated models: Figure 6 shows total loss in dollars (left y-axis), and the decision truth ratio (right y-axis) as functions of Cin. The decision/truth ratio is the number of transactions decided to be fraudulent, divided by the number of actual fraudulent transactions — this is a simplified way of knowing how the minimum risk is making decisions as Cin varies. Figure 7 shows a wider range for Cin and compares losses of classifications with and without using minimum risk (Bayesian decisions vs. purely prediction-based). Making decisions based on minimum risk result in smaller losses compared to decisions based purely on model predictions.
A comparison of losses incurred using risk minimization from calibrated vs. uncalibrated probabilities are presented in figures 8 and 9 (below). Making minimum risk decisions without probability calibrations resulted in greater losses and and many more false positives for the range of Cin plotted.
A fraud detection system that uses Bayesian minimum risk can lead to significant savings when compared to using model predictions alone, and it is important to have properly calibrated probabilities when doing so. Both the NN and RF models appear to have very similar loss for most Cin values when using this method, but the RF has slightly lower loss for much of the range. Regardless, choosing a model should be determined after knowing the precise value of Cin, as well as how many false negatives and false positives are acceptable.
The dataset was relatively small with very few instances of fraud, which means the performance of models could have high variance when predicting new data. The small number of positive instances also imposes some limitations on probability calibrations, possibly hindering the performance of risk minimization.


Consider the following function:
How many asterisks are printed by the method call
asterisksPrinter(i)?
(Either write a number, or write "infinite recursion".)
ANS
Note that there are two recursive calls.
Don't forget that the print statement will be exectued on the rollback for EACH of the recursive calls.


(Route : CIX Forums Menu, Upload File Command)
The Upload File dialog box allows you to upload a file for inclusion in a forum topic's file list.
This is a Hot Spot image. Move your mouse pointer over the image
and when the mouse pointer changes to the shape of a hand click
the left mouse button once for extra help.
Note that you can't directly add a file to a file list unless you are the moderator of the forum.
The Forum and Topic lists boxes will display the names of the current forum and topic.
In the File to upload text box enter the name of the path and filename of the file that you wish to upload. Click the Folder button if you want to locate the file.
You should now enter a short description of the file in the main body of the dialog box. This description will be used by the moderator of the forum when it is added to the file list. You can add a signature to the end of your description by choosing one from the Signature list.
When you click the Save button your file will be uploaded and a message sent to the moderator asking for the file to be added to the file list.
Note: This dialog box allows you upload a file to a CIX Forum and should not be confused with the Upload File command in the Mail Menu which will upload a file to your CIX Forums Mail Directory.


Problem 8. Trapezoid area
Write an expression that calculates trapezoid's area by given sides a and b and height h.
Please enter side a:
Please enter side b:
Please enter height:
Submit
Trapezoid area:


Police dog sniffs out suspects on the run in South Okanagan
by Steve ArstadTwo suspects fleeing police in the South Okanagan were nabbed, thanks in part, to the efforts of police service dog Haro.
RCMP officers from the Targeted Enforcement Unit in Penticton saw a woman they knew was prohibited from driving in the driver’s seat of a car with a stolen licence plate at around 11 a.m. Nov. 14, according to an RCMP media release.
A man in the passenger seat was also recognized as recently released from jail with a condition not to be in a motor vehicle. The two fled in the vehicle upon seeing police.
The vehicle was later located in Oliver where officers followed it from a distance as it travelled south to Osoyoos.
After taking a back road in an effort to elude police, the vehicle became stuck in the sand and the occupants fled the scene south along the shore of Osoyoos Lake.
Several officers attended the scene, along with police dog Haro. Following a lengthy search, the police dog and handler Cpl. Jason Goodfellow located the two, who were hiding in bushes.
Shane Pope, 29, of Penticton faces one count of breach while Connie Clark, 27, of Oliver has been charged with flight from police, dangerous driving and driving while prohibited.
“The great work and coordinated efforts of the South Okanagan regional detachments and Haro were paramount in this incident ending with the arrest and charges, and no injuries,” Osoyoos RCMP Sgt. Jason Bayda said the release.


Although both chrome os and mozilla os offer the ability to create native packaged applications (which behave like desktop applications ) using html , css and javascript it is not clear if they support all features of html 5. Applications made using mozilla os for example can not use webRTC at present. In chrome os however web rtc is supported.
So I wondered if it was possible to use html 5 web sockets on chrome apps. I could not find much documentation regarding my question so I decided to try it myself. I cloned the excellent github repository of samples and chose the simplest one (hello world) to test the functionality. Well it works. Here is my repo
To run a packaged app you need to go to tools-> extensions -> load unpacked extensions and navigate to where you have cloned the extension. It will then be available from the new tabs page in the extension categories.
There were a few interesting things I noticed while developing chrome apps.
My first experience with chrome apps has been quite positive. It is good to know that they support both Web sockets and web rtc. I wish that in the future it becomes possible to run these apps on mobiles. There is a large subset of applications that could benefit from it. I am planning to experiment with apps on mozilla os as well but there is still a lot to discover in chrome apps.


| Total net profit | -35.74 | Gross profit | 12.51 | Gross loss | -48.25 |
| Profit factor | 0.26 | Expected payoff | -0.67 | ||
| Absolute drawdown | -35.74 | Maximal drawdown (%) | 34.59 (-3007.83%) | ||
| Total trades | 53 | Profit trades (% of total) | 24 (45.28%) | Loss trades (% of total) | 29 (54.72%) |
| Long positions (won %) | 26 (57.69%) | Short positions (won %) | 27 (33.33%) | ||
| Largest | profit trade | 1.64 | loss trade | -4.32 | |
| Average | profit trade | 0.52 | loss trade | -1.66 | |
| Minimum | profit trade | 0.01 | loss trade | -0.11 | |
| Maximum | take profit in pips | 35.0 | stop loss in pips | -39.2 | |
| Average | take profit in pips | 10.2 | stop loss in pips | -24.8 | |
| Minimum | take profit in pips | -3.8 | stop loss in pips | -10.0 | |
| Maximum | Reward-Risk | 3.4 | Duration in minutes | 1404 | |
| Average | Reward-Risk | 0.5 | Duration in minutes | 206.3 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 5.0 (1.91) | consecutive losses (loss in money) | 12.0 (-19.6) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -9.99 | Gross profit | 6.23 | Gross loss | -16.22 |
| Profit factor | 0.38 | Expected payoff | -0.38 | ||
| Absolute drawdown | -10.62 | Maximal drawdown (%) | 11.9 (929.69%) | ||
| Total trades | 26 | Profit trades (% of total) | 15 (57.69%) | Loss trades (% of total) | 11 (42.31%) |
| Largest | profit trade | 1.0 | loss trade | -2.5 | |
| Average | profit trade | 0.42 | loss trade | -1.47 | |
| Minimum | profit trade | 0.01 | loss trade | -0.11 | |
| Maximum | take profit in pips | 20.2 | stop loss in pips | -39.2 | |
| Average | take profit in pips | 6.2 | stop loss in pips | -26.9 | |
| Minimum | take profit in pips | -3.8 | stop loss in pips | -10.7 | |
| Maximum | Reward-Risk | 1.3 | Duration in minutes | 599.0 | |
| Average | Reward-Risk | 0.3 | Duration in minutes | 146.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 5.0 (2.74) | consecutive losses (loss in money) | 5.0 (-7.23) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -25.75 | Gross profit | 6.28 | Gross loss | -32.03 |
| Profit factor | 0.2 | Expected payoff | -0.95 | ||
| Absolute drawdown | -25.75 | Maximal drawdown (%) | 24.34 (-1726.24%) | ||
| Total trades | 27 | Profit trades (% of total) | 9 (33.33%) | Loss trades (% of total) | 18 (66.67%) |
| Largest | profit trade | 1.64 | loss trade | -4.32 | |
| Average | profit trade | 0.7 | loss trade | -1.78 | |
| Minimum | profit trade | 0.02 | loss trade | -0.74 | |
| Maximum | take profit in pips | 35.0 | stop loss in pips | -37.2 | |
| Average | take profit in pips | 14.0 | stop loss in pips | -22.8 | |
| Minimum | take profit in pips | 0.2 | stop loss in pips | -10.0 | |
| Maximum | Reward-Risk | 3.4 | Duration in minutes | 1404.0 | |
| Average | Reward-Risk | 0.7 | Duration in minutes | 263.7 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 3.0 (1.44) | consecutive losses (loss in money) | 7.0 (-12.37) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |


Chapter 3 IM Data Specification
3.1 Introduction
The IM data is specified through the use indicators. Indicators consist of a numerator, denominator, and a type of count. The numerator and denominator are data elements, and the count specifies the applied operation. Counts can be a percentage, a number, or a ratio.
In this chapter, we are going to explore the IM indicators and how they are constructed in the Data Hub.
We’ll start by a short introduction to data elements and how they form the base/source of the IM indicators. We’ll then explore the different counts and wind up by showing you all the IM indicators.
3.2 Data Elements
Data elements form the base of IM indicators. As we mentioned in the previous chapter, they define how IM data are collected and stored in the IM Data Hub.
IM Data hub consists of: Global data elements - are shared across a number of countries, Country specific data elements - used by specific country data sets.
IM data elements are grouped into data elements groups by country and programs for easy access from the Analytics apps in DHIS2 (Pivot Table, Data Visualizer and Maps).
The data elements have the following key fields:
uid, name, shortName, and code
for uniquely identifying the data element in the IM Data Hub,description
to document more information about the data point,formName
that appears on the data entry form (in our case, data sets),domainType
that specifies the data model - aggregate or tracker (in our case, aggregate),aggregationType
defines how data is stored in the IM Data Hub.
3.3 Counts
Counts define the operation applied to the IM indicators calculations.
There are four main types of operations:
1 - Atomic (number)
- absolute numbers1 - Ratio
- a fraction of the numerator and denominator100 - percent
- a ratio multiplied by 100 percent1000 - per thousand
- a ratio multiplied by 1000
3.4 Indicators
Remember that an indicator has a numerator, a denominator and a count. In this section, you will notice that similar to data elements, indicators also have the uid, short name and code to uniquely identifying them in the IM Data Hub.
The IM PMP indicators are grouped into two types:
Global IM PMP indicators - computes PMP data for global access, Country Specific PMP indicators - computes PMP data for country access.
We present to you a list of all the IM indicators in section 3.4.1
3.4.1 Global IM PMP indicators
| code | name | shortName | numerator | denominator | count |
|---|---|---|---|---|---|
| DX indicators | |||||
| IM PMP DX 01 | IM PMP DX 01 - Percentage of confirmed malaria cases | IM PMP DX 01 - % of confirmed cases |
Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) id : #{UFGPsP6p9mm} + #{cZuXXzQuEL6} + #{EIkFrm5x8Sf} + #{gIFzQQKCMjb} |
Total number of suspected malaria cases who received a parasitological test id : #{N6j1f9LZiLf} + #{zsUZQV4CAd6} + #{BAZd7D3zQHN} + #{Rf40G3xMCqI} |
100 - Per Cent |
| IM PMP DX 01 NP | IM PMP DX 01 - Percentage of confirmed malaria cases (NP) | IM PMP DX 01 - % of confirmed cases (NP) |
Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) id : #{sZoMIJ8KFiw} |
Total number of suspected malaria cases who received a parasitological test id : #{uZWRcBbXVO5} |
100 - Per Cent |
| IM PMP DX 01 P | IM PMP DX 01 - Percentage of confirmed malaria cases (P) | IM PMP DX 01 - % of confirmed cases (P) |
Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) id : #{CrV5hkFVvdH} |
Total number of suspected malaria cases who received a parasitological test id : #{hoGKdACnBUs} |
100 - Per Cent |
| IM PMP DX 02 | IM PMP DX 02 - Percentage of patients with suspected malaria who received a parasitological test | IM PMP DX 02 - % of suspected cases tested |
Number of suspected malaria cases who received a parasitological test id : #{N6j1f9LZiLf} + #{BAZd7D3zQHN} + #{zsUZQV4CAd6} + #{Rf40G3xMCqI} |
Total number of suspected cases of malaria id : #{eHWYhy8vO8q} + #{euw633Z8zIo} + #{m2CdUYVjGe5} + #{LMMJ97M5sVl} |
100 - Per Cent |
| IM PMP DX 02 NP | IM PMP DX 02 - Percentage of patients with suspected malaria who received a parasitological test (NP) | IM PMP DX 02 - % of suspected cases tested (NP) |
Number of suspected malaria cases who received a parasitological test id : #{uZWRcBbXVO5} |
Total number of suspected cases of malaria id : #{DGZDsRtmqL7} |
100 - Per Cent |
| IM PMP DX 02 P | IM PMP DX 02- Percentage of patients with suspected malaria who received a parasitological test (P) | IM PMP DX 02 - % of suspected cases tested (P) |
Number of suspected malaria cases who received a parasitological test id : #{hoGKdACnBUs} |
Total number of suspected cases of malaria id : #{erW9vdRJVSE} |
100 - Per Cent |
| IM PMP DX 03 | IM PMP DX 03 - Percentage of health workers demonstrating competence in correctly classifying cases as not malaria, uncomplicated malaria, and severe malaria | IM PMP DX 03 - % of HWs able to classify cases |
Number of health workers who demonstrate correct procedures for correctly classifying cases according to global standards id : #{Grnm6jdzAJI} + #{t1vdNH0vIE2} + #{cVUt7HK83ag} + #{vXZ2RDwuyzA} |
Total number of health workers targeted during the reporting period id : #{SZKdCBFJd96} + #{blbz5pmEqoF} + #{pgsaevmKtbM} + #{ytbfyxZr1Ya} |
100 - Per Cent |
| IM PMP DX 04 | IM PMP DX 04 - Percentage of health workers demonstrating competence in malaria RDTs | IM PMP DX 04 - % of HWs competent in RDTs |
Number of health workers who score 90% or greater in preparation and reading of RDTs during the training post-test id : #{Mfkn1qCEQEL} + #{TDBa7IcPJAN} + #{eEWSWmY9ghg} + #{s5Ys2ABMGgE} |
Total number of health workers who completed a post-test during a training id : #{bWQoETmKYEh} + #{rSn9x2JQPJe} + #{soWdw75XP5d} + #{QniAsU4SnZr} |
100 - Per Cent |
| IM PMP DX 05 | IM PMP DX 05 - Percentage of health workers demonstrating competence in malaria microscopy | IM PMP DX 05 - % of HWs competent in microscopy |
Number of health workers who score 90% or greater in slide preparation and parasite detection during the training post-test id : #{mYlff5bXuZB} + #{x7CZVlQSaE6} + #{JygaBsHMOQ0} + #{jf8nKluaF2h} |
Total number of health workers who completed a post-test during a training id : #{f959MLmYSiX} + #{VdW9y4M8WAK} + #{XenaBO47ky4} + #{om6201JM4BF} |
100 - Per Cent |
| PMP DX 06 | IM PMP DX 06 - Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria | IM PMP DX 06 - % of facilities with quality DX |
Number of targeted facilities that meet 90% of greater on facility checklists for diagnosis during supervisory visits id : #{DTdMcXDeHFj} + #{Gj8mqX5twDU} + #{VQT4EOtPiwC} + #{uykAN5XlXKK} + #{TLuXiPIlTDu} + #{lYVEbBi1otu} |
Total number of targeted facilities who received a supervisory visit during the reporting period id : #{aRCKf2dnCpw} + #{oVIXBtQccHN} + #{teWZva5EkPM} |
100 - Per Cent |
| PMP DX 07 MC | IM PMP DX 07 - Percentage of targeted facilities with at least one provider trained in malaria diagnosis (microscopy) | IM PMP DX 07 - % of facilities trained (microsc) |
0 id : #{JJ9yeyBfn5V} + #{RjcTcWjtJEh} |
0 id : #{VX2UY9s7UTQ} |
100 - Per Cent |
| PMP DX 07 RDT | IM PMP DX 07 - Percentage of targeted facilities with at least one provider trained in malaria diagnosis (RDT) | IM PMP DX 07 - % of facilities trained on RDT |
Number of targeted facilities with one or more health workers trained in malaria diagnosis id : #{GFDsEzfZdjr} + #{N6oAUOjFBF0} + #{g1KI3UhtDoJ} |
Total number of targeted facilities id : #{VX2UY9s7UTQ} + #{YpbUdDBZjlA} + #{tWSq7bigbmf} |
100 - Per Cent |
| PMP DX 08 MC | IM PMP DX 08 - Percentage of targeted health workers trained in malaria laboratory diagnostics (microscopy) | IM PMP DX 08 - % of HWs trained on (microscopy) |
Number of health workers who complete the training course in malaria laboratory diagnostics (microscopy) id : #{f959MLmYSiX} + #{GCSv8Ni4KhE} + #{VdW9y4M8WAK} + #{XenaBO47ky4} |
Total number of targeted health workers id : #{opuW5lGx2Cc} + #{CX09R9Mdsj9} + #{VcPN46GIkwA} + #{a2RpS8B1eCM} |
100 - Per Cent |
| PMP DX 08 RDT | IM PMP DX 08 - Percentage of targeted health workers trained in malaria laboratory diagnostics (RDT) | IM PMP DX 08 - % of HWs trained on RDT |
Number of health workers who complete the training course in malaria laboratory diagnostics id : #{bWQoETmKYEh} + #{rSn9x2JQPJe} + #{QniAsU4SnZr} + #{soWdw75XP5d} |
Total number of targeted health workers id : #{opuW5lGx2Cc} + #{a2RpS8B1eCM} + #{CX09R9Mdsj9} + #{VcPN46GIkwA} |
100 - Per Cent |
| PMP DX 09 MC | IM PMP DX 09 - Percentage of targeted supervisors trained in supervision of malaria diagnostics (microscopy) | IM PMP DX 09 - % of SUPVs trained on microscopy |
Number of supervisors trained in supervision of malaria diagnostics id : #{RnEg1HPF7IR} + #{hbEgIBy1bIf} + #{zlWYhVAouup} + #{PgAauebV15e} + #{vg88HQNpSXz} |
Total number of targeted supervisors id : #{cGXuIzDaAEP} + #{MFpS8w5h9XS} + #{pVyN8ZZT7TA} + #{aZpDapygzg1} + #{O8ilhnRgkRW} |
100 - Per Cent |
| PMP DX 09 RDT | IM PMP DX 09 - Percentage of targeted supervisors trained in supervision of malaria diagnostics (RDT) | IM PMP DX 09 - % of SUPVs trained on RDT |
Number of supervisors trained in supervision of malaria diagnostics id : #{rvPl8m8kted} + #{sRz3zlhaJrH} + #{jZmH2n22eJf} + #{oFiMCwBOYLU} |
Total number of targeted supervisors id : #{cGXuIzDaAEP} + #{MFpS8w5h9XS} + #{pVyN8ZZT7TA} + #{aZpDapygzg1} |
100 - Per Cent |
| PMP DX 10 | IM PMP DX 10 - Percentage of targeted countries with national malaria diagnostic supervision tools that adhere to global standards | IM PMP DX 10 - % of countries with DX tools |
Number of targeted countries whose national malaria diagnostic supervision tools adhere to global standards id : #{y2aPasQa1gl} |
Total number of targeted countries id : #{y2aPasQa1gl} >= 0 |
100 - Per Cent |
| PMP DX 11 | IM PMP DX 11 - Percentage of targeted countries with national guidelines for malaria diagnosis that meet global standards | IM PMP DX 11 - % of countries with DX guidelines |
Number of targeted countries with national guidelines for malaria diagnosis that meet global standards/ id : #{MUKvaz7q2CD} |
Total number of targeted countries id : #{MUKvaz7q2CD} >= 0 |
100 - Per Cent |
| MIP indicators | |||||
| PMP MIP 25 | IM PMP MIP 25 - Percentage of pregnant women who received an ITN during routine ANC | IM PMP MIP 25 - % of pregnant women received ITN |
Number of pregnant women who received an insecticide-treated net (ITN) during routine antenatal care (ANC) id : #{p4bKkXlEOtC} + #{jmLXEBQDZAy} + #{mEuLOT08NfK} + #{kZAWqatScJ9} |
Total number of pregnant women attending antenatal visits (Number of first ANC visits as proxy in most countries’ RHIS) id : #{jNJNr4i82le} + #{IGyPotenyO2} + #{WdEGVnuWiDf} + #{KBe7hShEYAK} |
100 - Per Cent |
| PMP MIP 26 | IM PMP MIP 26 - Percentage of pregnant women who received three or more doses of IPTp | IM PMP MIP 26 - % of pregnant women with IPTp >=3 |
Number of pregnant women who received three or more doses of IPTp (IPT3) id : #{IGhCODoV6YY} + #{RS90YaqGgUN} + #{lVRMHcwVnDf} + #{CFaVHFhbS4v} |
Total number of pregnant women attending antenatal visits (Number of first ANC visits as proxy in most countries’ RHIS) id : #{jNJNr4i82le} + #{IGyPotenyO2} + #{WdEGVnuWiDf} + #{KBe7hShEYAK} |
100 - Per Cent |
| PMP MIP 27 | IM PMP MIP 27 - Percentage of pregnant women who received two doses of IPTp | IM PMP MIP 27 - % of pregnant women with IPTp = 2 |
Number of pregnant women who received two doses of IPTp (IPT2) id : #{Y73pLgWXfTh} + #{LF9PqTGXhBc} + #{inFlJdtBRGo} + #{SZgN28ZELh9} |
Total number of pregnant women attending antenatal visits (Number of first ANC visits as proxy in most countries’ RHIS) id : #{jNJNr4i82le} + #{IGyPotenyO2} + #{WdEGVnuWiDf} + #{SZgN28ZELh9} |
100 - Per Cent |
| PMP MIP 28 | IM PMP MIP 28 - Percentage of pregnant women who received one dose of IPTp | IM PMP MIP 28 - % of pregnant women with IPTp = 1 |
Number of pregnant women who received one dose of IPTp (IPT1) id : #{i5xgliv1YDl} + #{qk8A67ySRR0} + #{jrvQeHC4vrr} + #{uuWSsTIrVtU} |
Total number of pregnant women attending antenatal visits (Number of first ANC visits as proxy in most countries’ RHIS) id : #{jNJNr4i82le} + #{IGyPotenyO2} + #{WdEGVnuWiDf} + #{KBe7hShEYAK} |
100 - Per Cent |
| PMP MIP 29 | IM PMP MIP 29 - Percentage of targeted health workers demonstrating competence in treatment of MiP | IM PMP MIP 29 - % of HWs competent in MiP (TX) |
Number of health workers who score a pass mark on supervisory or quality improvement checklists measuring case management of MiP id : #{vKOVZ3Jckex} + #{AkJUjEErkX5} + #{RJVSWuXI87o} + #{S2sRLkbN81y} |
Total number of targeted health workers who received a supervisory visit during the reporting period id : #{t7LA2WZducS} + #{yOXzK6ElVJr} + #{gnjJPPJNSJF} + #{fJIele1NEHo} |
100 - Per Cent |
| PMP MIP 30 | IM PMP MIP 30 - Percentage of targeted health workers demonstrating competence in prevention of MiP | IM PMP MIP 30 - % of HWs competent in MiP (PREV) |
Number of health workers who score a pass mark on supervisory or quality improvement checklists measuring IPTp and counselling for MiP id : #{aOenVQmDtkc} |
Total number of targeted health workers who received a supervisory visit during the reporting period id : #{fpDelOtHjG4} |
100 - Per Cent |
| PMP MIP 31 | IM PMP MIP 31 - Percentage of health workers trained in IPTp | IM PMP MIP 31 - % of HWs trained on IPTp |
Number of health workers who complete the training course on IPTp id : #{yQ11Q5pQ2lx} + #{CxioZ7hV8e9} + #{eEITioVuAGg} + #{jKtX2TrReqh} |
Total number of targeted health workers id : #{yoNR9Ct37gT} + #{FVF9sYgBAH2} + #{wHuGUXeSGM8} + #{bf7z94n1SLP} |
100 - Per Cent |
| PMP MIP 32 | IM PMP MIP 32 - Percentage of targeted countries with national guidelines for prevention and treatment of MiP that meet global standards | IM PMP MIP 32 - % of countries with MiP guides |
Number of targeted countries with national guidelines for prevention and treatment of MiP that meet global standards id : #{C0UniRKgGAG} |
Total number of targeted countries id : #{C0UniRKgGAG}>=0 |
100 - Per Cent |
| PMP MIP 33 | IM PMP MIP 33 - Functional active RMNCH/MiP/ANC/community health Working Group | IM PMP MIP 33 - Active HWGs |
Functional/active malaria/MiP/ANC/community health or RMNCH Working Group with a MiP lens that meets periodically id : #{wHtxPnCGqBV} |
Functional/active malaria/MiP/ANC/community health or RMNCH Working Group with a MiP lens that meets periodically id : #{wHtxPnCGqBV}>=0 |
1 - Atomic (number) |
| SMC indicators | |||||
| PMP SMC 34 | IM PMP SMC 34 - Percentage of targeted children who receive all 4 doses of SMC in a round in intervention area (or all 3 per national guidance where only 3 doses are indicated) | IM PMP SMC 34 - % of children received 4 doses |
Number of targeted children receiving all recommended SMC doses during a campaign, in the intervention area id : #{ioU1w8yCIWv} |
Number of children in target age range in the intervention area id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| PMP SMC 35 | IM PMP SMC 35 - Percentage of targeted children who receive a dose of SMC in intervention area | IM PMP SMC 35 - % of children dosed |
Number of targeted children receiving a dose of SMC in the intervention area id : #{cOBjDRbvsyW} |
Number of children in target age range in the intervention area id : #{wr0GInmvDcB} |
100 - Per Cent |
| PMP SMC 36 | IM PMP SMC 36 - Percentage of targeted children who receive a dose of SMC in the first cycle | IM PMP SMC 36 - % of children dosed (cy. 1) |
Number of targeted children who received a dose of SMC during the first cycle id : #{HaSIpoMB2ub} |
Number of children in target age range in the intervention area id : #{pW2ZZhtd9Lz} |
100 - Per Cent |
| PMP SMC 37 | IM PMP SMC 37 - Percentage of targeted children who receive a dose of SMC in the second cycle | IM PMP SMC 37 - % of children dosed (cy. 2) |
Number of targeted children who received a dose of SMC during the second cycle id : #{wot02J4aRZm} |
Number of children in target age range in the intervention area id : #{AiA5kpzCOVw} |
100 - Per Cent |
| PMP SMC 38 | IM PMP SMC 38 - Percentage of targeted children who receive a dose of SMC in the third cycle | IM PMP SMC 38 - % of children dosed (cy. 3) |
Number of targeted children who received a dose of SMC during the third cycle id : #{ZCibgANrNQq} |
Number of children in target age range in the intervention area id : #{Q0hsAMcsvkm} |
100 - Per Cent |
| PMP SMC 39 | IM PMP SMC 39 - Percentage of targeted children who receive a dose of SMC in the fourth cycle | IM PMP SMC 39 - % of children dosed (cy. 4) |
Number of targeted children who received a dose of SMC during the fourth cycle id : #{Q71hAveCSGo} |
Number of children in target age range in the intervention area id : #{InNkmkUawo7} |
100 - Per Cent |
| PMP SMC 40 | IM PMP SMC 40 - Percentage of health workers trained to deliver SMC according to national guidelines | IM PMP SMC 40 - % of HWs trained on SMC |
Number of health workers who complete the training course on delivering SMC id : #{y6qGAzE5DRa} |
Total number of targeted health workers id : #{nE14FEYzkVX} |
100 - Per Cent |
| PMP SMC 41 | IM PMP SMC 41 - Percentage of targeted countries with annual SMC implementation plans | IM PMP SMC 41 - % of countries with ANN SMC plans |
Number of countries with annual operational SMC implementation plans id : #{hBYYaPLEFFf} |
Total number of targeted countries anticipated to have plans id : #{gN3w6Jx7q5h} |
100 - Per Cent |
| TL indicators | |||||
| PMP TL 42 | IM PMP TL 42 - Contribution to national, regional or global guidance/policy documents related to malaria (including RH) | IM PMP TL 42 - Contribution |
Contribution to national, regional guidance/policy documents or related to malaria (including RH) id : #{tJXmW64MUxf} |
Contribution to national, regional guidance/policy documents or related to malaria (including RH) id : 1 |
1 - Atomic (number) |
| PMP TL 43 | IM PMP TL 43 - Number of program activity outputs disseminated to the global health community | IM PMP TL 43 - Outputs to GH community |
By type of output (activity reports, operational research/evaluation reports, technical briefs, learning briefs, synthesis brief, discussion brief, infographic, case studies, peer-reviewed publications, webinars, oral/poster presentations, blog posts, short video clips, photos) By dissemination channel id : #{P4YDP2FNh3X} |
By type of output (activity reports, operational research/evaluation reports, technical briefs, learning briefs, synthesis brief, discussion brief, infographic, case studies, peer-reviewed publications, webinars, oral/poster presentations, blog posts, short video clips, photos) By dissemination channel id : 1 |
1 - Atomic (number) |
| PMP TL 44 | IM PMP TL 44 - Participation in targeted national, regional or global level Working group(s) and/or taskforce(s) | IM PMP TL 44 - Participation in TWGs. |
Participation in national, regional or global working group(s) or taskforce(s) related to malaria (including RH) id : #{XxfoDreiw9Z} |
Participation in national, regional or global working group(s) or taskforce(s) related to malaria (including RH) id : 1 |
1 - Atomic (number) |
| TX indicators | |||||
| PMP TX 12 | IM PMP TX 12 - Percentage of children under 5 appropriately treated for fever according to iCCM or country algorithms by community health workers | IM PMP TX 12 - % of CU5 fever treated |
Number of children under 5 correctly treated for malaria fever by community health worker id : #{sYGiQi29bny} + #{sHbqsn1y9zL} |
Number of CU5 presented to CHWs with fever id : #{jQCXu21pwiA} + #{pGLrWpOTB1m} |
100 - Per Cent |
| PMP TX 13 | IM PMP TX 13 - Percentage of confirmed severe malaria cases that were appropriately managed according to national guidelines | IM PMP TX 13 - % of confirmed severe cases |
Number of confirmed severe malaria cases that were appropriately managed according to national guidelines id : #{WY24SnaT4l7} |
Number of severe malaria cases reviewed id : #{WY24SnaT4l7} |
100 - Per Cent |
| PMP TX 13 NP | IM PMP TX 13 - Percentage of confirmed severe malaria cases that were appropriately managed according to national guidelines (NP) | IM PMP TX 13 - % of confirmed severe cases (NP) |
Number of confirmed severe malaria cases that were appropriately managed according to national guidelines (NP) id : #{W6NdGzWJurN} |
Number of severe malaria cases reviewed (NP) id : #{W6NdGzWJurN} |
100 - Per Cent |
| PMP TX 13 P | IM PMP TX 13 - Percentage of confirmed severe malaria cases that were appropriately managed according to national guidelines (P) | IM PMP TX 13 - % of confirmed severe cases (P) |
Number of confirmed severe malaria cases that were appropriately managed according to national guidelines (P) id : #{h5toMEwrcCi} |
Number of severe malaria cases reviewed (P) id : #{h5toMEwrcCi} |
100 - Per Cent |
| PMP TX 14 | IM PMP TX 14 - Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines | IM PMP TX 14 - % of uncomplicated cases |
Number of reported uncomplicated malaria cases that receive the appropriate first-line antimalarial treatment id : #{Aj2LczZnc35} + #{JLxVyp3qGve} + #{V6CvG07Copy} + #{i4BsFSVy41V} |
Total number of reported malaria cases during reporting period id : #{UFGPsP6p9mm} + #{PoyoVEewRIq} + #{EIkFrm5x8Sf} + #{cZuXXzQuEL6} + #{gIFzQQKCMjb} + #{y8geJquiy1f} + #{E04lKfKFlKc} |
100 - Per Cent |
| PMP TX 14 NP | IM PMP TX 14 - Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines (NP) | IM PMP TX 14 - % of uncomplicated cases (NP) |
Number of reported uncomplicated malaria cases that receive the appropriate first-line antimalarial treatment (NP) id : #{RYl937S07k0} + #{n9YAgSwzX8J} + #{Jz05wiLIOae} |
Total number of reported malaria cases during reporting period id : #{sZoMIJ8KFiw}+#{RnZK9U6WoAm}+ #{WuWNNgy0A1Z} + #{rKatBh8TmW2} + #{KcMPvVefT1h} |
100 - Per Cent |
| PMP TX 14 P | IM PMP TX 14 - Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines (P) | IM PMP TX 14 - % of uncomplicated cases (P) |
Number of reported uncomplicated malaria cases that receive the appropriate first-line antimalarial treatment id : #{Ldvf3MSggqO} + #{KZHiYuvgkMm} + #{PfB1cg2ktB9} |
Total number of reported malaria cases during reporting period id : #{CrV5hkFVvdH} + #{qXQ0d9NKQtI} + #{AmUi4XKbJ6X} + #{HiIx2iQ8Wxy} + #{YMiDfYBOpWk} + #{RfziOIqzJCP} |
100 - Per Cent |
| PMP TX 14 P (CL) | IM PMP TX 14 - Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines (P) (CL) | IM PMP TX 14 - % of uncomplicated cases (P) (CL) |
Number of reported uncomplicated malaria cases that receive the appropriate first-line antimalarial treatment id : #{Ldvf3MSggqO} |
Total number of reported malaria cases during reporting period id : #{CrV5hkFVvdH} + #{qXQ0d9NKQtI} |
100 - Per Cent |
| PMP TX 15 | IM PMP TX 15 - Percentage of targeted health workers demonstrating competence in management of severe malaria | IM PMP TX 15 - % of HWs competent in severe cases |
Number of health workers who score a pass mark on supervisory or quality improvement checklists measuring the diagnosis and management of severe malaria id : #{QYPXt95CyCY} + #{es8NwgtSAsX} + #{lomzmDp50ZG} + #{Ct1pZ0T1x2R} |
Health workers assessed on diagnosis and management of severe malaria id : #{W4wiC7ioFKS} + #{b3GBk3DB8ou} + #{g0OQgJCDs0U} + #{WNhkeZnc1XT} |
100 - Per Cent |
| PMP TX 16 | IM PMP TX 16 - Percentage of targeted health workers demonstrating competence in management of uncomplicated malaria | IM PMP TX 16 - % of HWs competent (uncomp cases) |
Number of health workers who score a pass mark on supervisory or quality improvement checklists measuring the diagnosis and treatment of uncomplicated malaria id : #{gmsvdmKMmP1} + #{l4uAwIXVrPp} + #{HsD3L7BZa5T} + #{umfGv9oznIa} |
Health workers assessed on diagnosis and management of uncomplicated malaria id : #{HcmlwDCmBSZ} + #{uNnp6IHODyP} + #{GtohCrGevVV} + #{Wq1oBD43sCc} |
100 - Per Cent |
| PMP TX 17 | IM PMP TX 17 - Percentage of targeted health workers demonstrating compliance to treatment with WHO guidelines for cases with positive malaria test results | IM PMP TX 17 - % of HWs adhered (+ve cases) |
Number of health workers who comply to treatment with a WHO-recommended antimalarial for cases with positive malaria test results during clinical assessment visits measured through direct observation during supervision visits id : #{Do9KBfS22qD} + #{eJSUmK8XCOw} + #{wVlGTvl4xvH} + #{E77z4UQBHJE} |
Health workers assessed on management of positive malaria cases id : #{Bli6iNQMtP5} + #{w1gYh10blt1} + #{ibu7rlMvF7s} + #{YCsROGVIXGz} |
100 - Per Cent |
| PMP TX 18 | IM PMP TX 18 - Percentage of health workers demonstrating adherence to negative test results according to global standards | IM PMP TX 18 - % of HWs adhered (-ve cases) |
Number of health workers demonstrating adherence to negative test results according to global standards measured through direct observation through supervision visits id : #{jhcbf1Yfhr5} + #{yChCRfBYBx2} + #{u6hbx56NqZQ} + #{Y0IgOx4kim8} |
Health workers assessed on management of negative malaria cases id : #{vskYxCrS9Hk} + #{FORs5r9LYU3} + #{oV5ypvYLOjj} + #{WvrndytTbHq} |
100 - Per Cent |
| PMP TX 19 | IM PMP TX 19 - Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality malaria case management | IM PMP TX 19 - % of facilities with CM standards |
Number of targeted facilities that meet 90% or greater on facility checklists for quality case management during supervisory visits id : #{Bjg3UQZTo7O} + #{vsI12KXd1od} |
Total number of targeted facilities who received a supervisory visit during the reporting period id : #{TpcOpmZBdLf} + #{XY77QRYfFiA} |
100 - Per Cent |
| PMP TX 20 | IM PMP TX 20 - Percentage of targeted health facilities regularly reporting routine malaria case data | IM PMP TX 20 - % of facilities reporting cases |
Number of health facilities reporting monthly routine malaria case load data on diagnosis and treatment within an agreed timescale at least 2 months in the past 3 months id : #{xV85TQT3GEj} |
Total number of target health facilities scheduled to report monthly routine malaria case load data during the reporting period id : #{pDisUBYkiUY} |
100 - Per Cent |
| PMP TX 21 | IM PMP TX 21 - Percentage of targeted health facilities that receive a supervisory visit | IM PMP TX 21 - % of facilities with SUPV visits |
Number of health facilities that receive a supervisory visit that covers malaria case management and/or malaria in pregnancy (MiP) id : #{TpcOpmZBdLf} |
Total number of health facilities targeted for supervision during the reporting period id : #{RncgLNY0xwy} |
100 - Per Cent |
| PMP TX 22 | IM PMP TX 22 - Percentage of health workers trained in management of severe malaria | IM PMP TX 22 - % of HWs trained on severe malaria |
Number of health workers who complete the training course on severe malaria case management id : #{Ly61ha78oUu} |
Total number of health workers targeted id : #{DoyuoVEARPV} |
100 - Per Cent |
| PMP TX 23 | IM PMP TX 23 - Percentage of targeted health workers trained according to national guidelines in malaria case management with ACTs | IM PMP TX 23 - % of HWs trained on ACTs |
Number of health workers who complete the national training course on malaria case management with ACTs id : #{wfxNAGkIedf} |
Total number of health workers targeted id : #{cqKHdgtfjID} |
100 - Per Cent |
| PMP TX 24 | IM PMP TX 24 - Percentage of targeted countries with national guidelines for malaria treatment that meet global standards | IM PMP TX 24 - % of countries with TX guidelines |
Number of targeted countries with national guidelines for malaria treatment that meet global standards id : #{YVLRuSGpePK} |
Total number of targeted countries id : #{YVLRuSGpePK}>0 |
100 - Per Cent |
| NA | IM PMP Tx 2 - Percentage of severe malaria cases appropriately managed | % of severe malaria cases appropriately managed |
Num id : #{km1lPVcORaY} |
Den id : #{W6NdGzWJurN} |
100 - Per Cent |
| NA | IM PMP Tx 3 - Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines | % of uncomplicated malaria cases managed |
Num id : #{RYl937S07k0} |
Den id : #{oWhqaiv26dB} |
100 - Per Cent |
3.4.2 Country IM PMP indicators
<IP_ADDRESS> Kenya
| code | name | shortName | numerator | denominator | count |
|---|---|---|---|---|---|
| DX indicators | |||||
| NA | KE PMP 01 - DX Percentage of patients with suspected malaria who received a parasitological test | KE PMP 01 - DX % tested suspected cases |
Num id : #{Rf40G3xMCqI} |
Den id : #{m2CdUYVjGe5} |
100 - Per Cent |
| NA | KE PMP 02 - DX Percentage of health workers demonstrating competence in malaria RDTs | KE PMP 02 - DX % HWs malaria RDTs |
Num id : #{s5Ys2ABMGgE} |
Den id : #{soWdw75XP5d} |
100 - Per Cent |
| NA | KE PMP 03 - DX Percentage of HCWs demonstrating competence in malaria microscopy | KE PMP 03 - DX % HWs malaria microscopy |
Num id : #{x7CZVlQSaE6} |
Den id : #{VdW9y4M8WAK} |
100 - Per Cent |
| NA | KE PMP 04 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (microscopy) | KE PMP 04 - DX % HFs malaria microscopy |
Num id : #{uykAN5XlXKK} |
Den id : #{HDK9LRt5rDv} |
100 - Per Cent |
| NA | KE PMP 04 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (RDT) | KE PMP 04 - DX % HFs malaria RDTs |
Num id : #{TLuXiPIlTDu} |
Den id : #{HDK9LRt5rDv} |
100 - Per Cent |
| NA | KE PMP 05 - DX Percentage of HCWs demonstrating competence or knowledgeable in correctly classifying uncomplicated and severe malaria | KE PMP 05 - DX % HWs classify cases correctly |
Num id : #{cVUt7HK83ag} |
Den id : #{blbz5pmEqoF} |
100 - Per Cent |
| NA | KE PMP 06 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (microscopy) | KE PMP 06 - DX % supervisors trained (microscopy) |
Num id : #{PgAauebV15e} |
Den id : #{pVyN8ZZT7TA} |
100 - Per Cent |
| NA | KE PMP 06 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (RDT) | KE PMP 06 - DX % supervisors trained (RDT) |
Num id : #{jZmH2n22eJf} |
Den id : #{pVyN8ZZT7TA} |
100 - Per Cent |
| NA | KE PMP 07 - DX Percentage of targeted counties with recommended malaria diagnostic supervision tools | KE PMP 07 - DX % counties w/malaria DX tools |
Num id : #{zTF23dP6a65} |
Den id : R{dfroFhEbqHJ.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | KE PMP 08 - DX DX Percentage of targeted health workers trained in malaria diagnostics (RDT) | KE PMP 08 - DX % HWs trained (RDT) |
Num id : #{soWdw75XP5d} |
Den id : #{a2RpS8B1eCM} |
100 - Per Cent |
| NA | KE PMP 08 - DX Percentage of targeted health workers trained in malaria diagnostics (microscopy) | KE PMP 08 - DX % HWs trained (microscopy) |
Num id : #{VdW9y4M8WAK} |
Den id : #{a2RpS8B1eCM} |
100 - Per Cent |
| NA | KE PMP 09 - DX Percentage of targeted facilities with at least one provider trained in RDT | KE PMP 09 - DX % HFs with 1 provider trained (RDT) |
Num id : #{g1KI3UhtDoJ} |
Den id : #{mZJOvYsr84U} |
100 - Per Cent |
| NA | KE PMP 10 - DX Percentage of targeted facilities with at least one provider trained in microscopy | KE PMP 10 - DX % HFs with 1 provider trained (mic) |
Num id : #{RjcTcWjtJEh} |
Den id : #{azuQmfnMNVU} |
100 - Per Cent |
| NA | KE PMP 11 - DX Percentage of targeted facilities with recommended guidelines/SOPs for malaria diagnosis | KE PMP 11 - DX % HFs with SOPs |
Num id : #{sjDRGLS9yjA} |
Den id : #{WrYkhf4k7pL} |
100 - Per Cent |
| TX indicators | |||||
| NA | KE PMP 12 - TX Percentage of suspected cases confirmed +ve for malaria | KE PMP 12 - TX % confirmed malaria cases |
Num id : #{gIFzQQKCMjb} |
Den id : #{Rf40G3xMCqI} |
100 - Per Cent |
| NA | KE PMP 13 - TX Percentage of confirmed uncomplicated malaria cases that received first-line antimalarial treatment according to recommended national guidelines | KE PMP 13 - TX % cases correctly treated |
Num id : #{V6CvG07Copy} |
Den id : #{gIFzQQKCMjb} |
100 - Per Cent |
| NA | KE PMP 14 - TX Percentage of laboratory confirmed severe malaria cases treated according to recommended guidelines (the administration of a recommended antimalarial) | KE PMP 14 - TX % severe cases correctly treated |
Num id : #{dumrnf9ppLQ} |
Den id : #{T32qa5ftQkW} |
100 - Per Cent |
| NA | KE PMP 15 - TX Percentage of targeted HCWs demonstrating compliance to treatment according to national guidelines for cases with positive malaria test results | KE PMP 15 - TX % HWs compliance positive cases |
Num id : #{E77z4UQBHJE} |
Den id : #{YCsROGVIXGz} |
100 - Per Cent |
| NA | KE PMP 16 - TX Percentage of HCWs demonstrating adherence to negative test results according to recommended guidelines | KE PMP 16 - TX % HWs compliance negative cases |
Num id : #{Y0IgOx4kim8} |
Den id : #{oV5ypvYLOjj} |
100 - Per Cent |
| NA | KE PMP 17 - TX Percentage of targeted HCWs demonstrating competence in management of severe malaria according to national guidelines | KE PMP 17 - TX % HWs competent severe cases |
Num id : #{Ct1pZ0T1x2R} |
Den id : #{WNhkeZnc1XT} |
100 - Per Cent |
| NA | KE PMP 18 - TX Percentage of targeted HCWs demonstrating competence in management of uncomplicated malaria according to national guidelines | KE PMP 18 - TX % HWs competent unc malaria |
Num id : #{umfGv9oznIa} |
Den id : #{GtohCrGevVV} |
100 - Per Cent |
| NA | KE PMP 19 - TX Percentage of targeted facilities that meet global standards (including appropriate materials, documentation, and qualified staff) for quality malaria case management | KE PMP 19 - TX % HFs compliant quality case mngt |
Num id : #{Sigw3MWtZ5d} |
Den id : #{YZCHWOIahjV} |
100 - Per Cent |
| NA | KE PMP 20 - TX Percentage of admitting facilities with HCWs trained in management of severe malaria | KE PMP 20 - TX % HFs provider trained (severe) |
Num id : #{JVOBcq2tayS} |
Den id : #{A20vEAmjRAS} |
100 - Per Cent |
| NA | KE PMP 21 - TX Percentage of health workers trained on malaria case management | KE PMP 21 - TX % HWs trained case mngt |
Num id : #{Y2Wizwyg2Zt} |
Den id : #{HKwLM21EySC} |
100 - Per Cent |
| NA | KE PMP 22 - TX Percentage of targeted health facilities that receive a supervisory visit | KE PMP 22 - TX % HFs supervised |
Num id : #{egcmJ7NFPBG} |
Den id : #{WRP6ICberkf} |
100 - Per Cent |
| NA | KE PMP 23 - TX Percentage of targeted health facilities regularly reporting routine malaria case data | KE PMP 23 - TX % HFs reporting on time |
Num id : #{A6is0WZ7UVI}+#{WUsfoJ01oa8} |
Den id : #{k0OSOd7UeDj}+#{Jz08Pq3eoMi} |
100 - Per Cent |
| MIP indicators | |||||
| NA | KE PMP 24 - MIP Percentage of pregnant women receiving a free ITN during first ANC visit | KE PMP 24 - MIP % pregnant women received ITN |
Num id : #{mEuLOT08NfK} |
Den id : #{WdEGVnuWiDf} |
100 - Per Cent |
| NA | KE PMP 25 - MIP Percentage of pregnant women who received three doses of IPTp according to recommended guidelines | KE PMP 25 - MIP % pregnant women IPT3 |
Num id : #{lVRMHcwVnDf} |
Den id : #{WdEGVnuWiDf} |
100 - Per Cent |
| NA | KE PMP 26 - MIP Percentage of pregnant women who received two doses of IPTp | KE PMP 26 - MIP % pregnant women IPT2 |
Num id : #{inFlJdtBRGo} |
Den id : #{WdEGVnuWiDf} |
100 - Per Cent |
| NA | KE PMP 27 - MIP Percentage of pregnant women who received one dose of IPTp | KE PMP 27 - MIP % pregnant women IPT1 |
Num id : #{jrvQeHC4vrr} |
Den id : #{WdEGVnuWiDf} |
100 - Per Cent |
| NA | KE PMP 28 - MIP Percentage of targeted HCWs demonstrating competence in prevention of MiP | KE PMP 28 - MIP % HWs competent MIP prevention |
Num id : #{ORROqdVXqV5} |
Den id : #{idmDOiFKv1Y} |
100 - Per Cent |
| NA | KE PMP 29 - MIP Percentage of targeted HCWs demonstrating competence in treatment of MiP | KE PMP 29 - MIP % HWs competent MIP treatment |
Num id : #{RJVSWuXI87o} |
Den id : #{fJIele1NEHo} |
100 - Per Cent |
| NA | KE PMP 30 - MIP Percentage of HCWs trained in MIP as per recommended guidelines | KE PMP 30 - MIP % HWs trained MIP |
Num id : #{jKtX2TrReqh} |
Den id : #{bf7z94n1SLP} |
100 - Per Cent |
| NA | KE PMP 31 - MIP Percentage of targeted facilities with recommended guidelines for prevention and treatment of MIP | KE PMP 31 - MIP % HFs guidelines prev and treat |
Num id : #{BTa5Qz98z3e} |
Den id : #{BdgvH1NHCpk} |
100 - Per Cent |
| NA | KE PMP 32 - MIP Functional/active MIP TWGs | KE PMP 32 - MIP Functional/active MIP TWGs |
Num id : #{iOf4wYGTQnz} |
Den id : #{Ou1JytOIUDS} |
100 - Per Cent |
| NA | KE PMP 33 - MIP Functional/active CM TWGs | KE PMP 33 - MIP Functional/active CM TWGs |
Num id : #{ISrVXrVhGWy} |
Den id : #{LELIkDnSPSg} |
100 - Per Cent |
| TL indicators | |||||
| NA | KE PMP 34 - TL Contribution to national, regional or global guidance/policy documents related to malaria (including RH) | KE PMP 34 - TL Contribution to national policies |
Num id : #{tJXmW64MUxf} |
Den id : 1 |
1 - Atomic (number) |
| NA | KE PMP 35 - TL Number of program activity outputs disseminated to the global health community | KE PMP 35 - TL Program outputs disseminated |
Num id : #{P4YDP2FNh3X} |
Den id : 1 |
1 - Atomic (number) |
| NA | KE PMP 36 - TL Participation in targeted national or regional level Working group(s) and/or taskforce(s) | KE PMP 36 - TL Participation in WGs |
Num id : #{XxfoDreiw9Z} |
Den id : 1 |
1 - Atomic (number) |
| NA | KE PMP 37 - TL Contribution to regional or global guidance/policy documents to related to malaria | KE PMP 37 - TL Contribution to guidance/policy |
Num id : #{OneitAtRzhO} |
Den id : 1 |
1 - Atomic (number) |
| NA | KE PMP 38 - TL Percentage of facilities with updated data wall charts showcasing malaria indicators | KE PMP 38 - TL % HFs data-wall charts on malaria |
Num id : #{cZbiIH1nPdE} |
Den id : #{qO8BdhE2NPm} |
100 - Per Cent |
| NA | KE PMP 39 - TL Percentage of IM focus counties conducting quarterly data-led review meetings | KE PMP 39 - TL % counties data-led review meetings |
Num id : #{EnT6cC7Kpzx} |
Den id : R{dfroFhEbqHJ.ACTUAL_REPORTS} |
100 - Per Cent |
<IP_ADDRESS> Ghana
| code | name | shortName | numerator | denominator | count |
|---|---|---|---|---|---|
| DX indicators | |||||
| NA | GH PMP 01 - DX Percentage of confirmed malaria cases | GH PMP 01 - DX % confirmed malaria cases |
Num id : #{cZuXXzQuEL6} |
Den id : #{zsUZQV4CAd6} |
100 - Per Cent |
| NA | GH PMP 01 - DX Percentage of confirmed malaria cases by test type | GH PMP 01 - DX % confirmed malaria cases by test |
Num id : #{C6fu4gyFJyt} |
Den id : #{PwOl1Z017wD} |
100 - Per Cent |
| NA | GH PMP 01 - DX Percentage of confirmed malaria cases (NP) | GH PMP 01 - DX % confirmed malaria cases (NP) |
Num id : #{rKatBh8TmW2} |
Den id : #{CpFbufOJ1rF} |
100 - Per Cent |
| NA | GH PMP 01 - DX Percentage of confirmed malaria cases (P) | GH PMP 01 - DX % confirmed malaria cases (P) |
Num id : #{YMiDfYBOpWk} |
Den id : #{vAjxUR9K0mp} |
100 - Per Cent |
| NA | GH PMP 02 - DX Percentage of patients with suspected malaria who received a parasitological test | GH PMP 02 - DX % suspected cases tested |
Num id : #{zsUZQV4CAd6} |
Den id : #{euw633Z8zIo} |
100 - Per Cent |
| NA | GH PMP 02 - DX Percentage of patients with suspected malaria who received a parasitological test by microscopy | GH PMP 02 - DX % suspected cases tested (micr) |
Num id : #{PwOl1Z017wD.LpJA6F7X60g} |
Den id : #{euw633Z8zIo} |
100 - Per Cent |
| NA | GH PMP 02 - DX Percentage of patients with suspected malaria who received a parasitological test by RDT | GH PMP 02 - DX % suspected cases tested (RDT) |
Num id : #{PwOl1Z017wD.QOpXmMLmbT4} |
Den id : #{euw633Z8zIo} |
100 - Per Cent |
| NA | GH PMP 02 - DX Percentage of patients with suspected malaria who received a parasitological test (NP) | GH PMP 02 - DX % suspected cases tested (NP) |
Num id : #{CpFbufOJ1rF} |
Den id : #{kpeH1yY1HA3} |
100 - Per Cent |
| NA | GH PMP 02 - DX Percentage of patients with suspected malaria who received a parasitological test (P) | GH PMP 02 - DX % suspected cases tested (P) |
Num id : #{vAjxUR9K0mp} |
Den id : #{dmDdKI5KYVQ} |
100 - Per Cent |
| NA | GH PMP 03 - DX Percentage of health workers demonstrating competence in malaria microscopy (parasite detection) | GH PMP 03 - DX % HWs parasite detection |
Num id : #{KGdsCrdl9ba} |
Den id : #{ubSPJnm2719} |
100 - Per Cent |
| NA | GH PMP 03 - DX Percentage of health workers demonstrating competence in malaria microscopy (slide preparation) | GH PMP 03 - DX % HWs slide preparation |
Num id : #{KSg5YQbFJY0} |
Den id : #{ubSPJnm2719} |
100 - Per Cent |
| NA | GH PMP 03 - DX Percentage of health workers demonstrating competence in malaria microscopy (species identification) | GH PMP 03 - DX % HWs species identification |
Num id : #{T0cJykJxWvk} |
Den id : #{ubSPJnm2719} |
100 - Per Cent |
| NA | GH PMP 04 - DX Percentage of health workers demonstrating competence in malaria RDTs | GH PMP 04 - DX % HWs malaria RDTs |
Num id : #{i8lD2Tc7BOM} |
Den id : #{olr3t0zkdUn} |
100 - Per Cent |
| NA | GH PMP 05 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (microscopy) | GH PMP 05 - DX % HFs malaria microscopy |
Num id : #{TKxb2PFjRwO} |
Den id : #{oVIXBtQccHN} |
100 - Per Cent |
| NA | GH PMP 05 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (RDT) | GH PMP 05 - DX % HFs malaria RDTs |
Num id : #{vvgD4UQFNye} |
Den id : #{oVIXBtQccHN} |
100 - Per Cent |
| NA | GH PMP 06 - DX Percentage of health workers demonstrating competence in correctly classifying cases as not malaria, uncomplicated malaria, and severe malaria | GH PMP 06 - DX % HWs malaria case mngt |
Num id : #{t1vdNH0vIE2} |
Den id : #{pgsaevmKtbM} |
100 - Per Cent |
| NA | GH PMP 07 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (microscopy) | GH PMP 07 - DX % supervisors trained (microscopy) |
Num id : #{zlWYhVAouup} |
Den id : #{aZpDapygzg1} |
100 - Per Cent |
| NA | GH PMP 07 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (RDT) | GH PMP 07 - DX % supervisors trained (RDT) |
Num id : #{oFiMCwBOYLU} |
Den id : #{aZpDapygzg1} |
100 - Per Cent |
| NA | GH PMP 08 - DX Percentage of targeted districts adhering to national malaria diagnostic supervision protocol | GH PMP 08 - DX % districts adherent dx tools |
Num id : #{tpADDhwJvm3} |
Den id : R{jVmnSOBUkeG.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | GH PMP 09 - DX Percentage of targeted health workers trained in malaria laboratory diagnostics (microscopy) | GH PMP 09 - DX % HWs trained (microscopy) |
Num id : #{XenaBO47ky4} |
Den id : #{VcPN46GIkwA} |
100 - Per Cent |
| NA | GH PMP 09 - DX Percentage of targeted health workers trained in malaria laboratory diagnostics (RDT) | GH PMP 09 - DX % HWs trained (RDT) |
Num id : #{QniAsU4SnZr} |
Den id : #{VcPN46GIkwA} |
100 - Per Cent |
| NA | GH PMP 10 - DX Percentage of targeted facilities with at least one provider trained in malaria parasitological diagnosis | GH PMP 10 - DX % HFs with 1 provider trained |
Num id : #{W0G6LUKSSnG} |
Den id : #{tWSq7bigbmf} |
100 - Per Cent |
| NA | GH PMP 11 - DX Percentage of targeted facilities with national guidelines for malaria diagnosis | GH PMP 11 - DX % HFs with national guidelines |
Num id : #{EU2SBKsgxLd} |
Den id : #{oVIXBtQccHN} |
100 - Per Cent |
| TX indicators | |||||
| NA | GH PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received the recommended antimalarial treatment according to national guidelines | GH PMP 12 - TX % cases correctly treated |
Num id : #{i4BsFSVy41V} |
Den id : #{cZuXXzQuEL6} + #{E04lKfKFlKc} |
100 - Per Cent |
| NA | GH PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received the recommended antimalarial treatment according to national guidelines (NP) | GH PMP 12 - TX % cases correctly treated (NP) |
Num id : #{Jz05wiLIOae} |
Den id : #{rKatBh8TmW2} + #{KcMPvVefT1h} |
100 - Per Cent |
| NA | GH PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received the recommended antimalarial treatment according to national guidelines (P) | GH PMP 12 - TX % cases correctly treated (P) |
Num id : #{PfB1cg2ktB9} |
Den id : #{YMiDfYBOpWk} + #{RfziOIqzJCP} |
100 - Per Cent |
| NA | GH PMP 13 - TX Percentage of severe malaria cases that were treated according to national guidelines | GH PMP 13 - TX % severe cases correctly treated |
Num id : #{oCpv0PXpm38} |
Den id : #{bi3pI9REwqS} |
100 - Per Cent |
| NA | GH PMP 14 - TX Percentage of children under 5 appropriately treated for fever according to CCM or country algorithms or national guidelines by community health workers (CHPS Staff) | GH PMP 14 - TX % CU5 correctly treated by CHWs |
Num id : #{F9NoFjuDa4W} |
Den id : #{x9V7Fnjnh3W} |
100 - Per Cent |
| NA | GH PMP 15 - TX Percentage of targeted health workers demonstrating compliance to treatment with WHO-recommended ACTs for cases with positive malaria test results | GH PMP 15 - TX % HWs compliance positive cases |
Num id : #{wVlGTvl4xvH} |
Den id : #{ibu7rlMvF7s} |
100 - Per Cent |
| NA | GH PMP 16 - TX Percentage of health workers demonstrating adherence to negative test results according to global standards | GH PMP 16 - TX % HWs compliance negative cases |
Num id : #{u6hbx56NqZQ} |
Den id : #{WvrndytTbHq} |
100 - Per Cent |
| NA | GH PMP 17 - TX Percentage of targeted health workers demonstrating competence in management of severe malaria according to WHO guidelines | GH PMP 17 - TX % HWs competent severe cases |
Num id : #{lomzmDp50ZG} |
Den id : #{g0OQgJCDs0U} |
100 - Per Cent |
| NA | GH PMP 18 - TX Percentage of targeted health workers demonstrating competence in management of uncomplicated malaria | GH PMP 18 - TX % HWs competent unc malaria |
Num id : #{HsD3L7BZa5T} |
Den id : #{Wq1oBD43sCc} |
100 - Per Cent |
| NA | GH PMP 19 - TX Percentage of targeted facilities that meet global standards (including appropriate materials, documentation, and qualified staff) for quality malaria case management | GH PMP 19 - TX % HFs compliant quality case mngt |
Num id : #{UVHLhFuqoVk} |
Den id : #{J0bw03xwqlH} |
100 - Per Cent |
| NA | GH PMP 20 - TX Percentage of targeted health workers trained in management of severe malaria according to national guidelines in malaria case management | GH PMP 20 - TX % HWs trained severe malaria |
Num id : #{SVODiwEcdgd} |
Den id : #{QCuGMfgsVaK} |
100 - Per Cent |
| NA | GH PMP 21 - TX Percentage of targeted health workers trained according to national guidelines in malaria case management with ACTs | GH PMP 21 - TX % HWs trained case mngt ACTs |
Num id : #{VfDLFFUYJzW} |
Den id : #{iQlzs8iDrPV} |
100 - Per Cent |
| NA | GH PMP 22 - TX Percentage of targeted health facilities that receive a quarterly/semi-annual supervisory visit | GH PMP 22 - TX % HFs supervised |
Num id : #{J0bw03xwqlH} |
Den id : #{nk4hdODaKK0} |
100 - Per Cent |
| NA | GH PMP 23 - TX Percentage of targeted health facilities regularly reporting routine malaria case data | GH PMP 23 - TX % HFs reporting on time |
Num id : #{Z0aqwa22PYb} + #{katkPagbEeB} + #{MwdunKjMqSS} + #{rGBsSAFdUmq} |
Den id : #{FJG59eImr6t} + #{S3oGeLMj5qW} + #{eKUPUTJnM8I} + #{t0AC67lGHwT} |
100 - Per Cent |
| NA | GH PMP 24 - TX Percentage of targeted districts with national guidelines for malaria treatment that meet national standards | GH PMP 24 - TX % districts guidelines on tx |
Num id : #{iiFzfmXIK3U} |
Den id : R{jVmnSOBUkeG.ACTUAL_REPORTS} |
100 - Per Cent |
| MIP indicators | |||||
| NA | GH PMP 25 - MIP Percentage of pregnant women who received an ITN during routine ANC visit/interaction | GH PMP 25 - MIP % pregnant women received ITN |
Num id : #{kZAWqatScJ9} |
Den id : #{KBe7hShEYAK} |
100 - Per Cent |
| NA | GH PMP 26 - MIP Percentage of pregnant women who received three or more doses of IPTp according to national guidelines | GH PMP 26 - MIP % pregnant women IPT3 |
Num id : #{CFaVHFhbS4v} |
Den id : #{KBe7hShEYAK} |
100 - Per Cent |
| NA | GH PMP 27 - MIP Percentage of pregnant women who received two doses of IPTp | GH PMP 27 - MIP % pregnant women IPT2 |
Num id : #{SZgN28ZELh9} |
Den id : #{KBe7hShEYAK} |
100 - Per Cent |
| NA | GH PMP 28 - MIP Percentage of pregnant women who received one dose of IPTp | GH PMP 28 - MIP % pregnant women IPT1 |
Num id : #{uuWSsTIrVtU} |
Den id : #{KBe7hShEYAK} |
100 - Per Cent |
| NA | GH PMP 29 - MIP Percentage of targeted health workers demonstrating competence in prevention of MiP | GH PMP 29 - MIP % HWs prevention MIP |
Num id : #{aqOOzx0bgiA} |
Den id : #{DjbIYfqQC8i} |
100 - Per Cent |
| NA | GH PMP 30 - MIP Percentage of targeted health workers demonstrating competence in treatment of MiP | GH PMP 30 - MIP % HWs treatment MIP |
Num id : #{S2sRLkbN81y} |
Den id : #{gnjJPPJNSJF} |
100 - Per Cent |
| NA | GH PMP 31 - MIP Percentage of targeted health workers trained in IPTp per national guidelines | GH PMP 31 - MIP % HWs trained IPTp |
Num id : #{eEITioVuAGg} |
Den id : #{wHuGUXeSGM8} |
100 - Per Cent |
| NA | GH PMP 32 - MIP Percentage of targeted districts with national guidelines for prevention and treatment of MiP that meet national standards | GH PMP 32 - MIP % districts guidelines on MIP |
Num id : #{JNsc7SinzVA} |
Den id : R{jVmnSOBUkeG.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | GH PMP 33 - MIP Number of national MiP/RH/ANC Working Group participated in by IM staff | GH PMP 33 - MIP # of national WGs joined |
Num id : #{RzhqmTVkT0P} |
Den id : 1 |
1 - Atomic (number) |
| TL indicators | |||||
| NA | GH PMP 39 - TL Number of program activity outputs disseminated to the global health community | GH PMP 39 - TL # of outputs disseminated |
Num id : #{P4YDP2FNh3X} |
Den id : 1 |
1 - Atomic (number) |
| NA | GH PMP 40 - TL Number of targeted national, regional or global level Working group(s) and/or taskforce(s) participated in | GH PMP 40 - TL # of WGs joined |
Num id : #{XxfoDreiw9Z} |
Den id : 1 |
1 - Atomic (number) |
| NA | GH PMP 41 - TL Number of regional or global guidance/policy documents contributed to malaria related issues | GH PMP 41 - TL # of documents contributed to |
Num id : #{tJXmW64MUxf} |
Den id : 1 |
1 - Atomic (number) |
| NA | GH PMP 42 - TL Percentage of facilities with updated data wall charts showcasing malaria indicators | GH PMP 42 - TL % HFs data-wall charts on malaria |
Num id : #{bJ4OXMnhmS0} |
Den id : R{IK0bdLXmBn0.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | GH PMP 43 - TL Percentage of IM districts conducting quarterly data-led review meetings | GH PMP 43 - TL % OUs data-led review meetings |
Num id : #{KBXEqUHeNXE} |
Den id : R{jVmnSOBUkeG.ACTUAL_REPORTS} |
100 - Per Cent |
<IP_ADDRESS> Demoncratic Republic of Congo
| code | name | shortName | numerator | denominator | count |
|---|---|---|---|---|---|
| DX indicators | |||||
| NA | CD PMP - DX Percentage of health workers demonstrating competence in malaria microscopy | CD PMP - DX % HWs competent in malaria microscopy |
Num id : #{jf8nKluaF2h} |
Den id : #{om6201JM4BF} |
100 - Per Cent |
| NA | CD PMP - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria | CD PMP - DX % HFs quality malaria dx |
Num id : #{lYVEbBi1otu} |
Den id : #{jGkzcQyNUNV} |
100 - Per Cent |
| NA | CD PMP - DX Percentage of targeted facilities with national guidelines for malaria diagnosis that meet global standards | CD PMP - DX % HFs meet global standards |
Num id : #{XSxU2y6GXrr} |
Den id : #{uRPZ7FLjfLc} |
100 - Per Cent |
| NA | CD PMP - DX Percentage of targeted provinces with national malaria diagnostic supervision tools that adhere to global standards | CD PMP - DX % provinces adhere global standards |
Num id : #{ajqXNRgblHk} |
Den id : R{ZCAcgsYrGTP.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | CD PMP - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics | CD PMP - DX % supervisors trained in malaria dx |
Num id : #{vg88HQNpSXz} |
Den id : #{O8ilhnRgkRW} |
100 - Per Cent |
<IP_ADDRESS> Mali
| code | name | shortName | numerator | denominator | count |
|---|---|---|---|---|---|
| DX indicators | |||||
| NA | ML PMP 01 - DX Percentage of patients with suspected malaria who received a parasitological test | ML PMP 01 - DX % suspected cases tested |
Num id : #{BAZd7D3zQHN} |
Den id : #{LMMJ97M5sVl} |
100 - Per Cent |
| NA | ML PMP 01 - DX Percentage of patients with suspected malaria who received a parasitological test (NP) | ML PMP 01 - DX % suspected cases tested (NP) |
Num id : #{OG6loaaPPta} |
Den id : #{sFNGVQb1U5L} |
100 - Per Cent |
| NA | ML PMP 01 - DX Percentage of patients with suspected malaria who received a parasitological test (P) | ML PMP 01 - DX % suspected cases tested (P) |
Num id : #{cYgEOZdVSu5} |
Den id : #{Lbm20lLkv6L} |
100 - Per Cent |
| NA | ML PMP 02 - DX Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) | ML PMP 02 - DX % confirmed malaria cases |
Num id : #{EIkFrm5x8Sf} |
Den id : #{BAZd7D3zQHN} |
100 - Per Cent |
| NA | ML PMP 02 - DX Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) (NP) | ML PMP 02 - DX % confirmed malaria cases (NP) |
Num id : #{WuWNNgy0A1Z} |
Den id : #{OG6loaaPPta} |
100 - Per Cent |
| NA | ML PMP 02 - DX Number of cases confirmed as malaria by a parasitological test (RDT or microscopy) (P) | ML PMP 02 - DX % confirmed malaria cases (P) |
Num id : #{AmUi4XKbJ6X} |
Den id : #{cYgEOZdVSu5} |
100 - Per Cent |
| NA | ML PMP 03 - DX Percentage of targeted health workers demonstrating competence in correctly classifying cases as negative (confirmed as not malaria), uncomplicated malaria, complicated malaria, and severe malaria | ML PMP 03 - DX % HWs competent case mngt |
Num id : #{vXZ2RDwuyzA} |
Den id : #{ytbfyxZr1Ya} |
100 - Per Cent |
| NA | ML PMP 04 - DX Percentage of health workers demonstrating competence in malaria RDTs | ML PMP 04 - DX % HWs malaria RDTs |
Num id : #{TDBa7IcPJAN} |
Den id : #{rSn9x2JQPJe} |
100 - Per Cent |
| NA | ML PMP 05 - DX Percentage of health workers demonstrating competence in malaria microscopy | ML PMP 05 - DX % HWs malaria microscopy |
Num id : #{PEOQBH2TD24} |
Den id : #{GCSv8Ni4KhE} |
100 - Per Cent |
| NA | ML PMP 06 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (microscopy) | ML PMP 06 - DX % HFs malaria microscopy |
Num id : #{Gj8mqX5twDU} |
Den id : #{teWZva5EkPM} |
100 - Per Cent |
| NA | ML PMP 06 - DX Percentage of targeted facilities that meet standards (including appropriate materials, documentation, and qualified staff) for quality diagnosis of malaria (RDT) | ML PMP 06 - DX % HFs malaria RDTs |
Num id : #{VQT4EOtPiwC} |
Den id : #{teWZva5EkPM} |
100 - Per Cent |
| NA | ML PMP 07 - DX Percentage of targeted facilities with at least one provider trained in malaria diagnosis | ML PMP 07 - DX % HFs with 1 provider trained |
Num id : #{N6oAUOjFBF0} |
Den id : #{YpbUdDBZjlA} |
100 - Per Cent |
| NA | ML PMP 08 - DX Percentage of targeted health workers trained in malaria laboratory diagnostics (microscopy) | ML PMP 08 - DX % HWs trained (microscopy) |
Num id : #{GCSv8Ni4KhE} |
Den id : #{CX09R9Mdsj9} |
100 - Per Cent |
| NA | ML PMP 08 - DX Percentage of targeted health workers trained in malaria laboratory diagnostics (RDT) | ML PMP 08 - DX % HWs trained (RDT) |
Num id : #{rSn9x2JQPJe} |
Den id : #{CX09R9Mdsj9} |
100 - Per Cent |
| NA | ML PMP 09 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (microscopy) | ML PMP 09 - DX % supervisors trained (microscopy) |
Num id : #{hbEgIBy1bIf} |
Den id : #{MFpS8w5h9XS} |
100 - Per Cent |
| NA | ML PMP 09 - DX Percentage of targeted supervisors trained in supervision of malaria diagnostics (RDT) | ML PMP 09 - DX % supervisors trained (RDT) |
Num id : #{sRz3zlhaJrH} |
Den id : #{MFpS8w5h9XS} |
100 - Per Cent |
| NA | ML PMP 10 - DX Percentage of targeted health districts with national malaria diagnostic supervision tools adhere to national standards | ML PMP 10 - DX % districts sup tools nat standards |
Num id : #{fEDZSnnqQM8} |
Den id : #{nUyU7k7knZD} |
100 - Per Cent |
| NA | ML PMP 11 - DX Percentage of targeted districts with national guidelines for malaria diagnosis that meet national standards | ML PMP 11 - DX % districts guideline nat standards |
Num id : #{Iu13qrpBgfW} |
Den id : #{nUyU7k7knZD} |
100 - Per Cent |
| TX indicators | |||||
| NA | ML PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received first-line antimalarial treatment according to national guidelines | ML PMP 12 - TX % cases correctly treated |
Num id : #{x4VF1RbQzQf} |
Den id : #{gZNPPAuMZTv} |
100 - Per Cent |
| NA | ML PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received first-line antimalarial treatment according to national guidelines (NP) | ML PMP 12 - TX % cases correctly treated (NP) |
Num id : #{Nr5DUqTxNBu} |
Den id : #{V64pOp9iOSO} |
100 - Per Cent |
| NA | ML PMP 12 - TX Percentage of malaria cases (presumed and confirmed) that received first-line antimalarial treatment according to national guidelines (P) | ML PMP 12 - TX % cases correctly treated (P) |
Num id : #{gb97L4YbngN} |
Den id : #{VHK6rCw6nCM} |
100 - Per Cent |
| NA | ML PMP 13 - TX Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines | ML PMP 13 - TX % unc cases correctly treated |
Num id : #{JLxVyp3qGve} |
Den id : #{DmSkJD4HLqS} |
100 - Per Cent |
| NA | ML PMP 13 - TX Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines (NP) | ML PMP 13 - TX % unc cases correctly treated (NP) |
Num id : #{n9YAgSwzX8J} |
Den id : #{ot3qZtzsOBh} |
100 - Per Cent |
| NA | ML PMP 13 - TX Percentage of uncomplicated malaria cases that received first-line antimalarial treatment according to national guidelines (P) | ML PMP 13 - TX % unc cases correctly treated (P) |
Num id : #{KZHiYuvgkMm} |
Den id : #{yTfVdx0NIOh} |
100 - Per Cent |
| NA | ML PMP 14 - TX Percentage of targeted health workers demonstrating compliance to treatment with WHO-recommended ACTs for cases with positive malaria test results | ML PMP 14 - TX % HWs compliance positive cases |
Num id : #{eJSUmK8XCOw} |
Den id : #{w1gYh10blt1} |
100 - Per Cent |
| NA | ML PMP 15 - TX Percentage of targeted health workers demonstrating adherence to negative test results according to global standards | ML PMP 15 - TX % HWs compliance negative cases |
Num id : #{yChCRfBYBx2} |
Den id : #{FORs5r9LYU3} |
100 - Per Cent |
| NA | ML PMP 16 - TX Percentage of targeted health workers demonstrating competence in management of complicated or severe malaria according to WHO guidelines | ML PMP 16 - TX % HWs competent sever cases |
Num id : #{es8NwgtSAsX} |
Den id : #{b3GBk3DB8ou} |
100 - Per Cent |
| NA | ML PMP 17 - TX Percentage of targeted health workers demonstrating competence in management of uncomplicated malaria according to national guidelines | ML PMP 17 - TX % HWs competent unc malaria |
Num id : #{l4uAwIXVrPp} |
Den id : #{uNnp6IHODyP} |
100 - Per Cent |
| NA | ML PMP 18 - TX Percentage of targeted facilities that meet global standards (including appropriate materials, documentation, and qualified staff) for quality malaria case management | ML PMP 18 - TX % HFs compliant quality case mngt |
Num id : #{vsI12KXd1od} |
Den id : #{XY77QRYfFiA} |
100 - Per Cent |
| NA | ML PMP 19 - TX Percentage of targeted health workers trained in management of complicated or severe malaria according to national guidelines in malaria case | ML PMP 19 - TX % HWs trained severe malaria |
Num id : #{RO6irxovRAO} |
Den id : #{ddWTWat3EDS} |
100 - Per Cent |
| NA | ML PMP 20 - TX Percentage of health workers trained according to national guidelines in malaria case management with ACTs | ML PMP 20 - TX % HWs trained case mngt ACTs |
Num id : #{r4Gt4QQOoFD} |
Den id : #{rJvwXMR6FUC} |
100 - Per Cent |
| NA | ML PMP 21 - TX Percentage of targeted health facilities that receive a quarterly supervisory visit | ML PMP 21 - TX % HFs supervised |
Num id : #{teWZva5EkPM} |
Den id : #{wfz9d5EpD2c} |
100 - Per Cent |
| NA | ML PMP 22 - TX Percentage of targeted health facilities regularly reporting routine malaria case data | ML PMP 22 - TX % HFs reporting on time |
Num id : #{iBcwqsqDLeT} |
Den id : R{Mzg2dfU0Sdj.ACTUAL_REPORTS} |
100 - Per Cent |
| NA | ML PMP 23 - TX Percentage of targeted districts with national guidelines for malaria treatment that meet national standards | ML PMP 23 - TX % districts treatment guidelines |
Num id : #{pklKswSY4dg} |
Den id : #{nUyU7k7knZD} |
100 - Per Cent |
| MIP indicators | |||||
| NA | ML PMP 24 - MIP Percentage of pregnant women who received an ITN during routine ANC visit/interaction | ML PMP 24 - MIP % pregnant women received ITN |
Num id : #{jmLXEBQDZAy} |
Den id : #{IGyPotenyO2} |
100 - Per Cent |
| NA | ML PMP 25 - MIP Percentage of pregnant women who received three or more doses of IPTp according to national guidelines (during ANC) | ML PMP 25 - MIP % pregnant women IPT3 |
Num id : #{RS90YaqGgUN} |
Den id : #{IGyPotenyO2} |
100 - Per Cent |
| NA | ML PMP 26 - MIP Percentage of pregnant women who received two doses of IPTp | ML PMP 26 - MIP % pregnant women IPT2 |
Num id : #{LF9PqTGXhBc} |
Den id : #{IGyPotenyO2} |
100 - Per Cent |
| NA | ML PMP 27 - MIP Percentage of pregnant women who received one dose of IPTp | ML PMP 27 - MIP % pregnant women IPT1 |
Num id : #{qk8A67ySRR0} |
Den id : #{IGyPotenyO2} |
100 - Per Cent |
| NA | ML PMP 28 - MIP Percentage of targeted health workers demonstrating competence in prevention of MiP | ML PMP 28 - MIP % HWs competent MIP prevention |
Num id : #{FGSiegFTyBL} |
Den id : #{YQ1zwUfv6Gx} |
100 - Per Cent |
| NA | ML PMP 29 - MIP Percentage of targeted health workers demonstrating competence in treatment of MiP | ML PMP 29 - MIP % HWs competent MIP treatment |
Num id : #{AkJUjEErkX5} |
Den id : #{yOXzK6ElVJr} |
100 - Per Cent |
| NA | ML PMP 30 - MIP Percentage of health workers trained in IPTp per national guidelines | ML PMP 30 - MIP % HWs trained IPTp |
Num id : #{CxioZ7hV8e9} |
Den id : #{FVF9sYgBAH2} |
100 - Per Cent |
| NA | ML PMP 31 - MIP A functional/active national MiP/MNCH/ANC Working Group | ML PMP 31 - MIP functional national working group |
Num id : #{wHtxPnCGqBV} |
Den id : 1 |
100 - Per Cent |
| SMC indicators | |||||
| NA | ML PMP 32 - SMC Percentage of targeted children who receive all 4 rounds doses of SMC during a campaign, in an intervention area (or all 3 per national guidance where only 3 doses are indicated) | ML PMP 32 - SMC % CU5 who received all doses |
Num id : #{ioU1w8yCIWv} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 33 - SMC Number of targeted children receiving a dose of SMC in the intervention area | ML PMP 33 - SMC % CU5 who received a dose |
Num id : #{cOBjDRbvsyW} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 34 - SMC Percentage of targeted children who receive dose of SMC in the first cycle | ML PMP 34 - SMC % CU5 who received a dose 1st wave |
Num id : #{HaSIpoMB2ub} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 35 - SMC Percentage of targeted children who receive dose of SMC in the second cycle | ML PMP 35 - SMC % CU5 who received a dose 2nd wave |
Num id : #{wot02J4aRZm} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 36 - SMC Percentage of targeted children who receive dose of SMC in the third cycle | ML PMP 36 - SMC % CU5 who received a dose 3rd wave |
Num id : #{ZCibgANrNQq} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 37 - SMC Percentage of targeted children who receive dose of SMC in the fourth cycle | ML PMP 37 - SMC % CU5 who received a dose 4th wave |
Num id : #{Q71hAveCSGo} |
Den id : #{jAD6Dh7z1d1} |
100 - Per Cent |
| NA | ML PMP 38 - SMC Percentage of health workers trained to deliver SMC according to national guidelines | ML PMP 38 - SMC % HWs trained on SMC |
Num id : #{y6qGAzE5DRa} |
Den id : #{nE14FEYzkVX} |
100 - Per Cent |
| NA | ML PMP 39 - SMC Percentage of targeted districts with annual SMC implementation plans | ML PMP 39 - SMC % annual operational SMC plans |
Num id : #{hBYYaPLEFFf} |
Den id : #{gN3w6Jx7q5h} |
100 - Per Cent |
| TL indicators | |||||
| NA | ML PMP 40 - TL Contribution to regional or national guidance/policy documents related to malaria (including RH) | ML PMP 40 - TL Contribution to national policies |
Num id : #{tJXmW64MUxf} |
Den id : 1 |
1 - Atomic (number) |
| NA | ML PMP 41 - TL Number of country-level program activity outputs disseminated to the national health community | ML PMP 41 - TL Program outputs disseminated |
Num id : #{P4YDP2FNh3X} |
Den id : 1 |
1 - Atomic (number) |
| NA | ML PMP 42 - TL Participation in targeted regional or global level Working group(s) and/or taskforce(s) | ML PMP 42 - TL Participation in WGs |
Num id : #{XxfoDreiw9Z} |
Den id : 1 |
1 - Atomic (number) |


Examine a CSR
openssl req -text -noout -verify -in oldreq.csr
Examine a Private Key
openssl rsa -in priv.key -check
Examine a Certificate
openssl x509 -in cert.crt -text -noout
Examine PKCS files
openssl pkcs12 -info -in key.pfx


Describe, if applicable, other assistance programmes to which the same request has been submitted and indicate the status (e.g. it is being processed, has already been delivered or has been turned down).Word Count: {{wordCount(document.sameRequests)}}


Carl Sagan
1934 -1996
Biography
Carl Edward Sagan was an American astronomer, cosmologist, astrophysicist, astrobiologist, author, science popularizer, and science communicator in astronomy and other natural sciences.
His ability to capture the imagination of millions and to explain difficult concepts in understandable terms is a magnificent achievement. The echo of his voice is still alive through his books, videos and many wise quotes. Carl Sagan was more than a just brilliant scientist. He was in many ways a man ahead of his time and he will never be forgotten.
Early Years
Carl Sagan was born on November 9, 1934, in Brooklyn, New York, the first of two children. Sagan’s interest in astronomy began early on, and when he was five his mother sent him to the library to find books on the stars. Soon after, his parents took him to the New York World’s Fair, where visions of the future piqued his interest further. He also quickly became a fan of the prevalent 1940s science-fiction stories in pulp magazines and was drawn in by reports of flying saucers that suggested extraterrestrial life.
Sagan graduated high school in 1951 at age 16 and headed to the University of Chicago, where experiments he conducted drove his fascination with the possibility of alien life. In 1955 Sagan graduated with a BA in physics, and he took his master’s a year later. Four years later, Sagan moved to California after obtaining a PhD in astronomy and astrophysics, landing at the University of California, Berkeley, as a fellow in astronomy. There, he helped a team develop an infrared radiometer for NASA’s Mariner 2 robotic probe.
Imagination will often carry us to worlds that never were. But without it we go nowhere.
The Rare Celebrity Scientist
In 1968, now a well-known quantity in the scientific realm, Sagan briefly served as a consultant on the Stanley Kubrick film 2001: A Space Odyssey, although a clash of personalities ensured the gig was short-lived. In the 1970s and 1980s, Sagan was the most well-known scientist in the United States, helped in no small part by the books he wrote. Works such as The Cosmic Connection: An Extraterrestrial Perspective (1973), Other Worlds (1975), The Dragons of Eden: Speculations on the Evolution of Human Intelligence (1977; Pulitzer Prize winner) and his 1985 novel, Contact (made into a film starring Jodie Fosterin 1997), all grabbed the attention of the scientific community and general audiences
The universe is not required to be in perfect harmony with human ambition.
Later Career
In 1980, Sagan co-founded the Planetary Society, an international nonprofit organization focusing on space exploration, and also launched the hugely influential TV series Cosmos: A Personal Voyage, which he wrote and hosted. He also wrote a companion book of the same name to accompany the series. Another of his famous works, Pale Blue Dot: A Vision of the Human Future in Space (1994), was the sequel to Cosmos and was inspired by the famous Pale Blue Dot photograph, which shows Earth as a mere speck in space. Sagan uses the Voyager 1 probe photo as a leaping-off point to discus humanity's place in the vast universe and his vision of the future.
Death
He died of pneumonia, a complication of the bone-marrow disease myelodysplasia, on December 20, 1996, at age 62. Eighteen years later, Cosmos was brought back to TV, this time with Neil DeGrasse Tyson taking on hosting duties and getting a whole new generation of viewers excited about what lies beyond the boundaries of Earth's atmosphere.
Read more on Wikipedia


{{template "styles" .}}
Co-Op Managment Database System
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This is for educational purposes only. 2017


September 2017 - June 2018
| Student 1 | Student 2 | Student 3 |
|---|---|---|
| Crs. 6 | Crs. 20 | Crs. 18 |
| Freshman | Freshman | Freshman |
| TX | NH | PA |
September 2016 - June 2017
| Student 1 | Student 2 |
|---|---|
| Crs. 6 | Crs. 10 |
| Senior | Senior |
| CA | MD |
September 2015 - June 2016
| Student 1 | Student 2 | Student 3 |
|---|---|---|
| Crs. 2 | Crs. 2 | Crs. 14 |
| Freshman | Freshman | Freshman |
| CA | GA | PA |


About
Hey There! My name is Dakota. I am a graduate of the University at Buffalo, with a Bachelor degree in Computer Science.Now that the boring stuff is out of the way, let's get into more interesting facts and tidbits about me.
Adventurer | Developer | Free Spirit
This is bold and this is strong. This is italic and this is emphasized.
This is superscript text and this is subscript text.
This is underlined and this is code: for (;;) { ... }
. Finally, this is a link.
Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan faucibus. Vestibulum ante ipsum primis in faucibus lorem ipsum dolor sit amet nullam adipiscing eu felis.
i = 0;
while (!deck.isInOrder())
{
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
| Name | Description | Price |
|---|---|---|
| Item One | Ante turpis integer aliquet porttitor. | 29.99 |
| Item Two | Vis ac commodo adipiscing arcu aliquet. | 19.99 |
| Item Three | Morbi faucibus arcu accumsan lorem. | 29.99 |
| Item Four | Vitae integer tempus condimentum. | 19.99 |
| Item Five | Ante turpis integer aliquet porttitor. | 29.99 |
| 100.00 |
| Name | Description | Price |
|---|---|---|
| Item One | Ante turpis integer aliquet porttitor. | 29.99 |
| Item Two | Vis ac commodo adipiscing arcu aliquet. | 19.99 |
| Item Three | Morbi faucibus arcu accumsan lorem. | 29.99 |
| Item Four | Vitae integer tempus condimentum. | 19.99 |
| Item Five | Ante turpis integer aliquet porttitor. | 29.99 |
| 100.00 |


Many apps will want to add custom Java code to implement native features, without the overhead of building and publishing a proper Capacitor plugin.
There are two ways to do this depending on whether or not you need to access that code from the WebView:
The easiest way to build custom native code that needs to be accessible in the WebView is to build
a local Capacitor plugin for it. In this case, building the plugin is as simple as building a class
that inherits from com.getcapacitor.Plugin
and uses the @NativePlugin()
and @PluginMethod()
annotations:
com/example/myapp/CustomNativePlugin.java
:
package com.example.myapp;
import com.getcapacitor.NativePlugin;
import com.getcapacitor.Plugin;
import com.getcapacitor.PluginCall;
import com.getcapacitor.PluginMethod;
@NativePlugin()
public class CustomNativePlugin extends Plugin {
@PluginMethod()
public void customCall(PluginCall call) {
String message = call.getString("message");
call.success();
}
}
Finally, register the plugin in your Activity:
// Other imports...
import com.example.myapp.CustomNativePlugin;
public class MainActivity extends BridgeActivity {
@Override
public void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
// Initializes the Bridge
this.init(savedInstanceState, new ArrayList<Class<? extends Plugin>>() {{
// Additional plugins you've installed go here
// Ex: add(TotallyAwesomePlugin.class);
add(CustomNativePlugin.class);
}});
}
}
If your code doesn't need to be accessible from the WebView, then simply add your code anywhere it needs to go. With Capacitor, you have full
control over your native project. Need to add a new event handler in your Activity? Just update MainActivity
and add it. The world is truly your oyster.


2nd Floor, G-2, Akruti Corporate Park, Lal Bahadur Shastri Marg, Ambedkar Nagar, Kanjurmarg West, Bhandup West, Mumbai, Maharashtra 400078


--- permalink: / --- {% assign title = 'Home' %} {% include header.html %}
Our goal is to increase awareness of physical, social, and electronic security. Our club participates in various security competitions, hosts demos and workshops, and invites speakers to come and share some of their real world experiences. All majors are welcome to join and participate.
Want to get connected? Join our slack channel.
{{ meeting.summary }}
{% for link in meeting.links%} {{ link.name }}