Africa is the region most affected by malaria in the world. Over ***** million cases of the disease were reported in the continent in 2022. From a country perspective, the Democratic Republic of the Congo registered the highest number of cases, some **** million, followed by Nigeria, with **** million cases. Overall, the total number of reported deaths due to the disease in Africa was around ****** as of 2022.

Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical component of the Global Technical Strategy for Malaria from 2016 to 2030. While traditional differential equation models can perform basic forecasting, supervised machine learning algorithms provide more accurate predictions, as demonstrated by a recent study using an elastic net model (REMPS). Nevertheless, current short-term prediction systems do not achieve the required accuracy levels for routine clinical practice. To improve in this direction, stacked hybrid models have been proposed, in which the outputs of several machine learning models are aggregated by using a meta-learner predictive model. In this paper, we propose an alternative specialist hybrid approach that combines a linear predictive model that specializes in the linear component of the malaria prevalence signal and a recurrent neural network predictive model that specializes in the non-linear residuals of the linear prediction, trained with a novel asymmetric loss. Our findings show that the specialist hybrid approach outperforms the current state-of-the-art stacked models on an open-source dataset containing 22 years of malaria prevalence data from the city of Ibadan in southwest Nigeria. The specialist hybrid approach is a promising alternative to current prediction methods, as well as a tool to improve decision-making and resource allocation for malaria control in high-risk countries.

The 2010 Nigeria Malaria Indicator Survey (2010 NMIS) was implemented by the National Population Commission (NPC) and the National Malaria Control Programme (NMCP). ICF International provided technical assistance through the MEASURE DHS programme, a project funded by the United States Agency for International Development (USAID), which provides support and technical assistance in the implementation of population and health surveys in countries worldwide. It was carried out from October to December 2010 on a nationally representative sample of more than 6,000 households. All women age 15-49 in the selected households were eligible for individual interviews. During the interviews, they were asked questions about malaria prevention during pregnancy and the treatment of fever among their children. In addition, the survey included testing for anaemia and malaria among children age 6-59 months using finger (or heel) prick blood samples. Test results were available immediately and were provided to the children’s parents or guardians. Thick blood smears and thin blood films were also made in the field and transported to the Department of Medical Microbiology and Parasitology at the College of Medicine, University of Lagos. Microscopy was performed to determine the presence of malaria parasites and to identify the parasite species. Slide validation was carried out by the University of Calabar Teaching Hospital in Calabar. The 2009-2013 National Strategic Plan for Malaria Control in Nigeria aims to massively scale up malaria control interventions in parts of the country. The 2010 Nigeria Malaria Indicator Survey (NMIS) was, therefore, designed to measure progress toward achieving the goals and targets of this strategic plan by providing data on key malaria indicators, including ownership and use of bed nets, diagnosis and prompt treatment of malaria using artemisinin-based therapy (ACT), indoor residual spraying, and behaviour change communication. The following are the specific objectives of the 2010 NMIS: To measure the extent of ownership and use of mosquito bed nets To assess the coverage of intermittent and preventive treatment programmes for pregnant women To identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications To measure the prevalence of malaria and anaemia among children age 6-59 months To determine the species of plasmodium parasite most prevalent in Nigeria To assess knowledge, attitudes, and practices regarding malaria in the general population

Global Malaria Cases Reported Share by Country (Units (Cases)), 2023 Discover more data with ReportLinker!

Extract from Abstract [Related Publication]

Background: maintaining the effectiveness of the currently recommended malaria vector control interventions while integrating new interventions will require monitoring key recommended indicators to identify threats to effectiveness including physiological and behavioural resistance to insecticides.

Methods: Country metadata on vector surveillance and control activities was collected using an online survey by national malaria control programmes or partner organization officials. Country and regional surveillance activities were analysed for alignment with indicators for priority vector surveillance objectives recommended by the World Health Organization. Surveillance activities were also compared for countries in the E2020 (eliminating countries) and countries with more intense transmission.

Extract from Survey:

With the support of the Bill and Melinda Gates Foundation, we are undertaking an evaluation of malaria vector surveillance in countries attempting malaria elimination in the E8, Greater Asia-Pacific Subregion and MesoAmerica. The overall objective is to establish a process to improve malaria vector surveillance, as we believe that good surveillance will maintain and improve the effectiveness of malaria vector control strategies. The process of improving vector surveillance encompasses several components. The following questionnaire which we are asking you to complete addresses the first component: to determine what surveillance activities are routinely (within the last year) carried out in the countries in which you work (if you are involved in malaria control in more than one country, please fill out a separate form for each country). This component includes a gap analysis of program capacity (including training) for vector surveillance as well as a data gap analyses to determine what vector parameters are being monitored, how they are measured and how the resulting vector information informs programmatic decisions. In the second component we will examine the capabilities of our surveillance tools (e.g., can the tools available provide the information that you require for effective decision-making or is there a technology gap). This analysis will be used to develop Target Product Profiles (TPPs) describing the requirements of future surveillance tools to improve how we monitor vectors.

We hope that this survey questionnaire will provide a high level overview of the surveillance activities in your countries and we will follow up with as many of you as possible to gather more detailed information on what you need to improve the effectiveness of malaria vector control. If you indicate that you routinely collect data on vectors, we would ask for your willingness to make this information available to allow us to access where we need to invest to understand the vectors of malaria better in order to understand where new emerging control methods might be most effective.

We appreciate your taking the time to fill out the survey. It is our hope that the analyses based on your responses will provide a strong argument for future investments to produce better training protocols as well as better methods for monitoring vectors and communicating that data to decision makers. Individual country data that is collected will be treated as confidential and will not disseminated. However, the summary data will be shared with the malaria control programs that participate in the survey as well as with the E8 Secretariat, the Asian Pacific Malaria Elimination Network, PAHO, AFRO and ALMA.

This dataset consists of 2 spreadsheets (saved in both Excel and Open Document formats):

• XLSForm_vector_surveillance_Multi-language: includes survey questions and choices in English, French and Spanish.
• Burkot_Vector surveillance survey_Aggregated dataset: summary results include type, label (questions/instructions), responses and counts.

This layer represents the estimated percentage of post-neonatal (1 month old to 5 years old) deaths due to malaria out of the total number of post-neonatal deaths for each country in 2015. Those estimates are provided by the World Health Organization and by the Maternal and Child Epidemiology Estimation Group (MCEE-John Hopkins University). Data for neonatal and under five children are also available by clicking in a specific country.You can access the report here:http://apps.who.int/iris/bitstream/10665/43840/1/9789241596435_eng.pdfFor more information and to access the raw data, visit the WHO website: http://apps.who.int/gho/data/view.main.ghe1002015-CH8?lang=en

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• Concordance and discordance between RDT and microscopy results
• Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

This report presents the findings of the 2012 Malawi Malaria Indicator Survey (2012 MMIS) conducted by the National Malaria Control Programme (NMCP) of the Ministry of Health from 28 March through 15 May 2012. The government of Malawi provided financial assistance in terms of in-kind contribution of personnel, office space, and logistical support. Financial support for the survey was provided by the United States Agency for International Development (USAID) from President’s Malaria Initiative funds through ICF International. ICF International also provided technical assistance, medical supplies, and equipment for the survey through the MEASURE DHS program, which is funded by USAID and is designed to assist developing countries in collecting data on fertility, family planning, and maternal and child health. The opinions expressed in this report are those of the authors and do not necessarily reflect the views of USAID. The Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG), a global technical advisory group providing monitoring and evaluation guidance for malaria control programmes, recommends that the MIS be conducted every two years within six weeks of the end of the rainy season in countries with endemic malaria transmission patterns, especially those in sub-Saharan Africa. For these reasons, in 2012, the NMCP conducted the second nationwide Malaria Indicator Survey in Malawi. The 2012 MIS used a standard set of instruments and protocol developed by RBM-MERG. These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardized survey management and data collection methodology. The package also includes standardized measurement of malaria parasite and anaemia prevalence among target populations to derive the malaria-related burden at the community level. The key objectives of the 2012 MIS were to: Measure the level of ownership and use of mosquito nets Assess coverage of the intermittent preventive treatment for pregnant women Identify treatment practices, including the use of specific antimalarial medications to treat malaria among children under 5 Measure the prevalence of malaria and anaemia among children age 6-59 months Assess knowledge, attitudes, and practices of malaria in the adult population Measure trends in key malaria indicators since the 2010 MDHS The 2012 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of three regions in Malawi: Northern, Central, and Southern.

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

Users can view maps, spatial, and statistical information drawn from different databases around the world. In addition, users can download data sets pertaining to prevalence and location of health facilities. Background The World Health Organization GeoNetwork is a geographic information management system that contains geo-referenced data sets and maps to facilitate the planning and monitoring of health related activities and health conditions. Information is available regarding the prevalence and location of health facilities. User Functionality Users must download the Geographic Information Systems (GIS) and Remote-Sensing (RSS) software applications to interact with the data tools, including digital maps, satellite images, and other geographic information. To obtain maps and other geographic information, users can search by term or geographic location or conduct an advanced search by time frame, year, and geographic location. There is a useful manual located under the “Help” tab, which enables users to learn more about GIS and how to use the GeoNetwork. Data Notes Data sources include: Food and Agriculture Organization of the United Nations (FAO), World Food Programme (WFP), and the United Nations Environment Programme (UNEP). The website announces datasets that have most recently been added to the GeoNetwork, but does not indicate the date it was updated.

Abstract

The 2020 Kenya Malaria Indicator Survey (2020 KMIS) was a cross-sectional household-based survey with a nationally representative sample of conventional households. The survey targeted women age 15-49 and children age 6 months to age 14 living within conventional households in Kenya. All women age 15-49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. In all sampled households, children age 6 months to age 14 were tested for anaemia and malaria.

The sample for the 2020 KMIS was designed to produce reliable estimates for key malaria indicators at the national level, for urban and rural areas separately, and for each of the five malaria endemic zones.

The 2020 KMIS was designed to provide information on the implementation of core malaria control interventions and serve as a follow-up to the previous malaria indicator surveys. The specific objectives of the 2020 KMIS were as follows: - To measure the extent of ownership of, access to, and use of mosquito nets - To assess coverage of intermittent preventive treatment of malaria during pregnancy - To examine fever management among children under age 5 - To measure the prevalence of malaria and anaemia among children age 6 months to age 14 - To assess knowledge, attitudes, and practices regarding malaria control - To determine the Plasmodium species most prevalent in Kenya

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-14
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and children age 0-14 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 KMIS followed a two-stage stratified cluster sample design and was intended to provide estimates of key malaria indicators for the country as a whole, for urban and rural areas, and for the five malaria-endemic zones (Highland epidemic prone, Lake endemic, Coast endemic, Seasonal, and Low risk).

The five malaria-endemic zones fully cover the country, and each of the 47 counties in the country falls into one or two of the five zones as follows: 1. Highland epidemic prone: Kisii, Nyamira, West Pokot, Trans-Nzoia, Uasin Gishu, Nandi, Narok, Kericho, Bomet, Bungoma, Kakamega, and Elgeyo Marakwet 2. Lake endemic: Siaya, Kisumu, Migori, Homa Bay, Kakamega, Vihiga, Bungoma, and Busia 3. Coast endemic: Mombasa, Kwale, Kilifi, Lamu, and Taita Taveta 4. Seasonal: Tana River, Marsabit, Isiolo, Meru, Tharaka-Nithi, Embu, Kitui, Garissa, Wajir, Mandera, Turkana, Samburu, Baringo, Elgeyo Marakwet, Kajiado, and West Pokot 5. Low risk: Nairobi, Nyandarua, Nyeri, Kirinyaga, Murang’a, Kiambu, Machakos, Makueni, Laikipia, Nakuru, Meru, Tharaka-Nithi, and Embu.

The survey utilised the fifth National Sample Survey and Evaluation Programme (NASSEP V) household master sample frame, the same frame used for the 2015 KMIS. The frame was used by KNBS from 2012 to 2020 to conduct household-based sample surveys in Kenya. It was based on the 2009 Kenya Population and Housing Census, and the primary sampling units were clusters developed from enumeration areas (EAs). EAs are the smallest geographical areas created for purposes of census enumeration; a cluster can be an EA or part of an EA. The frame had a total of 5,360 clusters and was stratified into urban and rural areas within each of 47 counties, resulting into 92 sampling strata with Nairobi and Mombasa counties being wholly urban.

The survey employed a two-stage stratified cluster sampling design in which, in the first stage of selection, 301 clusters (134 urban and 167 rural) were randomly selected from the NASSEP V master sample frame using an equal probability selection method with independent selection in each sampling stratum. The second stage involved random selection of a fixed number of 30 households per cluster from a roster of households in the sampled clusters using systematic random sampling.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four types of questionnaires were used for the 2020 KMIS: the Household Questionnaire, the Woman’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires were adapted to reflect issues relevant to Kenya. Modifications were determined after a series of meetings with various stakeholders from DNMP and other government ministries and agencies, nongovernmental organisations, and international partners. The Household and Woman’s Questionnaires in English and Kiswahili were programmed into Android tablets, which enabled the use of computer-assisted personal interviewing (CAPI) for data collection. The Biomarker Questionnaire, in English and Kiswahili, was filled out on hard copy and then entered into the CAPI system.

Cleaning operations

The 2020 KMIS questionnaires were programmed using Census and Survey Processing (CSPro) software. The program was then uploaded into Android-based tablets that were used to collect data via CAPI. The CAPI applications, including the supporting applications and the applications for the Household, Biomarker, and Woman’s Questionnaires, were programmed by ICF. The field supervisors transferred data daily to the CSWeb server, developed by the U.S. Census Bureau and located in Nairobi, for data processing on the central office computer at the KNBS office in Nairobi.

Data received from the field teams were registered and checked for any inconsistencies and outliers on the central office computer at KNBS. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in the central office computer at KNBS head office. The central office held data files which was used to produce final report tables and final data sets. CSPro software was used for data editing, cleaning, weighting, and tabulation.

Response rate

A total of 8,845 households were selected for the survey, of which 8,185 were occupied at the time of fieldwork. Among the occupied households, 7,952 were successfully interviewed, yielding a response rate of 97%. In the interviewed households, 7,035 eligible women were identified for individual interviews and 6,771 were successfully interviewed, yielding a response rate of 96%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2020 Kenya Malaria Indicator Survey (KMIS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2020 KMIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2020 KMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Number of enumeration areas completed, by month and malaria endemicity - Positive rapid diagnostic test (RDT) results, by month and malaria endemicity - Concordance and discordance between RDT and microscopy results - Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

This Malaria Behavior Survey (MBS) dataset includes nationally representative data on the determinants of malaria-related behaviors, collected in urban and rural areas of 14 administrative districts in Côte d’Ivoire. The MBS was conducted from September 2 to November 11, 2018. The purpose of the study was twofold: (1) To have a better understanding of the socio-demographic and ideational characteristics associated with malaria-related behaviors in Côte d'Ivoire and (2) To determine the appropriate orientation of programmatic activities to improve behaviors and influence the behavioral factors related to malaria. The research team initially grouped the administrative regions into three geographic zonal areas: North, Central, and South. However, Since more than half of the population of Côte d’Ivoire lives in the South zone, and inhabitants of Abidjan represent one-third of that population, the research team separated the district of Abidjan from the South zone for the purpose of calculating the required sample size. This study used a cross-sectional survey design with a random sample of women and men using structured questionnaires. Respondents were selected through a multi-step random. The research team collected relevant information from 5,969 households, 6,749 women and 1,930 men. The data collected are representative at national, regional as well as urban and rural levels.

Global Incidence of Malaria by Country, 2023 Discover more data with ReportLinker!

The 2014 MIS used a standard set of instruments and protocol developed by RBM Monitoring and Evaluation Reference Group (MERG). These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardised survey management and data collection methodology. The package also includes standardised measurement of malaria parasite and anaemia prevalence among target populations to derive the malariarelated burden at the community level. The key objectives of the 2014 MIS were to: ? Measure the level of ownership and use of mosquito nets ? Assess coverage of intermittent preventive treatment for pregnant women ? Identify treatment practises, including the use of specific antimalarial medications to treat malaria among children under 5 ? Measure the prevalence of malaria and anaemia among children age 6-59 months ? Assess knowledge, attitudes, and practises of malaria in the adult population ? Measure trends in key malaria indicators since the 2012 MIS The 2014 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions in Malawi: Northern, Central, and Southern.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

The most impacting features selected by Elastic Net regression per each group in each country.

"Ensure healthy lives and promote well-being for all at all ages : Some progress has been made against key mortality measures. Maternal mortality ratios have already fallen below the 2030 target in three-quarters of Pacific countries and territories, and one-half have achieved the under-five mortality rate target of fewer than 25 deaths per 100,000; The increasing burden of non-communicable diseases, both with respect to the risk of premature mortality and health care costs, is the dominant health issue in the Pacific region. A mixed pattern is found in the two lifestyle risk factors of alcohol and smoking, with three Pacific countries featuring among the top ten world countries in prevalence of current tobacco use among persons aged 15 years and older; Health worker density remains below WHO guidelines in most countries in the region; Malaria is still present in three countries (PNG, Solomon Islands and Vanuatu), although the incidence is decreasing due to awareness and preventative measures.

Find more Pacific data on PDH.stat.

BackgroundMalaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.Methodology and FindingsThe lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags.ConclusionThis study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

Data correct at time of upload (16 March 2022). Data maintained at https://www.malariagen.net/resource/30.This Figshare project provides information about data generated by the MalariaGEN Plasmodium vivax Genome Variation project (https://www.malariagen.net/parasite/p-vivax-genome-variation) using the version 4 pipeline for variant discovery and genotype calling. BACKGROUNDThe Plasmodium vivax Genome Variation Project supports groups around the world to establish the landscape of evolution in P. vivax populations and help guide informed control interventions by integrating whole genome sequencing with clinical and epidemiological studies. The Plasmodium vivax Genome Variation Project is comprised of multiple partner studies, uniting around collective ambitions for open genomic data resources, but each with their own respective research objectives and led by independent investigators all over the world. To address the need for comprehensive, large-scale genetic surveillance of P. vivax populations, we combined centralised sequencing capabilities with a standardised analysis pipeline for variant discovery and genotyping that resulted in whole genome sequencing data for partners to conduct downstream analysis in line with MalariaGEN’s guiding principles on equitable data sharing (https://www.malariagen.net/resource/1). This culminated in the open-access Plasmodium vivax Genome Variation project May 2016 data release (https://www.malariagen.net/data/p-vivax-genome-variation-may-2016-data-release) and associated analyses in Pearson et al, 2016 (https://www.nature.com/articles/ng.3599). This platform has provided a foundation to build upon for a second public data resource (v4), which sought to expand on this model to not only integrate more samples from partner studies, but to also include existing sample data generated by the wider scientific community. This is the first large-scale curation of malaria genome variation data across heterogeneous sequencing methodologies and locations, enabling community access to the largest curated dataset for epidemiological inferences across space and time, while simultaneously minimising the potential introduction of biases during the aggregation process with a standardised pipeline. This combined open resource contains a total of 1,895 samples, with the majority of all samples provided by VivaxGEN (1,025), and GlaxoSmith-Kline (GSK) (357), as well as 297 previously published samples from external studies. The data resource collectively represents 14 studies from 27 countries and 88 sampling locations, primarily between 2001-2017. Following on from the initial open data release, we have provided genomic variation data, including SNPs, indels, and tandem duplications. For ease of downstream analysis, we have also included information on population structure, calculated per-sample metrics of within-host diversity, and classified samples into four different types of drug resistance based on a limited set of published genetic markers.ABOUT THE DATA PIPELINEFull details of the methods can be found in the accompanying paper at https://www.nature.com/articles/ng.3599. The major changes from the v1 (May 2016 data release) pipeline are that we now a) map to the PvP01 reference genome rather than PvSal1 and b) use a pipeline based on current GATK best practices which is analogous to the Pf6 pipeline (https://www.malariagen.net/resource/26).CONTENTS OF THE RELEASEThis release contains details on contributing partner studies, sample metadata and key sample attributes inferred from genomic data, and genomic data including raw sequence reads. Further details and analytical results can be found in the accompanying data release paper.These data are available open access. Publications using these data should acknowledge and cite the source of the data using the following format: "This publication uses data from the MalariaGEN Plasmodium vivax Genome Variation Project as described in ‘An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples’. MalariaGEN et al, [DOI]. DATA RESOURCE1) Study information: Details of the 11 contributing partner studies, and 3 external studies, including description, contact information and key people. 2) Sample provenance and sequencing metadata: sample information including partner study information, location and year of collection, ENA accession numbers, and QC information for 1,895 samples from 27 countries. 3) Measure of complexity of infections: characterisation of within-host diversity (FWS) for 1,072 QC pass samples. 4) Drug resistance marker genotypes: genotypes at known markers of drug resistance for 1,895 samples, containing amino acid and copy number genotypes at 3 loci: dhfr, dhps, mdr1. 5) Inferred resistance status classification: classification of 1,072 QC pass samples into different types of resistance to 4 drugs or combinations of drugs: pyrimethamine, sulfadoxine, mefloquine, and sulfadoxine-pyrimethamine combination. 6) Drug resistance markers to inferred resistance status: details of the heuristics utilised to map genetic markers to resistance status classification. 7) Tandem duplication genotypes: genotypes for tandem duplications discovered in four regions of the genome.8) Genome regions and Genome regions index: a bed file (https://genome.ucsc.edu/FAQ/FAQformat.html#format1) classifying genomic regions as core genome or different classes of non-core genome in addition to tabix index file for genome regions file.9) Short variants genotypes: Genotype calls on 4,571,056 SNPs and short indels in 1,895 samples from 27 countries, available both as VCF and zarr files. These are available at: ftp://ngs.sanger.ac.uk/production/malaria/Resource/30.A README file describes in detail all the files included in the release, the format and interpretation of each column, and contains some tips and tricks for accessing the genotype data in VCF and zarr files. The VCF and zarr files in this release can be downloaded from the Wellcome Sanger Institute public FTP site using a freely available FTP client.

Users can find data on a range of global health topics like mortality, the burden of disease, infectious diseases, risk factors and health expenditures. Background The Global Health Observatory (GHO) database is the World Health Organization's main health statistics repository. Data is available for 193 World Health Organization member states on topics including but not limited to: Health related millennium goals, mortality, immunization, nutrition, infectious disease, non- communicable disease, tobacco control, violence, injuries, alcohol, HIV/AIDS, tuberculosis, malaria, water and sanitation, maternal and reproductive health, cho lera, child health, child nutrition, and road safety. User FunctionalityUsers can generate tables and charts according to country or region, health indicator, and time period. Data can also be compared across countries. Data can be filtered, tabulated, charted, and downloaded into Excel statistical software. These data are also published in statistical reports covering topics including: Alcohol and health, Child health, Cholera, HIV/AIDS, Malaria, Maternal and reproductive heal th, Non-communicable diseases, Public health and environment, Road safety, Tuberculosis, Tobacco control. Data Notes Data are derived from surveillance and household surveys. Years in which data were collected is indicated with these health statistics. Information is available for each WHO member country and international region. The most recent data is available from 2009.