In 2023, Nigeria accounted for nearly 26 percent of all malaria cases worldwide, by far the highest share of any country. The Democratic Republic of the Congo had the second-highest share of malaria cases that year with 12.6 percent, followed by Uganda with 4.8 percent. Malaria is an infectious disease spread by female mosquitoes. Symptoms include fever, fatigue, vomiting, and headache and if left untreated the disease may lead to death. The region most impacted by malaria In 2023, there were a total of 263,000 cases of malaria worldwide. The region of Africa accounted for 246,000 of these cases, making it by far the region most impacted by this deadly disease. In comparison, Southeast Asia reported four thousand malaria cases in 2023, while the Americas had just 548. However, incidence rates of malaria have decreased around the world over the past couple decades. In Africa, the incidence rate of malaria decreased from 369 per 1,000 at risk in the year 2000 to 223 per 1,000 at risk in 2022. Worldwide, the incidence rate of malaria decreased from 79 to 60 per 1,000 at risk during this period. How many people die from malaria each year? Although rates of malaria have decreased around the world, hundreds of thousands of people still die from malaria each year, with the majority of these deaths in Africa. In 2023, around 597,000 people died from malaria worldwide, with 569,000 of these deaths occurring in Africa. However, death rates from malaria have decreased in Africa, with a rate of 62.5 per 100,000 at risk in the year 2015 compared to a rate of 52.4 per 100,000 at risk in 2023. In 2023, Nigeria accounted for around 31 percent of all malaria deaths, while 11 percent of such deaths were in the Democratic Republic of the Congo.

In 2023, Brazil and Venezuela reported the highest number of presumed and confirmed malaria cases in Latin America, with over ******* and ******* infections, respectively. Colombia and Peru followed, reporting around ******* and ****** malaria cases that same year. Cases of malaria in Brazil have shown an overall decreasing trend in the last decade.

Malaria remains a public health crisis in Tanzania, with persistent morbidities and mortalities. Malaria etiology is multifactorial, with environmental factors playing a vital role in mosquito development and malaria transmission. In Tanzania and most of Sub-Saharan Africa, the Plasmodium falciparum parasite remains the most prevalent and virulent malaria parasite. Using data from the Tanzania Demographic and Health Surveys and spatio-temporal analysis, we explore the environmental determinants of P. falciparum across different regions in Tanzania over the last 2 decades. The hotspots analysis showed that the Kigoma and Kagera regions in the north-west of Tanzania as well as the Lindi and Mtwara regions in southern Tanzania were consistently hotspots of P. falciparum malaria from 2000 to 2020. Our findings also reveal and reinforce the role of environmental factors in mediating malaria epidemiology in Tanzania. Factors such as the use of insecticide-treated nets, population, evapotranspiration and aridity were often adversely associated with P. falciparum incidence. In contrast, vegetative landcover, temperature, precipitation, and the number of wet days were directly associated with P. falciparum in Tanzania. However, the relationship between these environmental factors and malaria prevalence varied temporally and spatially. Our findings further showed that, the two most important environmental factors that mediate P falciparum incidence in Tanzania over the last two decades were precipitation and aridity. Other vital predictors included the use of insecticide nets and the number of wet days. The findings provide policy pointers for targeted malaria interventions in Tanzania in the context of environmental change.

Malaria Diagnostics Market Outlook

According to our latest research, the global malaria diagnostics market size reached USD 920 million in 2024, supported by robust advancements in diagnostic technologies and increased funding for malaria control programs. The market is set to expand at a CAGR of 6.8% from 2025 to 2033, with the total market value forecasted to reach USD 1.74 billion by 2033. This growth is primarily driven by the rising incidence of malaria in endemic regions, increasing government and non-governmental initiatives for disease management, and the continuous evolution of diagnostic methods that offer higher sensitivity and specificity.

One of the primary growth factors for the malaria diagnostics market is the escalating prevalence of malaria in tropical and subtropical regions, particularly in sub-Saharan Africa and Southeast Asia. These regions account for the majority of the global malaria burden, prompting governments, non-governmental organizations, and international health agencies to invest heavily in malaria control and elimination programs. The World Health Organization (WHO) and other global health bodies have set ambitious targets for malaria reduction, which has translated into increased funding for the development and deployment of advanced diagnostic tools. The growing awareness regarding early and accurate diagnosis as a critical step in effective malaria management further fuels the demand for innovative diagnostic solutions.

Technological advancements play a pivotal role in driving the malaria diagnostics market forward. The shift from conventional microscopy to rapid diagnostic tests (RDTs) and molecular diagnostic techniques has revolutionized malaria detection, especially in resource-limited settings. RDTs offer quick results and do not require sophisticated laboratory infrastructure, making them ideal for remote and rural areas where malaria is most prevalent. Meanwhile, molecular diagnostics, including PCR-based methods, provide high sensitivity and specificity, enabling the detection of low-level parasitemia and mixed infections. These technological innovations not only enhance diagnostic accuracy but also support large-scale screening and surveillance programs, contributing significantly to market growth.

Another critical growth factor is the increasing collaboration between public and private sector stakeholders to combat malaria. Partnerships between governments, research institutions, diagnostic companies, and funding agencies have led to the development and commercialization of new diagnostic products tailored to the needs of endemic regions. These collaborations have also facilitated the implementation of quality assurance programs, capacity-building initiatives, and training for healthcare workers, ensuring the effective utilization of diagnostic tools. Furthermore, the growing emphasis on point-of-care diagnostics and the integration of digital health technologies are expected to create new avenues for market expansion in the coming years.

The introduction of Rapid Medical Diagnostic Kits has been a game-changer in the fight against malaria. These kits are designed to provide quick and accurate results, which are crucial in areas where timely diagnosis can significantly impact treatment outcomes. The portability and ease of use of these kits make them particularly valuable in remote regions with limited access to healthcare facilities. By enabling healthcare workers to diagnose malaria on-site, these kits help in reducing the time between diagnosis and treatment, which is essential in controlling the spread of the disease. Moreover, the affordability of these kits ensures that they can be widely distributed, making them a vital tool in global malaria eradication efforts.

From a regional perspective, the Asia Pacific and sub-Saharan Africa dominate the malaria diagnostics market due to their high disease burden and ongoing efforts to strengthen healthcare infrastructure. North America and Europe, while having a lower incidence of malaria, contribute to market growth through research and development activities and the adoption of advanced diagnostic technologies. Latin America and the Middle East & Africa regions are also witnessing increased investments in malaria diagnostics, driven by rising awareness and government-led initiatives. The m

Malaria remains a public health crisis in Tanzania, with persistent morbidities and mortalities. Malaria etiology is multifactorial, with environmental factors playing a vital role in mosquito development and malaria transmission. In Tanzania and most of Sub-Saharan Africa, the Plasmodium falciparum parasite remains the most prevalent and virulent malaria parasite. Using data from the Tanzania Demographic and Health Surveys and spatio-temporal analysis, we explore the environmental determinants of P. falciparum across different regions in Tanzania over the last 2 decades. The hotspots analysis showed that the Kigoma and Kagera regions in the north-west of Tanzania as well as the Lindi and Mtwara regions in southern Tanzania were consistently hotspots of P. falciparum malaria from 2000 to 2020. Our findings also reveal and reinforce the role of environmental factors in mediating malaria epidemiology in Tanzania. Factors such as the use of insecticide-treated nets, population, evapotranspiration and aridity were often adversely associated with P. falciparum incidence. In contrast, vegetative landcover, temperature, precipitation, and the number of wet days were directly associated with P. falciparum in Tanzania. However, the relationship between these environmental factors and malaria prevalence varied temporally and spatially. Our findings further showed that, the two most important environmental factors that mediate P falciparum incidence in Tanzania over the last two decades were precipitation and aridity. Other vital predictors included the use of insecticide nets and the number of wet days. The findings provide policy pointers for targeted malaria interventions in Tanzania in the context of environmental change.

Malaria remains a serious public health problem in Brazil despite a significant drop in the number of cases in the past decade. We conduct a comprehensive analysis of malaria transmission in Brazil to highlight the epidemiologically most relevant components that could help tackle the disease. We consider factors impacting on the malaria burden and transmission dynamics including the geographical occurrence of both autochthonous and imported infections, the distribution and abundance of malaria vectors and records of natural mosquito infections with Plasmodium. Our analysis identifies three discrete malaria transmission systems related to the Amazon rainforest, Atlantic rainforest and Brazilian coast, respectively. The Amazonian system accounts for 99% of all malaria cases in the country. It is largely due to autochthonous P. vivax and P. falciparum transmission by mosquitoes of the Nyssorhynchus subgenus, primarily Anopheles darlingi. Whilst P. vivax transmission is widespread, P. falciparum transmission is restricted to hotspot areas mostly in the States of Amazonas and Acre. This system is the major source of P. vivax exportation to the extra-Amazonian regions that are also affected by importation of P. falciparum from Africa. The Atlantic system comprises autochthonous P. vivax transmission typically by the bromeliad-associated mosquitoes An. cruzii and An. bellator of the Kerteszia subgenus. An. cruzii also transmits simian malaria parasites to humans. The third, widespread but geographically fragmented, system is found along the Brazilian coast and comprises P. vivax transmission mainly by An. aquasalis. We conclude that these geographically and biologically distinct malaria transmission systems require specific strategies for effective disease control.

Sleeping inside a mosquito treated net was the most popular malaria preventive measure for men and women in Nigeria, based on a study conducted in 2023, with ** percent of men and ** percent of women reporting using this measure. Spraying insecticide routinely came in second, with ** percent of men and ** percent of women reporting using this measure.

Malaria is a dangerous disease caused by a parasite that usually infects a certain type of mosquito, which in turn feeds on humans. In 2020, Nigeria registered some **** million cases of malaria.

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

Avian malaria has historically played an important role as a model in the study of human malaria, being a stimulus for the development of medical parasitology. Avian malaria has recently come back to the research scene as a unique animal model to understand the ecology and evolution of the disease, both in the field and in the laboratory. Avian malaria is highly prevalent in birds and mosquitoes around the world and is amenable to laboratory experimentation at each stage of the parasite's life cycle. Here, we take stock of 5 years of experimental laboratory research carried out using Plasmodium relictum SGS1, the most prevalent avian malaria lineage in Europe, and its natural vector, the mosquito Culex pipiens. For this purpose, we compile and analyse data obtained in our laboratory in 14 different experiments. We provide statistical relationships between different infection-related parameters, including parasitaemia, gametocytaemia, host morbidity (anaemia) and transmission rates to mo...

As malaria is being pushed back on many frontiers and global case numbers are declining, accurate measurement and prediction of transmission becomes increasingly difficult. Low transmission settings are characterised by high levels of spatial heterogeneity, which stands in stark contrast to the widely used assumption of spatially homogeneous transmission used in mathematical transmission models for malaria. In the present study an individual-based mathematical malaria transmission model that incorporates multiple parasite clones, variable human exposure and duration of infection, limited mosquito flight distance and most importantly geographically heterogeneous human and mosquito population densities was used to illustrate the differences between homogeneous and heterogeneous transmission assumptions when aiming to predict surrogate indicators of transmission intensity such as population parasite prevalence or multiplicity of infection (MOI). In traditionally highly malaria endemic regions where most of the population harbours malaria parasites, humans are often infected with multiple parasite clones. However, studies have shown also in areas with low overall parasite prevalence, infection with multiple parasite clones is a common occurrence. Mathematical models assuming homogeneous transmission between humans and mosquitoes cannot explain these observations. Heterogeneity of transmission can arise from many factors including acquired immunity, body size and occupational exposure. In this study, we show that spatial heterogeneity has a profound effect on predictions of MOI and parasite prevalence. We illustrate, that models assuming homogeneous transmission underestimate average MOI in low transmission settings when compared to field data and that spatially heterogeneous models predict stable transmission at much lower overall parasite prevalence. Therefore it is very important that models used to guide malaria surveillance and control strategies in low transmission and elimination settings take into account the spatial features of the specific target area, including human and mosquito vector distribution.

BackgroundThe efficient allocation of financial resources for malaria control using appropriate combinations of interventions requires accurate information on the geographic distribution of malaria risk. An evidence-based description of the global range of Plasmodium falciparum malaria and its endemicity has not been assembled in almost 40 y. This paper aims to define the global geographic distribution of P. falciparum malaria in 2007 and to provide a preliminary description of its transmission intensity within this range. Methods and FindingsThe global spatial distribution of P. falciparum malaria was generated using nationally reported case-incidence data, medical intelligence, and biological rules of transmission exclusion, using temperature and aridity limits informed by the bionomics of dominant Anopheles vector species. A total of 4,278 spatially unique cross-sectional survey estimates of P. falciparum parasite rates were assembled. Extractions from a population surface showed that 2.37 billion people lived in areas at any risk of P. falciparum transmission in 2007. Globally, almost 1 billion people lived under unstable, or extremely low, malaria risk. Almost all P. falciparum parasite rates above 50% were reported in Africa in a latitude band consistent with the distribution of Anopheles gambiae s.s. Conditions of low parasite prevalence were also common in Africa, however. Outside of Africa, P. falciparum malaria prevalence is largely hypoendemic (less than 10%), with the median below 5% in the areas surveyed. ConclusionsThis new map is a plausible representation of the current extent of P. falciparum risk and the most contemporary summary of the population at risk of P. falciparum malaria within these limits. For 1 billion people at risk of unstable malaria transmission, elimination is epidemiologically feasible, and large areas of Africa are more amenable to control than appreciated previously. The release of this information in the public domain will help focus future resources for P. falciparum malaria control and elimination.

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.

Abstract

Since 1995, the National Malaria Control Programme (NMCP) and its partners have been implementing and scaling up malaria interventions in all parts of the country. To determine the progress made in malaria control and prevention in Uganda, the Uganda Malaria Indicator Survey (UMIS) was implemented in 2009 and again in 2014-15 to provide data on key malaria indicators including mosquito net ownership and use, as well as prompt treatment using ACT.

The main objective of the UMIS is to obtain population-based estimates on malaria indicators including the prevalence of malaria and anaemia to inform strategic planning and programme evaluation. Specific objectives are: 1. To obtain estimates of the magnitude and distribution of anaemia and malaria parasitemia among children age 0-59 months 2. To estimate core malaria programme coverage indicators • Measure the extent of ownership and use of mosquito bed nets • Assess coverage of the intermittent preventive treatment programme for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Assess percentage of children under five with fever for whom advice or treatment was sought • Determine the species of plasmodium parasite most prevalent in children age 0-59 months 3. To measure indicators of knowledge, attitudes, and behaviour related to malaria control 4. To determine the factors associated with malaria parasitemia and anaemia

Geographic coverage

National

Analysis unit

• Household
• Children age 0-5
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) was designed to provide most of the key malaria indicators for the country as a whole, for urban and rural areas, and for 10 survey regions.

In addition, three study domains based on malaria endemicity were created to provide selected malaria indicators addressing NMCP/MOH programmatic needs: 1) to evaluate the effect of interventions such as indoor residual spraying (IRS) in the 10 districts in the north, 2) to provide baseline indicators for the 14 districts planned for future IRS programmes, and 3) provide estimates separately for high altitude areas with low malaria burden. The three study domains are arranged as follows: Domain 1: ten (10) districts in which IRS programmes are currently implemented; Domain 2: fourteen (14) districts planned for future IRS programmes (to provide baseline estimates); Domain 3: ten (10) high-altitude districts (low malaria burden areas).

Apart from the three study domains above, the region of Karamoja was over-sampled in order to be comparable to a DHS region, and the urban areas of Wakiso and Mukono districts, together with Kampala, were combined to form a special 'Greater Kampala' zone.

Each of the 10 regions and the 3 study domains comprise multiple administrative districts that share a similar malaria burden or have specific malaria prevention efforts. The capital city, Kampala, comprises its own district and is entirely urban.

The sampling frame used for the 2014-15 UMIS was the preparatory frame for the Uganda Population and Housing Census, which was conducted in August 2014. Provided by the Uganda Bureau of Statistics (UBOS), the sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks, and prisons.

The 2014-15 UMIS sample was selected using a stratified two-stage cluster design consisting of 210 clusters, with 44 in urban areas and 166 in rural areas. In the first stage, 20 sampling strata were created and clusters were selected independently from each stratum by a probability-proportional-to-size selection. In the selected clusters, a complete listing of households and a mapping exercise was conducted from 25 October to 20 November 2014, with the resulting list of households serving as the sampling frame for the selection of households in the second stage.

In the second stage of the selection process, 28 households were selected in each cluster by equal probability systematic sampling. Because of the nonproportional allocation of the sample to the different regions and study domains, the sample is not self-weighting. Weighting factors have been added to the data file so that the results will be representative at the national and regional level as well as the survey domain level.

All women age 15-49 who were either permanent residents of the households in the 2014-15 UMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 0-59 months who were either permanent residents of the sampled households or visitors present in the households on the night before the survey were eligible to be tested for malaria and anaemia.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 UMIS used two questionnaires: a Household Questionnaire and a Woman’s Questionnaire for women age 15-49 in the selected households. Both of these instruments were based on the model Malaria Indicator Survey questionnaires developed by the Roll Back Malaria Monitoring and Evaluation Research Group, as well as other questionnaires from previous surveys conducted in Uganda, including the 2009 UMIS. The Technical Working Group organised stakeholders’ meetings in Kampala to review the draft questionnaires. Stakeholders comprised a range of potential users, including government institutions, nongovernmental organisations, and interested donor groups. The questionnaires were translated from English into six local languages (Ateso/Karamajong, Luganda, Lugbara, Luo, Runyankole/Rukiga, and Runyoro/Rutoro).

The Household Questionnaire captured data on all usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for the individual interview and children eligible for anaemia and malaria testing. The Household Questionnaire was also used to collect responses on indicators of ownership and use of mosquito bed nets. In addition, the Household Questionnaire collected data on housing conditions and assets to calculate the measures of household wealth.

The Woman’s Questionnaire was used to collect data from women age 15-49 years, including: background characteristics (age, education, etc.); reproductive history (number of births, survival of births, etc.); current pregnancy status, intermittent preventive treatment for malaria during recent pregnancies; and antimalarial treatment for children under five with recent fever). It also collected information on knowledge about malaria.

Cleaning operations

All questionnaires for the 2014-15 UMIS were returned to the data processing centre at the UBOS headquarters in Kampala. Activities performed included office editing, data entry, and editing of computeridentified inconsistencies. The data were processed by a team consisting of one data entry supervisor, one assistant supervisor, 24 data entry operators, and 7 staff who performed tasks related to questionnaire administration, office editing, and secondary editing. Data entry and editing were accomplished using CSPro software. The process of office editing and data processing was initiated in January 2015 and completed in mid- February 2015.

Response rate

A total of 5,802 households were selected for the sample, of which 5,494 were occupied. Of the occupied households, 5,345 were successfully interviewed, yielding a response rate of 97 percent. The response rate among households in rural areas was slightly higher (98 percent) than the response rate in urban areas (96 percent).

In the interviewed households, 5,494 women were identified as eligible for the individual interview; interviews were completed with 5,322 women, yielding a response rate of 97 percent. The eligible women’s response rate does not differ by urban or rural residence. The principal reason for non-response among eligible women was failure to find individuals at home despite repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the selected 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 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) to minimize 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 2014-15 UMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is 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.

A sampling error is usually measured in terms of the standard error for a

BackgroundInfection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated.Methodology/Principal FindingsA total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).Conclusions/SignificanceWe have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.

Malaria poses a risk to approximately 3.3 billion people or approximately half of the world's population. Most malaria cases occur in Sub-Saharan Africa. Asia, Latin America, and to a lesser extent the Middle East and parts of Europe are also affected. According to the Global Malaria Report published by the World Health Organization (WHO), malaria was present in 106 countries and territories in 2010; and there were 216 million estimated cases of malaria and nearly 0.7 million deaths - mostly among children living in Africa.

In this research, we have estimated current population exposed to malaria - by country. In our computation, we have made the geographical distinction of areas with high, medium, low prevalence ("endemicity") of malaria in each country based on the Global malaria atlas compiled by the Malaria Atlas Project (MAP) of the Oxford University. The data are based on 24,492 parasite rate surveys (Plasmodiumfalciparum. 24,178; Plasmodium vivax. 8,866) from an aggregated sample of 4,373,066 slides prepared from blood samples taken in 85 countries. The MAP study employs a new cartographic technique for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007. These estimates are then compared with those derived under existing surveillance-based approaches to arrive at the final data used in the malaria mapping (Hay et al., 2009). (http://www.map.ox.ac.uk/media/maps/pdf/mean/World_mean.pdf, accessed 2012) Malaria maps generally separate the malaria endemicity into three broad categories by Plasmodium falciparum parasite rate (PfPR), a commonly reported index of malaria transmission intensity: PfPR < 5% as low endemicity, PfPR 5%-40% as medium/intermediate endemicity, and PfPR > 40% as high endemicity.

In our research, global mapping techniques were used to estimate population exposed to malaria. The malaria endemicity maps were overlaid on global population maps from Landscan 20051 (Dobson, 2000) and country-level population exposure in the three endemicity areas were computed. Due to the spatial reference of the data and the number of observations in the combined data, the use of Geographic Information Systems functions from ESRI ArcGIS (v 9.3.1) were used and automated in the python (v 2.5) language.

In 2023, there were approximately ***** cases of malaria reported in the European Union, an increase of over 1,000 cases compared to 2022. In this year, France had the most reported cases with over *****, followed by Germany at *** cases. This statistic displays the number of reported cases of malaria in Europe in 2023, by country.

Knowledge of the signs and symptoms of malaria in young children is vital, and combating low awareness of signs and symptoms of malaria is a priority of National Malaria control Programme (NMCP). Although there are a variety of symptoms caused by malaria, fever is the most common and should be recognized by caregivers as possibly being the result of malaria. All children under age 5 with fever should be tested for malaria and, for those who are infected,treated within 24 hours of the onset of fever.

Malaria Ag Rapid Test Kits Market Outlook

The global market size for Malaria Ag Rapid Test Kits was valued at approximately USD 700 million in 2023 and is projected to reach around USD 1.15 billion by 2032, growing at a CAGR of 5.5% during the forecast period. The growth factor for this market is primarily driven by the rising prevalence of malaria, particularly in regions with limited access to healthcare services, coupled with an increasing demand for quick and reliable diagnostic tools.

One of the major growth factors contributing to the Malaria Ag Rapid Test Kits market is the escalating incidence of malaria cases globally, especially in sub-Saharan Africa and Southeast Asia. Malaria continues to be a significant public health concern, with millions of new cases reported annually. The need for rapid and accurate diagnosis to initiate timely treatment is paramount in curbing the disease's spread and reducing mortality rates. Rapid test kits offer a convenient and effective solution, especially in remote areas where access to advanced healthcare facilities is limited.

Another crucial driver for the market is the growing emphasis on early and accurate diagnosis of malaria to reduce the disease burden. Healthcare authorities and organizations, including the World Health Organization (WHO), have been advocating for the widespread adoption of rapid diagnostic tests (RDTs) as part of their malaria control programs. The ease of use, affordability, and quick results offered by rapid test kits make them an attractive option for both healthcare providers and patients, thereby fueling market growth.

The increasing investment in research and development activities aimed at improving the sensitivity and specificity of malaria rapid test kits is also expected to propel market growth. Technological advancements have led to the development of next-generation test kits that offer enhanced accuracy and reliability. Furthermore, the integration of digital technologies and mobile health applications with rapid test kits is emerging as a notable trend, facilitating better disease surveillance and management.

From a regional perspective, the market dynamics vary significantly across different geographies. The Asia Pacific region, particularly countries like India and China, is witnessing substantial growth due to high malaria prevalence and increased government initiatives aimed at malaria eradication. In contrast, North America and Europe are experiencing moderate growth due to lower malaria incidence but increasing awareness and adoption of preventive measures among travelers and expatriates. Africa remains the largest market, driven by the high burden of malaria and the need for accessible diagnostic solutions.

Product Type Analysis

The Malaria Ag Rapid Test Kits market is segmented by product type into Pf/Pv Rapid Test Kits, Pf Rapid Test Kits, Pv Rapid Test Kits, and Others. Among these, Pf/Pv Rapid Test Kits hold a significant share of the market due to their ability to detect both Plasmodium falciparum and Plasmodium vivax infections simultaneously. These dual-detection kits are highly valued in regions where both malaria strains are prevalent, as they offer comprehensive diagnostic coverage in a single test, thereby streamlining the diagnostic process.

Pf Rapid Test Kits are specifically designed to detect Plasmodium falciparum, the most deadly malaria parasite responsible for the majority of malaria-related deaths worldwide. The demand for these kits is particularly high in sub-Saharan Africa, where Plasmodium falciparum is predominant. The market for Pf Rapid Test Kits is driven by the need for prompt and accurate diagnosis to initiate life-saving treatment and prevent complications associated with severe malaria.

Pv Rapid Test Kits, on the other hand, are tailored for detecting Plasmodium vivax, a malaria parasite more commonly found in regions like Southeast Asia and Latin America. Although less fatal than Plasmodium falciparum, Plasmodium vivax can cause recurrent infections and significant morbidity. The increasing focus on eliminating all forms of malaria, including Plasmodium vivax, is driving the demand for Pv Rapid Test Kits in these regions.

Other types of malaria rapid test kits, which may include those designed for detecting other less common Plasmodium species or offering unique features like enhanced sensitivity, also contribute to the market. These kits are often used in specialized settings or research initiatives aimed at understanding and controlling malaria transmissi

The evolutionary relationships among the apicomplexan blood pathogens known as the malaria parasites (order Haemosporida), some of which infect nearly 200 million humans each year, has remained a vexing phylogenetic problem due to limitations in taxon sampling, character sampling, and the extreme nucleotide base composition biases that are characteristic of this clade. Previous phylogenetic work on the malaria parasites has often lacked sufficient representation of the broad taxonomic diversity within the Haemosporida or the multi-locus sequence data needed to resolve deep evolutionary relationships, rendering our understanding of haemosporidian life history evolution and the origin of the human malaria parasites incomplete. Here we present the most comprehensive phylogenetic analysis of the malaria parasites conducted to date, using samples from a broad diversity of vertebrate hosts that includes numerous enigmatic and poorly known haemosporidian lineages in addition to genome-wide mul...

Malaria Test Kit Market Outlook

The global malaria test kit market size was valued at $XX billion in 2023 and is projected to reach $XX billion by 2032, growing at a CAGR of XX% during the forecast period. The market is being driven by several growth factors, including the increasing prevalence of malaria in certain regions, advancements in diagnostic technology, and growing awareness about the importance of early detection for effective treatment.

One of the primary growth factors for the malaria test kit market is the rising incidence of malaria, especially in tropical and subtropical regions. Malaria continues to be a significant public health challenge, affecting millions of people worldwide. Governments and international health organizations are investing heavily in malaria control and eradication programs, which include the provision of effective diagnostic tools. This growing focus on combating malaria is expected to drive the demand for malaria test kits significantly.

Additionally, advancements in diagnostic technologies have led to the development of more accurate and efficient malaria test kits. Rapid diagnostic tests (RDTs) and molecular diagnostics have revolutionized the way malaria is diagnosed, offering quicker and more reliable results than traditional methods like microscopy. The increasing adoption of these advanced diagnostic tools in both clinical and research settings is contributing to the expansion of the malaria test kit market. The continuous innovation in diagnostic technologies is expected to further propel market growth.

Another critical growth factor is the growing awareness about the importance of early detection and treatment of malaria. Early diagnosis significantly improves treatment outcomes and reduces the risk of severe complications and mortality. Health campaigns and educational programs conducted by governments, NGOs, and healthcare providers are raising awareness about the signs and symptoms of malaria and the need for prompt testing. This heightened awareness is driving the demand for malaria test kits in both endemic and non-endemic regions.

From a regional perspective, the Asia Pacific region holds the largest share of the malaria test kit market, followed by Africa. These regions have the highest burden of malaria cases, which drives the demand for diagnostic tools. In contrast, North America and Europe, with lower malaria incidence rates, have smaller market shares. However, the increasing trend of international travel and migration could lead to a rise in imported malaria cases in these regions, thereby creating a potential demand for malaria test kits. Government initiatives and funding in these regions also play a vital role in shaping the market dynamics.

Product Type Analysis

The malaria test kit market can be broadly segmented into three product types: Rapid Diagnostic Tests (RDTs), Microscopy, and Molecular Diagnostics. Each of these product types plays a unique role in diagnosing malaria, and their market dynamics are shaped by various factors, including technological advancements, ease of use, and accuracy.

Rapid Diagnostic Tests (RDTs) are one of the most widely used methods for diagnosing malaria. These tests are popular due to their simplicity, speed, and cost-effectiveness. RDTs can provide results in less than 30 minutes, making them ideal for use in remote and resource-limited settings. The growing demand for point-of-care diagnostics, particularly in endemic regions, is driving the RDT segment. Additionally, ongoing research and development efforts are focused on improving the sensitivity and specificity of these tests, further enhancing their market potential.

Microscopy has been the traditional gold standard for malaria diagnosis for many years. This method involves examining blood smears under a microscope to detect the presence of malaria parasites. While microscopy is highly accurate and allows for species identification and parasite quantification, it requires skilled laboratory personnel and is time-consuming. Despite these limitations, microscopy remains an important diagnostic tool in many healthcare settings, particularly where laboratory infrastructure is well-established. The segment's growth is supported by efforts to improve training and quality control in microscopy-based diagnostics.

Molecular Diagnostics represents the most advanced segment in the malaria test kit market. Techniques such as Polymerase Chain Reaction (PCR) offer unparalleled sensitivity and specificity, enabling the detection of low-