Background The use of seasonal malaria chemoprevention (SMC) to prevent malaria in children has expanded rapidly since it was recommended by the World Health Organisation (WHO) in 2012. Deriving consistent and comparable estimates of SMC coverage and adherence is challenging due to varied deployment approaches, coverage indicators, and implementing partners. Understanding where and to what extent SMC has been implemented is crucial for optimising deployment strategies and incorporating SMC into models of malaria burden. Methods Comprehensive campaign data were used to map where and when SMC was implemented. Household surveys provided robust measures of coverage and adherence, but were not available for all locations and years. We developed and validated a framework to select the most accurate method for imputing missing coverage and adherence data. Full-course adherence was calculated as the proportion of eligible children receiving the full 3-day treatment per cycle. Findings We provide the first standardised sub-annual and sub-national estimates and maps of SMC coverage and full-course adherence. Monthly estimates capture shifts in the timing and frequency of SMC implementation. The scope and population coverage of SMC has increased considerably since 2012. In 2024, across the 19 countries, we estimate that there were 204 (187-219) million child-months of treatments with the first dose and 185 (167-202) million child-months of treatments with reported adherence to the full three-day course. Interpretation We found widespread increase in uptake of SMC, with more children are being reached and covered with SMC for longer deployment periods. Our results provide a resource to support future evaluation of SMC deployment strategies and impact on regional and national malaria burden.

Background Malaria in urban areas is a growing concern in most sub-Saharan African countries. The growing threats of Anopheles stephensi and insecticide resistance magnify this concern and hamper elimination efforts. It is therefore imperative to identify areas, within urban settings, of high-risk of malaria to help better target interventions. Methods In this study, we combined a set of environmental, climatic, and urban covariates with observed data from a malaria prevalence study and used geospatial methods to predict malaria risk in the Greater Accra Region of Ghana. Georeferenced data from 12,371 surveyed children aged between 6 months and 10 years were included in the analysis. Results Predicted malaria prevalence in this age group ranged from 0 to 52%. Satellite-driven data on tasselled cap brightness, enhanced vegetation index and a combination of urban covariates were predictive of malaria prevalence in the study region. We produced a map that quantified the probability of malaria prevalence exceeding 10%. Conclusions This map revealed areas within the districts earmarked for malaria elimination that have high malaria risk. This work is providing evidence for use by the National Malaria Elimination Program and District Health Managers in planning and deploying appropriate malaria control strategies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Funding for the PhD work of the corresponding author is from the Higher Degree Research Scholarship from the Curtin University, Western Australia. This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation INV-009390/OPP1197730. The conclusions and opinions expressed in this work are those of the author(s) alone and shall not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Please note works submitted as a preprint have not undergone a peer review process. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The original study was approved by the Ethical review boards of the Ghana health service. Anonymized data for this work was obtained from the Kintampo Health Research Centre through the Ghana National Malaria Elimination Programme. This study is within a PhD projected under the Malaria Atlas Project with ethical approval from the Ethics committee of Curtin University under HRE2021-0734. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available on reasonable request, subject to ethics, governance, and privacy considerations. The code for the analysis is available on the personal GitHub portal of the corresponding author and will be made available when necessary.

Since their first detection in 2010, Plasmodium falciparum malaria parasites lacking the P. falciparum histidine-rich protein 2 gene (pfhrp2) have been observed in 40 of 47 surveyed countries, as documented by the World Health Organization. These genetic deletions reduce detection by the most widely used rapid diagnostic tests, prompting three countries to switch to alternative diagnostics. However, manufacturing of alternative rapid diagnostic tests has not been scaled up and there are no World Health Organization-prequalified combination tests that use P. falciparumPlasmodium lactate dehydrogenase. The continuing spread of pfhrp2 and/or pfhrp3 (pfhrp2/3) deletions threatens malaria control, creating an emerging public health crisis. Here we use mathematical modeling informed by current pfhrp2/3 deletion prevalence and a literature review to assess the global risk of pfhrp2/3 deletions. We identify ten priority countries for surveillance and predict that the primary spread in Africa will move southward from the Horn of Africa through East Africa within 20 years. Despite variation in modeled timelines due to uncertainty in model parameters, four countries yet to switch rapid diagnostic tests are consistently classified as high risk under a range of model assumptions. This updated model offers refined predictions to guide pfhrp2/3 policy and prioritize future surveillance efforts and innovation.

Eradication and elimination strategies for lymphatic filariasis (LF) primarily rely on multiple rounds of annual mass drug administration (MDA), but also may benefit from vector control interventions conducted by malaria vector control programs. We aim to examine the overlap in LF prevalence and malaria vector control to identify potential gaps in program coverage. We used previously published geospatial estimates of LF prevalence from the Institute for Health Metrics and Evaluation, as well as publicly available insecticide-treated net (ITN) access (proportion of the total population with access to ITNs) and use (proportion of the total population that slept under an ITN) estimates among the total population and malaria Plasmodium falciparum parasite rates (PfPR) from the Malaria Atlas Project (MAP). We aggregated the 5x5 km2 estimates of LF prevalence estimates and ITN estimates to the implementation unit (IU) level using fractional aggregation, for 33 LF and malaria-endemic locations in Africa, and then overlaid the IU-level aggregates. In this analysis, ITN coverage was low in areas where LF is common, with 51.7% (90/174) of high-LF-prevalence-IUs having both access and use estimates under 40%. Most (67.8%; 61/90) of these low-ITN-coverage, high-LF-prevalence locations were also categorized as high- or highest-prevalence for malaria by PfPR, suggesting suboptimal ITN coverage even in some malaria-co-endemic locations. Even in IUs with high LF prevalence but low malaria prevalence, almost half (48.2%; 39/81) had high levels of access to ITNs. When accounting for population, however, gaps in ITN access in such areas were evident: more individuals lived in high-LF, low-malaria IUs with low ITN access (8.68 million) than lived in high-LF, low-malaria IUs with high ITN access (6.76 million). These results suggest that relying on current malaria vector control programs alone may not provide sufficient ITN coverage for high LF prevalence areas. Opportunities for coordinated vector control programs in places where LF and malaria prevalence are high but ITN coverage is low - or additional ITN distribution in high-LF, low-malaria locations - should be explored to help achieve elimination goals.

Objectives: To characterise small-area geographical variation in the prevalence of diabetes in Australian youth. Methods: A combined statistical reconstruction and small-area estimation algorithm was applied to privacy-modulated data from the 2021 Australian Census. The census instrument and reconstruction accuracy was examined by comparisons against a hospital-based register and community register. Diabetes prevalence maps were created from the small-area estimates. Results: The median and interquartile range of estimated diabetes prevalence by small-area unit under our geospatial smoothing model were 1.76 [1.49–1.97] cases per 1000 population for those aged 0–14 years and 5.2 [4.4–5.9] cases per 1000 population for those aged 15–19 years old. Concentrations of elevated prevalence were identified in the vicinities of regional towns across South-East Queensland, regional New South Wales and regional Victoria. Across each of Australia’s five largest cities a gradient of decreasing youth diabetes prevalence from the outer suburbs to the urban centre was identified. Conclusion: Diabetes burden is systematically higher among rural and peri-urban resident youth in Australia compared with their urban counterparts. Implications for Public Health: Hotspots of prevalence in regional areas deserve attention from public health authorities.

The implications of climate change for malaria eradication in the 21st century remain poorly resolved. Many studies have focussed on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control, and the socioeconomic environment, including the disruptive impact of extreme weather. Here we integrate 25 years of data on climate, malaria burden, control interventions, socioeconomic factors, and extreme weather events in Africa. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario, we estimate the future impact of climate change on malaria burden in Africa, accounting for both ecological and disruptive effects. Our findings suggest climate change could lead to 123 million (projection range 49.5 million - 203 million) additional malaria cases and 532,000 (195,000 - 912,000) additional deaths in Africa between 2024 and 2050 under current control levels. Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50-94%) of additional cases and 93% (70%-100%) of additional deaths. Most increases are due to intensification in existing endemic areas rather than range expansion, with significant regional variation in impact. These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress toward malaria eradication in Africa. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Funding for this work was primarily from the Bill and Melinda Gates Foundation (INV-055192, INV-075583). PWG is also supported by an NHMRC Investigator Grant (2025280). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The downscaled and bias-corrected CMIP6 climate projections used in this analysis are available from: https://registry.opendata.aws/nex-gddp-cmip6/. Citations to other supporting datasets (on historical climate variables, flood and hydrological modelling, cyclone modelling, topology, population density, remotely sensed landcover classifications, socioeconomic indicators, malaria infection prevalence and control coverage) are provided in the manuscript or in full in Supplementary Annex Table 1.

BACKGROUND:Bacillus Calmette-Guérin (BCG) protects children from severe tuberculosis and remains the only licensed vaccine for tuberculosis. Subnational estimates of BCG coverage are essential for identifying underserved populations across Africa. This study aimed to map BCG vaccination coverage in Africa from 1990 to 2022. METHODS:We conducted an advanced Bayesian geostatistical analysis, integrating spatial covariates through stacked ensemble modelling and adjusting for spatial and residual effects to estimate BCG coverage. The analysis included 110 demographic and health surveys, and multiple indicator household surveys, comprising data on 180 449 children from 35 countries in Africa. BCG vaccination coverage among children aged 12-23 months was estimated at a 5 × 5 km spatial resolution. These estimates were then aggregated to second administrative and national levels over the study period weighting by the same age child population. Coverage changes were estimated by comparing coverage metrics in 1990 and 2022. The population-weighted number of unvaccinated children was also estimated at first administrative level. The likelihood of second administrative-level areas achieving the Global Vaccine Action Plan target of 80% coverage threshold in 2020 was quantified using posterior exceedance probabilities. FINDINGS:Between 1990 and 2022, BCG vaccination coverage increased by at least 25% in 85·32% (95% uncertainty interval 84·11-86·50) of second administrative level areas, with an average increase of 40·52%. Despite this progress, significant geographical disparities remain within and between countries. In 2022, many local areas still had low BCG coverage, including countries with high national BCG vaccination rates. Some countries showed notable improvements, but only a few (such as Burundi, Rwanda, Uganda, Gambia, and Togo) have achieved the Global Vaccine Action Plan target of 80% coverage in all second administrative level areas by 2020. INTERPRETATION:Despite significant progress in BCG vaccination across Africa over the last three decades, notable geographical disparities persist. These findings suggest a need to prioritise resources and boost BCG coverage, especially in underserved areas. FUNDING:Curtin University Strategic Scholarship and Australian National Health and Medical Research Council. TRANSLATIONS:For the Amharic, Icibemba and Swahili translations of the abstract see Supplementary Materials section.

BACKGROUND:Since its inception in 2005, the US President's Malaria Initiative (PMI) has played a major role in the reductions in malaria morbidity and mortality observed across Africa. With the status of PMI funding and operations currently uncertain, we aimed to quantify the impact that a fully functioning PMI would have on malaria cases and deaths in Africa during 2025. METHODS:We combined detailed spatiotemporal information on planned 2025 PMI and non-PMI malaria commodity procurement and distribution in Africa (focusing on insecticide-treated bednets, artemisinin-based combination therapies, seasonal malaria chemoprevention, and indoor residual spraying of insecticides) with spatiotemporal Bayesian models of intervention coverage and Plasmodium falciparum transmission and burden in Africa. By comparing coverage scenarios with and without planned PMI contributions (ie, business-as-usual and no-PMI scenarios), we estimated the number of malaria cases and deaths that PMI investment would avert in 2025. For all interventions, the no-PMI scenario was defined in the absence of any mitigation strategies. Coverage supported by PMI under the business-as-usual scenario was assumed to be lost. FINDINGS:We estimated that business-as-usual PMI contributions to vector control, seasonal malaria chemoprevention, and routine malaria treatment in Africa would avert 13·6 million (95% uncertainty interval [UI] 11·4-16·4 million) malaria cases and 104 000 (69 000-161 000) deaths in 2025. These estimates represent 11·3% (95% UI 9·9-12·9) of the total burden of malaria morbidity and 37·5% (34·2-41·5) of the total burden of malaria mortality in PMI's focus geographies across 27 African countries. These estimates do not account for the additional impact of PMI-supported provision of diagnostics or severe case management commodities, nor preventive treatment for pregnant individuals, which would further lessen these burdens. INTERPRETATION:PMI investment in supporting procurement and distribution of malaria control commodities would directly translate into millions of malaria cases averted and approximately 100 000 lives saved across its focus geographies in Africa throughout 2025. FUNDING:National Health and Medical Research Council, Australia.

Background:Vietnam, as one of the countries in the Greater Mekong Subregion, has committed to eliminating all malaria by 2030. Declining case numbers highlight the country's progress, but challenges including imported cases and pockets of residual transmission remain. To successfully eliminate malaria and to prevent reintroduction of malaria transmission, geostatistical modelling of vulnerability (importation rate) and receptivity (quantified by the reproduction number) of malaria is critical. Methods:Case data from 2019 to 2022 was used to train a range of network and geostatistical models, from which strategically useful metrics were computed. These metrics include vulnerability, which was estimated from the abundance of imported cases, and receptivity, which was estimated based on a transmission model linking cases as well as environmental covariate data. Findings:There is considerable spatiotemporal heterogeneity in the computed metrics. Importations are concentrated in the South Central Coast and Central highlands regions. The importation rate of Plasmodium falciparum is around 2.5 times higher than that of P. vivax. The mean computed reproduction number is less than one, which is consistent with the historical decline in cases and eventual elimination. There are, however, places where the estimated reproduction number can temporarily exceed one, which explains the seasonal case resurgence observed in the case data. The reproduction number is typically higher in forested areas. Interpretation:Receptivity and vulnerability to malaria is spatiotemporally heterogeneous in Vietnam. Despite the average reproduction number being less than one, the spatial pockets and temporal windows of elevated reproduction number could prevent timely elimination of malaria or even lead to a reversal of progress. The predictive maps presented in this paper can inform appropriate intervention strategies to advance goals of malaria elimination. Funding:This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [INV-055192 and INV-009390/OPP1197730]. The conclusions and opinions expressed in this work are those of the author(s) alone and shall not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Please note works submitted as a preprint have not undergone a peer review process. This work also includes funding support from the Australian Government, National Health and Medical Research Council (Award No: GNT2025280) and Telethon Trust, Western Australia.

BACKGROUND:Worldwide, tuberculosis (TB) remains the leading cause of death from infectious diseases. Africa is the second most-affected region, accounting for a quarter of the global TB burden, but there is limited evidence whether there is subnational variation of TB prevalence across the continent. Therefore, this study aimed to estimate sub-national and local TB prevalence across Africa. METHODS:We compiled geolocated data from 50 population-based surveys across 14 African countries. A total of 212 data points were identified and linked to covariates assembled from publicly available sources. Bayesian geostatistical modelling was used to predict TB prevalence across Africa, and results were aggregated to estimate number of TB cases at national and subnational levels. RESULTS:Here we estimate 1.28 million TB cases (95% uncertainty interval [UI] 0.14-4.87) across 14 countries, with marked spatial variations. The highest cases are estimated in Nigeria (460,247 95% UI 7954-1,783,106), and Mozambique (120,622 95%UI 20,027-321,177) while the lowest in Guinea-Bissau (1952 95%UI 154-7365) and Rwanda (2207 95% UI 1050-9225). National TB prevalence range from 0.25 to 7.32 per 1000 with significant variation at higher spatial resolution. Temperature (°C) (OR = 1.27; 95% CrI: 1.20-1.35), precipitation (mm) (OR = 1.34; 95% CrI: 1.26-1.40), and access to city (minute) (OR = 1.21; 95% CrI: 1.14-1.25) are positively associated with TB prevalence, while altitude (m) (OR = 0.83; 95% CrI: 0.78-0.87) is negatively associated. CONCLUSIONS:We find substantial variations in TB prevalence at national, sub-national, and local levels in Africa. These considerable spatial variations suggest the need for geographically targeted interventions to control TB in Africa.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

In the thirteen years since the first report of pfhrp2-deleted parasites in 2010, the World Health Organization (WHO) has found that 40 of 47 countries surveyed worldwide have reported pfhrp2/3 gene deletions. Due to a high prevalence of pfhrp2/3 deletions causing false-negative HRP2 RDTs, in the last five years, Eritrea, Djibouti and Ethiopia have switched or started switching to using alternative RDTs, that target pan-specific-pLDH or P. falciparum specific-pLDH alone of in combination with HRP2. However, manufacturing of alternative RDTs has not been brought to scale and there are no WHO prequalified combination tests that use Pf-pLDH instead of HRP2 for P. falciparum detection. For these reasons, the continued spread of pfhrp2/3 deletions represents a growing public health crisis that threatens efforts to control and eliminate P. falciparum malaria. National malaria control programmes, their implementing partners and test developers desperately seek pfhrp2/3 deletion data that can inform their immediate and future resource allocation. In response, we use a mathematical modelling approach to evaluate the global risk posed by pfhrp2/3 deletions and explore scenarios for how deletions will continue to spread in Africa. We incorporate current best estimates of the prevalence of pfhrp2/3 deletions and conduct a literature review to estimate model parameters known to impact the selection of pfhrp2/3 deletions for each malaria endemic country. We identify 20 countries worldwide to prioritise for surveillance and future deployment of alternative RDT, based on quickly selecting for pfhrp2/3 deletions once established. In scenarios designed to explore the continued spread of deletions in Africa, we identify 10 high threat countries that are most at risk of deletions both spreading to and subsequently being rapidly selected for. If HRP2-based RDTs continue to be relied on for malaria case management, we predict that the major route for pfhrp2 deletions to spread is south out from the current hotspot in the Horn of Africa, moving through East Africa over the next 20 years. We explore the variation in modelled timelines through an extensive parameter sensitivity analysis and despite wide uncertainties, we identify three countries that have not yet switched RDTs (Senegal, Zambia and Kenya) that are robustly identified as high risk for pfhrp2/3 deletions. These results provide a refined and updated prediction model for the emergence of pfhrp2/3 deletions in an effort to help guide pfhrp2/3 policy and prioritise future surveillance efforts and innovation.

We assess progress towards improved case management of childhood diarrhea in Nigeria over a period of targeted health systems reform from 2013 to 2018. Individual and community data from three Demographic and Health Survey rounds are leveraged in a geospatial model designed for stratified estimation by venue of treatment seeking and State. Our analysis reveals a highly regionalised health system undergoing rapid change. Nationally, there have been substantial increases in the proportion of children under 5 years old with diarrhea receiving the recommended oral rehydration therapy after seeking treatment at either a health clinic (0.57 [0.44-0.69; 95% CI] in 2008; 0.70 [0.54-0.83] in 2018) or chemist/pharmacy (0.28 [0.17-0.42] in 2008; 0.48 [0.31-0.64] in 2018). Yet State-level variations in venue attendance and performance by venue have conspired to hold the overall proportion receiving this potentially life-saving therapy (0.45 [0.35-0.55] in 2018) to well-below ideal coverage levels. High performing states that have demonstrated significant improvements include Kano, Jigawa and Borno, while under-performing states that have suffered declines in coverage include Kaduna and Taraba. The use of antibiotics is not recommended for mild cases of childhood diarrhea yet remains concerningly high nationally (0.27 [0.19-0.36] in 2018) with negligible variation between venues. Antibiotic use rates are particularly high in Enugu, Kaduna, Taraba, Kano, Niger and Kebbi, yet welcome reductions were identified in Jigawa, Adamawa and Osun. These results support the conclusions of previous studies and build the strength of evidence that urgent action is needed throughout the multi-tiered health system to improve the quality and equity of care for common childhood illnesses in Nigeria.

Testing and treating symptomatic malaria cases is crucial for case management, but it may also prevent future illness by reducing mean infection duration. Measuring the impact of effective treatment on burden and transmission via field studies or routine surveillance systems is difficult and potentially unethical. This project uses mathematical modeling to explore how increasing treatment of symptomatic cases impacts malaria prevalence and incidence. Leveraging the OpenMalaria stochastic agent-based transmission model, we first simulated an array of transmission intensities with baseline effective treatment coverages of 28

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Background:Bhutan has achieved a substantial reduction in both malaria morbidity and mortality over the last two decades and is aiming for malaria elimination certification in 2025. However, a significant percentage of malaria cases in Bhutan are imported (acquired in another country). The aim of the study was to understand how importation drives local malaria transmission in Bhutan. Methods:Information on geo-located individual-level laboratory-confirmed malaria cases between 2016 and 2020 was obtained from the Bhutan Vector-borne Disease Control Program. Records included the date of diagnosis and treatment, type of cases classified as indigenous or imported, and malaria species. Hawkes Processes were used to study the role of imported malaria in local transmission in Bhutan. We imposed 15 days delay for a mosquito to become infectious in the model. Findings:There were 285 cases during the study period and 58.6% (159) were imported malaria. 71.1% (113) of these imported cases were Plasmodium vivax and 73.6% (117) were from India. The model suggested that a person remains infectious for 8 days for Plasmodium falciparum malaria but over 19 days for P. vivax. The background intensity from imported malaria cases was much greater for P. vivax cases (maximum 0.17) resulting in more importations than P. falciparum cases (maximum 0.06). However, model fitting suggested that local P. falciparum transmission was mainly driven by importations but additional factors such as relapse played a role for P. vivax. Interpretation:Imported malaria cases are key drivers of transmission within Bhutan, with most cases since 2016 being P. vivax. Control programmes should be devised to target interventions towards the P. vivax strain and test those who are more likely to bring in imported malaria cases or acquire it from returning travellers. Funding:None.

BackgroundMalaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017-2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.MethodsA Bayesian geostatistical framework incorporating population data and treatment-seeking propensity was developed. The models incorporated static and dynamic factors and accounted for spatial heterogeneity.ResultsResults showed a significant decline in malaria cases in Laos over the five-year period and a shift in transmission patterns. While the north became malaria-free, the south experienced ongoing transmission with sporadic outbreaks.ConclusionThe risk maps provided insights into changing transmission patterns and supported risk stratification. These risk maps are valuable tools for malaria control in Laos, aiding resource allocation, identifying intervention gaps, and raising public awareness. The study enhances understanding of malaria transmission dynamics and facilitates evidence-based decision-making for targeted interventions in high-risk areas.

BACKGROUND:Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country.METHODS:TB prevalence data were obtained from the Ethiopia national TB prevalence survey and from a comprehensive review of published reports. Geospatial covariates were obtained from publicly available sources. A random effects meta-analysis was used to estimate a pooled prevalence of TB at the national level, and model-based geostatistics were used to estimate the spatial variation of TB prevalence at sub-national and local levels. Within the MBG Plugin Framework, a logistic regression model was fitted to TB prevalence data using both fixed covariate effects and spatial random effects to identify drivers of TB and to predict the prevalence of TB.RESULTS:The overall pooled prevalence of TB in Ethiopia was 0.19% [95% confidence intervals (CI): 0.12%-0.28%]. There was a high degree of heterogeneity in the prevalence of TB (I2 96.4%, P <0.001), which varied by geographical locations, data collection periods and diagnostic methods. The highest prevalence of TB was observed in Dire Dawa (0.96%), Gambela (0.88%), Somali (0.42%), Addis Ababa (0.28%) and Afar (0.24%) regions. Nationally, there was a decline in TB prevalence from 0.18% in 2001 to 0.04% in 2009. However, prevalence increased back to 0.29% in 2014. Substantial spatial variation of TB prevalence was observed at a regional level, with a higher prevalence observed in the border regions, and at a local level within regions. The spatial distribution of TB prevalence was positively associated with population density.CONCLUSION:The results of this study showed that TB prevalence varied substantially at sub-national and local levels in Ethiopia. Spatial patterns were associated with population density. These results suggest that targeted interventions in high-risk areas may reduce the burden of TB in Ethiopia and additional data collection would be required to make further inferences on TB prevalence in areas that lack data.