Abstract Background Malaria transmission in Uganda is heterogenous, so the national malaria program needs information about the distribution of malaria to develop appropriate policies. While population-based community surveys estimate Plasmodium falciparum parasite rate ( Pf PR), they are too infrequent and sparse for routine malaria management. Health facility data is routinely collected and covers a large geographic scope, but the data is collected passively, variable in quality, and potentially highly biased. We aimed to triangulate test positivity rate (TPR) from health facility data to survey estimated Pf PR data in Uganda to create monthly, high-resolution Pf PR estimates. Methods Using matched health facility and survey data, we fit a multi-level logistic regression model that accounted for clustering at the district and region level, to predict Pf PR from TPR. Additional covariates were explored to select a final model that reduced bias while prioritizing its utility for programmatic tasks. Model predictions were validated against observed Pf PR and used to generate monthly district-level prevalence estimates from 2016 to 2024. Regional and national level estimates were made by weighting district level estimates by population. Results The final model included a smoothed TPR term and proportion of severe malaria cases at a district-month level. Predicted Pf PR was strongly positively correlated with the observed survey Pf PR (Pearson’s rank correlation rho =0.79, p<0.001). National estimates derived from predicted Pf PR aligned well with survey estimates from the same time and area. Conclusion Health Management Information System (HMIS) data, when paired with research data, can be used to estimate malaria prevalence with high spatial and temporal resolution. Estimates can be tested and models can be updated to help malaria programs best leverage facility data. In the context of declining survey frequency, HMIS-based modeling offers a resilient and cost-effective alternative for malaria surveillance and programmatic decision-making in Uganda and similar high-burden settings.

Background. Long-lasting insecticidal nets (LLINs) are commonly distributed through mass distribution campaigns (MDCs). MDCs are commonly followed by substantial loss of LLINs, affecting coverage. Here, data from an MDC and a malaria indicator survey (MIS) on Bioko Island were used to characterize LLIN mobility. Methods. Between October 2014 and July 2015, an MDC reached most of the households of the island. Distributed nets were marked with a unique code corresponding to the community where they were distributed. An MIS conducted in August and September 2015 allowed to measure LLIN loss and mobility by verifying the number of existing nets and their community code. Multivariate models identified factors associated with LLINs with mismatched codes. A source-sink analysis measuring risk gradient between the source community and the sink household was used to assess the overall effect of migrating nets. Results. The MIS revealed that 40.4% of LLINs distributed during the MDC had been lost. Among the surviving nets (7,393/12,397), community codes were visible only for 3,413 (46.2%), and 551 (16.1%) revealed a mismatched code. The factor most strongly associated with mismatched LLINs was open eaves (OR: 1.88; 95% CI: 1.48 – 2.38, p < 0.001). The source sink analysis showed that while the majority of LLINs migrated from lower to higher risk areas (62.3%), almost a third migrated in the opposite direction. Conclusion. While redistribution had a positive impact on some communities by serving populations at higher risk of transmission, many households in high-risk areas lost their nets to areas with lower risk. This, coupled with the significant LLIN loss post-MDC, pointed to critical inefficiencies and prompted a shift in LLIN distribution strategy from MDC to demand-driven fixed distribution points.

Abstract Timely processing of routine health facility surveillance data is essential for responsive malaria control, yet the path from raw electronic reports to actionable intelligence remains a largely undocumented challenge in endemic countries. We present an open-source Extract-Transform-Load (ETL) software system and accompanying metadata R package ( ramptools ) that together convert raw DHIS2 health facility data into cleaned, version-controlled, analysis-ready datasets for Uganda’s National Malaria Elimination Division (NMED). The ETL pipeline is implemented in R and deployed on a cloud platform with scripts running on automated schedules to keep the database current. The system extracts malaria indicators from two successive DHIS2 instances via the DHIS2 Web API, applies a two-stage outlier detection algorithm combining variance-based screening with STL decomposition, imputes missing values through seasonal interpolation, enforces logical consistency constraints across indicator cascades, and aggregates facility-level data through a six-level administrative hierarchy. All raw data are stored in an append-only versioned schema, enabling reconstruction of the database state at any historical point. The ramptools package provides standardized metadata—including an indicator crosswalk mapping 118 indicators across DHIS2 instances, a location hierarchy of 11,229 organizational units, geolocated health facility attributes, and administrative boundary shapefiles—as lazy-loaded R data objects. We validate the system through a proof-of-principle outbreak detection application that consumes ETL outputs to compute district-level outbreak indices via kernel-smoothed time series analysis, deployed as an interactive Shiny dashboard. The software has been in development since 2020, processing weekly and monthly data for approximately 8,700 health facilities. All code is open source (MIT license) and hosted on GitHub.

The implications of climate change for malaria eradication this century remain poorly resolved1,2. Many studies focus on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control and the socioeconomic environment, including disruption from extreme weather3,4. Here we integrate 25 years of African data on climate, malaria burden and control, socioeconomic factors, and extreme weather. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario5, we estimate the future impact of climate change on malaria burden in Africa, including both ecological and disruptive effects. Our findings indicate that climate change could lead to 123 million (projection range 49.5 million to 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 stem from 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 towards malaria eradication.

Over the last 20 years, malaria transmission on Bioko Island, Equatorial Guinea has declined dramatically thanks to the implementation of robust malaria control activities, centered around island-wide indoor residual spraying (IRS). In 2024 Bioko Island experienced a lapse in malaria control funding, and as a result vector control activities (including IRS) were interrupted. However, agreements with funders allowed for both a previously planned malaria indicator survey (MIS) and the subsequent implementation of indoor residual spraying (IRS) in late 2024. This study analyses routine case data from public health facilities from 2019-2024 and annual cross-sectional MIS data from 2019- 2024 using interrupted time series methods to quantify the impact of the interruption and reestablishment of control activities on Bioko Island. In 2024, the number of confirmed cases reported was 41% higher than the 2021-2023 average, and the Plasmodium falciparum prevalence rate ( Pf PR) rose by three percentage points. Statistical modeling estimated that 25.3% (95% CI 12.0-36.3%) of 2024 cases were avertable if control activities had been maintained, and that the interruption was associated with an increased Pf PR in 2024, above previous trends (adjusted OR 1.16, 95% CI 1.05-1.29). Moreover, the reintroduction in IRS in late 2024 was found to have averted an estimated additional 7.0% (95% CI 3.0-10.2%) increase in confirmed cases. In just one year with interruptions to control, malaria transmission and burden quickly resurged on Bioko Island. However, Bioko’s experience demonstrates that the reestablishment of control activities can equally rapidly contain, and reverse resurgence associated with control interruptions.

Mosquito ecology and behavior and malaria parasite development display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g. urbanization, socioeconomic development, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor can be masked by others. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We review two broad types of study that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology, and ii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many. Our work outlines gaps in modelling that we believe are priorities for future research.

The epidemiology of Plasmodium falciparum malaria presents a unique set of challenges due to the complicated dynamics of infection, immunity, disease, and detection. Studies of malaria epidemiology commonly measure malaria parasite densities or prevalence, but since malaria is so complex with so many factors to consider, a complete mathematical synthesis of malaria epidemiology has been elusive. Here, we take a new approach. From a simple model of malaria exposure and infection in human cohorts as they age, we develop random variables describing the multiplicity of infection (MoI) and the age of infection (AoI). Next, using the MoI and AoI distributions, we develop random variables describing parasite densities, parasite counts, and detection. We also derived a random variable describing the age of the youngest infection (AoY), which can be used to compute approximate parasite densities in complex infections. Finally, we derive a simple system of differential equations with hybrid variables that track the mean MoI, AoI and AoY, and we show it matches the complex probabilistic system with reasonable accuracy. We can thus compute the state of any individual chosen at random from the population in two ways. The same approach - pairing random variables and hybrid models - can be extended to model other features of malaria epidemiology, including disease, malaria immunity, treatment and chemoprotection, and infectiousness. The computational simplicity of hybrid models has some advantages over compartmental models and stochastic individual-based models, and with the supporting probabilistic framework, provide a sound basis for a synthesis of observational malaria epidemiology.

Objective:To test 50% indoor residual spraying coverage (percentage of households sprayed) for non-inferiority against the recommended 80% coverage for malaria control. Methods:Indoor residual spraying was done in 2021 and 2022 on Bioko, Equatorial Guinea, in a control arm (80% coverage) and intervention arm (50% coverage) with 37 clusters each. We assessed malaria infection in a representative sample of the population during annual surveys using rapid diagnostic tests. We compared the change in the odds of Plasmodium falciparum infection between baseline and post-intervention using difference-in-differences analysis within a survey-weighted binomial generalized linear model. Given differences between the arms at baseline, we adjusted the model for indoor residual spraying coverage at baseline. Findings:Relative to baseline, the odds of malaria infection post-intervention were 1.11 (95% confidence interval, CI: 0.81-1.52) in the 80% arm and 0.97 (95% CI: 0.72-1.29) in the 50% arm. In the adjusted model, the change in the odds of P. falciparum infection was no greater in the intervention arm than in the control arm (odds ratio: 0.89; 95% CI: 0.58-1.36), with the upper CI being lower than the non-inferiority margin of 1.43. Conclusion:There was no evidence that 50% coverage was inferior in preventing malaria, which supports the use of this target in settings where this level makes indoor residual spraying feasible by increasing the cost-effectiveness and equity of the intervention.

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.

Malaria remains a leading cause of morbidity and mortality worldwide, with sub Saharan Africa bearing the highest burden. Stalled progress under an inadequate budget and the expectation that funding would be further constrained call for an evaluation of a worst case scenario to appreciate malaria transmission potential in the absence of interventions. We examine a scenario in which all funding for interventions ceases for several years, allowing population immunity to adjust to a new equilibrium amid a surge in transmission, while economic development, climate, and demographics remain static, which we define as the transmission niche or baseline prevalence. The baseline proposed here is a set of stratified PfPr2-10 observations to select only samples with low intervention histories, preserving diverse epidemiological contexts. We further developed a bespoke contrastive deep learning architecture applied to 30 meter resolution images from Landsat 8 satellites, generating a detailed vector covariate set based on observable land status, which helps produce state of the art malaria estimates, an improvement over all previous covariate-based models. These features were integrated into a Bayesian MCMC model with regularisation to estimate baseline PfPr2-10 and R0 to generate a map of malaria incidence over all of Sub-Saharan Africa. Using population data from the World Malaria Report 2024, our model estimates 422 (275-582) million cases across sub-Saharan Africa for this worst case baseline scenario, reflecting a 131% increase compared to previous business-as-usual baseline scenario estimates. Our analyses highlight the effect of long-term benefits of two decades of investments for malaria control and the critical need for sustained intervention efforts and informed policy-making to mitigate potential resurgences in malaria transmission ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement KC acknowledges the grant support from William Demant Fonden (24-3395). SB acknowledges support from the Novo Nordisk Foundation via the Novo Nordisk Young Investigator Award (NNF20OC0059309), which also funds KC and AK. SB acknowledges the Danish National Research Foundation (DNRF160) through the chair grant which also supports NS. SB acknowledges support from The Eric and Wendy Schmidt Fund For Strategic Innovation via the Schmidt Polymath Award (G-22-63345). SB acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1) funded by the UK Medical Research Council (MRC). This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking. SB is funded by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency, Imperial College London and LSHTM (grant code NIHR200908). DAD is supported by a Novo Nordisk Fonden Data Science Emerging Investigator grant (NNF23OC0084647). Disclaimer-The views expressed are those of the author(s) and not necessarily those of the NIHR, UK Health Security Agency or the Department of Health and Social Care. DAD acknowledges support from the Novo Nordisk Foundation via the Emerging Data Science Investigator award (NNF23OC0084647). SM would like to acknowledge National Research Foundation via The NRF Fellowship Class of 2023 (NRF-NRFF15-2023-0010) award. ### 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 study used Demographic Health Surveys Data- https://www.dhsprogram.com/Data/, World Malaria Report Data- https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024, and LandSat 8 Satellite data- https://developers.google.com/earth-engine/datasets/catalog/landsat 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 All data produced in the present study are available upon request at the different data sources, available online at https://www.dhsprogram.com/Data/, https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024, and https://developers.google.com/earth-engine/datasets/catalog/landsat

Mosquito dispersal plays an important role in mosquito ecology and mosquito-borne pathogen transmission. While reaction-diffusion and patch-based models with simple flux assumptions for emigration have played a predominant role in modeling mosquito dispersal, mosquito behavioral ecology - in particular, the process of searching for resources - is usually ignored by diffusion-based models. We thus set out to analyze mosquito movement using highly mimetic models, to see what we could learn from a different approach. Here, we explore mosquito dispersal in behavioral state microsimulation models, in which mosquitoes are in behavioral states and search for the required resources that are distributed on a landscape. Models of this sort are laborious and challenging to work with, so we developed ramp.micro, an R package to build, solve, analyze, and visualize behavioral state microsimulation models for mosquitoes. We show that even when resources are distributed randomly and uniformly, mosquito populations tend to form highly spatially structured communities. We also show that some heterogeneity in mosquito population densities is attributed to features of a network defined by searching and the spatial distribution of resources. These models highlight the importance of understanding mosquito behaviors and resource availability as factors structuring mosquito population movement. Motivated by these dynamics, spatial models for mosquito ecology and mosquito-borne pathogen transmission would benefit from considering resource availability as a factor affecting mosquito movement and dispersal.

Indoor residual spraying (IRS) is one of the core vector control interventions available to malaria control programs. Normative and scientific guidance has long held that very high IRS coverage (at least 80 to 85% houses sprayed) is necessary to provide community protection, but there is little evidence backing these recommendations, in large part due to the operational and ethical concerns that conducting appropriate trials of differing IRS coverage levels would raise. The present study leverages data from four years of targeted IRS implementation on Bioko Island, Equatorial Guinea, to estimate a dose–response curve of IRS coverage. Due to the observational nature of the data, a double robust causal inference technique was utilized. The results suggest that at all spatial scales examined a threshold providing community protection was reached at much lower coverage levels than previously assumed (30 to 50%). Sensitivity analysis corroborated this result across multiple methods, but there was less agreement on whether extremely high coverage ( ≥ 85%) has additional benefit. A secondary analysis of the impact of changing operational coverage targets found that significantly reducing coverage targets (to 30 to 60%) could provide nearly the same protection as maintaining the existing 80% target in Bioko. While these findings are limited in strength by the observational nature of the data and may be specific to the context of Bioko Island, they raise important questions for further research on how IRS coverage impacts epidemiological outcomes and on how malaria control programs should set programmatic IRS coverage targets.

BACKGROUND:Malaria remains a leading cause of illness and death globally, with countries in sub-Saharan Africa bearing a disproportionate burden. Global high-resolution maps of malaria prevalence, incidence, and mortality are crucial for tracking spatially heterogeneous progress against the disease and to inform strategic malaria control efforts. We present the latest such maps, the first since 2019, which cover the years 2000-22. The maps are accompanied by administrative-level summaries and include estimated COVID-19 pandemic-related impacts on malaria burden. METHODS:We initially modelled prevalence of Plasmodium falciparum malaria infection in children aged 2-10 years in high-burden African countries using a geostatistical modelling framework. The model was trained on a large database of spatiotemporal observations of community infection prevalence; environmental and anthropogenic covariates; and modelled intervention coverages for insecticide-treated bednets, indoor residual spraying, and effective treatment with an antimalarial drug. We developed an additional model to incorporate disruptions to malaria case management caused by the COVID-19 pandemic. The resulting high-resolution maps of infection prevalence from 2000 to 2022 were subsequently translated to estimates of case incidence and malaria mortality. For other malaria-endemic countries and for Plasmodium vivax estimates, we used routine surveillance data to model annual case incidence at administrative levels. We then converted these estimates to infection prevalence and malaria mortality, and spatially disaggregated administrative-level results to produce high-resolution maps. Lastly, we combined the modelled outputs to produce global maps and summarised tables that are suitable for assessing changing malaria burden from subnational to global scales. FINDINGS:We found an ongoing plateau in rates of malaria infection prevalence and case incidence within sub-Saharan Africa, with consistent year-on-year improvements not evident since 2015. Due to the concentration of malaria burden in sub-Saharan Africa and the region's rapid population growth relative to other endemic regions, we estimate that 2022 had 234·8 (95% uncertainty interval 179·2-299·0) million clinical cases of P falciparum malaria, the most since 2004. Despite these findings, deaths from malaria continued to decline in sub-Saharan Africa and consequently globally after 2015, except for the COVID-19-impacted years of 2020-22. Similarly, progress in reducing P falciparum and P vivax morbidity outside Africa continued despite stalled progress globally. However, a major malaria outbreak in Pakistan following intense flooding in 2022 resulted in a reversal in this improving trend and contributed heavily to the global total of 12·4 (10·7-14·8) million clinical cases of P vivax malaria. Within Africa, we found that the plateau in infection prevalence occurred earlier in more densely populated areas, whereas more sparsely populated regions have continued a trajectory of modest improvement. INTERPRETATION:The unprecedented investment in malaria control since the early 2000s has averted an enormous amount of malaria burden. However, case incidence rates in Africa have flattened, and with a rapidly growing population at risk, the number of P falciparum cases in Africa, and thus globally, is now comparable to levels before the surge of investment. Outside Africa progress against malaria morbidity continued after 2015, but a resurgence of P vivax cases in 2022 underscores the fragility of progress against malaria in the face of climatic shocks. COVID-19-related disruptions led to increased malaria cases and deaths, but the impact was less severe than feared, in part because endemic countries continued to prioritise malaria control during the pandemic. Nevertheless, improved tools and strategies remain urgently needed to regain momentum against this disease. FUNDING:Bill & Melinda Gates Foundation and Australian National Health and Medical Research Council.

Importation of malaria infections is a suspected driver of sustained malaria prevalence on areas of Bioko Island, Equatorial Guinea. Quantifying the impact of imported infections is difficult because of the dynamic nature of the disease and complexity of designing a randomized trial. We leverage a six-month travel moratorium in and out of Bioko Island during the initial COVID-19 pandemic response to evaluate the contribution of imported infections to malaria prevalence on Bioko Island. Using a difference in differences design and data from island wide household surveys conducted before (2019) and after (2020) the travel moratorium, we compare the change in prevalence between areas of low historical travel to those with high historical travel. Here, we report that in the absence of a travel moratorium, the prevalence of infection in high travel areas was expected to be 9% higher than observed, highlighting the importance of control measures that target imported infections.

BackgroundSince 2015, malaria vector control on Bioko Island has relied heavily upon long-lasting insecticidal nets (LLIN) to complement other interventions. Despite significant resources utilised, however, achieving and maintaining high coverage has been elusive. Here, core LLIN indicators were used to assess and redefine distribution strategies.MethodsLLIN indicators were estimated for Bioko Island between 2015 and 2022 using a 1x1 km grid of areas. The way these indicators interacted was used to critically assess coverage targets. Particular attention was paid to spatial heterogeneity and to differences between urban Malabo, the capital, and the rural periphery.ResultsLLIN coverage according to all indicators varied substantially across areas, decreased significantly soon after mass distribution campaigns (MDC) and, with few exceptions, remained consistently below the recommended target. Use was strongly correlated with population access, particularly in Malabo. After a change in strategy in Malabo from MDC to fixed distribution points, use-to-access showed significant improvement, indicating those who obtained their nets from these sources were more likely to keep them and use them. Moreover, their use rates were significantly higher than those of whom sourced their nets elsewhere.ConclusionsStriking a better balance between LLIN distribution efficiency and coverage represents a major challenge as LLIN retention and use rates remain low despite high access resulting from MDC. The cost-benefit of fixed distribution points in Malabo revealed significant advantages, offering a viable alternative for ensuring access to LLINs to those who use them.

Background Rapid diagnostic tests (RDTs) that detect Plasmodium falciparum histidine-rich protein-2 (PfHRP2) are exclusively deployed in Uganda, but deletion of the pfhrp2/3 target gene threatens their usefulness as malaria diagnosis and surveillance tools. Methods A cross-sectional survey was conducted at 40 sites across four regions of Uganda in Acholi, Lango, W. Nile and Karamoja from March 2021 to June 2023. Symptomatic malaria suspected patients were recruited and screened with both HRP2 and pan lactate dehydrogenase (pLDH) detecting RDTs. Dried blood spots (DBS) were collected from all patients and a random subset were used for genomic analysis to confirm parasite species and pfhrp2 and pfhrp3 gene status. Plasmodium species was determined using a conventional multiplex PCR while pfhrp2 and pfhrp3 gene deletions were determined using a real-time multiplex qPCR. Expression of the HRP2 protein antigen in a subset of samples was further assessed using a ELISA. Results Out of 2435 symptomatic patients tested for malaria, 1504 (61.8%) were positive on pLDH RDT. Overall, qPCR confirmed single pfhrp2 gene deletion in 1 out of 416 (0.2%) randomly selected samples that were confirmed of P. falciparum mono-infections. Conclusion These findings show limited threat of pfhrp2/3 gene deletions in the survey areas suggesting that HRP2 RDTs are still useful diagnostic tools for surveillance and diagnosis of P. falciparum malaria infections in symptomatic patients in this setting. Periodic genomic surveillance is warranted to monitor the frequency and trend of gene deletions and its effect on RDTs.

OBJECTIVES:The accuracy of malaria rapid diagnostic tests is threatened by Plasmodium falciparum with pfhrp2/3 deletions. This study compares gene deletion prevalence determined by multiplex real time polymerase chain reaction (qPCR) and conventional polymerase chain reaction (cPCR) using existing samples with clonality previously determined by microsatellite genotyping. METHODS:Multiplex qPCR was used to estimate prevalence of pfhrp2/3 deletions in three sets of previously collected patient samples from Eritrea and Peru. The qPCR was validated by multiplex digital polymerase chain reaction. Sample classification was compared with cPCR, and receiver operating characteristic curve analysis was used to determine the optimal ΔCq threshold that aligned the results of the two assays. RESULTS:qPCR classified 75% (637 of 849) of samples as single, and 212 as mixed-pfhrp2/3 genotypes, with a positive association between clonality and proportion of mixed-pfhrp2/3 genotype samples. The sample classification agreement between cPCR and qPCR was 75.1% (95% confidence interval [CI] 68.6-80.7%) and 47.8% (95% CI 38.9-56.9%) for monoclonal and polyclonal infections. The qPCR prevalence estimates of pfhrp2/3 deletions showed almost perfect (κ = 0.804, 95% CI 0.714-0.895) and substantial agreement (κ = 0.717, 95% CI 0.562-0.872) with cPCR for Peru and 2016 Eritrean samples, respectively. For 2019 Eritrean samples, the prevalence of double pfhrp2/3 deletions was approximately two-fold higher using qPCR. The optimal threshold for matching the assay results was ΔCq = 3. CONCLUSIONS:Multiplex qPCR and cPCR produce comparable estimates of gene deletion prevalence when monoclonal infections dominate; however, qPCR provides higher estimates where multi-clonal infections are common.

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible—ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project’s documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Abstract Background Artemisinin-based combination therapy (ACT) is currently recommended for treatment of uncomplicated malaria. However, the emergence and spread of partial artemisinin resistance threatens their effectiveness for malaria treatment in sub-Saharan Africa where the burden of malaria is highest. Early detection and reporting of validated molecular markers (pfk13 mutations) in Plasmodium falciparum is useful for tracking the emergence and spread of partial artemisinin resistance to inform containment efforts. Methods Genomic surveillance was conducted at 50 surveillance sites across four regions of Uganda in Karamoja, Lango, Acholi and West Nile from June 2021 to August 2023. Symptomatic malaria suspected patients were recruited and screened for presence of parasites. In addition, dried blood spots (DBS) were collected for parasite genomic analysis with PCR and sequencing. Out of 563 available dried blood spots (DBS), a random subset of 240 P. falciparum mono-infections, confirmed by a multiplex PCR were selected and used for detecting the pfk13 mutations by Sanger sequencing using Big Dye Terminator method. Regional variations in the proportions of pfk13 mutations were assessed using the chi square or Fisher’s exact tests while Kruskal–Wallis test was used to compare absolute parasite DNA levels between wild type and mutant parasites. Results Overall, 238/240 samples (99.2%) contained sufficient DNA and were successfully sequenced. Three mutations were identified within the sequenced samples; pfk13 C469Y in 32/238 (13.5%) samples, pfk13 A675V in 14/238 (5.9%) and pfk13 S522C in (1/238 (0.42%) samples across the four surveyed regions. The prevalence of pfk13 C469Y mutation was significantly higher in Karamoja region (23.3%) compared to other regions, P = 0.007. The majority of parasite isolates circulating in West Nile are of wild type (98.3), P = 0.002. Relative parasite DNA quantity did not differ in samples carrying the wild type, C469Y and A675V alleles (Kruskal–Wallis test, P = 0.6373). Conclusion Detection of validated molecular markers of artemisinin partial resistance in multiple geographical locations in this setting provides additional evidence of emerging threat of artemisinin partial resistance in Uganda. In view of these findings, periodic genomic surveillance is recommended to detect and monitor levels of pfk13 mutations in other regions in parallel with TES to assess potential implication on delayed parasite clearance and associated treatment failure in this setting. Future studies should consider identification of potential drivers of artemisinin partial resistance in the different malaria transmission settings in Uganda.

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework-human population strata, patches, and aquatic habitats-interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the "vectorial capacity matrix," a human transmitting capacity distribution matrix, and threshold conditions. An R package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.