Malaria Journal

Malaria molecular surveillance (MMS) is becoming increasingly common in endemic settings and has been proposed as a tool for monitoring parasite transmission to inform programmatic decision-making. However, the conditions under which parasite genetic metrics provide interpretable signals for broader use cases, such as assessing intervention impacts and detecting importation, remain under-characterized. We present EMOD with Full Parasite Genetics (FPG), a simulation framework designed to explore how parasite genetic metrics arise from transmission, intervention, importation, and sampling processes at programmatically relevant timescales. Using seasonal scenarios across a range of transmission intensities, we demonstrate three principal findings. First, genetic metrics can detect insecticide-treated net intervention impacts at seasonal and yearly timescales, but the strength, timing, and form of the relationship between genetic and epidemiological measures vary by metric and sampling timing. Second, importation can break the expected relationship between parasite genetic diversity from local transmission intensity at very low incidence, allowing low-transmission settings with substantial importation to maintain elevated diversity metrics. Third, convenience sampling practices, including sample size, collection timing, and the clinical composition of sampled populations, introduce non-random biases in genetic metric estimation in a way that obscures the true transmission signal. Together, these findings show that parasite genetic metrics can support operational surveillance, but that their interpretation depends on transmission context, importation, metric choice, and sampling design. EMOD FPG provides a framework for evaluating these dependencies in future setting-specific analyses and for guiding the interpretation of parasite genetic data across sites and over time.

Introduction: Malaria during pregnancy is a major risk factor for low birth weight (LBW) in newborns, which in turn negatively affects the growth and development of the child. The World Health Organization (WHO) recommended interventions for pregnant women living in malaria endemic countries that include the use of intermittent preventive treatment in pregnancy (IPTp). However, WHO asserts that the coverage of pregnant women taking the recommended doses of IPTp are still very low. The primary goal of this study was to estimate the effects of increasing the coverage of doses of IPTp and to assess the effect of pregnancy timing in relation to seasonal transmission on malaria infections during pregnancy and neonates with LBW. We explored these effects in moderate and high transmission settings. Methods and Findings: A compartmental mathematical model depicting malaria during pregnancy with IPTp doses was formulated to analyze the effects of IPTp, insecticide treated net (ITN) use and seasonal variations in moderate and high malaria transmission settings. Our simulation findings suggest that increasing both ITN use and IPTp dose coverages to high levels, prevents 90% and 84% clinical cases for pregnancies starting in August in moderate and high transmission, respectively. Our model predicts that increasing the coverage of the first dose of IPTp to 90%, while lowering subsequent doses, averts 44% and 37% LBW cases for the August cohort in moderate and high transmission settings, respectively. Unprotected pregnancies overlapping the January peak in rainfall and malaria incidence during the third trimester experience the highest LBW burden. Conclusions: The highest IPTp coverage prevents the highest number of LBWs providing evidence of the benefits of scaling up IPTp. Overall, our results demonstrate that increasing ITN use has a substantial impact in reducing clinical malaria cases during pregnancy and improves birth outcomes. This highlights its importance as a key intervention, and the health benefits it would provide for malaria control goals for pregnant women. Pregnancies that overlap with the epidemic peaks in later trimesters lead to a rise in LBWs, indicating the necessity of protecting pregnant women at risk of malaria infection till delivery.

Gene expression analysis in malaria parasites has been used to define transcriptional regulatory networks but has been used less frequently to characterize parasite response to drug treatment or to show how parasites may evade killing. Here, we applied single-cell RNA sequencing (scRNA-seq) to hundreds of thousands of individually infected asynchronous red blood cells to evaluate the parasites response to treatment with three chemotypes that can be used for treatment (artemisinin) or prophylaxis and treatment (atovaquone, ganaplacide). We found that each treatment gave rise to different cell populations with different transcriptional profiles. Comparing single cell transcription patterns in compound-treated cells, to transcript patterns observed previously with synchronized cells showed an enrichment of cells expressing gametocyte-associated genes after artemisinin treatment but fewer lifecycle perturbations after treatment with the two other compounds. In contrast, bulk analysis showed an enrichment of pyrimidine biosynthesis transcripts for atovaquone treatment. Our results show that scRNA-seq may be used to profile diverse drug responses across many lifecycle stages and to potentially classify drug classes. ImportanceDetermining the mechanism of action (MOA) of compounds with antimalarial activity remains a key activity in both drug development and drug resistance studies but remains challenging for some chemotypes. Here we highlight the potential of single cell transcriptional sequencing to augment the process of MOA deconvolution. We develop a new analytical pipeline that involves comparing single cell transcription patterns to existing profiles from synchronized parasites to comprehensively characterize life cycle stage enrichments that may be observed after chemical perturbations. We also show that transcriptional feedback regulation may be present for some drug classes.

Malaria control programs are increasingly tailored at subnational scales; however, neighboring areas remain connected through human mobility, allowing parasite importation that may undermine independently timed interventions. Although the spatial targeting of control has been the focus of extensive research, the epidemiological consequences of temporal misalignment in intervention deployment across interconnected regions remain to be elucidated. We investigate how asynchronous timing of malaria interventions affects transmission dynamics using a two-patch susceptible-infected-susceptible metapopulation model. We compare synchronous and asynchronous intervention schedules and quantify their impact using measures of excess cumulative incidence attributable to asynchrony. The measure that will be used for this purpose is referred to as Asynchrony Induced Growth (AIG). Across a range of 10,000 parameter combinations, asynchronous implementation has been observed to result in a heightened incidence compared to synchronized deployment, though the impact is typically negligible in most endemic settings. Sensitivity analyses indicate that the impact is most significant when interventions are highly effective, infectious duration is brief, and transmission intensity approaches the elimination threshold. In such circumstances, asynchrony has the potential to substantially inflate case numbers, delay transmission interruption, or even prevent elimination entirely. In illustrative scenarios that reflect realistic settings, synchronizing interventions has been shown to avert large numbers of infections and shorten elimination timelines by years to decades. These findings demonstrate that, beyond spatial targeting, temporal coordination of interventions across connected areas can meaningfully enhance malaria control and elimination. Coordinated timing may be particularly valuable for cross-border or near-elimination programs and should be considered in operational planning and resource allocation.

Use of zooprophylaxis as a malaria control strategy has been recommended historically, but a complex relationship exists between animal ownership and malaria infection, with mixed associations described in the literature. We sought to characterize this relationship spatially and temporally in malaria-endemic regions of Africa. We used data from 392,843 individuals from 66 Demographic and Health surveys from countries within Africa to investigate the association between household animal ownership and Plasmodium infection. We used Bayesian models with Integrated Nested Laplace Approximation to incorporate spatially varying coefficient processes, allowing the association of interest to vary over space, time, and within strata of vector species occurrence, land cover, and number of animals owned by households. Spatially varying intercept models showed that ownership of cattle, chickens/poultry, goats, horses/donkeys/mules, pigs, and sheep was broadly associated with malaria infection, with odds ratios ranging from 1.55 to 1.67. However, spatially varying slope models revealed considerable heterogeneity, with odds ratio estimates for all animal types demonstrating both protective and harmful effects varying from 0.33 to 3.33 both subnationally and across time. We found no evidence that modification by vector species, number of animals owned, and land cover fully explained the variation in estimates. Unobserved localized cultural, behavioral, or ecological factors likely modify the association between animal ownership and malaria prevalence. Further exploring the nature of this relationship over space and time will be important to understanding how context-specific One Health dynamics between humans, animals and the environment affect malaria prevention and control efforts.

Background Vector control is essential to malaria control and elimination. National Malaria Programmes (NMPs) must make complicated decisions about vector control in the face of evolving epidemiology, biological threats like insecticide resistance, a growing vector control toolbox, and an increasingly constrained funding landscape. The WHO recently published a manual on subnational tailoring of malaria strategies, but limited efforts have been made to understand how NMPs prioritize data and factors that impact decision-making in practice. This study explores vector control decision-making processes, enablers, and barriers across 12 African malaria programmes. Methods We conducted semi-structured interviews with 13 NMP managers or designated representatives from 12 African countries. Interviews were conducted virtually via Zoom or in-person, audio-recorded, transcribed, and thematically analyzed using content analysis. Participants described the interventions in use, decision-making factors, stratification approaches, perspectives on new tools, and operational challenges. Results Insecticide-treated bed nets (ITNs) and indoor residual spraying (IRS) are the core interventions in all countries, with limited but growing use of larval source management, mainly larviciding. Vector control tool selection is driven by WHO guidance, resistance profiles and patterns, epidemiological trends, operational feasibility, and donor funding priorities. Sub-national stratification is widely applied; however, limited analytic and modeling capacity hinder consistent application. Gaps in entomological data result in incomplete data availability to guide stratification. New vector control tools were perceived as promising options, albeit constrained by cost, limited evidence, regulatory delays, and community acceptability. Funding emerged as the dominant driver of decisions, shaping intervention choices regardless of country preference. Participants emphasized substantial gaps in vector control protection related to residual transmission, outdoor biting, insecticide resistance, and unprotected populations living in temporary structures or associated with high-risk occupations. Conclusions Vector control decision-making among NMPs is shaped by an interplay of scientific evidence, operational realities, and external funding dynamics. Strengthening entomological surveillance, enhancing SNT analytic and model output interpretation capacity, securing sustainable financing, and improving community engagement are critical to advancing tailored deployment of tools. Decision-support frameworks that reflect the complexities facing NMPs may further enhance evidence-based, context-specific vector control planning.

Background: Reliable inference of Plasmodium vivax recurrence states - relapse, recrudescence and reinfection (the ``3Rs'') - improves estimates of antimalarial efficacy. The R package Pv3Rs features a Bayesian model designed for P. vivax molecular correction, i.e., using parasite genetic data to infer recurrence states. The model is an extension of a prototype built to analyse microsatellite data from the Vivax History (VHX) and Best Primaquine Dose (BPD) trials. Methods: We re-analysed data from 212 VHX and BPD trial participants (493 recurrences) using Pv3Rs, comparing results with those from the prototype and with genetic relatedness estimated using Dcifer, a tool for estimating relatedness based on identity-by-descent. Posterior recurrence state probabilities were computed using both uniform and time-to-event priors: artificial but equal prior probabilities facilitate posterior interpretation, while time-to-event priors leverage all available information and enable re-computation of failure rates. Relatedness estimates were used to identify and correct instances of model misspecification. Results: The Pv3Rs model generated posterior probabilities for all recurrences and was able to jointly model data on all episodes per participant for 89% of participants, compared with 73% using the prototype. Recurrence state probabilities were broadly consistent across methods, though the Pv3Rs model elevated reinfection probabilities slightly. Relatedness estimates exposed various outliers consistent with half-sibling parasites and/or genotyping errors. Outlier correction impacted some per-participant failure probabilities, but reinfection-adjusted radical-cure failure rates of high-dose primaquine remained near 3%, in line with previous findings. Conclusion: Re-analysis of VHX and BPD P. vivax genetic data restates earlier reinfection-adjusted efficacy estimates. It demonstrates the increased computational capability and misspecification sensitivity of Pv3Rs, highlighting a need for careful analyses. Using relatedness-based diagnostics alongside model-based inference, we were able to harness the advantages of model-based inference and provide a framework for future P. vivax molecular correction.

Rectal artesunate (RAS) is a pre-referral intervention recommended for children with suspected severe malaria in remote settings where injectable treatment is not readily available. Although clinical trials have demonstrated efficacy, less is known about the behavioural and health system factors influencing effectiveness under routine conditions. A convergent parallel mixed-methods design was used to assess implementation of Zambia's RAS intervention package across three districts: Serenje, Chama, and Mwinilunga. A retrospective case-tracking investigation of all 300 children with suspected severe malaria recorded by community health workers (CHWs) assigned to study facilities examined progression and attrition across the severe malaria care cascade. In-depth interviews and focus group discussions with caregivers, CHWs, and other stakeholders explored barriers and facilitators influencing progression. Among 300 enrolled children, early attrition occurred due to negative rapid diagnostic test results. Of 239 RDT-positive children, 218 (91.2%) received RAS. Referral completion was lower; among 261 children referred and followed up at health facilities, 209 (80.1%) were confirmed to have completed referral. Of 186 children diagnosed with severe malaria at the facility, 167 (89.8%) received both injectable artesunate and follow-on artemether-lumefantrine. Patterns of disengagement varied by district, with Serenje demonstrating the most consistent progression, Chama experiencing the largest drop-off at RAS administration, and Mwinilunga showing the lowest completion of follow-on treatment. Qualitative findings revealed strong community appreciation for RAS despite stockouts, alongside social and behavioural barriers, including gendered responsibilities, transport challenges, and confusion following symptom improvement, that discouraged referral completion. RAS can be a life-saving intervention when embedded within strong health systems and community structures. Zambia's experience underscores the need for comprehensive implementation strategies that extend beyond drug distribution to include sustained CHW training, reliable commodity supply, functional referral systems, and meaningful caregiver engagement.

Between 2015 and 2025, Cambodia reported a 99.9% decline in the number of cases of malaria. To aid acceleration of elimination, the National Center for Parasitology, Entomology and Malaria Control (CNM) implemented a package of interventions known as the Last Mile (LM) elimination program. The aim of this study was to determine the impact of the LM program on case numbers and evaluate the coverage of interventions. LM was rolled out between November 2020 and December 2023 in villages reporting a locally acquired case of Plasmodium falciparum or mixed infection with P. falciparum and P. vivax and included combinations of targeted drug administration (TDA), intermittent preventative treatment for forest goers (IPTf), active fever screening (AFS), the recruitment of a village or mobile malaria worker (VMW/MMW) and the top-up of insecticide-treated bed nets (ITN) depending on the vulnerability and receptivity of the village. A total of 103 full and 82 partial villages in seven provinces were included. Two rounds of TDA were administered, with a total of 10,678 individuals (67.6%) given during the first round and 9,678 (62.3%) during the second round. Coverage varied by province with none meeting the recommended threshold of 80%. IPTf was implemented each month among 35% (n=35) of full LM villages and 56% (n=42) of partial LM villages. A total of 11.7% (n=12) of full LM villages implemented AFS consistently on a weekly basis. Controlled interrupted time series showed no statistically significant difference in the number of malaria cases before and after the implementation of LM. Although we were unable to prove a statistically significant impact of LM, likely due to the small number of cases prior to LM, it is important to add to the limited evidence-based for Accelerator Strategies in countries approaching the elimination of malaria. Furthermore, findings from the feasibility and impact of individual interventions were used to change policy at the national level.

Purpose: This study developed a Bayesian hierarchical spatio-temporal modeling framework to analyze factors and trends in malaria risk across Ghana's 16 administrative regions from 2020 to 2024. The aim was to identify statistically significant areas with elevated or persistent malaria risk, to inform targeted intervention planning and support the National Malaria Elimination Program. Methods: This study utilized malaria incidence data from the Ghana Health Service's District Health Information Management System-II covering the years 2020 to 2024. Meteorological data were sourced from the Visual Crossing Weather Data, and regional population estimates were obtained from the Ghana Statistical Service. To analyze the data, a Bayesian hierarchical spatiotemporal model with a Negative Binomial (NB) likelihood was implemented using Integrated Nested Laplace Approximation to account for overdispersion. The model included Conditional Autoregressive priors for structured spatial effects, first-order random walk priors for temporal dependence, and spatio-temporal interaction terms. Additionally, Local Indicators of Spatial Association (LISA) analysis with 999 conditional permutations was conducted to identify statistically significant spatial clusters, including high-high hotspots and low-low cold spots. Results: The NB model significantly outperformed the Poisson model, leading to a reduction in the dispersion statistic from 9,227.55 to 1.11. Humidity with a 1-month lag showed the strongest positive association with malaria risk, while the ultraviolet index had the greatest protective effect. Predictive relative risk maps identified persistent high-risk clusters in the northern and northwestern regions, specifically Upper West, Upper East, Bono, Ahafo, and Western North. LISA analysis indicated that Bono-Ahafo has been a stable high-high cluster from 2020 to 2023, while Ashanti has remained a consistent low-high anomaly. Additionally, Greater Accra and Central regions formed a significant low-low cluster in 2024. Conclusion: The Bayesian hierarchical spatio-temporal framework effectively characterized the complex transmission dynamics of malaria in Ghana. It revealed significant spatial dependence, temporal correlation, and interactions between these factors. By identifying persistent high-risk clusters and statistically significant spatial associations, this framework provides essential evidence to guide resource allocation. These findings support Ghana's National Malaria Elimination Program Strategic Plan (2024-2028) by enabling targeted interventions in hotspots and optimizing the use of limited resources to sustain progress in low-transmission areas.

Artemisinin partial resistance has not yet been reported in southern Africa. Therefore, the magnitude of the spread of artemisinin partial resistance in this region is yet to be quantified. Using a two strain metapopulation modelling framework, we explored possible spread of artemisinin partial resistance in eight connected countries with high level of human movement. We explored three scenarios in which artemisinin partial resistance may first enter circulation: low malaria transmission level country; high malaria transmission level country and all countries and compared to an artemisinin partial resistance free scenario. Partial rank correlation coefficient sensitivity analysis was performed to identify key parameters that drive artemisinin partial resistance spread. Our model simulations show that high mobility between countries can increase the spread of mutations associated with delayed clearance. Suggesting that artemisinin partial resistance will be confirmed (>5% partial resistant cases) after 14 years of circulation if it is to appear in southern Africa. We confirm that human movement, both human-to-mosquito and mosquito-to-human probabilities of transmission, were significant and highly sensitive parameters in the spread of artemisinin partial resistance. Human mobility between countries can facilitate the spread of artemisinin partial resistance. More research is needed to identify strategies to preserve the efficacy of artemisinin-based combination therapies in the presence of partial artemisinin resistance, which may eventually lead to treatment failure and necessitate regimen replacement.

Background In 2017, Zambia adopted surveillance as a core intervention towards achieving malaria elimination. Among the surveillance strategies is the malaria case investigation and response 1-3-7 (MCIR 1-3-7), which has been piloted in two low-incidence districts in the Southern Province since 2021. The study aimed to assess the implementation of MCIR 1-3-7 under programmatic conditions. It examined the timeliness, and completeness of the MCIR 1-3-7 activities, including the completeness of data entry in surveillance forms, and explored the experiences and perspectives of healthcare workers involved in the pilot. Methods A mixed-methods design was employed to assess the MCIR 1-3-7. Using a descriptive cross-sectional design, quantitative data were collected from 19 healthcare facilities in the two districts to assess the timeliness and completeness of MCIR 1-3-7. Additionally, 12 qualitative interviews were conducted with 29 healthcare workers from 11 of the 19 healthcare facilities. The interviews were voice-recorded and then transcribed manually. A codebook was developed using an iterative process to explore the facilitators and barriers encountered by healthcare workers in implementing the MCIR 1-3-7 intervention. All the visited facilities were purposively selected based on logistical convenience. Results This study retrospectively assessed 510 malaria cases that were diagnosed between January 2022 and June 2023, presenting at 19 health facilities: 283 cases in Chikankata and 227 in Mazabuka districts. A total of 278 cases (54.5%) were deemed to have been imported from outside the district, province, or country, while 45.5% (232/510) of the cases were classified as transmitted locally. Overall, 29.6% of case notification forms were found to be complete. Twelve interviews with 29 healthcare workers revealed a lack of transportation modalities as the main obstacle in executing the MCIR 1-3-7 intervention. The healthcare workers also indicated that monetary incentives, and supportive supervision would help them succeed in implementing this intervention. Conclusions The MCIR 1-3-7 has the potential to accelerate elimination in areas with low-transmission of malaria in Zambia. This study highlights opportunities to improve future implementation of the MCIR 1-3-7 intervention via strengthening supportive supervision, availing job aids, and ensuring access to malaria commodities as the intervention expands.

Global efforts to control and eliminate malaria are threatened by the emergence and spread of antimalarial drug resistance. The World Health Organization recommends surveillance of molecular markers of resistance as a complementary approach to therapeutic efficacy studies. Here, we report the first regional analysis of malaria drug resistance markers from genomic surveillance across six southern African countries spanning diverse transmission intensities and geographies. Dried blood spots, collected from rapid diagnostic test-positive individuals in Angola, Eswatini, Namibia, and Zambia in 2023, Mozambique in 2022, and South Africa between 2022 and 2024 using a standardized collection method, were analyzed using a Plasmodium falciparum targeted amplicon sequencing protocol. The distribution of resistance markers was spatially heterogeneous. Markers of artemisinin partial resistance (ART-R) were rare, never detected in >1.3% of samples from any country. However, over 30% of samples from eastern Namibia and Zambia's Western and Central Provinces carried the candidate kelch13 P441L ART-R marker. Other ART-R markers (kelch13 R515K, P553L, P574L, A675V) were detected at low frequencies in all countries except Mozambique. The wild type mdr1 N86 allele, potentially associated with reduced lumefantrine susceptibility, was near fixation across all countries. The sulfadoxine-pyrimethamine (SP) resistance dhps-dhfr quintuple mutation was approaching fixation in most districts, except northern Angola, where dhps K540E prevalence was lower and the crt K76T chloroquine resistance marker more frequently detected. This pronounced spatial heterogeneity underscores the need for timely high-resolution local resistance data generation and sharing to safeguard antimalarial drug efficacy and guide malaria control and elimination strategies across the region.

Background: Artemisinin-based combination therapies remain the mainstay of malaria control strategies; nevertheless, the advent of genetic markers linked to partial artemisinin resistance in Plasmodium falciparum has elicited substantial concern across African settings. To assess the prevalence, geographic distribution, and clinical associations of these molecular markers, we undertook a systematic review and meta-analysis of observational cohort studies.Methods: We conducted a search of cohort studies published between January 2015 and June 2025, following PRISMA 2020 guidelines. We queried databases including PubMed/MEDLINE, Scopus, Web of Science, and CINAHL. Eligibility required prospective enrollment of patients, longitudinal monitoring (therapeutic efficacy studies), and pfkelch13 propeller domain genotyping.Results: A meta-analytical synthesis of 888 isolates from six core prospective cohorts revealed a pooled prevalence of 6% (95% CI: 2.1%-11.8%) for validated pfkelch13 mutations. A profound geographic dichotomy was identified: while West and Central African cohorts maintained a 0% prevalence, East African hotspots showed significant expansion, with prevalence reaching 12.8% in Rwanda and up to 25.5% in Northern Uganda; high statistical heterogeneity (, ) reflects this biological divergence. Conclusions: These findings highlight the established and expanding presence of artemisinin partial resistance in East Africa. Standardized surveillance is essential to adapt malaria control policies across the continent. Keywords: Africa; artemisinin resistance; clinical indicators; pfkelch13 gene; molecular markers; partial resistance; Plasmodium falciparum.

Abstract Background Malaria remains a major public health challenge in Ghana, despite recent reductions in cases due to various interventions. The endemicity of the disease varies across regions, influenced by diverse seasonal and temporal factors that support mosquito proliferation and malaria cases. This study used a Generalised Additive Models to explore the impact of weather conditions on malaria cases in Ghana. Methods Generalised Additive Models were used to examine the nonlinear effects of weather conditions on malaria cases. Monthly aggregated malaria cases from the District Health Information Management System II and average monthly rainfall and temperature data from the Ghana Meteorological Agency were analysed, covering 2012 to 2023. Regional Generalised Additive Models incorporating weather variables were developed, fitted, and validated against observed data using model diagnostics to identify the most suitable model for each region. Results The analysis revealed complex temporal patterns in malaria cases across Ghana, influenced by seasonal and long-term trends. Regions constituting the Coastal and Transitional Forest zones exhibited bimodal peak malaria seasons, while the Guinea Savannah showed a unimodal peak. Significant interactions between rainfall and temperature were identified, particularly in the Eastern region, where higher rainfall combined with temperatures around 27-28 {degrees}C were associated with higher malaria cases, reflecting the complex and region-specific nature of meteorological influences. Conclusions The findings point to the dynamic and heterogeneous nature of malaria caseloads in Ghana, emphasising the need for region-specific control strategies tailored to local climatic conditions. A key recommendation is the systematic integration of meteorological data into the National Malaria Data Repository to enable continuous monitoring of climatic influences and support timely, evidence-based intervention decisions. Future research should incorporate socio-economic factors, intervention coverage data, vector surveillance, and demographic characteristics into mathematical modelling frameworks for a more comprehensive understanding of malaria cases in Ghana.

Malaria remains a major public health challenge in sub-Saharan Africa, with pronounced spatial and temporal variation in transmission intensity that complicates effective control strategies. Accurate classification of transmission states is essential for guiding targeted interventions and strengthening early warning systems. This study develops a machine learning framework for the classification of malaria transmission states in Kenya using monthly panel data from 47 counties spanning the period 2015 to 2025. Transmission was categorised into four operationally relevant states based on incidence thresholds. Four supervised learning models, namely multinomial logistic regression, random forest, extreme gradient boosting, and support vector machine, were trained using temporally lagged features and evaluated under a forward chaining validation scheme to preserve temporal structure. Model performance was assessed using accuracy, macro averaged F1 score, Matthews correlation coefficient, and Brier score, complemented by calibration analysis. Extreme gradient boosting achieved the best overall performance, with accuracy of 0.9918, macro averaged F1 score of 0.9647, and Matthews correlation coefficient of 0.9831, alongside the lowest Brier score of 0.0031, indicating highly reliable probability estimates. Feature importance analysis revealed that lagged incidence, vegetation index, precipitation, and insecticide treated net coverage were the most influential predictors. Partial dependence analysis demonstrated nonlinear relationships and clear seasonal patterns in transmission dynamics. The findings show that machine learning approaches can accurately classify malaria transmission states while providing interpretable and well calibrated outputs for decision making. This framework offers a practical tool for supporting malaria surveillance and resource allocation. Further validation in different epidemiological settings is recommended to assess generalisability.

Maternal health during pregnancy is critical for favorable birth outcomes and long-term wellbeing of both mothers and infants. Women in rural, malaria-endemic regions face unique biological and socioeconomic challenges that may increase the risk of adverse pregnancy outcomes (APOs). This study investigated the incidence and determinants of APOs among pregnant women attending antenatal care at Webuye sub-County Hospital in Western Kenya, a rural malaria-endemic setting. We conducted a retrospective cohort analysis utilizing previously collected data of 300 women enrolled during early pregnancy and followed through delivery. Maternal demographic, clinical, and infection-related factors were assessed, and associations with APOs were evaluated using chi-square tests and multivariable logistic regression. Maternal age and gestational age at enrollment were significantly associated with malaria history (P<0.001). Maternal BMI abnormality (124.5/1000 pregnancies), anemia (99.3/1000), fetal or neonatal death (81.3/1000), and preterm birth (43.8/1000) were observed (all P<0.001), suggesting a substantial burden. Younger mothers (<20 years) and older mothers (>35 years) were significantly more likely to develop anemia (P =0.026), and prior malaria infection further increased anemia risk (P =0.02). Abnormal urinalysis findings indicative of urinary tract infection were significantly associated with low birthweight (P =0.031). No significant associations were found between APOs and infant sex, parity, gravidity, or maternal ABO blood type. These findings highlight a substantial burden of APOs in this rural population, exceeding national and global estimates. Strengthening malaria prevention, nutritional support, urinary infection screening, and encouraging early antenatal care attendance are critical to improving maternal and neonatal outcomes. Targeted interventions for adolescent and older mothers, along with enhanced point-of-care diagnostics, may reduce preventable complications in similar resource-limited, malaria-endemic settings.

1In 2024-2025 a multi-centre study involving seven international laboratories was conducted with the support of the World Health Organization (WHO). The aim of the study was to establish and validate discriminating concentrations (DCs) in WHO bottle bioassays for monitoring susceptibility to broflanilide and isocycloseram in Anopheles gambiae s.s., An. funestus, An. stephensi and Aedes aegypti. The following values are recommended for adoption as DCs for broflanilide: 10 {micro}g/bottle for An. gambiae and Ae. aegypti, 15 {micro}g/bottle for An. funestus, and 25 {micro}g/bottle for An. stephensi. The recommended DCs for isocycloseram are 15 {micro}g/bottle for Ae. aegypti, 30 {micro}g/bottle for An. gambiae, 50 {micro}g/bottle for An. stephensi, and 60 {micro}g/bottle for An. funestus. Based on the experiences of conducting this study, which represents the application of a generic protocol for establishing discriminating concentrations produced by the WHO, technical recommendations are made on the generation and analysis of DC data for insecticides in future.

Malaria transmission in Nigeria is highly seasonal and climate-sensitive, yet routine surveillance and meteorological datasets remain underutilized for predictive modelling at subnational levels. This study modelled seasonal malaria incidence trends in Nasarawa State, Nigeria using routine surveillance and climatic data. A retrospective ecological time-series study was conducted using monthly confirmed malaria incidence data from all 13 Local Government Areas of Nasarawa State between 2021 and 2025. Rainfall and temperature were examined as the climatic predictors. Seasonal decomposition and cross-correlation analyses were performed to identify the temporal patterns and lag structures. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models were developed using the Box-Jenkins framework. Model performance was evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Malaria incidence showed pronounced seasonal peaks, with the highest transmission occurring during the rainy season. Cross-correlation analysis identified rainfall at a one-month lag and contemporaneous temperature as significant predictors of malaria incidence. The SARIMAX model outperformed the univariate SARIMA model, achieving strong predictive accuracy (MAPE = 8.7%). Forecast projections indicate sustained transmission with a peak incidence expected between June and August 2026. Malaria transmission in Nasarawa follows a predictable seasonal pattern that is influenced by climatic variability. Incorporating rainfall and temperature into SARIMAX models improves the forecasting performance and provides evidence supporting climate-informed malaria surveillance and preparedness in endemic settings.

Introduction: Malaria, primarily transmitted by Anopheles mosquitoes, remains a major public health concern in Maiduguri, Borno State, Nigeria. While conventional control methods (e.g., ITNs) face challenges due to insecticide resistance and accessibility constraints, many communities rely on locally sourced natural products. This study aimed to assess the prevalence, usage patterns, and associated factors of these natural alternatives. Methods: A cross-sectional survey was conducted across three purposefully selected communities in Maiduguri (Mairi, Furi, Lagos Street). A total of 450 household heads were interviewed using a structured questionnaire, collecting data on socio-demographics, specific natural products used, method of application, frequency, and perceived efficacy. Data were analyzed using descriptive statistics and binary logistic regression. Results: Overall usage prevalence of natural products was high at 68.4%. The most common products identified were Neem (Azadirachta indica) extract (45.9%) and burnt Lemon Grass (Cymbopogon citratus) (31.2%). Usage pattern was predominantly indoor fumigation (burning), and over 70% of users prepared the products crudely at home. Logistic regression revealed that rural residence (Odds Ratio (OR): 2.1; p<0.01) and low education level (OR: 1.8; p<0.05) were significant independent predictors of higher natural product reliance. Conclusion: Natural products constitute a widely adopted, community-driven vector control method in Borno State. The high prevalence and association with vulnerable populations suggest an urgent need to standardize the preparation and application of these products for potential integration into regional malaria control programs. Keywords: Anopheles, Adulticides, Borno State, Malaria, Natural Repellents, Vector Control, Usage Pattern.