Climate-Driven Malaria Transmission Dynamics with Human Awareness and Optimal Control: A Deterministic Mathematical Modeling Approach.

Taking into account the influence of climatic factors and age in the mathematical modeling and analysis of the dynamic transmission of malaria

Climate change and malaria in west Africa: a review of strategies to handle one of the world's most prominent anthroponoses.

Climate change is intensifying the threat of malaria in Africa by altering the geographical range, seasonality, and transmission intensity of the disease. Rising temperatures promote mosquito breeding and parasite development, whereas extreme weather events, particularly flooding, disrupt control efforts and create breeding sites, enabling their spread into previously unaffected highland areas. These factors pose an evolving public health challenge. Malaria-control strategies, often designed for stable climatic conditions, risk being outpaced by rapid environmental changes. However, little is known regarding how national policies integrate climate adaptation into strategic planning. We qualitatively analysed national malaria strategic plans and related policy documents from 11 west and central African countries, published between 2015 and 2025. We assessed incorporation of climate and weather information into malaria surveillance, early-warning systems, and vector-control planning. Although most countries recognise the influence of climate and seasonality on malaria transmission, explicit integration of climate data into planning and surveillance remains scarce. Ghana and Nigeria use advanced data-driven approaches, including predictive agent-based modelling and subnational stratification, to guide interventions, whereas Togo, Benin, Senegal, and The Gambia show emerging progress. However, most national malaria strategic plans remain reactive rather than predictive because of data, technical, and coordination gaps. This Review highlights policy and implementation gaps in integrating climate adaptation within malaria-control programmes. We recommend climate-resilient malaria strategies centred on enhanced surveillance, integrated meteorological forecasting, and strengthened institutional capacity to support adaptive, evidence-based interventions.

Transmission Dynamics and Optimal Control of Malaria-Dengue Co-infection: A Cost-Effectiveness Analysis

This study presents a comprehensive deterministic compartmental model for malaria-dengue co-infection incorporating distinct vector populations (Anopheles for malaria and Aedes for dengue), disease-specific progression pathways, and co-infection compartments. The model is rigorously analyzed to establish positivity, boundedness, disease-free and endemic equilibria, and basic reproduction numbers. Optimal control theory is applied to evaluate six time-dependent intervention strategies: insecticide-treated bed nets (ITNs), indoor residual spraying (IRS), environmental management, personal protection measures, treatment compliance, and vaccination. Model parameters are estimated using Brazilian malaria and dengue case data (2011-2023). Sensitivity analysis identifies key parameters influencing disease transmission. Cost-effectiveness analysis using infection averted ratio (IAR), average cost-effectiveness ratio (ACER), and incremental cost-effectiveness ratio (ICER) reveals that the combined implementation of all six control measures (Strategy 9) is most cost-effective, averting 9,200 infections (35.38% reduction) at an ACER of 3,756.536. These findings provide crucial guidance for designing economically efficient intervention strategies in resource-constrained co-endemic settings.

Guiding malaria elimination interventions: a data-driven approach to resource optimization in Benin, West Africa.

The findings reveal pronounced spatial dependence in malaria prevalence, with transmission patterns strongly associated with temperature and vegetation cover, and Integrating spatial modelling with environmental data offers a powerful framework for refining malaria control strategies and accelerating progress towards elimination targets.

Data-Driven Geospatial Modeling and Forecasting of Malaria Burden to Support Control and Elimination in Africa

Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.

The effect of natural climate variability on future vector-borne disease suitability: A mathematical modelling study

Climate factors such as temperature and rainfall influence vector-borne disease dynamics. Consequently, climate change is affecting the global distribution of diseases such as dengue. Natural climate variability (NCV), which adds noise to the climate signal, alters the future climate trajectory, but is rarely analysed in climate-health analyses. In this study, extending a published model of climate suitability for Ae. aegypti , we consider a climate-sensitive dengue transmission model. Using 100 climate projections up to 2100 from the Community Earth System Model (a climate model including climate change and NCV), each run under the same shared socioeconomic pathway scenario, we generate 100 equally plausible simulations of the future basic reproduction number of dengue globally and the population at risk. We quantify the difference in transmission suitability between the most-suitable and least-suitable projections (i.e., uncertainty due to NCV) and demonstrate that NCV affects future suitability for transmission, dominating epidemiological parameter uncertainty in many locations. While the global population at risk from dengue (and, as we show, other vector-borne diseases) is expected to increase, NCV affects its precise value. Our findings demonstrate that NCV should be routinely incorporated into climate-sensitive epidemiological projections, and a key output of our research is the provision of a methodological framework for this. Accounting for NCV will enable public health policy decisions to be informed by the range of possible future outcomes.