BACKGROUND:
Malaria remains a major public health challenge in Madagascar, with transmission patterns that are heterogeneous across ecological zones and seasons. This heterogeneity is strongly influenced by climatic factors, particularly temperature and rainfall, which modulate mosquito population dynamics and Plasmodium transmission to humans. To analyze these complex relationships between climate, vectors, and human populations, modeling approaches are commonly used, although the extent to which vector biology is incorporated varies across models. This study aims to develop a malaria transmission model that integrates the full Anopheles life cycle and vector–human interactions.

METHODS:
The model couples a human epidemiological SEIR (Susceptible, Exposed, Infectious, Recovered) framework with Anopheles population dynamics. The vector component explicitly represents aquatic stages (eggs, larvae, and pupae) as well as multiple adult mosquito stages. Key climatic factors, including temperature and rainfall, were integrated into the model. Numerical simulations were conducted for three ecologically distinct districts of Madagascar (Farafangana, Morondava, and Maevatanana) over the period 2014–2017.

RESULTS:
Model simulations reveal a strong seasonal pattern in malaria incidence, with transmission peaks during warm and rainy periods. Marked differences were observed across the study districts, with Farafangana exhibiting the highest incidence levels, followed by Morondava and Maevatanana. Simulated temporal trends were consistent with independent epidemiological estimates. Spatially explicit results highlight substantial intra-district heterogeneity, with areas of increased risk, particularly following extreme climatic events such as cyclones.

CONCLUSIONS:
By integrating climatic variability, human infection processes, and vector life cycle dynamics, this spatiotemporal modeling approach provides a realistic representation of malaria transmission across different Malagasy contexts. The findings highlight the potential of climate-sensitive tools to identify high-risk periods and locations and to support targeted malaria surveillance and control strategies from a public health perspective.