The Mandoto District in the central highlands of Madagascar experiences year-round transmission of Plasmodium vivax ( P. vivax ) and Plasmodium falciparum ( P. falciparum ) . Monthly malaria case data from 27 health centers across Mandoto between 2019 and 2024 were analyzed alongside meteorological data to understand transmission dynamics and forecast potential influences of climate change using descriptive, cross-correlation, and seasonal autoregressive integrated moving average forecast models. Over a period of 6 years, 276,318 rapid diagnostic tests (RDTs) were performed, yielding a 39.6% positivity rate, totaling 109,428 malaria cases. After 2021, when multispecies RDTs became available, 71.5% of cases were attributed to P. falciparum , and 28.5% were attributed to P. vivax . Both species were co-endemic across all health centers, with the western region experiencing a higher transmission risk. Malaria cases peaked in January, with a second peak from April to June after the rainy season, and declined between July and September. Precipitation and temperature effectively revealed the seasonality of malaria dynamics, thereby improving model accuracy. Plasmodium falciparum exhibited stronger associations with precipitation and temperature variability. The present study highlights that combining time-series modeling with precipitation and temperature data can help predict malaria cases and support timely planning and resource allocation.
Rajaonarimirana et al. (Tue,) studied this question.