AI-based Malaria incidence prediction under current and future Climate in southern Ethiopia

Malaria remains a significant public health problem, with environmental factors like climate being key drivers of its transmission. Southern Ethiopia experiences high malaria burden, with fluctuating rates influenced by complex interplays between climate and land use, parasite, vectors, and human. The current malaria prediction models used by the country struggles to account for these complexities, without considering the combined effects of these factors. This is partly due to the unavailability of high-resolution climate and land-use data representing the complex Ethiopian landscape leading to inaccurate malaria forecasts. Therefore, this proposal aims to harness the power of artificial intelligence (AI) to downscale coarse-resolution climate simulations and integrating Kebele-scale malaria data to predict malaria incidence at fine-spatial scale, considering the past, current, and predicted/projected future climate scenarios. To this end, AIM-Clim project improves and sustains the health of local community by developing a web-based malaria prediction tool. The tool can be used for understanding the relationship between climate, land-use, and malaria contribute to long-term climate-adaptation strategies such as community-based surveillance. Furthermore, it can also be used as an early-warning system about malaria outbreaks, empowering local health authorities to optimally and proactively deploy anti-malaria interventions and thus contributing to the National Malaria Elimination Strategic Plan and SDGs Goals (Goals 3-5, and 13).