Advanced Spatio-Temporal Neural Networks for Malaria Prevalence Forecasting in Sub-Saharan Africa

Abstract Accurate malaria prevalence forecasting is critical for timely interventions in Sub-Saharan Africa, where the disease remains a major health burden. Traditional regression models often struggle to capture the complex interactions between the epidemiological, environmental, and spatial factors that drive malaria transmission. This study develops and compares three distinct deep learning approaches for predicting the Plasmodium falciparum parasite rate in children (PfPR2−10), using historical prevalence data from 1900 to 2015 and climate variables such as temperature and precipitation sourced from NASA’s POWER project. The first framework is a Spatiotemporal Transformer developed to capture intricate local dependencies between covariates. The second is a Patch-based Time Series Transformer (PatchTST) designed for robust point forecasting across temporal dimensions. The third approach employs a Spatio-Temporal Neural Ordinary Differential Equation (Neural ODE) model that learns the underlying continuous-time dynamics governing malaria prevalence, enabling smooth temporal interpolation and extrapolation. All three models were further extended with uncertainty estimation using Monte Carlo Dropout, providing probabilistic forecasts in addition to point predictions. Our findings highlight a key architectural trade-off: for this dataset, characterized by short time series and strong cross-feature interactions, the Spatiotemporal Transformer yielded the highest predictive accuracy. Conversely, while the Neural ODE demonstrated slightly lower point-forecast accuracy, it effectively captured temporal continuity and uncertainty dynamics, offering deeper insights into malaria’s spatio-temporal evolution. This comparative analysis provides a nuanced perspective, offering decision-makers a choice between highly accurate models for direct forecasting and probabilistic frameworks for risk assessment and strategic planning. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data is available from Harvard Dataverse, V1, under a CC-BY 4.0 license. Source - http://dx.doi.org/10.7910/DVN/Z29FR0 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Authors’ email: vikalpsri090102{at}gmail.com, sanikavaidya11{at}gmail.com, a-tridane{at}uaeu.ac.ae Updated author details. Added authors contribution details. Funding statement and competing interest statement is also added. Data Availability All data produced are available online at http://dx.doi.org/10.7910/DVN/Z29FR0 Climate data can be provided upon reasonable request to the authors.