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Malaria remains a major public health challenge in Ghana’s Ashanti Region, where climatic factors such as temperature and rainfall strongly influence transmission. The VECTRI model, a climate-driven malaria model widely applied across Africa, has not been evaluated to predict malaria cases in Ashanti region, Ghana. This study assesses VECTRI’s performance in predicting malaria cases at monthly timescales and its ability to capture spatio-temporal variations and observed malaria case data from the Ghana Health Service. The results revealed VECTRI simulations reproduced the seasonal bimodal peaks of malaria cases, albeit with a one-month lag, and demonstrated a significant positive correlation with observed cases (R = 0.74, p = 0.006). The model, however, exhibited a mean bias error of -287.05, indicating general underestimation of cases, with over-prediction during wet periods and under-prediction in dry seasons. Persistent malaria hotspots were identified, including Adansi North, Obuasi, Sekyere Afram Plains, Sekyere Central, Ejura Sekyedumase, and Kumasi. Despite discrepancies likely due to misdiagnosis, reporting biases, or patient mobility, VECTRI proved capable of reproducing seasonal and spatial malaria dynamics in Ashanti region.
Keywords: VECTRI, Malaria, Evaluation, Cases, Ghana.