Empirical modeling of Malaria Cases in Erstwhile Mpohor District, Ghana

Abstract

This study develops a unified empirical modeling framework to analyze malaria incidence in the Mpohor District of Ghana via monthly surveillance data from 2009–2014 (N = 45,080 cases). The study constructed City-specific, demographic, cumulative, and deviation-from-linearity models to characterize transmission dynamics across four communities (Ayiem, Banso, Manso, and Mpohor) and three population groups (under 5, above 5, pregnant women). Mpohor and Banso reported the highest burdens (14,329 and 14,548 cases, respectively), whereas Ayiem reported a consistently lower incidence. Cumulative modeling revealed an approximately linear long-term increase (slope ≈ 624.1 cases/month; R² = 0.999), whereas deviation analysis revealed outbreak-linked anomalies in 2010, 2012, and 2013. Polynomial trend analysis detected an inflection point around month 80, indicating a shift from an acceleratedto a decelerating transmission phase. Strong intercity correlations (r = 0.998 between Banso and Mpohor) suggest shared ecological drivers. These empirical tools provide an interpretable analytical system for district-level malaria monitoring, enabling targeted vector control and improved forecasting in resource-constrained settings.