ABSTRACT This study presents a novel, context‐specific mathematical modeling framework for predicting and monitoring COVID‐19 transmission in Rwanda, addressing the increasing demand for integrated and data‐driven public health decision‐support tools. The model incorporates localized epidemiological dynamics, transmission pathways, and major intervention measures, including lockdowns, vaccination, quarantine, and home‐based care, which is considered a critical factor influencing disease transmission. Using a system of differential equations, the framework models transition among infected, home‐based care, hospitalized, recovered, and deceased populations. The model was calibrated and validated using Rwanda's national COVID‐19 surveillance data collected between March 2020 and December 2022, covering multiple epidemic waves and intervention phases. Calibration utilized time‐series data on confirmed cases, active infections, hospitalizations, intensive care unit admissions, recoveries, and mortality. Detailed home‐based care records improved the estimation of transmission and recovery parameters. Parameter optimization was performed through least‐squares fitting and cross‐validation techniques. Model performance was assessed using the root mean square error (RMSE) and the coefficient of determination ( R 2 ) metrics. Validation through out‐of‐sample predictions demonstrated strong accuracy in capturing infection trends and healthcare burden. An interactive dashboard complements the framework by enabling real‐time analytics, forecasting, and evidence‐based public health decision‐making.
Ntaganda et al. (2026) studied this question.