Van Wyk, MAMcDonald, Andre MNgwako, MTNyandoro OT, OTZhang, F, MA McDonald2025-12-032025-12-032025-11978-1-64368-634-9http://hdl.handle.net/10204/14485Accurate and early estimation of Susceptible-Infected-Recovered (SIR) epidemiology model parameters in infectious epidemics can enhance planning and resource allocation, thereby mitigating the adverse impacts on affected populations. Focusing on the basic SIR epidemiology model, in this paper we examine the scenario of known incidence rate (e.g., cases per day). Even though the SIR model is nonlinear, we obtain an exact least squares solution that is linear in simple algebraic functions of the SIR model’s parameters, the infection rate, recovery rate and total population. Linear least squares solutions lend themselves to be applied to only a selected time period, to the censoring of unreliable measurements such as obvious outliers as well as to enable iterative update of the parameter estimates as new data (i.e., measurements) become available. We present numerical results for both simulated and real-world COVID-19 data to demonstrate the practical utility and accuracy of the proposed method. The proposed method demonstrates advantages over state-of-the-art approaches while also providing reliable parameter estimates.FulltextenKnown incidence rateNovel least squares estimatorsSusceptible-Infected-RecoveredSIR modelConference Presentationn/a