Research Article
A Mathematical Model for the Spread of Monkeypox in Uganda: An SIR Model with Demographic Effects
- By David Chepkonga, Amos Kipkorir Langat, Caroline Njue, John Mutinda Kamwele - 12 Sep 2025
- Applied Mathematics on Science and Engineering, Volume: 2(2025), Issue: 2, Pages: 1 - 7
- https://doi.org/10.65036/amse221
- Received: 25.03.2025; Accepted: 17.08.2025; Published: 12.09.2025
Abstract: Monkeypox, a viral zoonotic disease caused by the monkeypox virus and primarily transmitted through close contact with infected animals and humans, poses a growing public health challenge, especially in endemic regions like Uganda. With its potential for human-tohuman transmission and an increasing number of reported cases in recent years, monkeypox can lead to significant morbidity and mortality if not controlled effectively. This study employs a Susceptible-Infected-Recovered (SIR) compartmental model to rigorously analyze the transmission dynamics of a hypothetical monkeypox outbreak in Uganda, a country endemic to the disease. The model is parameterized using epidemiological data from existing literature, including transmission rates, recovery rates, and contact patterns, to construct realistic outbreak scenarios. We perform a series of simulations over a 200-day period to estimate the basic reproduction number (R0), identify key transmission drivers, and assess the effectiveness of various public health interventions such as mass vaccination, isolation, and public awareness campaigns. The simulation results reveal that, in the absence of intervention, the outbreak reaches a peak around Day 70, with approximately 45% of the population infected, underscoring the high transmissibility of the virus. Strategic early interventions, particularly mass vaccination and rapid isolation of cases, are shown to significantly reduce R0 to below the epidemic threshold, demonstrating their critical importance in outbreak management. These findings underscore the necessity for robust, proactive public health strategies and can inform policy decisions in Uganda and other regions facing similar epidemiological threats. The model framework presented here can also be adapted to other infectious diseases, offering a versatile tool for public health preparedness and response planning.