Modeling monkeypox transmission with a compartmental framework to evaluate testing, isolation and public awareness strategies

Monkeypox (mpox), a viral infectious disease affecting both humans and non-human primates, has posed significant public health challenges, particularly in light of recent global outbreaks. This study develops a deterministic mathematical model to analyze the transmission dynamics of mpox, with a focus on the effects of self-isolation, testing, and public awareness in mitigating the spread of the virus. The model is calibrated using incidence data from the United States and employs a nonlinear least squares fitting method to reflect the observed epidemic trends accurately. The analysis identifies key parameters influencing the spread of mpox, including the transmission probabilities between human to human and mammal to human, as well as the effectiveness of public awareness campaigns. Stability analysis of the model reveals that the mpox-free equilibrium is locally asymptotically stable (LAS) when the basic reproduction number is less than 1, indicating disease elimination. Conversely, when, the presence of a stable endemic equilibrium suggests the potential for sustained transmission if control measures are not adequately implemented. Sensitivity analysis reveals that factors like transmission rates, public awareness levels, and isolation rates significantly impact the disease’s spread, providing insights into which interventions may be most effective. Numerical simulations demonstrate the potential of targeted strategies, showing that even partial adherence to isolation and public awareness strategies can significantly reduce new infections. A comprehensive approach combining effective isolation, expanded testing, and targeted awareness campaigns is crucial in managing mpox outbreaks. This approach provides valuable guidance for public health strategies, supporting the design of interventions that can prevent the spread of mpox and ensure better preparedness for future outbreaks.

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