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arXiv · 2309.00981

A mathematical model for understanding and controlling monkeypox transmission dynamics in the United States and its implications for future epidemic management

Md. Azmir Ibne Islam·M H M Mubassir·Arindam Kumar Paul·Sharmin Sultana Shanta
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Abstract

Background: Although the outbreak of human monkeypox (mpox) caused by the monkeypox virus (MPXV) has slowed down around the world, little is known about the short-term dynamics of this disease. This limited information highlights the critical need to assess the underlying interventions. Method: To identify and re-examine the key pattern of the disease, a modified logistic growth model is presented and analysed in this paper. Our main focus is on the two non-pharmaceutical interventions: policies aimed at reducing human-to-human transmission and animal-to-human transmission. We incorporated these two strategies in the model as control parameters to understand their short-term significance on the epidemic, and to analyse their strengths in minimizing the infected cases. We used mpox data set of the United States from 10 May 2022 to 31 December 2022 in the model and estimated the baseline parameters. Results: The model reveals a complying acceptance to the US data set. Model simulations highlight that preventive measures could play important roles in controlling the deadly spread of the disease in the year of 2022. During the transmission period, better outcomes could have been possible to achieve in the US if both controls were brought to action simultaneously. Conclusion: Our study reflects that continuous application of the preventive strategies might be an effective tool to prevent the short-term outbreak of mpox or similar diseases. Moreover, such strategies could play supporting roles during pre- and post-vaccination periods.

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Md. Azmir Ibne Islam, M H M Mubassir, Arindam Kumar Paul, Sharmin Sultana Shanta. 2024-11-23. A mathematical model for understanding and controlling monkeypox transmission dynamics in the United States and its implications for future epidemic management. https://doi.org/10.1016/j.dcit.2024.100031

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