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Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models

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Abstract

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease’s transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

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Acknowledgements

We are thankful to Babasaheb Bhimrao Ambedkar University for providing us with the basic research infrastructure, which helped us in carrying out this work.

Funding

This work is supported by the Council of Scientific and Technology Research- Uttar Pradesh, Government of Uttar Pradesh, through Project No. CST/D-1310.

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Author notes

  1. Vishwajeet Singh and Saif Ali Khan have contributed equally to this work.

Authors and Affiliations

  1. Directorate of Online Education, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, 576104, India

    Vishwajeet Singh

  2. Department of Statistics, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, Uttar Pradesh, 226025, India

    Saif Ali Khan & Subhash Kumar Yadav

  3. Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, Uttar Pradesh, 226025, India

    Yusuf Akhter

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  1. Vishwajeet Singh
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Contributions

SKY and YA: conceived the idea. SAK and VS: retrieved the data and performed the analysis. SKY: wrote the first draft. SKY and YA: edited the final version and coordinated the overall study.

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Correspondence to Subhash Kumar Yadav or Yusuf Akhter.

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Singh, V., Khan, S.A., Yadav, S.K. et al. Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models. Curr Microbiol 81, 15 (2024). https://doi.org/10.1007/s00284-023-03531-6

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