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Monkeypox: a model-free analysis.

V R Saiprasad¹, R Gopal¹, D V Senthilkumar²

¹Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401 India.

European Physical Journal Plus

|February 14, 2023

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Monkeypox (MPXV) outbreaks were analyzed using power law distributions and Echo State Networks (ESN). This approach characterized country-specific infection rates and growth patterns, offering insights into disease spread dynamics.

Area of Science:

Epidemiology and Public Health
Computational Biology and Bioinformatics
Infectious Disease Modeling

Background:

The 2022 global outbreak of monkeypox (MPXV), a zoonotic orthopoxvirus, necessitated rapid analysis of its spread.
Understanding disease transmission dynamics and growth patterns is crucial for effective public health interventions.
Previous epidemiological studies have employed various statistical and computational methods to track infectious disease outbreaks.

Purpose of the Study:

To systematically analyze daily cumulative infection data from the global monkeypox outbreak.
To characterize country-specific infection rates and disease spread patterns using power law distributions.
To predict and forecast monkeypox transmission using a reservoir computing-based Echo State Network (ESN).

Main Methods:

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Model-free analysis of cumulative infection data using power law distributions.
Application of Echo State Networks (ESN), a type of reservoir computing, for disease spread prediction.
Comparative analysis of power law findings with the 1907 smallpox outbreak in Tokyo.

Main Results:

Power law distributions were used to characterize country-specific infection rates and identify potential saturation states in disease spread.
The Echo State Network (ESN) model provided predictions and forecasts for monkeypox transmission.
Results from the ESN model were validated against the power law scaling exponent, showing significant correlation.

Conclusions:

The study successfully applied power law analysis and ESN to characterize and forecast monkeypox spread.
The findings provide insights into the growth patterns and potential saturation of the monkeypox outbreak.
The validated correlation between statistical and machine learning methods enhances confidence in predicting infectious disease dynamics.