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Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China

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Abstract

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and suggestions, which have effectively improved the quality of this paper.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Changshu Institute of Technology, Changshu, 215500, China

    Xingyu Gao

  2. GE HealthCare Technologies Inc, No. 1 Huatuo Road, Shanghai, 201210, China

    Yuchao Xu

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  1. Xingyu Gao
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  2. Yuchao Xu
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Correspondence to Xingyu Gao.

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Gao, X., Xu, Y. Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China. Bull Math Biol 86, 71 (2024). https://doi.org/10.1007/s11538-024-01300-5

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