Predicting the trend of leptospirosis in China via a stochastic model with vector and environmental transmission

Xiangyun Shi; Dan Zhou; Xueyong Zhou; Fan Yu; Xiangyun Shi; Dan Zhou; Xueyong Zhou; Fan Yu

doi:10.3934/era.2024176

Electronic Research Archive

2024, Volume 32, Issue 6: 3937-3951. doi: 10.3934/era.2024176

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Research article

Predicting the trend of leptospirosis in China via a stochastic model with vector and environmental transmission

School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China

Received: 04 May 2024 Revised: 28 May 2024 Accepted: 04 June 2024 Published: 13 June 2024

A stochastic model of leptospirosis with vector and environmental transmission is established in this paper. By mathematical analysis of the model, the threshold for eliminating the disease is obtained. The partial rank correlation coefficient was used to analyze the parameters that have a greater impact on disease elimination, and a sensitivity analysis was conducted on the parameters through numerical simulation. Further, combined with the data of leptospirosis case reports in China from 2003 to 2021, two parameter estimation methods, Least Squares method (LSM) and Markov Chain Monte Carlo-Metropolis Hastings method (MCMC-MH), are applied to estimate the important parameters of the model and the future trend of leptospirosis in China are predicted.
- stochastic epidemic model,
- leptospirosis,
- disease elimination,
- parameter estimation,
- disease prediction
Citation: Xiangyun Shi, Dan Zhou, Xueyong Zhou, Fan Yu. Predicting the trend of leptospirosis in China via a stochastic model with vector and environmental transmission[J]. Electronic Research Archive, 2024, 32(6): 3937-3951. doi: 10.3934/era.2024176

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Abstract

A stochastic model of leptospirosis with vector and environmental transmission is established in this paper. By mathematical analysis of the model, the threshold for eliminating the disease is obtained. The partial rank correlation coefficient was used to analyze the parameters that have a greater impact on disease elimination, and a sensitivity analysis was conducted on the parameters through numerical simulation. Further, combined with the data of leptospirosis case reports in China from 2003 to 2021, two parameter estimation methods, Least Squares method (LSM) and Markov Chain Monte Carlo-Metropolis Hastings method (MCMC-MH), are applied to estimate the important parameters of the model and the future trend of leptospirosis in China are predicted.

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