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<p>This paper presents a stochastic model to study the spread of infectious diseases within two distinct populations, focusing on low-prevalence scenarios. The model captures disease transmission through direct contact and environmental contamination, with one population acting as the primary source of infection. We analyze the average and asymptotic behavior of the model, providing conditions for stochastic stability. Parameter estimation is addressed using both maximum likelihood and Bayesian neural network approaches, with applications to synthetic and real-world data. The model is applied to a measles outbreak in Japan, demonstrating its effectiveness in understanding disease dynamics and informing public health strategies.</p>

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Statistical inference for two-population stochastic epidemic models

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Ibrahim Bouzalmat,
  • Zaineb Smida,
  • Adel Settati

摘要

This paper presents a stochastic model to study the spread of infectious diseases within two distinct populations, focusing on low-prevalence scenarios. The model captures disease transmission through direct contact and environmental contamination, with one population acting as the primary source of infection. We analyze the average and asymptotic behavior of the model, providing conditions for stochastic stability. Parameter estimation is addressed using both maximum likelihood and Bayesian neural network approaches, with applications to synthetic and real-world data. The model is applied to a measles outbreak in Japan, demonstrating its effectiveness in understanding disease dynamics and informing public health strategies.