Bucyibaruta Georges, Dean C B, Torabi Mahmoud
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
Infect Dis Model. 2023 May 6;8(2):471-483. doi: 10.1016/j.idm.2023.04.008. eCollection 2023 Jun.
We develop a discrete time compartmental model to describe the spread of seasonal influenza virus. As time and disease state variables are assumed to be discrete, this model is considered to be a discrete time, stochastic, Susceptible-Infectious-Recovered-Susceptible (DT-SIRS) model, where weekly counts of disease are assumed to follow a Poisson distribution. We allow the disease transmission rate to also vary over time, and the disease can only be reintroduced after extinction if there is a contact with infected individuals from other host populations. To capture the variability of influenza activities from one season to the next, we define the seasonality with a 4-week period effect that may change over years. We examine three different transmission rates and compare their performance to that of existing approaches. Even though there is limited information for susceptible and recovered individuals, we demonstrate that the simple models for transmission rates effectively capture the behaviour of the disease dynamics. We use a Bayesian approach for inference. The framework is applied in an analysis of the temporal spread of influenza in the province of Manitoba, Canada, 2012-2015.
我们开发了一个离散时间 compartmental 模型来描述季节性流感病毒的传播。由于时间和疾病状态变量被假定为离散的，该模型被认为是一个离散时间、随机的易感-感染-康复-易感（DT-SIRS）模型，其中每周的疾病计数被假定遵循泊松分布。我们允许疾病传播率也随时间变化，并且只有在与来自其他宿主群体的感染个体接触后，疾病在灭绝后才能重新引入。为了捕捉不同季节间流感活动的变异性，我们用一个可能随年份变化的4周周期效应来定义季节性。我们研究了三种不同的传播率，并将它们的性能与现有方法进行比较。尽管关于易感个体和康复个体的信息有限，但我们证明，简单的传播率模型有效地捕捉了疾病动态的行为。我们使用贝叶斯方法进行推断。该框架应用于对2012 - 2015年加拿大曼尼托巴省流感的时间传播分析。
