单载体时变载体传染病模型的贝叶斯公式

V. Deo, G. Grover, Ravi Vajala, Chandra Bhan Yadav
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引用次数: 0

摘要

本文采用Weiss(1965)定义的时变载体传染病模型作为贝叶斯框架对其参数进行估计。提出了一种完整的方法结构,用于估计从流行病开始的时间t后m个易感人群中k个的相对感染率和存活概率。该方法已提出,假设一个单一的携带者,以简化研究的行为有效性拟合贝叶斯模型相对于时间和相对感染率。此外,所提出的模型已在两个真实数据集上实施——瑞士采尔马特的伤寒流行数据和印度喀拉拉邦的Covid-19流行数据。结果表明,所提出的方法产生的可靠预测与最大似然估计值和预期流行病学模式一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Formulation of Time-Dependent Carrier-Borne Epidemic Model with a Single Carrier
In this paper, the time dependent carrier-borne epidemic model defined by Weiss in 1965 has been adopted into a Bayesian framework for the estimation of its parameters. A complete methodological structure has been proposed for estimating the relative infection rate and probability of survival of k out of m susceptibles after time t from the start of the epidemic. The methodology has been proposed assuming a single carrier to simplify the study of the behavioral validity of the fitted Bayesian model with respect to time and relative infection rate. Further, the proposed model has been implemented on two real data sets- the typhoid epidemic data from Zermatt in Switzerland and the Covid-19 epidemic data from Kerala in India. Results show that the proposed methodology produces reliable predictions which are consistent with those of the maximum likelihood estimates and with expected epidemiological patterns.
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