Improving estimates of waning immunity rates in stochastic SIRS models with a hierarchical framework

IF 3 Q2 INFECTIOUS DISEASES
P. Alahakoon, J. McCaw, P. Taylor
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引用次数: 1

Abstract

Most disease pathogens require onward transmission for their continued persistence. It is necessary to have continuous replenishment of the population of susceptibles, either through births, immigration, or waning immunity in recovered individuals. Consider the introduction of an unknown infectious disease into a fully susceptible population where it is not known how long immunity is conferred once an individual recovers. If the disease takes off, the number of infectives will typically decrease to a low level after the first major outbreak. During this period, the disease dynamics will be highly influenced by stochastic effects and there is a non-zero probability that the epidemic will die out. This is known as an epidemic fade-out. If the disease does not die out, the susceptible population may be replenished by the waning of immunity, and a second wave may start. In this study, we describe an experiment where we generated synthetic outbreak data from independent stochastic SIRS models in multiple communities. Some of the outbreaks faded-out and some did not. By conducting Bayesian parameter estimation independently on each outbreak, as well as under a hierarchical framework, we investigated if the waning immunity rate could be correctly identified. When the outbreaks were considered independently, the waning immunity rate was incorrectly estimated when an epidemic fade-out was observed. However, the hierarchical approach improved the parameter estimates. This was particularly the case for those communities where the epidemic faded out.
用分层框架改进随机SIRS模型中免疫下降率的估计
大多数疾病病原体需要继续传播才能持续存在。有必要通过出生、移民或康复者免疫力下降,不断补充易感人群。考虑将一种未知的传染病引入完全易感人群,在这种人群中,不知道一旦个人康复,免疫力会被赋予多长时间。如果疾病蔓延,在第一次大规模爆发后,感染者的数量通常会降至较低水平。在此期间,疾病动力学将受到随机效应的高度影响,并且流行病消亡的概率为非零。这被称为流行病消退。如果这种疾病没有消失,易感人群可能会因免疫力下降而得到补充,第二波疫情可能会开始。在这项研究中,我们描述了一个实验,在该实验中,我们从多个社区的独立随机SIRS模型中生成了合成的疫情数据。有些疫情逐渐消失,有些则没有。通过对每次疫情独立进行贝叶斯参数估计,并在分级框架下,我们研究了免疫力下降率是否可以正确识别。当独立考虑疫情时,当观察到疫情消退时,对免疫力下降率的估计是错误的。然而,分层方法改进了参数估计。对于那些流行病逐渐消失的社区来说,情况尤其如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
18.30
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