Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.

个体对人群内和人群间感染传播的贡献差异很大。影响传染病传播的三个关键流行病学宿主特征：易感性（获得感染的倾向）、传染性（将感染传播给他人的倾向）和可恢复性（快速恢复的倾向）。旨在减少疾病传播的干预措施可能以改善这些特征中的任何一个为目标，但缺乏获得风险估计的必要统计方法。在本文中，我们介绍了一种名为 SIRE（代表“易感性、传染性和可恢复性估计”）的新型软件工具，它首次允许同时估计单核苷酸多态性 (SNP) 的遗传效应，以及非遗传对这三个不可观察的宿主特征的影响。SIRE 实施了一种灵活的贝叶斯算法，该算法适用于广泛的疾病监测数据，包括记录的个体感染和/或恢复时间或疾病诊断测试结果的任意组合。模拟了不同的遗传和非遗传法规和数据场景（代表现实记录方案）以验证 SIRE 并评估它们对参数估计的精度、准确性和偏差的影响。该分析表明，除了少数例外，SIRE 提供了与所有三种宿主性状相关的无偏、准确的参数估计。对于大多数情况，可以以最高精度估计与可恢复性相关的 SNP 效应，然后是敏感性。对于传染性，许多个体很少的流行病比相反的情况具有更大的统计能力来识别 SNP 效应。重要的是，即使在对个体感染或生存状态的测量不完整、经过审查和相对不频繁的情况下，也可以获得对 SNP 和其他影响的精确估计，尽管需要更多的个体才能产生同等的精度。SIRE 代表了一种新工具，用于分析广泛的实验和现场疾病数据，旨在发现和验证 SNP 以及控制传染病传播的其他因素。