Estimation of multiple transmission rates for epidemics in heterogeneous populations
Estimation of multiple transmission rates for epidemics in heterogeneous populations
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DOI：
10.1073/pnas.0706461104
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发表时间：
2007-12-18
影响因子：
11.1
通讯作者：
Gilligan, Christopher A.
中科院分区：
文献类型：
--
作者：
Cook, Alex R.;Otten, Wilfred;Gilligan, Christopher A.
流行病学建模的主要挑战之一是通过对传播率的实际估计来参数化模型，以便分析控制策略和预测疾病结果。通过结合重复实验、贝叶斯统计推断和随机建模，我们引入并说明了一种通过两相镶嵌（包括有利和不利宿主）来估计感染传播的传播参数的策略。我们关注具有局部扩散的流行病，并使用易感感染者 (S-I) 流行病学模型的渗滤范式制定了一组空间明确的随机转移概率。 S-I 渗滤模型进一步推广，允许多种感染源，包括外部接种和宿主间感染。我们使用贝叶斯推理和马尔可夫链蒙特卡罗模拟将模型拟合到通过复制的植物群体传播猝倒病的连续快照，这些植物群体的有利和不利宿主的相对比例不同，并且传播率随时间变化。通过使用偏差信息标准来比较这些传输速率的流行病学上合理的参数形式。我们的结果表明，两相系统有四种传播率，对应于受感染的供体和易感受体的每种组合。了解传播率的数量和大小可以确定异质人群中传播的主要途径。最后，我们展示了如果不考虑多种传播率，可能会高估或低估异质环境中流行病的传播率，这可能导致控制策略的明显失败或低效。
One of the principal challenges in epidemiological modeling is to parameterize models with realistic estimates for transmission rates in order to analyze strategies for control and to predict disease outcomes. Using a combination of replicated experiments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strategy to estimate transmission parameters for the spread of infection through a two-phase mosaic, comprising favorable and unfavorable hosts. We focus on epidemics with local dispersal and formulate a spatially explicit, stochastic set of transition probabilities using a percolation paradigm for a susceptible-infected (S-I) epidemiological model. The S-I percolation model is further generalized to allow for multiple sources of infection including external inoculum and host-to-host infection. We fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapshots of damping-off disease spreading through replicated plant populations that differ in relative proportions of favorable and unfavorable hosts and with time-varying rates of transmission. Epidemiologically plausible parametric forms for these transmission rates are compared by using the deviance information criterion. Our results show that there are four transmission rates for a two-phase system, corresponding to each combination of infected donor and susceptible recipient. Knowing the number and magnitudes of the transmission rates allows the dominant pathways for transmission in a heterogeneous population to be identified. Finally, we show how failure to allow for multiple transmission rates can overestimate or underestimate the rate of spread of epidemics in heterogeneous environments, which could lead to marked failure or inefficiency of control strategies.