Time series regression model for infectious disease and weather
Time series regression model for infectious disease and weather
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DOI：
10.1016/j.envres.2015.06.040
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发表时间：
2015-10-01
影响因子：
8.3
通讯作者：
Hashizume, Masahiro
中科院分区：
文献类型：
--
作者：
Imai, Chisato;Armstrong, Ben;Hashizume, Masahiro
时间序列回归法已经发展起来，并长期用于评估空气污染和天气与非传染性疾病死亡率或发病率的短期关联。然而，将这种传统的回归方法应用到传染病中，却没有得到很好的探索，并提出了一些新的问题。我们讨论并提出了在这种分析中经常出现的五个问题的潜在解决方案：免疫群体的变化，强自相关性，广泛的合理滞后结构和关联模式，季节性调整，和大的过度分散。潜在的方法说明了数据集的霍乱病例和降雨量从孟加拉国和流感和温度在东京。虽然这篇文章的重点是传统的时间序列回归在传染病和天气因素中的应用，但我们也简要介绍了替代方法，包括数学建模，小波分析和自回归积分移动平均（ARIMA）模型。对标准时间序列回归实践提出的修改包括使用过去病例的总和作为免疫人口的代理，和使用滞后疾病计数的对数来控制由于真实传染而引起的自相关，这两种方法都是由“易感染-传染-恢复”（SIR）模型驱动的。滞后结构和关联模式的复杂性通常可以通过生物机制来了解，并通过使用分布式滞后非线性模型来探索。对于过度分散模型，应考虑替代分布模型，如拟泊松和负二项分布。时间序列回归可用于调查传染病对天气的依赖性，但可能需要修改以考虑特定于此背景的特征。(C)2015作者爱思唯尔公司出版
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues.We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion.The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models.Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. (C) 2015 The Authors. Published by Elsevier Inc.