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Time series regression model for infectious disease and weather
http://hdl.handle.net/10069/35718
http://hdl.handle.net/10069/35718bb219eb2-281e-449d-bdf8-aab33b33883a
名前 / ファイル | ライセンス | アクション |
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EnvRes142_319.pdf (1.3 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2015-08-03 | |||||
タイトル | ||||||
タイトル | Time series regression model for infectious disease and weather | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Climate | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Infectious disease | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Method | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Time series | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Weather | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Imai, Chisato
× Imai, Chisato× Armstrong, Ben× Chalabi, Zaid× Mangtani, Punam× Hashizume, Masahiro |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
書誌情報 |
Environmental Research 巻 142, p. 319-327, 発行日 2015-10 |
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出版者 | ||||||
出版者 | Academic Press Inc. | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 00139351 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1016/j.envres.2015.06.040 | |||||
権利 | ||||||
権利情報 | c 2015 Elsevier Inc. Open Access article distributed under the terms of CC BY. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Environmental Research, 142, pp.319-327; 2015 |