Time series regression model for infectious disease and weather.


Imai, C; Armstrong, B; Chalabi, Z; Mangtani, P; Hashizume, M; (2015) Time series regression model for infectious disease and weather. Environmental research, 142. pp. 319-327. ISSN 0013-9351 DOI: 10.1016/j.envres.2015.06.040

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Abstract

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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Faculty of Public Health and Policy > Dept of Social and Environmental Health Research
Research Centre: Centre for the Mathematical Modelling of Infectious Diseases
PubMed ID: 26188633
Web of Science ID: 363602800038
URI: http://researchonline.lshtm.ac.uk/id/eprint/2255535

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