MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1016/j.annepidem.2007.03.020

Медицинская библиотека · Annals of epidemiology · 2007

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

Hu Wenbiao, Tong Shilu, Mengersen Kerrie, Connell Des

Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

PURPOSE: Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system.

METHODS

Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis.

RESULTS

Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted.

CONCLUSIONS

The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

Для цитирования: Hu Wenbiao, Tong Shilu, Mengersen Kerrie, Connell Des. Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.. Annals of epidemiology. 2007. DOI: 10.1016/j.annepidem.2007.03.020.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.