Effect of temperature and precipitation on salmonellosis cases in South-East Queensland, Australia: an observational study

Abstract

Objective: Foodborne illnesses in Australia, including salmonellosis, are estimated to cost over $A1.25 billion annually. The weather has been identified as being influential on salmonellosis incidence, as cases increase during summer, however time series modelling of salmonellosis is challenging because outbreaks cause strong autocorrelation. This study assesses whether switching models is an improved method of estimating weather-salmonellosis associations.

Design: We analysed weather and salmonellosis in South-East Queensland between 2004 and 2013 using 2 common regression models and a switching model, each with 21-day lags for temperature and precipitation.

Results: The switching model best fit the data, as judged by its substantial improvement in deviance information criterion over the regression models, less autocorrelated residuals and control of seasonality. The switching model estimated a 5 °C increase in mean temperature and 10 mm precipitation were associated with increases in salmonellosis cases of 45.4% (95% CrI 40.4%, 50.5%) and 24.1% (95% CrI 17.0%, 31.6%), respectively.

Conclusions: Switching models improve on traditional time series models in quantifying weather-salmonellosis associations. A better understanding of how temperature and precipitation influence salmonellosis may identify where interventions can be made to lower the health and economic costs of salmonellosis.

Keywords: Australia; disease outbreaks; rainfall; salmonellosis; temperature.

Figure 1

Estimates of the per cent change in cases per 5°C increase in temperature and 10 mm increase in precipitation for each model.

Figure 2

Overall per cent change in cases by daily mean temperature and by days of lag following a day with a mean temperature of 30°C, and per 1 mm change in daily precipitation and by days of lag after a day with 75 mm precipitation.