The importance of climatic factors and outliers in predicting regional monthly campylobacteriosis risk in Georgia, USA

Int J Biometeorol. 2014 Nov;58(9):1865-78. doi: 10.1007/s00484-014-0788-6. Epub 2014 Jan 24.


Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R(2)) was used for evaluating training (1999-2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.

MeSH terms

  • Campylobacter Infections / diagnosis
  • Campylobacter Infections / epidemiology*
  • Climate*
  • Data Interpretation, Statistical*
  • Forecasting
  • Georgia / epidemiology
  • Humans
  • Incidence
  • Population Surveillance / methods*
  • Prognosis
  • Reproducibility of Results
  • Risk Assessment / methods
  • Seasons*
  • Sensitivity and Specificity
  • Spatio-Temporal Analysis
  • Temperature*
  • Weather*