Seasonality in six enterically transmitted diseases and ambient temperature

Epidemiol Infect. 2007 Feb;135(2):281-92. doi: 10.1017/S0950268806006698.


We propose an analytical and conceptual framework for a systematic and comprehensive assessment of disease seasonality to detect changes and to quantify and compare temporal patterns. To demonstrate the proposed technique, we examined seasonal patterns of six enterically transmitted reportable diseases (EDs) in Massachusetts collected over a 10-year period (1992-2001). We quantified the timing and intensity of seasonal peaks of ED incidence and examined the synchronization in timing of these peaks with respect to ambient temperature. All EDs, except hepatitis A, exhibited well-defined seasonal patterns which clustered into two groups. The peak in daily incidence of Campylobacter and Salmonella closely followed the peak in ambient temperature with the lag of 2-14 days. Cryptosporidium, Shigella, and Giardia exhibited significant delays relative to the peak in temperature (approximately 40 days, P<0.02). The proposed approach provides a detailed quantification of seasonality that enabled us to detect significant differences in the seasonal peaks of enteric infections which would have been lost in an analysis using monthly or weekly cumulative information. This highly relevant to disease surveillance approach can be used to generate and test hypotheses related to disease seasonality and potential routes of transmission with respect to environmental factors.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Campylobacter Infections / epidemiology*
  • Campylobacter Infections / transmission
  • Climate*
  • Cryptosporidiosis / epidemiology*
  • Cryptosporidiosis / transmission
  • Disease Outbreaks
  • Dysentery, Bacillary / epidemiology*
  • Dysentery, Bacillary / transmission
  • Giardiasis / epidemiology*
  • Giardiasis / transmission
  • Hepatitis A / epidemiology
  • Hepatitis A / transmission
  • Humans
  • Massachusetts / epidemiology
  • Models, Statistical
  • Salmonella Infections / epidemiology*
  • Salmonella Infections / transmission
  • Seasons*
  • Temperature