Foodborne and waterborne gastrointestinal infections and their associated outbreaks are preventable, yet still result in significant morbidity, mortality and revenue loss. Many enteric infections demonstrate seasonality, or annual systematic periodic fluctuations in incidence, associated with climatic and environmental factors. Public health professionals use statistical methods and time series models to describe, compare, explain and predict seasonal patterns. However, descriptions and estimates of seasonal features, such as peak timing, depend on how researchers define seasonality for research purposes and how they apply time series methods. In this review, we outline the advantages and limitations of common methods for estimating seasonal peak timing. We provide recommendations improving reporting requirements for disease surveillance systems. Greater attention to how seasonality is defined, modelled, interpreted and reported is necessary to promote reproducible research and strengthen proactive and targeted public health policies, intervention strategies and preparedness plans to dampen the intensity and impacts of seasonal illnesses.

E. coli O157 can be transmitted to humans by three primary (foodborne, environmental, waterborne) and one secondary (person-to-person transmission) pathways. A regression model and quantitative microbiological risk assessments (QMRAs) were applied to determine the relative importance of the primary transmission pathways in NE Scotland. Both approaches indicated that waterborne infection was the least important but it was unclear whether food or the environment was the main source of infection. The QMRAs over-predicted the number of cases by a factor of 30 and this could be because all E. coli O157 strains may not be equally infective and/or the level of infectivity in the dose–response model was too high. The efficacy of potential risk mitigation strategies to reduce human exposure to E. coli O157 using QMRAs was simulated. Risk mitigation strategies focusing on food and environment are likely to have the biggest impact on infection figures.