Seasonal synchronization of foodborne outbreaks in the United States, 1996–2017

Abstract

Modern food systems represent complex dynamic networks vulnerable to foodborne infectious outbreaks difficult to track and control. Seasonal co-occurrences (alignment of seasonal peaks) and synchronization (similarity of seasonal patterns) of infections are noted, yet rarely explored due to their complexity and methodological limitations. We proposed a systematic approach to evaluate the co-occurrence of seasonal peaks using a combination of L-moments, seasonality characteristics such as the timing (phase) and intensity (amplitude) of peaks, and three metrics of serial, phase-phase, and phase-amplitude synchronization. We used public records on counts of nine foodborne infections abstracted from CDC’s FoodNet Fast online platform for the US and ten representative states from 1996 to 2017 (264 months). Based on annualized and trend-adjusted Negative Binomial Harmonic Regression (NBHR) models augmented with the δ-method, we determined that seasonal peaks of Campylobacter, Salmonella, and Shiga toxin-producing Escherichia Coli (STEC) were tightly clustered in late-July at the national and state levels. Phase-phase synchronization was observed between Cryptosporidium and Shigella, Listeria, and Salmonella (ρ = 0.51, 0.51, 0.46; p < 0.04). Later peak timing of STEC was associated with greater amplitude nationally (ρ = 0.50, p = 0.02) indicating phase-amplitude synchronization. Understanding of disease seasonal synchronization is essential for developing reliable outbreak forecasts and informing stakeholders on mitigation and preventive measures.

Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998–2019

“…In prior research, we found variations in seasonal peak timing across pathogens using harmonic regression modeling [ 33 , 34 , 35 ]. Such fluctuations could occur for a variety of social and environmental reasons [ 36 , 37 , 38 , 39 ].…”

How Seasonality of Malnutrition Is Measured and Analyzed

“…Our study in Australia used surveillance data on Ross River virus and Barmah Forest virus to identify peak timing, as well as to estimate the impact of a false-positive Barmah Forest virus epidemic in 2013 [ 53 ]. We had recently demonstrated the summertime synchronization of foodborne outbreaks in the USA using the national surveillance data [ 54 ]. Thus, secondary data could be extremely useful as part of formative research, for extrapolating seasonal trends from smaller and context specific primary seasonality research, as well as for general forecasting and modeling.…”

Investigating seasonal patterns in enteric infections: a systematic review of time series methods

“…Descriptive studies often reported linear fluctuations in mean and median incidence over time or the sum and percentage of infections by consecutive seasons or months (CID 1-11). Studies frequently estimated seasonal peak timing by identifying the calendar month or season with the highest sum, percentage or average incidence of infections (CID [12][13][14][15][16][17][18]. Study rationales aimed to understand trends in infection severity for creating, modifying or evaluating public health interventions.…”

“…Precise estimation of peak timing helps identify differences in seasonal patterns by pathogen, subpopulation and geographic location [ 12 ]. Synchronisation of pathogens' peaks suggests possible co-infections and shared food/water- or environmental-drivers of infection [ 12 , 16 , 17 ]. Lags between peaks of infections and their drivers, best assessed with more granular temporal data, inform forecasts of peak incidence for early outbreak warnings [ 12 , 18 , 19 ].…”