Abstract
We fit a power law distribution to US foodborne disease outbreaks to assess under-detection and underreporting. We predicted that 788 fewer than expected small outbreaks were identified annually during 1998–2017 and 365 fewer during 2018–2019, after whole-genome sequencing was implemented. Power law can help assess effectiveness of public health interventions.
Each year in the United States, >800 foodborne outbreaks are reported, causing >14,000 illnesses and >800 hospitalizations (1–3). Foodborne outbreaks range from small, localized outbreaks, such as those associated with a locally contaminated meal shared by family or friends, to large, multistate outbreaks associated with a contaminated food that is widely distributed. Selection and information biases, pathogen testing methods, and outbreak size can affect detection, investigation, and reporting (4). However, few methods are available to estimate the extent of outbreak under detection and underreporting.
Outbreaks can be considered natural occurrences with a mathematical relationship between frequency and size. Several studies have used a power law distribution, where one variable is proportional to the power of another, to help describe disease outbreaks or transmission (5–9). We examined the mathematical relationship between foodborne outbreak frequency and size to estimate the number of expected outbreaks of different sizes, comparing power law, log-normal, and exponential distributions by using censored and complete data to clarify under detection and underreporting.
FoodWorld 