Two biases in incubation time estimation related to exposure.

BACKGROUND: Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature. METHODS: We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution. RESULTS: Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile. CONCLUSION: We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.

Towards robust and accurate estimates of the incubation time distribution, with focus on upper tail probabilities and SARS-CoV-2 infection.

Quarantine length for individuals who have been at risk for infection with SARS-CoV-2 has been based on estimates of the incubation time distribution. The time of infection is often not known exactly, yielding data with an interval censored time origin. We give a detailed account of the data structure, likelihood formulation and assumptions usually made in the literature: (i) the risk of infection is assumed constant on the exposure window and (ii) the incubation time follows a specific parametric distribution. The impact of these assumptions remains unclear, especially for the right tail of the distribution which informs quarantine policy. We quantified bias in percentiles by means of simulation studies that mimic reality as close as possible. If assumption (i) is not correct, then median and upper percentiles are affected similarly, whereas misspecification of the parametric approach (ii) mainly affects upper percentiles. The latter may yield considerable bias. We suggest a semiparametric method that provides more robust estimates without the need of a parametric choice. Additionally, we used a simulation study to evaluate a method that has been suggested if all infection times are left censored. It assumes that the width of the interval from infection to latest possible exposure follows a uniform distribution. This assumption gave biased results in the exponential phase of an outbreak. Our application to open source data suggests that focus should be on the level of information in the observations, as expressed by the width of exposure windows, rather than the number of observations.

Multiple Sources of the Outbreak of Legionnaires' Disease in Genesee County, Michigan, in 2014 and 2015.

BACKGROUND: A community-wide outbreak of Legionnaires' disease (LD) occurred in Genesee County, Michigan, in 2014 and 2015. Previous reports about the outbreak are conflicting and have associated the outbreak with a change of water source in the city of Flint and, alternatively, to a Flint hospital. OBJECTIVE: The objective of this investigation was to independently identify relevant sources of Legionella pneumophila that likely resulted in the outbreak. METHODS: An independent, retrospective investigation of the outbreak was conducted, making use of public health, health care, and environmental data and whole-genome multilocus sequence typing (wgMLST) of clinical and environmental isolates. RESULTS: Strong evidence was found for a hospital-associated outbreak in both 2014 and 2015: a) 49% of cases had prior exposure to Flint hospital A, significantly higher than expected from Medicare admissions; b) hospital plumbing contained high levels of L. pneumophila; c) Legionella control measures in hospital plumbing aligned with subsidence of hospital A-associated cases; and d) wgMLST showed Legionella isolates from cases exposed to hospital A and from hospital plumbing to be highly similar. Multivariate analysis showed an increased risk of LD in 2014 for people residing in a home that received Flint water or was located in proximity to several Flint cooling towers. DISCUSSION: This is the first LD outbreak in the United States with evidence for three sources (in 2014): a) exposure to hospital A, b) receiving Flint water at home, and c) residential proximity to cooling towers; however, for 2015, evidence points to hospital A only. Each source could be associated with only a proportion of cases. A focus on a single source may have delayed recognition and remediation of other significant sources of L. pneumophila. https://doi.org/10.1289/EHP5663.