Estimating the Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) Virus Infections

Abstract

A novel avian influenza virus, influenza A(H7N9), emerged in China in early 2013 and caused severe disease in humans, with infections occurring most frequently after recent exposure to live poultry. The distribution of A(H7N9) incubation periods is of interest to epidemiologists and public health officials, but estimation of the distribution is complicated by interval censoring of exposures. Imputation of the midpoint of intervals was used in some early studies, resulting in estimated mean incubation times of approximately 5 days. In this study, we estimated the incubation period distribution of human influenza A(H7N9) infections using exposure data available for 229 patients with laboratory-confirmed A(H7N9) infection from mainland China. A nonparametric model (Turnbull) and several parametric models accounting for the interval censoring in some exposures were fitted to the data. For the best-fitting parametric model (Weibull), the mean incubation period was 3.4 days (95% confidence interval: 3.0, 3.7) and the variance was 2.9 days; results were very similar for the nonparametric Turnbull estimate. Under the Weibull model, the 95th percentile of the incubation period distribution was 6.5 days (95% confidence interval: 5.9, 7.1). The midpoint approximation for interval-censored exposures led to overestimation of the mean incubation period. Public health observation of potentially exposed persons for 7 days after exposure would be appropriate.

Association between the Severity of Influenza A(H7N9) Virus Infections and Length of the Incubation Period

“…By 15 June 2015, 655 laboratory-confirmed cases were reported in mainland China. The mean incubation period of human infections was around 3.4 days [2], similar to the incubation period for human infections with influenza A(H5N1) [3], and longer than the incubation period for human infections with seasonal influenza viruses [4].…”

“…A simple approach to estimate the incubation period distribution from interval-censored data is to impute the midpoint of the exposure interval for each patient, and then estimate the distribution based on these 'exact' incubation times. However, this approach is somewhat naïve, and is likely to overestimate incubation period distributions which tend to be right-skewed [2]. Therefore to estimate the incubation period distribution, we fitted a Weibull distribution allowing for interval censoring [2], estimating the shape and scale parameters using Markov Chain Monte Carlo in a Bayesian framework [5].…”

“…However, this approach is somewhat naïve, and is likely to overestimate incubation period distributions which tend to be right-skewed [2]. Therefore to estimate the incubation period distribution, we fitted a Weibull distribution allowing for interval censoring [2], estimating the shape and scale parameters using Markov Chain Monte Carlo in a Bayesian framework [5]. We assumed that the incubation period distribution had different parameters among the non-fatal cases and the fatal cases and we consequently estimated two different couple of parameters (k, θ) of the Weibull distribution using MCMC.…”

“…All laboratory-confirmed human cases of avian influenza A(H7N9) virus infection were notified to the Chinese Center for Disease Control and Prevention (China CDC) and relevant clinical and epidemiological data was recorded in an electronic database [2,3]. Data extracted for this study included age, sex, geographical location, underlying conditions and dates of illness onset and hospital admission for cases reported from March 2013 through August 2014.…”

“…Exposure data were available for 203/395 (51%) of cases. In the majority of those cases, the information on exposure was recorded as intervals of 2 or more days during which infection was thought to have occurred rather than exact dates of presumed infection [2]. For the subset of cases without available exposure data, we assumed their incubation time fell in the interval (0,14) days [2].…”