On the estimation of the reproduction number based on misreported epidemic data

Abstract

Epidemic data often suffer from underreporting and delay in reporting. In this paper, we investigated the impact of delays and underreporting on estimates of reproduction number. We used a thinned version of the epidemic renewal equation to describe the epidemic process while accounting for the underlying reporting system. Assuming a constant reporting parameter, we used different delay patterns to represent the delay structure in our model. Instead of assuming a fixed delay distribution, we estimated the delay parameters while assuming a smooth function for the reproduction number over time. In order to estimate the parameters, we used a Bayesian semiparametric approach with penalized splines, allowing both flexibility and exact inference provided by MCMC. To show the performance of our method, we performed different simulation studies. We conducted sensitivity analyses to investigate the impact of misspecification of the delay pattern and the impact of assuming nonconstant reporting parameters on the estimates of the reproduction numbers. We showed that, whenever available, additional information about time-dependent underreporting can be taken into account. As an application of our method, we analyzed confirmed daily A(H1N1) v2009 cases made publicly available by the World Health Organization for Mexico and the USA.

Burden of influenza, respiratory syncytial virus, and other respiratory viruses and the completeness of respiratory viral identification among respiratory inpatients, Canada, 2003‐2014

“…These new datasets and new approaches of measuring the burden of influenza should be compared with the regression‐based estimates of excess burden as a way of assessing representativeness and as an aid to interpreting any differences. When dealing with near real‐time data, delays in reporting make the interpretation of weekly data considerably more complex 26, 27, 28. Use of an ARI case definition to identify cases for viral identification will result in the identification of a much larger proportion of hospitalizations due to influenza than use of an ILI definition, although at a cost of considerably higher testing rates 25.…”

“…Though overall, the morbidity burden for influenza, RSV, and ORV is similar; the age distribution is very different with the rate of RSV- When dealing with near real-time data, delays in reporting make the interpretation of weekly data considerably more complex. [26][27][28] Use of an ARI case definition to identify cases for viral identification will result in the identification of a much larger proportion of hospitalizations due to influenza than use of an ILI definition, although at a cost of considerably higher testing rates. 25…”

An exact method for quantifying the reliability of end-of-epidemic declarations in real time

“…While these could bias z s and alter declaration times, some of these more realistic dynamics can be included as future extensions. We can adjust for delays by applying nowcasting techniques [24] and include heterogeneity by using a negative binomial renewal model [1]. Future generalisations of our method will consider how data about reporting trends (e.g.…”

“…This is a standard model for under-reporting [6,24] and implies the following statistical relationship…”

Spatiotemporal Evolution of Ebola Virus Disease at Sub-National Level during the 2014 West Africa Epidemic: Model Scrutiny and Data Meagreness

“…Unfortunately we do not have data to assess the magnitude or the variability of underreporting. Also, inconsistent reporting with undocumented backlogging and the absence of dates of disease onset may affect the accuracy of the estimates and need to be taken into consideration when interpreting the results [ 27 ]. Furthermore, the district-specific SEIR model is a mathematical model assuming a deterministic disease process.…”