COVID-19 underreporting and its impact on vaccination strategies

Abstract

Background: Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests' implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death.

Methods: We present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model.

Results: For the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization.

Conclusions: pBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies.

Keywords: Epidemiological models; Numerical simulation; Stable rates of hospitalization and death; Underreported infections; Underreporting estimation; Vaccination strategies.

Fig. 1

Methodological workflow for the underreporting quantification

Fig. 2

Left: Daily numbers of performed and positive tests of COVID-19 in Chicago. Right: The corresponding daily positive rate. The period is 01-Mar-2020 to 23-Dec-2020. The solid horizontal line represents a reference of 10%

Fig. 3

Daily rates of hospitalization (left), death (center) and death amongst hospitalized individuals (right) from 01-Mar-2020 to 23-Dec-2020, in Chicago. The dark area shows the period when the rate seems to stabilize, i.e., 01-Aug2020 to 05-Oct-2020. The horizontal solid lines represent the median of the rates observed during 01-Aug-2020 to 05-Oct-2020

Fig. 4

Daily rates of hospitalization (left), death (center) and death amongst hospitalized individuals (right) from 01-Mar-2020 to 23-Dec-2020, in Chicago, for each age range. The dark area shows the period when the rate seems to stabilize, i.e., 01-Aug-2020 to 05-Oct-2020. The horizontal solid lines represent the median values of the rates observed during 01-Aug-2020 to 05-Oct-2020

Fig. 5

Schematic representation of the SEIR-type model in Eqs. (345678910)–(11)

Fig. 6

Corrected and reported series of daily infections in Chicago from 01-Mar-2020 to 23-Dec-2020, using the rates of hospitalization (left column) and death (right column) from Table 1. First row uses the daily reports, the second uses daily reports by gender, and the third one uses daily reports by age range. The filled envelopes are 90% confidence intervals (CIs)