Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate

Abstract

Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly spread around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R₀. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

Keywords: Basic Reproduction Number; COVID-19; Growth rate; Models; Statistical.

Fig. 1

Classifying the epidemic curve of novel coronavirus disease (COVID-19) into exponential or linear growth phases for different geographic areas and the time-series of the daily number of tests and the corresponding COVID-19 positivity rate in Japan. Time series data of the reported COVID-19 cases in Italy (A), Japan (B), California, US (G), hospitalized cases in Japan (E), severe cases in Japan (F). Shaded and white areas separate periods when the observed cumulative incidence curve grows exponentially or linearly, respectively. To classify the type of growth profile, linear regression and non-linear regression (exponential function) were utilized while the best model fit was selected using the Akaike Information Criteria (AIC). Time series data of cumulative number of PCR tests in Japan (C) and the cumulative COVID-19 positivity rate among tested samples (D). Dashed lines in (B), (C) and (D) denote the boundary changes the number of PCR test, between 3rd March and 4th March 2020.