key: cord-0682692-dz1lfwzp
authors: Omori, Ryosuke; Mizumoto, Kenji; Chowell, Gerardo
title: Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate
date: 2020-04-19
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2020.04.021
sha: 7bd9387120184b5000590e4b374cac6757a2f7e0
doc_id: 682692
cord_uid: dz1lfwzp

Abstract Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly propagated to 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, R 0. 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.

Highlight  Reported cases may not reflect true epidemic growth due to limited testing capacity  Numbers of hospitalized and severe cases may be less biased  Case series by date of symptoms onset can eliminate the bias from testing capacity

Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly propagated to 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 timeseries 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. 

Our findings reveal an exponential growth phase for the COVID-19 epidemic in Italy during the entire study period ( fig. 1(A) ), while the epidemic in Japan shows linear growth during the first few weeks. At face value, this pattern would suggest that Japan was facing a constant infection risk in spite of a growing epidemic ( fig. 1(B) ). Linear growth is apparent until 5th March 2020 in Japan, a trend that suggests that the number of cases exceeded testing capacity during that period. Indeed, a drastic increase in testing rate was observed on 4th March 2020 ( fig. 1(C) ), which in turn, changes the cumulative positive rate drastically ( fig. 1(D) ) [4] . Furthermore, the growths trends in the numbers of both hospitalized and severe cases (requiring intubation or admission to ICU), which may be less biased from limited testing capacity, show signs of exponential growth ( fig. 1(E) and (F)) [4] .

Alternative explanations for the early linear growth phase other than saturated testing capacity could be considered. First, it could be the result of preferential testing for highly-suspicious samples leading to a bias in the positive rate among samples. Another possibility that could be invoked is the effect of interventions, e.g., containment, on epidemic growth. Finally, transmission driven by sporadic outbreaks/clusters might lead to linear growth. Furthermore, we also found that the trajectory of the epidemic in California, United States, started with a discontinuous increase from zero counts on 11th March, 2020, following an exponential growth ( fig. 1(G) ). This abrupt increase is probably the result of testing delays, reporting delays or difficulties in identifying COVID-19 cases during the early phase of the outbreak, a pattern that is also misleading to the public.

Our results indicate that changes in testing rates which could result from limited diagnostic testing capacity could mask the epidemic's growth rate, which has public health implications. Specifically, the derivation of the basic reproduction number during the early phase of outbreak and the effective reproduction number during the course of the epidemic are key quantities that often rely on time-series of reported cases. Hence, biased epidemic trends can lead to incorrect inferences of metrics characterizing the transmission potential. Ideally, modelers are interested in case series by date of symptoms onset to mitigate this bias, but the date of symptoms onset becomes largely unavailable for epidemics of rapid dissemination such as COVID-19. Data on the testing strategy including testing rates as well as estimates of the reporting probability and ascertainment bias could help derive reliable inferences of epidemiological and transmission parameters [5] [6] [7] . Trends in numbers of hospitalized/severe cases may also capture the epidemic growth profile. However, linear growth was observed after a brief period of exponential growths, likely reflecting other mechanisms involved (highly variable times from symptoms onset to admission, heterogeneous severity levels in the exposed population) or other biases than testing capacity, e.g., highly specific testing among hospitalized individuals, limited hospital bed capacity. Further analysis is required to use them.

We declare that we have no conflict of interest. 

World Health Organization

World Health Organization, WHO Director-General's opening remarks at the media briefing on COVID-19 -11

Ministry of Health, Labour and Welfare

Real-time quantification of the next-generation matrix and age-dependent forecasting of pandemic influenza H1N1 2009 in Japan

Transmission potential of COVID-19 in South Korea

Estimating Risk for Death from 2019 Novel Coronavirus Disease, China