![]() mitigation versus containment), and that moderate control measures will not achieve measurable public health benefit. ![]() For such a fast-epidemic growth, our results suggest that very strong and active control measures need to be implemented as early as possible regardless of the public health goal (e.g. We show that in most countries, the detection rate of infected individuals is in general low, and COVID-19 spreads very fast in these countries. We fit models to both case incidence data and death count data collected from eight European countries and the US in March 2020. Here, we designed a simple methodology to disentangle the epidemic growth from confounding factors, such as underreporting, delays in case confirmation and changes in surveillance intensity. Although it is possible that deaths from COVID-19 were not recorded as death from COVID-19 during very early phase of an epidemic when people are unaware of community transmission of COVID-19 ( 12) or during the relatively late phase of the epidemic when health care system is overwhelmed, this represents only a small fraction of cases in the countries we examine in this work, and is unlikely to significantly affect the death count curve when the death count increases exponentially. The time series of death counts reflects the growth of an epidemic reliably, with a delay in onset determined by the time between infection to death. Here, we argue that death and the cause of death are usually recorded reliably and are less affected by surveillance intensity changes or delay in confirmation than case counts. Simply fitting an exponential curve to case confirmation data may lead to erroneous conclusions when confounding factors are not taken into account or estimated from other sources of data. low detection rate, changes in surveillance intensity and delays in case confirmation. ![]() However, a major challenge to the inference of the growth of COVID-19 is that as a result of a fast-growing outbreak and a sizable infected population with no or mild-to-moderate symptoms ( 9, 10), case confirmation data is influenced by many factors in addition to the true epidemic growth, including substantial underreporting ( 11), i.e. Third, it is important for accurate estimation of the basic reproductive number, R 0, which in turn is used for many control measures, including evaluation of the vaccine/herd immunity threshold needed to stop transmission ( 6, 8). Second, it sets the baseline for evaluation of effectiveness of intervention strategies. First, it is crucial for forecasting the epidemic trajectory, the burden on health care systems and potential health and economic damage, so that appropriate and timely responses can be prepared. However, it was not clear whether COVID-19 can spread in other countries as fast as in Wuhan, China.Īccurate estimation of the rate of epidemic growth is important for many practical aspects. A fast epidemic spread is consistent with multiple other lines of evidence, such as the rapid increase of the epidemic curve by symptom onset published by China CDC ( 7) and the growth in the number of death cases in Hubei, China during late January 2020 ( 6). However, using domestic travel data and two distinct approaches, we estimated that the epidemic in Wuhan grew much faster than initially estimated, and the growth rate is likely to be between 0.21–0.3/day, translating to a doubling time between 2.3 to 3.3 days, and an R 0 approximately at 5.7 with a large confidence interval ( 6). Initially, it was suggested that the epidemic grew at 0.1–0.14/day, leading to an epidemic doubling time of 5–7 days ( 2 – 5). Estimation of the rate of early epidemic spread in Wuhan, China, lead to different conclusions. As of March 31, 2020, the global pandemic lead to more than 800,000 total confirmed cases and 40,000 deaths. ![]() ![]() It has spread rapidly and caused a global pandemic within a short period of time. COVID-19 originated in Wuhan China in Dec, 2019 ( 1). ![]()
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