Estimating internationally imported cases during the early COVID-19 pandemic

Abstract

Early in the COVID-19 pandemic, when cases were predominantly reported in the city of Wuhan, China, local outbreaks in Europe, North America, and Asia were largely predicted from imported cases on flights from Wuhan, potentially missing imports from other key source cities. Here, we account for importations from Wuhan and from other cities in China, combining COVID-19 prevalence estimates in 18 Chinese cities with estimates of flight passenger volume to predict for each day between early December 2019 to late February 2020 the number of cases exported from China. We predict that the main source of global case importation in early January was Wuhan, but due to the Wuhan lockdown and the rapid spread of the virus, the main source of case importation from mid February became Chinese cities outside of Wuhan. For destinations in Africa in particular, non-Wuhan cities were an important source of case imports (1 case from those cities for each case from Wuhan, range of model scenarios: 0.1-9.8). Our model predicts that ​18.4 (8.5 - 100)​ COVID-19 cases were imported to 26 destination countries in Africa, with most of them (90%) predicted to have arrived between 7th January (+/-10 days) and 5th February (+/- 3 days), and all of them predicted prior to the first case detections. We finally observed marked heterogeneities in expected imported cases across those locations. Our estimates shed light on shifting sources and local risks of case importation which can help focus surveillance efforts and guide public health policy during the final stages of the pandemic. We further provide a time window for the seeding of local epidemics in African locations, a key parameter for estimating expected outbreak size and burden on local health care systems and societies, that has yet to be defined in these locations.

Estimating unobserved SARS-CoV-2 infections in the United States

“…Similar information has proven useful in past public health emergencies however, such as Zika and Ebola ( 44 , 45 ), and our accounting for uncertainty about R , which is one of the more likely parameters to vary across contexts, spans a wide range of possibilities that could apply to the United States ( 21 ). Third, we did not make use of airline data to model importation ( 46 ), which could be used to refine assumptions about unobserved importations in future applications of our method ( 47 ).…”

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

“…Equation 2 below provides the exact differential equations governing the model. (2) In order to determine the values of the parameters defining the flows between states, we use a least squares regression performed at seven day intervals in the datasets to which we fit. This allows the model to take into account the time dependent nature of the parameters, which rely on factors such as social distancing regulations and changes in testing capacity.…”

“…As of 4 November 2020, there have been more than 47.6 million confirmed cases globally with more than million confirmed deaths 1 . However, these numbers and the current mapping of disease spread present an incomplete picture of the outbreak largely due to the lack of adequate testing, particularly as undetected infected cases are the main source of disease spread [2][3][4][5][6][7] . It is estimated that the reported detection rate of actual COVID-19 cases is only 1-2% 5 .…”

Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes

“…As of 25 September 2020, there have been more than 32.1 million confirmed cases globally with more than 980,000 confirmed deaths (1). However, these numbers and the current mapping of disease spread present an incomplete picture of the outbreak largely due to the lack of adequate testing, particularly as undetected infected cases are the main source of disease spread (2)(3)(4)(5)(6)(7). It is estimated that the reported detection rate of actual COVID-19 cases is only 1-2% (5).…”