Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study

Lai, Shengjie; Bogoch, Isaac I.; Ruktanonchai, Nick W.; Watts, Alexander; Lu, Xin; Yang, Weizhong; Yu, Hongjie; Khan, Kamran; Tatem, Andrew J.

doi:10.1101/2020.02.04.20020479

Cited by 155 publications

(156 citation statements)

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“…We defined imported cases as those with known travel history from China (of those, 83% had travel history from Hubei province, and 17% from unknown locations in China 1 ). Estimates on daily air travel volume were obtained from Lai et al 8 henceforth referred to as simply the GHS 2 index. We classify locations with GHS 2 index above the 80th percentile as high surveillance locations, those with GHS 2 index below the 20th percentile as low surveillance locations, with all others classified as locations with intermediate surveillance.…”

Section: Methodsmentioning

confidence: 99%

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Niehus

Munoz

Taylor

et al. 2020

Preprint

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Risk of COVID-19 infection in Wuhan has been estimated using imported case counts of international travelers, often under the assumption that all cases in travelers are ascertained. Recent work indicates variation among countries in detection capacity for imported cases. Singapore has historically had very strong epidemiological surveillance and contact-tracing capacity and has shown in the COVID-19 epidemic evidence of a high sensitivity of case detection. We therefore used a Bayesian modeling approach to estimate the relative imported case detection capacity for other countries compared to that of Singapore.We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% -64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 -4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, we found that the ability to detect imported cases relative to Singapore among high surveillance locations is 40% (95% HPDI 22% -67%), among intermediate surveillance locations it is 37% (95% HPDI 18% -68%), and among low surveillance locations it is 11% (95% HPDI 0% -42%). Using a simple mathematical model, we further find that treating all travelers as if they were residents (rather than accounting for the brief stay of some of these travelers in Wuhan) can modestly contribute to underestimation of prevalence as well. We conclude that estimates of case counts in Wuhan based on assumptions of perfect detection in travelers . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. : medRxiv preprintterm can be well approximated as We plot this approximation of V given doubling times . V ≈ 1 − e −(r+γ)d aligned with 4 , a range of durations of detectable infection and a range of lengths of stay 2 ( Figure 2 ). Results:We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% -64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 -4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore, which leads to 1.8 (95% CI 0.5 -3.4) undetected cases per detected case. The ability to detect imported cases among high surveillance countries is 40% (95% HPDI 22% -67%), among intermediate surveillance countries it is 37% (95% HPDI 18% -68%), and among low surveillance countries it is 11% (95% HPDI 0% -42%). : medRxiv preprint 7In this paper, we have aimed to test two assumptions underlying the estimation of incidence at the epicentre of the SARS-Cov2 outbreak. The first of these is that the capacity for detection of international imported cases is 100% sensitive and specific across locations. While we know of no reason to doubt specificity of detection, we tested the assumption of perfec...

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Section: Methodsmentioning

confidence: 99%

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Niehus

Munoz

Taylor

et al. 2020

Preprint

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“…The initial reporting of observed cases occurred during the traditional Chinese New Year, when the largest population movement takes place every year (Ai et al, 2020). Further, Wuhan is a highly populated city with more than 11 million residents and is connected to many cities in China through public transportation, such as buses, trains, and flights (Lai et al, 2020;Read, Bridgen, Cummings, Ho, & Jewell, 2020). In the absence of pharmaceutical interventions, rapid action was required by the Chinese government to mitigate transmission within and outside of Wuhan.…”

Section: Introductionmentioning

confidence: 99%

“…On January 23, 2020, the Chinese government implemented a strict lockdown of Wuhan, followed by several nearby cities in subsequent days; the lockdowns include temporarily suspending all public transportation and advising residents to remain at home (Du et al, 2020;Wu et al, 2020). Further, many high-speed rail stations and airports have implemented screening measures to detect travelers with a fever, specifically those traveling from Wuhan, and those with a fever are referred to public hospitals (Lai et al, 2020;Wu et al, 2020). Within hospitals, patients who fulfill clinical and epidemiological characteristics of 2019-nCoV are immediately isolated.…”

Section: Introductionmentioning

confidence: 99%

Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020

Roosa

Lee

Luo

et al. 2020

Infectious Disease Modelling

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a b s t r a c tThe initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409e7496 additional confirmed cases in Hubei and 1128e1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588e13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th e 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.

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“…Since then others have used flight and other data to highlight countries at most risk of importation of SARS-CoV-2 from China. 8,9 These papers have then used the infectious disease vulnerability index (IDVI) as an assessment of how at risk a country is to local transmission. 8,9 In countries where importations are occurring, it will be important to quantify the risk of onward transmission occurring and the extent of this transmission.…”

Section: Introductionmentioning

confidence: 99%

“…8,9 These papers have then used the infectious disease vulnerability index (IDVI) as an assessment of how at risk a country is to local transmission. 8,9 In countries where importations are occurring, it will be important to quantify the risk of onward transmission occurring and the extent of this transmission. Kucharski et al 10 considered this generally given the number of importations using a probabilistic model, and Wu et al estimated a probability of transmission within cities outside China.…”

Section: Introductionmentioning

confidence: 99%

Estimating number of global importations of COVID-19 from Wuhan, risk of transmission outside mainland China and COVID-19 introduction index between countries outside mainland China

Sun

Dickens

Chen

et al. 2020

Preprint

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Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study

Abstract: Objective: To estimate the potential risk and geographic range of Wuhan novel coronavirus (2019-nCoV) spread within and beyond China from

Cited by 155 publications

References 24 publications

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020

Estimating number of global importations of COVID-19 from Wuhan, risk of transmission outside mainland China and COVID-19 introduction index between countries outside mainland China

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