Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19

Hortaçsu, Ali; Liu, Jiarui; Schwieg, Timothy

doi:10.1016/j.jeconom.2020.07.047

Cited by 50 publications

(24 citation statements)

References 32 publications

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“…To account for underreported air travel cases, the number of unreported COVID-19 cases in the general global population is assumed to apply. Recent estimates indicate global under-reporting to be a 10-fold [ 6 , 7 ], 23-fold ]8[ and 54-fold [ 9 ] multiplier of the current reported cases.…”

Section: Methodsmentioning

confidence: 99%

Probability and estimated risk of SARS-CoV-2 transmission in the air travel system

Pang

Jones

Waite

et al. 2021

Travel Medicine and Infectious Disease

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Background As an emerging virus, SARS-CoV-2 and the risk of transmission during air travel is of high interest. This paper is a retrospective estimate of the probability of an infectious passenger in the air travel system transmitting the SARS-CoV-2 virus to a fellow passenger. Methods Literature was reviewed from May–September 2020 to identify COVID-19 cases related to air travel. The studies were limited to publicly available literature for passengers; studies of flight crews were not reviewed. A novel quantitative approach was developed to estimate air travel transmission risk that considers secondary cases, the overall passenger population, and correction factors for asymptomatic transmission and underreporting. Results There were at least 2866 index infectious passengers documented to have passed through the air travel system in a 1.4 billion passenger population. Using correction factors, the global risk of transmission during air travel is estimated at 1:1.7 million; acknowledging that assumptions exist around case detection rate and mass screenings. Uncertainty in the correction factors and a 95% credible interval indicate risk ranges from 1 case for every 712,000 travelers to 1 case for every 8 million travelers. Conclusion The risk of COVID-19 transmission on an aircraft is low, even with infectious persons onboard.

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Section: Methodsmentioning

confidence: 99%

Probability and estimated risk of SARS-CoV-2 transmission in the air travel system

Pang

Jones

Waite

et al. 2021

Travel Medicine and Infectious Disease

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“…Typically, reported cases represent a subset of the total number of infected cases, as sometimes only the most severe cases of symptoms are reported. On the basis of Liu et al (2020) [ 6 , 10 ], our approach is based on the knowledge of the data of new reported cases (typically weekly) over the time course of the pandemic.…”

Section: Methodsmentioning

confidence: 99%

“…In other words, we cannot convert our cumulative reported exponential function to the least-square log transformation, since we have an error

that we need to estimate. In previous studies, an a priori constant has to be chosen, and not estimated, for

[ 10 ]; then, a fitted least-square log transformation was applied to the cumulative data. In this paper, we estimate the parameters directly using the self-starting functions proposed [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

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Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Sallahi

Park

El-Mellouhi

et al. 2021

Biology

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Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

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“…One recent work attempts to estimate the level of under-reporting in the USA during the first half of March 2020 using travel data from epicentres. 2 Another study 3 uses a Bayesian analysis to get an estimate of the cumulative number of unreported cases in the USAS up to 18 April 2020.…”

Section: Introductionmentioning

confidence: 99%

Estimating under-reporting of COVID-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio

Unnikrishnan¹,

Mangalathu²,

Kutty

2021

BMJ Open

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ObjectivesThe COVID-19 pandemic has spread to all states in India. Due to limitations in testing coverage, the true extent of the spread may not be fully reflected in the reported cases. In this study, we obtain time-varying estimates of the fraction of COVID-19 infections reported in the different states.MethodsFollowing a methodology developed in prior work, we use a delay-adjusted case fatality ratio to estimate the true fraction of cases reported in different states. We also develop a delay adjusted test positivity estimation method and study the relationship between the estimated test positivity rate for each state and the estimated fraction of cases reported.SettingWe apply this method of analysis to all Indian states reporting at least 100 deaths as of 10 October 2020.ResultsOur analysis suggests that delay-adjusted case fatality ratios observed in different states range from 0.47% to 3.55%. The estimated fraction of cases reported in different states ranges from 39% to 100% for an assumed baseline case fatality ratio of 1.38%, from 18.6% to 100% for an assumed baseline case fatality ratio of 0.66%, and from 2.8% to 19.7% for an assumed baseline case fatality ratio of 0.1%. We also demonstrate a statistically significant negative relationship between the fraction of cases reported in each state and the testing positivity rate.ConclusionsThe estimates provide a means to quantify and compare the trends of reporting and the true level of current infections in different states. This information may be used to guide policies for prioritising testing in different states, and also to analyse the time-varying effects of different quarantine measures adopted in different states.

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Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19

Cited by 50 publications

References 32 publications

Probability and estimated risk of SARS-CoV-2 transmission in the air travel system

Probability and estimated risk of SARS-CoV-2 transmission in the air travel system

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Estimating under-reporting of COVID-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio

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