Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak

Park, Sang Woo; Bolker, Benjamin M.; Champredon, David; Earn, David J. D.; Li, Michael; Weitz, Joshua S.; Grenfell, Bryan T.; Dushoff, Jonathan

doi:10.1098/rsif.2020.0144

Cited by 105 publications

(92 citation statements)

References 49 publications

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“…where R 0 = β/μ 2 and f (a) = μ 1 μ 2 μ 1 −μ 2 (e −μ 2 a − e −μ 1 a ). Beyond SIR and SEIR models, epidemiologists have approximated generation time distributions directly from contact tracing data, and several such studies have been conducted using early SARS-CoV-2 outbreak data from China (see [28] for a summary). The generation times are often presumed to follow gamma distributions, as the latter provide a flexible statistical model for the time between the onset of symptoms for infector/infectee pairs within a transmission chain (the serial interval), and the distribution of serial intervals is taken as an estimate of the unobservable times between infections (which is what the generation time density f (a) is meant to represent).…”

Section: Age-of-infection Dependent Transmissionmentioning

confidence: 99%

“…The first is the gamma distribution with mean 8.87 days and standard deviation 4.02 days, drawn from Li et al's [25] study of early transmission dynamics in Wuhan referred to earlier, which is widely cited as the first detailed analysis of early SARS-CoV-2 transmission. The second is also a gamma distribution but with mean (standard deviation) equal to 8.50 (6.07) days; this is based on parameter point estimates in the Bayesian meta-analysis conducted by Park et al [28]. These two forward generation time densities are displayed in Fig.…”

Section: Controlling Outbreaks Via Repeat Testing and Isolationmentioning

confidence: 99%

“…This model is flexible in allowing users to simulate many different testing scenarios, but it is perhaps most useful Two estimated generation time distributions found in published studies. We refer to these distributions as featuring relatively early transmission [28] and late transmission [25] in identifying the limits of outbreak control for alternative repeat-testing policies. The proposed approach is to first identify an acceptable control level of infection within a defined time periord, such as 5% of the student population over the course of a semester.…”

Section: Controlling Outbreaks Via Repeat Testing and Isolationmentioning

confidence: 99%

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Repeat SARS-CoV-2 testing models for residential college populations

Chang

Crawford

Kaplan

2020

Health Care Manag Sci

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Residentialcolleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

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Section: Age-of-infection Dependent Transmissionmentioning

confidence: 99%

Section: Controlling Outbreaks Via Repeat Testing and Isolationmentioning

confidence: 99%

Section: Controlling Outbreaks Via Repeat Testing and Isolationmentioning

confidence: 99%

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Repeat SARS-CoV-2 testing models for residential college populations

Chang

Crawford

Kaplan

2020

Health Care Manag Sci

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“…The basic reproductive number (R0 value) of SARS-CoV-2 is still controversial. The early estimates of R0 value for SARs-CoV-2 ranged from 2.2-3.1 [23,24]. The recent estimates, however, indicate that the number of SARS-CoV-2 infected individuals doubles every 2.4 days during the early epidemic, and R0 is likely to range from 4.7-6.6 [25][26][27][28].…”

Section: Zoonotic Origins Of Human Coronavirusesmentioning

confidence: 98%

Zoonotic and reverse zoonotic events of SARS-CoV-2 and their impact on global health

Munir

Ashraf

Munir

et al. 2020

Emerging Microbes & Infections

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Coronaviruses (CoVs) are enveloped, positive sense, single-stranded RNA viruses. The viruses have adapted to infect a large number of animal species, ranging from bats to camels. At present, seven CoVs infect humans, of which Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for causing the Coronavirus Disease 2019 (COVID-19) in humans. Since its emergence in late 2019, SARS-CoV-2 has spread rapidly across the globe. Healthcare systems around the globe have been stretched beyond their limits posing new challenges to emergency healthcare services and critical care. The outbreak continues to jeopardize human health, social life and economy. All known human CoVs have zoonotic origins. Recent detection of SARS-CoV-2 in pet, zoo and certain farm animals has highlighted its potential for reverse zoonosis. This scenario is particularly alarming, since these animals could be potential reservoirs for secondary zoonotic infections. In this article, we highlight interspecies SARS-CoV-2 infections and focus on the reverse zoonotic potential of this virus. We also emphasize the importance of potential secondary zoonotic events and the One-Health and One-World approach to tackle such future pandemics.

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“…wherein T is the total population. The biological parameters σ m and γ m are fixed at values reported in the COVID-19 literature [16][17][18] .…”

Section: Mean-β Modelmentioning

confidence: 99%

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

Tiwari

Vyasarayani

Chatterjee

2020

Preprint

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People in many countries are now infected with COVID-19. By now, it is clear that the number of people infected is much more than the number of reported cases. To estimate the infected but undetected/unreported cases using a mathematical model, we can use a parameter called the probability of quarantining an infected individual. This parameter exists in the time-delayed SEIQR model (Scientific Reports, article number: 3505). Two limiting cases of a network of such models are used to estimate the undetected population. The first limit corresponds to the network collapsing onto a single node and is referred to as the mean-$\beta$ model. In the second case, the number of nodes in the network is infinite and results in a continuum model, treating the infectivity as statistically distributed. We use a shifted Pareto distribution to model the infectivity. This distribution has a long tail and incorporates the presence of super-spreaders that contribute to the disease progression. While both the models capture the {\em detected} numbers equally well, the predictions of {\em affected} numbers from the continuum model are more realistic. Results suggest that affected people outnumber detected people by one to two orders of magnitude in Spain, UK, Italy, and Germany.

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Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak

Abstract: A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number R 0 —the average number of secondary cases caused by a primary case… Show more

Cited by 105 publications

References 49 publications

Repeat SARS-CoV-2 testing models for residential college populations

Repeat SARS-CoV-2 testing models for residential college populations

Zoonotic and reverse zoonotic events of SARS-CoV-2 and their impact on global health

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

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