The timing of COVID-19 transmission

Ferretti, Luca; Ledda, Alice; Wymant, Chris; Zhao, Lele; Ledda, Virginia; Dörner, Lucie Abeler-; Kendall, Michaela; Nurtay, Anel; Cheng, Hao-Yuan; Ng, Ta-Chou; Lin, Hsien-Ho; Hinch, Rob; Masel, Joanna; Kilpatrick, A. Marm; Fraser, Christophe

doi:10.1101/2020.09.04.20188516

Cited by 119 publications

(225 citation statements)

References 54 publications

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“…Based on a review of 17 studies reporting serial intervals or generation times, Casey et al estimated that 56.1% of transmission occurred in the pre-symptomatic period [14]. Ferretti et al found that the peak of transmission occurred at the time of symptom onset with 41% of transmission events occurring before symptoms onset and another 35% on the day of and day after symptom onset [15]. We estimate that 50% of infectiousness occurs before symptom onset.…”

Section: Plos Onementioning

confidence: 78%

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Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

Bhatia

Klausner

2020

PLoS ONE

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We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.

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Section: Plos Onementioning

confidence: 78%

“…Several transmission studies have estimated that roughly half of infection transmission occurs pre-symptomatically [13][14][15]. He et al estimated that 44% (95% CI, 30-57%) of secondary cases were infected during the index cases' pre-symptomatic stage [13].…”

Section: Plos Onementioning

confidence: 99%

Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

Bhatia

Klausner

2020

PLoS ONE

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“…1). The timing of onward transmission is determined by empirical distributions based on multiple studies of transmission pairs (Ferretti et al, 2020a). These distributions are: the generation time distribution describing the time interval between the infection of an infector and infectee ( Fig.…”

Section: Quantifying the Benefit Of Quarantinementioning

confidence: 99%

“…A detailed discussion about the relationships between these distributions can be found in Lehtinen et al (2020). (Ferretti et al, 2020a). B) The infectivity profile follows a shifted Student's t distribution (Ferretti et al, 2020a).…”

Section: Quantifying the Benefit Of Quarantinementioning

confidence: 99%

Quantifying the impact of quarantine duration on COVID-19 transmission

Ashcroft

Lehtinen

Bonhoeffer

2020

Preprint

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The numbers of confirmed cases of SARS-CoV-2 infection are increasing in many places. Consequently, the number of individuals placed into quarantine is increasing too. The large number of individuals in quarantine has high societal and economical costs, as well as infringing on the freedom of the individual. This has led to a vigorous debate about the duration of quarantine, particularly in light of the potentially low specificity of quarantine (i.e. low probability of quarantined individuals indeed being infected). We present a mathematical model that leverages empirically determined distributions of incubation period, infectivity, and generation time to quantify how the duration of quarantine affects transmission. With this model we address the impact of shortening the quarantine for returning travellers and traced contacts of confirmed cases, both in terms of prevented transmission and the ratio of prevented transmission to days spent in quarantine. We also consider the impact of i) test-and-release strategies; ii) additional hygiene measures imposed upon release after a negative test; iii) the development of symptoms during quarantine; iv) the relationship between quarantine duration and adherence; and v) the specificity of quarantine. When considering the benefit versus cost utility of quarantine, we find that the diminishing impact of longer quarantine on transmission prevention may support a quarantine duration below 10 days, particularly for returning travellers. A greater gain of utility can be achieved through a test-and-release strategy, and this can be even further strengthened by imposed hygiene measures post-release. We also find that unless a test-and-release strategy is considered, the specificity of quarantine does not affect the optimal duration of quarantine. Therefore, the argument that we should shorten quarantine because of lack of specificity is misguided.

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“…We slightly tweaked our settings for pre-symptomatic Transmission Risk Levels to agree with later epidemiological work. 45 A final source of information comes from detailed Taiwanese contact tracing 46 , who found a 1.0% symptomatic attack rate (95% CI 0.6-1.6%) for those exposed within five days of symptom onset, and 0% (95% CI 0-0.4%) for those exposed after. Risk from exclusively pre-symptomatic exposure was 0.7% (95% CI 0.2%-2.4%).…”

Section: Transmission Risk Levelsmentioning

confidence: 99%

Quantifying SARS-CoV-2 infection risk within the Google/Apple exposure notification framework to inform quarantine recommendations

Wilson

Aviles

Beamer

et al. 2020

Preprint

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Background: Bluetooth-based exposure notification apps can supplement manual contact tracing to reduce SARS-CoV-2 transmission. Their speed, scalability, and privacy preservation are generally acknowledged, but less exploited are smartphones' accurate measurement of duration, and ability to automatically calculate risk from multiple inputs. Methods: We model uncertainty in the shape of an exhaled virus-containing plume, inhalation parameters, and distance as a function of Bluetooth attenuation. We assume relative rates of viral shedding depend on the timing of exposure relative to symptom onset. We calibrate an exponential dose-response curve on the basis of the infection probabilities of household contacts. The conditional probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been free of symptoms, decreases during quarantine, with shape determined by the distribution of incubation periods, proportion of asymptomatic cases, and distribution of asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. Findings: As an example of our calculations, fifteen minutes of close contact with a high-shedding individual, given a 15% asymptomatic infection rate and no testing, would require 5- and 14-day quarantine for their risk of current or future infectiousness to fall below 0.84% and 0.14% risk, respectively. Interpretation: The Covid-Watch app is currently programmed either to use a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration. Either threshold can be set by public health authorities.

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The timing of COVID-19 transmission

Cited by 119 publications

References 54 publications

Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

Quantifying the impact of quarantine duration on COVID-19 transmission

Quantifying SARS-CoV-2 infection risk within the Google/Apple exposure notification framework to inform quarantine recommendations

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