Optimal COVID-19 quarantine and testing strategies

Wells, Chad R.; Townsend, Jeffrey P.; Pandey, Abhishek; Moghadas, Seyed M.; Krieger, Gary R.; Singer, Burton H.; McDonald, Robert H.; Fitzpatrick, Meagan C.; Galvani, Alison P.

doi:10.1038/s41467-020-20742-8

Cited by 185 publications

(185 citation statements)

References 39 publications

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“…For both models, we then used point estimates of fitted model parameters to infer the distributions of the generation time (Figure 2A), the time from onset of symptoms to transmission (TOST; Figure 2B) and the serial interval (Figure 2C). The TOST distribution (which characterises the relative expected infectiousness of a host at each time from symptom onset, as opposed to from infection [13,20,26,28,29]) predicted using the mechanistic model was more concentrated around the time of symptom onset compared to that obtained using the independent transmission and symptoms model (Figure 2B), as was found in [12].…”

Section: Inferring Generation Times From Uk Household Datamentioning

confidence: 53%

Inference of SARS-CoV-2 generation times using UK household data

Hart

Abbott

Endo

et al. 2021

Preprint

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The distribution of the generation time (the interval between individuals becoming infected and passing on the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to SARS-CoV-2 data from January-March 2020 compared to existing models. However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March-November 2020 in the UK, we provide updated estimates of the generation time. We consider both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% CrI 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (6.0 days, 5.2-7.0 days) and a similar standard deviation (4.9 days, 4.0-6.3 days). Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, continued data collection and analysis is necessary to inform future public health policy decisions.

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Section: Inferring Generation Times From Uk Household Datamentioning

confidence: 53%

Inference of SARS-CoV-2 generation times using UK household data

Hart

Abbott

Endo

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The generation time and TOST distributions indicate the average infectiousness of a host at each time since infection and time since symptom onset, respectively ( He et al, 2020 ; Fraser, 2007 ). These distributions are important for assessing the effectiveness of public health measures such as isolation ( Ashcroft et al, 2021 ; Wells et al, 2021 ) and contact tracing ( Ferretti et al, 2020a ; Fraser et al, 2004 ; Davis et al, 2020 ). Estimates of the SARS-CoV-2 generation time have typically involved an assumption that a host’s infectiousness is independent of their symptom status ( Ferretti et al, 2020a ; Deng et al, 2020 ; Ganyani et al, 2020 ; Knight and Mishra, 2020 ; Lehtinen et al, 2021 ; Figure 1B , left).…”

Section: Introductionmentioning

confidence: 99%

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing

Hart

Maini

Thompson

2021

eLife

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Background:Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing.Methods:Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector–infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches.Results:The mechanistic method provides an improved fit to data from SARS-CoV-2 infector–infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms.Conclusions:High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced.Funding:Engineering and Physical Sciences Research Council (EPSRC).

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“…We account for uncertainties around the epidemic dynamic in the class through a stochastic compartmental model and around the screening plan implementation through random generation of groups, when required by the plan, and random allocation of the new infections across them. Our work is not far from others which used mathematical models or simulations to investigated relevant issues during the COVID-19 emergency, such as the definition of optimal quarantine strategies [11] or optimal pool size in pooled testing [12,13]. scenarios, potentially more dangerous in terms of infection spread within and outside the class [14].…”

Section: Discussionmentioning

confidence: 99%

Screening plans for SARS-CoV-2 based on sampling and rotation: an example in the school setting

Baccini

Cereda

2021

Preprint

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Screening plans for prevention and containment of SARS-CoV-2 infection should take into account the epidemic context, the fact that undetected infected individuals may transmit the disease, and that the infection spreads through outbreaks, creating clusters in the population. In this paper, we compare the performance of six screening plans based on poorly sensitive individual tests, in detecting infection outbreaks at the level of single classes in a school context. The performance evaluation is done by simulating different epidemic dynamics within the class during the five weeks following the day of the first infection. The plans have different costs in terms of number of individual tests required for the screening and are based on recurrent evaluations on all students or subgroups of students in rotation. Especially in scenarios where the rate of contagion is high, at an equal cost, testing half of the class in rotation every week appears to be better in terms of sensitivity than testing all students every two weeks. Similarly, testing one-fourth of the students every week is comparable with testing all students every two weeks, despite the first one is a much cheaper strategy. In the presence of natural clusters in the population, testing subgroups of individuals belonging to the same cluster in rotation may have a better performance than testing all the individuals less frequently. The proposed simulations approach can be extended to evaluate more complex screening plans than those presented in the paper.

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Optimal COVID-19 quarantine and testing strategies

Cited by 185 publications

References 39 publications

Inference of SARS-CoV-2 generation times using UK household data

Inference of SARS-CoV-2 generation times using UK household data

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing

Screening plans for SARS-CoV-2 based on sampling and rotation: an example in the school setting

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