Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study
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Abstract
The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco’s shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, −20.1%–81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.
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SciScore for 10.1101/2020.06.09.20125831: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This model exhibits the following limitations. We assumed constant reproduction numbers Rt before and after the adoption of San Francisco’s shelter in place policy, so this model cannot detect more detailed changes in Rt. The model assumes uniform dynamics across all cases in a simulation of a county, not accounting for specific …
SciScore for 10.1101/2020.06.09.20125831: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This model exhibits the following limitations. We assumed constant reproduction numbers Rt before and after the adoption of San Francisco’s shelter in place policy, so this model cannot detect more detailed changes in Rt. The model assumes uniform dynamics across all cases in a simulation of a county, not accounting for specific geographic and demographic heterogeneity. The city and county of San Francisco is modeled in isolation, without migration or transmission across county lines (since we anticipate more transmission within than between counties). Detection rates are assumed to be constant per capita; a gradually rising detection rate can cause an upward bias [28]. If testing is limited by available resources, the assumption of a per capita rate may not be justified. Alternative hypotheses for the unknown interval distributions of generation time and time to detection should be evaluated. In particular, the impact of the amount of transmission occurring before onset of symptoms, and the tail of long times to detection, should be examined carefully. The distribution of generation intervals is affected by censoring of transmission after cases’ detection dates, which shifts generation intervals to shorter times, so that our baseline interval distribution should ideally be for generation times in the absence of detection, but our estimates are taken from populations with some amount of detection. This could lead to bias toward shorter generation times than is accurate. Heter...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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