Controlling COVID-19 via test-trace-quarantine
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Abstract
Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.
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SciScore for 10.1101/2020.07.15.20154765: (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
Software and Algorithms Sentences Resources Analyses were performed with Covasim version 2.0.0. Covasimsuggested: NoneThe ordinary least squares method from the Python package statsmodels was used for the fit. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations of this study. First, we do not consider geographical clustering, so cannot model hotspots or outbreaks in specific areas. Although this may not be crucial given that interventions were …
SciScore for 10.1101/2020.07.15.20154765: (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
Software and Algorithms Sentences Resources Analyses were performed with Covasim version 2.0.0. Covasimsuggested: NoneThe ordinary least squares method from the Python package statsmodels was used for the fit. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations of this study. First, we do not consider geographical clustering, so cannot model hotspots or outbreaks in specific areas. Although this may not be crucial given that interventions were set at a county-wide level, subsequent phases of the response may require more localized policy actions. Second, there is continued debate around how susceptibility and transmissibility vary by age and with comorbidities; the model parameters reflect the best available evidence to date (32), but new evidence is continually coming to light. Third, there is considerable uncertainty around other crucial characteristics of both SARS-CoV-2 transmission (including the extent to which it is seasonal, and the proportion of asymptomatic and presymptomatic transmission) and the impact of interventions (such as mask efficacy). We have handled these uncertainties by calibrating extensively to data (Fig. 1A-C), and by propagating remaining uncertainties in parameters (Fig. 1E) through all scenarios. However, additional data on transmission characteristics would nonetheless help refine our understanding of the most important transmission pathways, as well as how to disrupt them. In summary, we have shown that (a) agent-based models can be fit to detailed epidemic time trends and age distributions (Fig. 1), as well as make accurate forecasts (Fig. 5); (b) an idealized test-trace-quarantine program with no capacity constraints can control an epidemic even at high rates of transmi...
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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SciScore for 10.1101/2020.07.15.20154765: (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
Software and Algorithms Sentences Resources All simulations were run using Covasim version 1.5.0. Covasimsuggested: NoneThe ordinary least squares method from the Python package statsmodels was used for the fit. Pythonsuggested: (IPython, SCR_001658)A comparison of the main features and use cases of each of Covasim’s default network model options is shown in Table S4 and schematically in Fig. Covasim’ssuggested: None2.7 Software architecture … SciScore for 10.1101/2020.07.15.20154765: (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
Software and Algorithms Sentences Resources All simulations were run using Covasim version 1.5.0. Covasimsuggested: NoneThe ordinary least squares method from the Python package statsmodels was used for the fit. Pythonsuggested: (IPython, SCR_001658)A comparison of the main features and use cases of each of Covasim’s default network model options is shown in Table S4 and schematically in Fig. Covasim’ssuggested: None2.7 Software architecture Covasim was developed for Python 3.7 using the SciPy (scipy.org) ecosystem (66). scipysuggested: (SciPy, SCR_008058)It uses NumPy (numpy.org), Pandas (pandas.pydata.org), and Numba (numba.pydata.org) for fast numerical computing; Matplotlib (matplotlib.org) and Plotly (plotly.com) for plotting; and Sciris (sciris.org) for data structures, parallelization, and other utilities. Matplotlibsuggested: (MatPlotLib, SCR_008624)All plotting outputs are configurable, and results can also be saved in Excel, JSON, or NumPy formats for further processing. NumPysuggested: (NumPy, SCR_008633)Data from additional tools added to each annotation on a weekly basis.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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