Optimal strategies for quarantine stopping in France – General expected patterns of strategies focusing on contact between age groups
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Abstract
Due to the COVID-19 pandemic, many countries have implemented a complete lockdown of their population that may not be sustainable for long. To identify the best strategy to replace this full lockdown, sophisticated models that rely on mobility data have been developed. In this study, using the example of France as a case-study, we develop a simple model considering contacts between age classes to derive the general impact of partial lockdown strategies targeted at specific age groups. We found that epidemic suppression can only be achieved by targeting isolation of young and middle age groups with high efficiency. All other strategies tested result in a flatter epidemic curve, with outcomes in (e.g. mortality and health system over-capacity) dependent of the age groups targeted and the isolation efficiency. Targeting only the elderly can decrease the expected mortality burden, but in proportions lower than more integrative strategies involving several age groups. While not aiming to provide quantitative forecasts, our study shows the benefits and constraints of different partial lockdown strategies, which could help guide decision-making.
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SciScore for 10.1101/2020.04.21.20073932: (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 study has several limitations that deserve discussion. First, we used a deterministic model that does not consider the stochasticity in epidemic dynamics. Stochasticity is particularly relevant for predicting the tails of the epidemic curve, but its impact decreases as the number of cases increase (i.e. the bell of the curve). Thus, …
SciScore for 10.1101/2020.04.21.20073932: (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 study has several limitations that deserve discussion. First, we used a deterministic model that does not consider the stochasticity in epidemic dynamics. Stochasticity is particularly relevant for predicting the tails of the epidemic curve, but its impact decreases as the number of cases increase (i.e. the bell of the curve). Thus, while it is unlikely to affect our conclusions, it may have resulted in an underestimation of the total epidemic time under different scenarios. Second, there is still uncertainty in the precise value of several parameters used in our model, and reports of confirmed COVID-19 cases and associated deaths are highly sensitive to testing rates and official case definitions. As a result, our assumptions may not hold true if there is a significant deviation in parameter values or between the real and reported burden of the epidemic. Third, we made multiple simplifying assumptions about age-specific contact rates, and we did not include transmission from pre-symptomatic cases. We used broad groups to roughly represent dynamics of populations whose primary activity is education, work or retirement so that we could envision age-specific measures for these groups, but in reality, these categories are fluid (e.g. individuals who start working at 18 years old, or who retire at 50). Moreover, we assumed that reducing contact rates within an age group (e.g. closing a school) would not impact contact rates with other groups (e.g. increased contact at home)....
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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