Optimal lockdown strategies for SARS-CoV2 mitigation— an Indian perspective
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Abstract
We sought to identify optimal temporal windows for lockdown-based mitigation strategies on infectious disease spreads. An age-structured multi-compartmental Susceptible- Infected-Recovered (SIR) model was used to estimate infection spreads under parametric variation of lockdown intensity and duration from the data of SARS-CoV2 cases in India between January to July, 2020. The resulting parameter values were used to simulate lockdown outcomes for a wide range of start times and durations. Lockdowns were simulated as intervention strategies that modified weights assigned to social contact matrices for work, school and other places. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Our analysis shows that lockdown outcomes depend sensitively on the timing of imposition and that it is possible to minimize lockdown durations while limiting case loads to numbers below the hospitalization thresholds. Such timing based effects arise naturally from the non-linear nature of SIR dynamics.
Notation
N Total Population
S Number of susceptible individuals
I Number of infected individuals
R Number of recovered/removed individuals
β Per-individual disease transmission rate
γ Recovery rate
τ Lockdown start-time
Δ Duration of lockdown
p Post-lockdown coefficient
h Total number of hospital beds
ξ Maximum fraction of infected individuals
ξ 0 Hospitalization threshold
Article activity feed
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SciScore for 10.1101/2020.07.31.20165662: (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:Assumptions and Limitations: We conclude by laying out the inherent assumptions and possible limitations of the model presented here. Most importantly, our model falls in the class of conceptual models to understand which broad lockdown strategy is effective, not necessarily a detailed plan for implementing the lockdown across diverse …
SciScore for 10.1101/2020.07.31.20165662: (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:Assumptions and Limitations: We conclude by laying out the inherent assumptions and possible limitations of the model presented here. Most importantly, our model falls in the class of conceptual models to understand which broad lockdown strategy is effective, not necessarily a detailed plan for implementing the lockdown across diverse socio-culturally bound populations. For example, our model ignores temporal delays that are likely to exist between lockdown announcement and effective implementation. We think that such delays would not amount to substantial differences in disease dynamics as it is possible that the effects of the delay are offset by a gradual reduction in social mobility preceding the actual lockdown announcement. Additionally, this assumption simplifies the model considerably. Certainly country specific delays would change the predictions from the model. Second, by assuming a single value for ξ0, the model implicitly assumes spatial homogeneity in the distribution of medical infrastructure across the country. However, both disease burden and medical infrastructure is known to be heterogeneously distributed across the country [Klein et al., 2020]. Detailed modelling at the level of individual states and districts could potentially solve the problem but at considerable computational costs. We decided to favour model simplicity since our focus was on assessing optimal lockdown scenarios. Third, it is well-known that the number of reported cases is a function of ...
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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 17, 18, 19 and 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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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