Less Wrong COVID-19 Projections With Interactive Assumptions
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Abstract
COVID-19 pandemic is an enigma with uncertainty caused by multiple biological and health systems factors. Although many models have been developed all around the world, transparent models that allow interacting with the assumptions will become more important as we test various strategies for lockdown, testing and social interventions and enable effective policy decisions. In this paper, we developed a suite of models to guide the development of policies under different scenarios when the lockdown opens. These had been deployed to create an interactive dashboard called COVision which includes the Agent-based Models (ABM) and classical compartmental models (CCM). Our tool allows simulation of scenarios by changing the strength of lockdown, basic reproduction number(R0), asymptomatic spread, testing rate, contact rate, recovery rate, incubation period, leakage in lockdown etc. We optimized ABM and CCMs and evaluated them on multiple error metrics. Out of these models in our suite, ABM was able to capture the data better than CCMs. Our evaluation suggests that ABM models were able to capture the dynamic nature of the epidemic for a longer duration of time while CCMs performed inefficiently. We computed R0 using CCMs which were found to be decreasing with lockdown duration, indicating the effectiveness of policies in different states of India. Models have been deployed on a dashboard hosted at http://covision.tavlab.iiitd.edu.in which allows users to simulate outcomes under different parameters and will allow the policymakers to make informed decisions and efficient monitoring of the covid19 pandemic in India.
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SciScore for 10.1101/2020.06.06.20124495: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2020.06.06.20124495: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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