A multiagent coronavirus model with territorial vulnerability parameters
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Abstract
We developed a simple and user-friendly simulator called MD Corona that is based on a multiagent model and describes the transmission dynamics of coronavirus for a given location considering three setting parameters: population density, social-isolation rate, and effective transmission probability. The latter is represented by the Coronavirus Protection Index (CPI) - a measurement of a given territory’s vulnerability to the coronavirus that includes characteristics of the health system and socioeconomic development as well as infrastructure. The dynamic model also relies on other real epidemiological parameters. The model is calibrated by using immunity surveys and provides accurate predictions and indications of the different spread dynamic mechanisms. Our simulation studies clearly demonstrate the existence of multiple epidemic curves in the same city due to different vulnerabilities to the virus across regions. And it elucidates the phenomenon of the epidemic slowing despite a reduction in social-distancing policies, understood as a consequence of “local protection bubbles.” The simulator can be used for scientific outreach purposes, bringing science closer to the general public in order to raise awareness and increase engagement about the effectiveness of social distancing in reducing the transmissibility of the virus, but also to support effective actions to mitigate the spread of the virus.
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SciScore for 10.1101/2020.10.25.20218735: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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 …
SciScore for 10.1101/2020.10.25.20218735: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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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