Two New Models for Epidemics with Application to the COVID-19 Pandemic in the United States, Italy, and the United Kingdom
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
The Distributed Logistic Model and the Adaptive Logistic Model of epidemics are formulated and used to study the course of cases and deaths during the COVID-19 pandemic. The distributed model is designed to account for a spread of initiation times of hot spots across a country; it does especially well at capturing the initial and linear phases of epidemics. The adaptive model accounts for the development of social mitigation factors, and does especially well at capturing the declining phases of epidemics. The historical data for the U.S., Italy, and the U.K. are analyzed in detail. The parameters of the fits to the two models provide complementary information about the pandemic. The initial infection rate constant was r 0 ≃ 0.29 day −1 for each country, and the effective infection rate constants evolved with time in essentially the same way for each. This suggests that mitigation effects were equally effective in all three countries. Analysis with the distributed model suggests that it took somewhat different times T for the epidemic to spread across each country, with T (US)≃ 50 days significantly greater than the T ‘s of Italy or the U.K. The mortality ratio in the U.S. was about 0.061 while in Italy and U.K. it was much larger at about 0.15.
Article activity feed
-
SciScore for 10.1101/2020.07.13.20152686: (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 10.1101/2020.07.13.20152686: (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.
-
