Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing
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Abstract
We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.
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SciScore for 10.1101/2020.04.21.20073890: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One important caveat of our analysis of the effects of social distancing is that our statistical modeling approach is geared towards countries with relatively large death totals. Our model has little to say about countries like South Korea (about 230 deaths on April 15th) that have used extensive testing and social distancing to successfully contain the COVID-19 pandemic. Much more work will have to be done to understand this in …
SciScore for 10.1101/2020.04.21.20073890: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:One important caveat of our analysis of the effects of social distancing is that our statistical modeling approach is geared towards countries with relatively large death totals. Our model has little to say about countries like South Korea (about 230 deaths on April 15th) that have used extensive testing and social distancing to successfully contain the COVID-19 pandemic. Much more work will have to be done to understand this in greater detail. We emphasize that our statistical model is neither capable of, nor designed to, understand what will happen if policies change substantially. All of the countries we have analyzed have adopted significant social distancing protocols. Since we do not explicitly incorporate the effects of policy in our fits, we have no way of asking about what will happen if these policies change significantly. Nonetheless, there seem to be some general lessons to be learned. First, despite all the variation across regions and countries, cases and deaths seem to quickly cross over from exponential to power law growth. Similar behavior was observed in other epidemics including HIV/AIDS and the 2014 Ebola outbreak [24, 28]. This suggests that it is useful to plot all data in both log-linear and log-log scales. Second, it cautions against making extrapolations far into the future based on exponential growth, since power law growth seems to be quite consistent and generic. Finally, to better understand the origin of these dynamics, we performed simulations o...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
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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