Superspreaders and high variance infectious diseases
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
A well-known characteristic of recent pandemics is the high level of heterogeneity in the infection spread: not all infected individuals spread the disease at the same rate and some individuals (superspreaders) are responsible for most of the infections. To quantify the effects of this phenomenon, we analyze the effect of the variance and higher moments of the infection distribution on the spread of the disease. Working in the framework of stochastic branching processes, we derive an approximate analytical formula for the probability of avoiding an outbreak in the high variance regime of the infection distribution, verify it numerically and analyze its regime of validity in various examples. We perform population based simulations and show that, as predicted by the mathematical model, it is possible for an outbreak not to occur in the high variance regime even when the basic reproduction number R 0 is larger than 1. The applicability of our results to the current COVID-19 is restricted to scenarios where imposed measures are able to reduce significantly the number of infected individuals and the high basic reproduction number. We note that our analysis may find implications in general information spread scenarios.
Article activity feed
-
SciScore for 10.1101/2020.09.06.20189258: (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 10.1101/2020.09.06.20189258: (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.
-
-
