Simulating Pandemic Disease Spread and the Impact of Interventions in Complex Societal Networks
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Abstract
Introduction
Projecting disease spread is challenging because of the heterogeneous nature of human interactions, including both natural societal interactions and how they change in response to pandemics. Simulations can provide important guidance regarding the likely impact of interventions on an assumed base case.
Methods
This paper uses assumptions based on the COVID-19 pandemic to construct a Susceptible-Infectious-Recovered model representative of US society, focusing on the interrelationships of groups with differing contact networks (essential/non-essential workers and urban/non-urban populations). The model is used to explore the impact of interventions (reduced interactions, vaccinations and selective isolation) on overall and group-specific disease spread.
Results
In the absence of herd immunity, temporary interventions will only reduce the overall number of disease cases moderately and spread them over a greater period of time unless they virtually eliminate disease and no new infections occur from exogenous sources. Vaccinations can provide stronger benefit, but can be limited by efficacy and utilization rates.
Conclusions
While a highly effective and broadly utilized vaccine might halt disease spread, some combination of increased long-term surveillance and selective isolation of the most vulnerable populations might be necessary to minimize morbidity and mortality if only moderately effective vaccines are available.
Article activity feed
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SciScore for 10.1101/2020.10.28.20221820: (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
Software and Algorithms Sentences Resources Modeling was conducted using Microsoft Excel Professional Plus 2016. Microsoft Excel Professional Plussuggested: NoneResults 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 …
SciScore for 10.1101/2020.10.28.20221820: (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
Software and Algorithms Sentences Resources Modeling was conducted using Microsoft Excel Professional Plus 2016. Microsoft Excel Professional Plussuggested: NoneResults 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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