ArticlePLoS computational biology2021
Dynamics of COVID-19 under social distancing measures are driven by transmission network structure.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
46 citing papers in PubMed.
- Evaluating adherence to COVID-19 quarantine protocols at two international ground crossings in Savannakhet Province, Lao PDR.Tropical medicine and health · 2026Article
- The role of social determinants in COVID-19 hospitalization disparities by migration status in Stockholm, Sweden. A population-based cohort study.Communications medicine · 2026Article
- Remarks on a Scaling Theory of Spread of COVID-19 with an Application to the Case of Bulgaria.Entropy (Basel, Switzerland) · 2026Article
- Designing network based intervention strategies for epidemics of infectious diseases from edge based infection probability.Scientific reports · 2026Article
- Identifying Social-Epidemiological Roles Associated with Viral Exposure Using Regular Equivalence Blockmodeling.medRxiv : the preprint server for health sciences · 2025Article
- Evaluation of Kindergarten Through Grade 12 All-Cause Absenteeism Data as an Indicator and Predictor of Respiratory Disease, 2018-2022.Public health reports (Washington, D.C. : 1974) · 2025Article
- Epidemic evolutionarily stable strategies within an age-structured host population.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning analysis of the effects of COVID-19 on migration patterns.Scientific reports · 2024Article
- Bridging the Gap: Integrating Knowledge from the Study of Social Network Analysis and Infectious Disease Dynamics in Human and Nonhuman Primates.Annual review of anthropology · 2024Article
- Digital Health Technology Use Across Socioeconomic Groups Prior to and During the COVID-19 Pandemic: Panel Study.JMIR public health and surveillance · 2024Article
- Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control.Scientific reports · 2024Article
- Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England.PLoS pathogens · 2024Article
- Risk communication and community engagement capacity in the Eastern Mediterranean Region: a call for action.BMJ global health · 2024Article
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- Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study.Epidemiology and infection · 2023Article
- Association of close-range contact patterns with SARS-CoV-2: a household transmission study.eLife · 2023Article
- Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK.Royal Society open science · 2023Article
- The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.Royal Society open science · 2023Article
- 2021-2022 monitoring, early warning, and forecasting of global infectious diseases.Journal of biosafety and biosecurity · 2022Article
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5 authors.
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Abstract
In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.