ArticlePloS one2021
Impact of mobility restriction in COVID-19 superspreading events using agent-based model.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of spatial epidemiological modeling approaches applied during the COVID-19 pandemic.BMC public health · 2026Pooled it
- Global Distribution and Dispersal Pathways of Riparian Invasives: Perspectives Using Alligator Weed (Plants (Basel, Switzerland) · 2026Review
- A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.medRxiv : the preprint server for health sciences · 2025Article
- Cognitively-plausible reinforcement learning in epidemiological agent-based simulations.Frontiers in epidemiology · 2025Article
- 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 trade-off between COVID-19 and mental diseases burden during a lockdown: Mathematical modeling of control measures.Infectious Disease Modelling · 2023Article
- Article
- A systematic review of COVID-19 transport policies and mitigation strategies around the globe.Transportation research interdisciplinary perspectives · 2022Article
- Does Social Distancing Matter for Infectious Disease Propagation? An SEIR Model and Gompertz Law Based Cellular Automaton.Entropy (Basel, Switzerland) · 2022Article
- Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach.Journal of transport & health · 2022Article
- Fractional Stochastic Differential Equation Approach for Spreading of Diseases.Entropy (Basel, Switzerland) · 2022Article
- Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review.Frontiers in public health · 2022Review
- The fading impact of lockdowns: A data analysis of the effectiveness of Covid-19 travel restrictions during different pandemic phases.PloS one · 2022Article
- Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection.Stochastic environmental research and risk assessment : research journal · 2022Article
- COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves.PloS one · 2022Article
- Superspreading in the emergence of COVID-19 variants.Trends in genetics : TIG · 2021Review
- A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy.Nonlinear dynamics · 2021Article
- Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations.PloS one · 2021Article
- Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA.MDM policy & practiceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 pandemic is an immediate major public health concern. The search for the understanding of the disease spreading made scientists around the world turn their attention to epidemiological studies. An interesting approach in epidemiological modeling nowadays is to use agent-based models, which allow to consider a heterogeneous population and to evaluate the role of superspreaders in this population. In this work, we implemented an agent-based model using probabilistic cellular automata to simulate SIR (Susceptible-Infected-Recovered) dynamics using COVID-19 infection parameters. Differently to the usual studies, we did not define the superspreaders individuals a priori, we only left the agents to execute a random walk along the sites. When two or more agents share the same site, there is a probability to spread the infection if one of them is infected. To evaluate the spreading, we built the transmission network and measured the degree distribution, betweenness, and closeness centrality. The results displayed for different levels of mobility restriction show that the degree reduces as the mobility reduces, but there is an increase of betweenness and closeness for some network nodes. We identified the superspreaders at the end of the simulation, showing the emerging behavior of the model since these individuals were not initially defined. Simulations also showed that the superspreaders are responsible for most of the infection propagation and the impact of personal protective equipment in the spreading of the infection. We believe that this study can bring important insights for the analysis of the disease dynamics and the role of superspreaders, contributing to the understanding of how to manage mobility during a highly infectious pandemic as COVID-19.
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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.