ArticleChaos, solitons, and fractals2020
A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Agent-based simulation of seasonal malaria chemoprevention strategy in Southern Tanzania: comparing dihydroartemisinin-piperaquine with or without primaquine.Malaria journal · 2026Article
- Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data.PloS one · 2024Article
- The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024Article
- Generative design for COVID-19 and future pathogens using stochastic multi-agent simulation.Sustainable cities and society · 2023Article
- An age-dependent immuno-epidemiological model with distributed recovery and death rates.Journal of mathematical biology · 2023Article
- Characterization of superspreaders movement in a bidirectional corridor using a social force model.Frontiers in public health · 2023Article
- Article
- Assessment Model for Rapid Suppression of SARS-CoV-2 Transmission under Government Control.Tropical medicine and infectious disease · 2022Article
- The Fitness-Corrected Block Model, or how to create maximum-entropy data-driven spatial social networks.Scientific reports · 2022Article
- The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions.Pattern recognition · 2022Article
- Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level.Environmental research · 2022Article
- Crowd dynamics research in the era of Covid-19 pandemic: Challenges and opportunities.Safety science · 2022Article
- Socio-economic analysis of short-term trends of COVID-19: modeling and data analytics.BMC public health · 2022Article
- Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method.JMIR public health and surveillance · 2022Article
- Fractional-order backstepping strategy for fractional-order model of COVID-19 outbreak.Mathematical methods in the applied sciences · 2022Article
- Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework.Scientific reports · 2022Article
- Uncertainty quantification in Covid-19 spread: Lockdown effects.Results in physics · 2022Article
- Data based model for predicting COVID-19 morbidity and mortality in metropolis.Scientific reports · 2021Article
- Estimation of COVID-19 recovery and decease periods in Canada using delay model.Scientific reports · 2021Article
- A multi-scale agent-based model of infectious disease transmission to assess the impact of vaccination and non-pharmaceutical interventions: The COVID-19 case.Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen) · 2021Article
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2 authors.
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Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we reveal that pre-symptomatic transmission accelerates the onset of the exponential growth of cases. After that, we demonstrate that the persistence of SARS-CoV-2 on hard surfaces determines the number of cases reached during the peak of the epidemic. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China.
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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.