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Harweg T, Wagner M, Weichert F. Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:545. [PMID: 36612868 PMCID: PMC9819456 DOI: 10.3390/ijerph20010545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.
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Affiliation(s)
- Thomas Harweg
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
| | - Mathias Wagner
- Department of Pathology, University of Saarland Medical School, Homburg Saar Campus, Kirrberger Strasse 100, 66424 Homburg Saar, Saarland, Germany
| | - Frank Weichert
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
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Bushaj S, Yin X, Beqiri A, Andrews D, Büyüktahtakın İE. A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-33. [PMID: 36187178 PMCID: PMC9512996 DOI: 10.1007/s10479-022-04926-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 05/12/2023]
Abstract
In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas.
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Affiliation(s)
- Sabah Bushaj
- Department of Management Information Systems and Analytics, School of Business and Economics, SUNY Plattsburgh, Plattsburgh, NY USA
| | | | - Arjeta Beqiri
- Department of Management Information Systems and Analytics, School of Business and Economics, SUNY Plattsburgh, Plattsburgh, NY USA
| | - Donald Andrews
- Trinity College Dublin, School of Natural Sciences, Dublin, Ireland
| | - İ. Esra Büyüktahtakın
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA USA
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Kühn MJ, Abele D, Binder S, Rack K, Klitz M, Kleinert J, Gilg J, Spataro L, Koslow W, Siggel M, Meyer-Hermann M, Basermann A. Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany. BMC Infect Dis 2022; 22:333. [PMID: 35379190 PMCID: PMC8978163 DOI: 10.1186/s12879-022-07302-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. METHODS We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. RESULTS We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. CONCLUSIONS Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.
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Affiliation(s)
- Martin J Kühn
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
| | - Daniel Abele
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Sebastian Binder
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Kathrin Rack
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Margrit Klitz
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jan Kleinert
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jonas Gilg
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Luca Spataro
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Wadim Koslow
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Martin Siggel
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Achim Basermann
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2022; 49:1265-1293. [PMID: 34276105 PMCID: PMC8275455 DOI: 10.1007/s11116-021-10210-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/09/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J. Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H. Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2021; 49:1265-1293. [PMID: 34276105 DOI: 10.1101/2020.06.20.20135186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/28/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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