Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Hackl, Jürgen; Dubernet, Thibaut Jean Pierre

doi:10.3390/fi11040092

Cited by 86 publications

(65 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most of the existing studies utilize discrete-event simulation approach (Schmitt and Singh, 2012, Ivanov, 2017a, 2017b, Schmitt et al, 2017, Ivanov and Rozhkov, 2017, Macdonald et al, 2018, Ivanov, 2019, Tan et al, 2020 while some studies use agent-based (Li and Chan, 2013, Hou et al, 2018 and system dynamics (Wilson, 2007, Aboah et al, 2019 methods, too. A very few studies (e.g., Hackl and Dubernet, 2019) have incorporated the simulation and transportation disruptions during the epidemic crises.…”

Section: Simulation-based Sc Risk Modelingmentioning

confidence: 99%

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Ivanov

2020

Transportation Research Part E: Logistics and Transportation Re

1,583

1,348

View full text Add to dashboard Cite

Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decisionmakers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.

show abstract

Section: Simulation-based Sc Risk Modelingmentioning

confidence: 99%

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Ivanov

2020

Transportation Research Part E: Logistics and Transportation Re

1,583

1,348

View full text Add to dashboard Cite

show abstract

“…A similar method was also used in other ABMs to overcome the computational burden. 36 Every child agent in the model was assigned an immunization status for receiving the pneumococcal conjugate vaccine (PCV) based on the WHO and United Nations Children's Fund (UNICEF) immunization coverage estimates in 2017 for Ethiopia. 37 Daily pneumococcal disease incidence rates were applied based on the child agent's PCV immunization status and vaccine efficacy.…”

Section: Methodsmentioning

confidence: 99%

Impact of Antibiotic Resistance on Treatment of Pneumococcal Disease in Ethiopia: An Agent-Based Modeling Simulation

Chen

Stringer

Eguale

et al. 2019

The American Journal of Tropical Medicine and Hygiene

View full text Add to dashboard Cite

Antimicrobial resistance (AMR) is a growing threat to global health. Although AMR endangers continued effectiveness of antibiotics, the impact of AMR has been poorly estimated in low-income countries. This study sought to quantify the effect of AMR on treatments for pediatric pneumococcal disease in Ethiopia. We developed the DREAMR (Dynamic Representation of the Economics of AMR) model that simulate children younger than 5 years who acquire pneumococcal disease (pneumonia, meningitis, and acute otitis media) and seek treatment from various health facilities in Ethiopia over a year. We examined the AMR levels of three antibiotics (penicillin, amoxicillin, and ceftriaxone), treatment failures, and attributable deaths. We used the cost-of-illness method to assess the resulting economic impact of AMR from a societal perspective by estimating the direct and indirect treatment costs and productivity losses. Findings showed that AMR against antibiotics that were used to treat pneumococcal disease led to 195,763 treatment failures per year, which contributed to 2,925 child deaths annually in Ethiopia. Antimicrobial resistance resulted in a first-line treatment failure rate of 29.4%. In 1 year, the proportion of nonsusceptible Streptococcus pneumoniae bacteria increased by 2.1% and 0.5% for amoxicillin and penicillin, and reduced by 0.3% for less commonly used ceftriaxone. Annual costs of AMR to treat pneumococcal disease were around US$15.8 million, including US$3.3 million for ineffective first-line treatments, US$3.7 million for second-line treatments, and US$8.9 million for long-term productivity losses. Antibiotic stewardship to reduce misuse and overuse of antibiotics is essential to maintain the effectiveness of antibiotics, and lessen the health and economic burden of AMR.

show abstract

“…Therefore real-world data on contact networks are rare [30,45,23,32,43] and not available for large-scale populations. A reasonable approach is to generate the data synthetically, for instance by using mobility and population data based on geographical diffusion [46,17,36,3]. For instance, this has been applied to the influenza virus [33].…”

Section: Related Workmentioning

confidence: 99%

Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

Großmann

Backenköhler

Wolf

2020

Preprint

View full text Add to dashboard Cite

In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic's dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODEmodels. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.

show abstract

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Cited by 86 publications

References 34 publications

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Impact of Antibiotic Resistance on Treatment of Pneumococcal Disease in Ethiopia: An Agent-Based Modeling Simulation

Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

Contact Info

Product

Resources

About