A Health Policy Simulation Model of Smallpox and Ebola Haemorrhagic Fever

Kurahashi, Setsuya; Terano, Takao

doi:10.1007/978-3-319-19728-9_34

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…An agent-based simulation model of Smallpox and Ebola Hemorrhagic fever based on Epstein's model is discussed in [19]. The model has used the SIR model and reveals the spread of the infection by contact process among people.…”

Section: Policy Modelling For Pandemicsmentioning

confidence: 99%

“…Since the recent emergence of the severe acute respiratory syndrome, 2019 novel coronavirus (2019-nCoV) from Wuhan, China [1] spread across China and to many other countries making it a global health concern. The World Health Organization announced a new name for the epidemic disease caused by 2019-nCoV as coronavirus disease/COVID- 19. The studies have demonstrated that COVID-19 transmission is possible through human to human interaction through droplets or close contact with an infected person [2].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation

Dunuwila

Rajapakse

2020

2020 2nd International Conference on Advancements in Computing (ICAC)

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COVID-19 pandemic has become a major concern due to its rapid spread throughout the world. We can observe some countries are successful in formulating effective strategies for managing the pandemic, while some are struggling. The research is based on the question of formulating effective policies for COVID-19 to reduce community transmission. While many countries are suffering from the pandemic, it is a critical issue that the policymakers should be concerned with formulating effective policies to address the problem. We use computational methods to foresee the future by creating a simulation model based on multi-agent and simulation methodology because it is not always possible to predict the future state of a complex adaptive system. The data are collected through a survey and the literature to calibrate the model parameters to build a constructive and realistic model. Once the model is constructed, the simulation results are compared with the real-world observations to validate the model. The implementation of the model follows an iterative process for improving the validity of the model. This paper presents the conceptual model of the system being investigated and its initial implementation, which needs to be calibrated further with empirical data before using it as a decision support tool.

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Section: Policy Modelling For Pandemicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation

Dunuwila

Rajapakse

2020

2020 2nd International Conference on Advancements in Computing (ICAC)

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“…Operations Research (OR) and mathematical modeling methods have been widely used to determine optimal resource allocation strategies to control an epidemic disease. Those approaches include simulations (Ajelli et al, 2016; Kurahashi and Terano, 2015; Siettos et al, 2015; Wells et al, 2015), differential equations (Craft et al, 2005; Kaplan et al, 2003), network models (Berman and Gavious, 2007; Longini Jr et al, 2007; Porco et al, 2004; Riley and Ferguson, 2006), resource allocation analysis (Nguyen et al, 2017; Shaw and Schwartz, 2010; Tebbens and Thompson, 2009; Zaric et al, 2000), and stochastic compartmental models (Funk et al, 2017; Kibis et al, 2020; Lekone and Finkenstädt, 2006; Tanner et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Yin

Büyüktahtakın

2021

Preprint

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Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multistage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

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“…We aim to study the relationship between antibody holding rate of men and the spread of infection by constructing infection of rubella virus with the agent-based model and repeating simulation experiment on a computer. Although our previous study described the infectious disease model of smallpox and Ebola [6], this paper proposes a new model of rubella which has caused crucial problems for pregnant women in recent years. In Sect.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Infectious Disease Model of Rubella Outbreaks

Kurahashi¹

2019

Agents and Multi-Agent Systems: Technologies and Applications 2019

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This study proposes a simulation model of rubella. SIR (Susceptible, Infected, Recovered) model has been widely used to analyse infectious diseases such as influenza, smallpox, bioterrorism, to name a few. On the other hand, agentbased model begins to spread in recent years. The model enables to represent the behaviour of each person on the computer. It also reveals the spread of infection by simulation of the contact process among people in the model. The study designs a model based on smallpox and Ebola fever model in which several health policies are decided such as vaccination, the gender-specific workplace and so on. The infectious simulation of rubella, which has not yet vaccinated completely for men in Japan, is implemented in the model. As results of experiments using the model, it has been found that preventive vaccine to all the men is crucial factors to prevent the spread in women.

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A Health Policy Simulation Model of Smallpox and Ebola Haemorrhagic Fever

Cited by 7 publications

References 7 publications

Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation

Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

An Agent-Based Infectious Disease Model of Rubella Outbreaks

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