An object simulation model for modeling hypothetical disease epidemics – EpiFlex

Hanley, Brian P.

doi:10.1186/1742-4682-3-32

Cited by 8 publications

(11 citation statements)

References 30 publications

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“…To consider cases where a fraction of the population is immune to the disease, either from previous exposure and recovery, or by immunization or natural immunity, we define a parameter called fractional immunity, denoted as Π, which shows the fraction of the agent population that is immune at the outset of a simulation. This notion of fractional immunity is similar to that seen in studies that seek to model the effect of a pre-existing or acquired immunity already present in a significant fraction of the population [36,37].…”

Section: System Parameterssupporting

confidence: 53%

Modeling the Spread and Control of COVID-19

Trivedi¹,

Sreenivas

Rao

2021

Systems

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Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters, such as the extent to which hygiene and social distancing are observed in a population. Our results provide qualitative indications of the effects of various policies and parameters, for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing is more important than personal hygiene, and that the growth of infection is significantly reduced for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.

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Section: System Parameterssupporting

confidence: 53%

Modeling the Spread and Control of COVID-19

Trivedi¹,

Sreenivas

Rao

2021

Systems

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“…flute is a stochastic individual‐based modelling platform capable of simulating large‐scale spread of influenza and evaluation of intervention measures against pandemic influenza across major metropolitan areas or the continental US (Chao et al, 2010). influsim is a simple deterministic SEIR compartmental model that captures heterogeneous mixing (Eichner et al., 2007), while epiflex is a stochastic individual‐based model which can simulate other diseases such as HIV and smallpox in addition to influenza (Hanley, 2006). Pandemic Influenza Policy Model ( pipm ) is an agent‐based model specifically designed for military settings (Feighner et al., 2009).…”

Section: Resultsmentioning

confidence: 99%

“…These new methods and approaches included: extending stochastic models to allow for variable length of infectious period and heterogeneity in contact rates (Addy et al., 1991); models to estimate the R 0 of within and between household transmission of influenza virus (Fraser, 2007); to improve computational efficiency of large‐scale spatial stochastic individual‐based models through algorithm refinement including the use of an R 0 parameter rather than per contact transmission probability (Tsai et al., 2010); and the development of aggregate (system dynamic) models that capture the influence of contact network structures using basic reproductive ratios derived from the network structures (Aparicio and Pascual, 2007). Seven articles related solely to the spread of influenza (Flahault et al., 1994; Grais et al., 2003, 2004; Lavenu et al., 2004; Boni et al., 2009; Ohkusa and Sugawara, 2009; Rios‐Doria and Chowell, 2009) and three focused on the development of modelling software (Hanley, 2006; Eichner et al., 2007; Feighner et al., 2009).…”

Section: Resultsmentioning

confidence: 99%

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

Dorjee

Poljak

Revie

et al. 2012

Zoonoses and Public Health

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Increasing incidences of emerging and re-emerging diseases that are mostly zoonotic (e.g. severe acute respiratory syndrome, avian influenza H5N1, pandemic influenza) has led to the need for a multidisciplinary approach to tackling these threats to public and animal health. Accordingly, a global movement of 'One-Health/One-Medicine' has been launched to foster collaborative efforts amongst animal and human health officials and researchers to address these problems. Historical evidence points to the fact that pandemics caused by influenza A viruses remain a major zoonotic threat to mankind. Recently, a range of mathematical and computer simulation modelling methods and tools have increasingly been applied to improve our understanding of disease transmission dynamics, contingency planning and to support policy decisions on disease outbreak management. This review provides an overview of methods, approaches and software used for modelling the spread of zoonotic influenza viruses in animals and humans, particularly those related to the animal-human interface. Modelling parameters used in these studies are summarized to provide references for future work. This review highlights the limited application of modelling research to influenza in animals and at the animal-human interface, in marked contrast to the large volume of its research in human populations. Although swine are widely recognized as a potential host for generating novel influenza viruses, and that some of these viruses, including pandemic influenza A/H1N1 2009, have been shown to be readily transmissible between humans and swine, only one study was found related to the modelling of influenza spread at the swine-human interface. Significant gaps in the knowledge of frequency of novel viral strains evolution in pigs, farm-level natural history of influenza infection, incidences of influenza transmission between farms and between swine and humans are clearly evident. Therefore, there is a need to direct additional research to the study of influenza transmission dynamics in animals and at the animal-human interface.

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“…To consider cases where a fraction of the population is immune to the disease, either from previous exposure and recovery, or by immunization or natural immunity, we define a parameter called fractional immunity , denoted Π, which gives the fraction of the agent population that is immune at the outset of a simulation. This notion of fractional immunity is similar to that seen in studies that seek to model the effect of a pre-existing or acquired immunity already present in a significant fraction of the population [17, 37]. Hospitals are considered to have two types of resources—normal beds and ICU beds.…”

Section: Agent Modelmentioning

confidence: 91%

Modeling the Spread and Control of COVID-19

Trivedi¹,

Sreenivas

Rao

2020

Preprint

View full text Add to dashboard Cite

Data-centric models of COVID-19 have been tried, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease, and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters such as the extent to which hygiene and social distancing are observed in a population. Our results give qualitative indications of the effects of various policies and parameters; for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing matters more than personal hygiene, and that the growth of infection comes down significantly for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.

show abstract

An object simulation model for modeling hypothetical disease epidemics – EpiFlex

Cited by 8 publications

References 30 publications

Modeling the Spread and Control of COVID-19

Modeling the Spread and Control of COVID-19

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

Modeling the Spread and Control of COVID-19

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