Estimating the potential for disease spread in horses associated with an equestrian show in Ontario, Canada using an agent-based model

ObjectiveTo develop a computational model to assess the risk of epidemics in global mass gatherings and evaluate the impact of various measures of prevention and control of infectious diseases.IntroductionGlobal Mass gatherings (MGs) such as Olympic Games, FIFA World Cup, and Hajj (Muslim pilgrimage to Makkah), attract millions of people from different countries. The gathering of a large population in a proximity facilitates transmission of infectious diseases [1]. Attendees arrive from different geographical areas with diverse disease history and immune responses. The associated travel patterns with global events can contribute to a further disease spread affecting a large number of people within a short period and lead to a potential pandemic. Global MGs pose serious health threats and challenges to the hosting countries and home countries of the participants [2]. Advanced planning and disease surveillance systems are required to control health risks in these events. The success of computational models in different areas of public health and epidemiology motivates using these models in MGs to study transmission of infectious diseases and assess the risk of epidemics. Computational models enable simulation and analysis of different disease transmission scenarios in global MGs. Epidemic models can be used to evaluate the impact of various measures of prevention and control of infectious diseases.MethodsThe annual event of the Hajj is selected to illustrate the main aspects of the proposed model and to address the associated challenges. Every year, more than two million pilgrims from over 186 countries arrive in Makkah to perform Hajj with the majority arriving by air. Foreign pilgrims can stay at one of the holy cities of Makkah and Madinah up to 30-35 days prior the starting date of the Hajj. The long duration of the arrival phase of the Hajj allows a potential epidemic to proceed in the population of international pilgrims. Stochastic SEIR (Susceptible−Exposed−Infected−Recovered) agent-based model is developed to simulate the disease transmission among pilgrims. The agent-based model is used to simulate pilgrims and their interactions during the various phases of the Hajj. Each agent represents a pilgrim and maintains a record of demographic data (gender, country of origin, age), health data (infectivity, susceptibility, number of days being exposed or infected), event related data (location, arrival date and time), and precautionary or health-related behaviors.Each pilgrim can be either healthy but susceptible to a disease, exposed who are infected but cannot transmit the infection, or infectious (asymptomatic or symptomatic) who are infected and can transmit the disease to other susceptibles. Exposed individuals transfer to the infectious compartment after 1/α days, and infectious individuals will recover and gain immunity to that disease after 1/γ days. Where α is the latent period and γ is the infectious period. Moving susceptible individuals to exposed compartment depends on a successful disease transmission given a contact with an infectious individual. The disease transmission rate is determined by the contact rate and thetransmission probability per contact. Contact rate and mixing patterns are defined by probabilistic weights based on the features of infectious pilgrims and the duration and setting of the stage where contacts are taking place. The initial infections are seeded in the population using two scenarios (Figure 1) to measure the effects of changing, the timing for introducing a disease into the population and the likelihood that a particular flight will arrive with one or more infected individuals.ResultsThe results showed that the number of initial infections is influenced by increasing the value of λ and selecting starting date within peak arrival days. When starting from the first day, the average size of the initial infectious ranges from 0.05% to 1% of the total arriving pilgrims. Using the SEIR agent-based model, a simulation of the H1N1 Influenza epidemic was completed for the 35-days arrival stage of the Hajj. The epidemic is initiated with one infectious pilgrim per flight resulting in infected 0.5% of the total arriving pilgrims. As pilgrims spend few hours at the airport, the results obtained from running the epidemic model showed only new cases of susceptible individuals entering the exposed state in a range of 0.20% to 0.35% of total susceptibles. The number of new cases is reduced by almost the same rate of the number of infectious individuals following precautionary behaviors.ConclusionsA data-driven stochastic SEIR agent-based model is developed to simulate disease spread at global mass gatherings. The proposed model can provide initial indicators of infectious disease epidemic at these events and evaluate the possible effects of intervention measures and health-related behaviors. The proposed model can be generalized to model the spread of various diseases in different mass gatherings, as it allows different factors to vary and entered as parameters.References1. Memish ZA, Stephens GM, Steffen R, Ahmed QA. Emergence of medicine for mass gatherings: lessons from the Hajj. The Lancet infectious diseases. 2012 Jan 31;12(1):56-65.2. Chowell G, Nishiura H, Viboud C. Modeling rapidly disseminating infectious disease during mass gatherings. BMC medicine. 2012 Dec 7;10(1):159.

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Learning differential equation models from stochastic agent-based model simulations

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0987 ◽

2021 ◽

Vol 18 (176) ◽

Author(s):

John T. Nardini ◽

Ruth E. Baker ◽

Matthew J. Simpson ◽

Kevin B. Flores

Keyword(s):

Differential Equation ◽

Cell Biology ◽

Coarse Grained ◽

Disease Spread ◽

Agent Based Model ◽

Agent Based Models ◽

Agent Based ◽

Model Simulations ◽

Differential Equation Models ◽

Model Dynamics

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth–death–migration model commonly used to explore cell biology experiments and a susceptible–infected–recovered model of infectious disease spread.

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Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19

Systems ◽

10.3390/systems9020041 ◽

2021 ◽

Vol 9 (2) ◽

pp. 41

Author(s):

Elizabeth Hunter ◽

John D. Kelleher

Keyword(s):

Complex System ◽

Infectious Disease ◽

Emerging Infectious Diseases ◽

Disease Outbreaks ◽

Basic Reproductive Number ◽

Disease Spread ◽

Reproductive Number ◽

Agent Based Model ◽

School Closures ◽

Agent Based

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

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Farmers’ heterogeneous motives, voluntary vaccination and disease spread: an agent-based model

European Review of Agricultural Economics ◽

10.1093/erae/jbz041 ◽

2019 ◽

Vol 47 (3) ◽

pp. 1201-1222 ◽

Cited By ~ 5

Author(s):

Jaap Sok ◽

Egil A J Fischer

Keyword(s):

Animal Health ◽

Communication Strategy ◽

Vaccine Uptake ◽

Disease Spread ◽

Agent Based Model ◽

Agent Based ◽

Disease Epidemiology ◽

Base Scheme ◽

Livestock Disease ◽

Specific Scheme

Abstract Animal health authorities responsible for effective voluntary livestock disease control need to consider the dynamic interplay between farmers’ collective behaviour and disease epidemiology. We present an agent-based model to simulate vaccination scheme designs that differ in expected adverse vaccine effects, communication strategies and subsidy levels. Specific scheme designs improve the vaccine uptake by farmers at the start of a livestock disease epidemic compared with a base scheme of minimal communication and subsidy. The results suggest that motivational mechanisms activated by a well-designed risk communication strategy are equally or more effective in increasing vaccination uptake than providing more financial compensation.

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An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State

10.1101/2020.10.19.20215517 ◽

2020 ◽

Author(s):

Amitava Datta ◽

Peter Winkelstein ◽

Surajit Sen

Keyword(s):

New York ◽

Public Space ◽

Public Transportation ◽

New York State ◽

Virtual Community ◽

Disease Spread ◽

Grocery Stores ◽

Agent Based Model ◽

Agent Based ◽

York State

We introduce a simple agent based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent and simulate the spread of a pandemic and subsequently validate it by using publicly available COVID-19 data. Our simulation tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection, recovering asymptomatically, or getting hospitalized. The virtual community is divided into family groups with 2-6 individuals in each group. Agents interact with other agents in virtual public places like at grocery stores, on public transportation and in offices. We initially seed the virtual community with a very small number of infected individuals and then monitor the disease spread and hospitalization over a period of fifty days, which is a typical time-frame for the initial spread of a pandemic. An uninfected or asymptomatic agent is randomly selected from a random family group in each simulation step for visiting a random public space. An uninfected agent contracts infection if the public place is occupied by other infected agents. We have calibrated our simulation rounds according to the size of the population of the virtual community for simulating realistic exposure of agents to a contagion. Our simulation results are consistent with the publicly available hospitalization and ICU patient data from different communities of varying sizes in New York state. Our model can predict the trend in epidemic spread and hospitalization from a set of simple parameters and could be potentially useful in exploring strategies to keep a community safe.

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Cost–benefit of limited isolation and testing in COVID-19 mitigation

Scientific Reports ◽

10.1038/s41598-020-75640-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Andreas Eilersen ◽

Kim Sneppen

Keyword(s):

Short Duration ◽

Disease Transmission ◽

Social Groups ◽

Cost Benefit ◽

Mitigation Strategies ◽

Disease Spread ◽

Agent Based Model ◽

Social Contacts ◽

Agent Based ◽

The Cost

Abstract The international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent. In this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures. We find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost–benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown. A targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its effect increases when testing is more widespread.

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