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

Systems ◽  
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.

scholarly journals An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208775 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

scholarly journals Beyond R 0 : heterogeneity in secondary infections and probabilistic epidemic forecasting

2020 ◽  
Vol 17 (172) ◽  
pp. 20200393 ◽  
Author(s):  
Laurent Hébert-Dufresne ◽  
Benjamin M. Althouse ◽  
Samuel V. Scarpino ◽  
Antoine Allard
Keyword(s):  
Random Network ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Epidemic Size ◽  
Classic Result ◽  
Secondary Infections

The basic reproductive number, R 0 , is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R 0 . Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R 0 and the underlying heterogeneity. Importantly, epidemics with lower R 0 can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R 0 , heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R 0 .

scholarly journals Beyond R0: Heterogeneity in secondary infections and probabilistic epidemic forecasting

2020 ◽  
Author(s):  
Laurent Hébert-Dufresne ◽  
Benjamin M. Althouse ◽  
Samuel V. Scarpino ◽  
Antoine Allard
Keyword(s):  
Random Network ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Epidemic Size ◽  
Secondary Infections ◽  
Outbreak Size

The basic reproductive number — R0 — is one of the most common and most commonly misapplied numbers in public health. Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R0 [1–3]. Here, we show how to predict outbreak size using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. To do so, we reformulate and extend a classic result from random network theory [4] that relies on contact tracing data to simultaneously determine the first moment (R0) and the higher moments (representing the heterogeneity) in the distribution of secondary infections. Further, we show the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19, the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0 when predicting epidemic size.

scholarly journals Exact solution of infection dynamics with gamma distribution of generation intervals

2020 ◽  
Author(s):  
Alexei Vazquez
Keyword(s):  
Infectious Disease ◽  
Gamma Distribution ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Infectious Disease Outbreaks ◽  
Infection Dynamics ◽  
Infectious Disease Dynamics ◽  
Analytical Expressions ◽  
Interval Distribution

Infectious disease outbreaks are expected to grow exponentially in time but their initial dynamics is less known. Here I derive analytical expressions for the infectious disease dynamics with a gamma distribution of generation intervals. Excluding the exponential distribution, the outbreak grows as a power law at short times. At long times the dynamics is exponential with a growth rate determined by the basic reproductive number and the parameters of the generation interval distribution. These analytical expressions can be deployed to do better estimates of infectious disease parameters.

scholarly journals Correction: An open-data-driven agent-based model to simulate infectious disease outbreaks

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211245 ◽  
Author(s):  
Elizabeth Hunter ◽  
Brian Mac Namee ◽  
John Kelleher
Keyword(s):  
Infectious Disease ◽  
Open Data ◽  
Disease Outbreaks ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Infectious Disease Outbreaks

scholarly journals Huanglongbing Model under the Control Strategy of Discontinuous Removal of Infected Trees

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1164
Author(s):  
Weiwei Ling ◽  
Pinxia Wu ◽  
Xiumei Li ◽  
Liangjin Xie
Keyword(s):  
Infectious Disease ◽  
Global Stability ◽  
Control Strategy ◽  
Theoretical Basis ◽  
Dynamic Properties ◽  
Transmission Mechanism ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Vector Borne ◽  
Citrus Trees

By using differential equations with discontinuous right-hand sides, a dynamic model for vector-borne infectious disease under the discontinuous removal of infected trees was established after understanding the transmission mechanism of Huanglongbing (HLB) disease in citrus trees. Through calculation, the basic reproductive number of the model can be attained and the properties of the model are discussed. On this basis, the existence and global stability of the calculated equilibria are verified. Moreover, it was found that different I0 in the control strategy cannot change the dynamic properties of HLB disease. However, the lower the value of I0, the fewer HLB-infected citrus trees, which provides a theoretical basis for controlling HLB disease and reducing expenditure.

Employing Agent-based Modeling to Mitigate Infectious Disease Spread

2021 ◽  
Vol 65 (1) ◽  
pp. 390-394
Author(s):  
Michael Schwartz ◽  
Paul Oppold ◽  
Boniface Noyongoyo ◽  
Peter Hancock
Keyword(s):  
Infectious Disease ◽  
Viral Infections ◽  
Agent Based Modeling ◽  
Disease Spread ◽  
Effective Measure ◽  
Agent Based ◽  
Personal Learning ◽  
Business Operations ◽  
Interpersonal Factors ◽  
Reduce Risk

The current pandemic has tested systems in place as to how to fight infectious diseases in many countries. COVID-19 spreads quickly and is deadly. However, it can be controlled through different measures such as physical distancing. The current project examines through simulation model of the UCF Global building the potential spread of an infectious disease via AnyLogic Personal Learning Edition (PLE) 8.7.0 on a laptop running Windows 10. The goal is to determine the environmental and interpersonal factors that could be modified to reduce risk of illness while maintaining typical business operations. Multiple experiments were ran to see when there is a potential change in infection and spread rate. Results show that increases occur with density between 400 and 500. To curtail the spread it is therefore important to limit contact through physical distancing for it has been proven an effective measure for reducing the spread of viral infections.

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