Utilizing model characteristics to obtain efficient parallelization in the context of agent based epidemiological models

2007 ◽  
Author(s):  
Steven Naron ◽  
Segev Wasserkrug
Keyword(s):  
Epidemiological Models ◽  
Agent Based ◽  
Efficient Parallelization

scholarly journals Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach

2017 ◽  
Author(s):  
Sébastien Picault ◽  
Yu-Lin Huang ◽  
Vianney Sicard ◽  
Pauline Ezanno
Keyword(s):  
Knowledge Engineering ◽  
Control Measures ◽  
Epidemiological Models ◽  
Multi Agent Systems ◽  
Epidemiological Modeling ◽  
Agent Based ◽  
Computational Sciences ◽  
Multi Agent ◽  
Procedural Concerns ◽  
Development Methods

The development of computational sciences has fostered major advances in life sciences, but also led to reproducibility and reliability issues, which become a crucial stake when simulations are aimed at assessing control measures, as in epidemiology. A broad use of software development methods is a useful remediation to reduce those problems, but preventive approaches, targeting not only implementation but also model design, are essential to sustainable enhancements. Among them, AI techniques, based on the separation between declarative and procedural concerns, and on knowledge engineering, offer promising solutions. Especially, multilevel multi-agent systems, deeply rooted in that culture, provide a generic way to integrate several epidemiological modeling paradigms within a homogeneous interface. We explain in this paper how this approach is used for building more generic, reliable and sustainable simulations, illustrated by real-case applications in cattle epidemiology.

scholarly journals A Statistical Examination of Distinct Characteristics Influencing the Performance of Vector-Borne Epidemiological Agent-Based Simulation Models

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 166-196
Author(s):  
Anna Paula Galvão Scheidegger ◽  
Henrique dos Santos Maxir ◽  
Amarnath Banerjee
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Human Behavior ◽  
Simulation Models ◽  
Disease Spread ◽  
Epidemiological Models ◽  
Agent Based Simulation ◽  
Single Strain ◽  
Agent Based ◽  
Vector Borne

The spread of infectious diseases is a complex system in which pathogens, humans, the environment, and sometimes vectors interact. Mathematical and simulation modelling is a suitable approach to investigate the dynamics of such complex systems. The 2019 novel coronavirus (COVID-19) pandemic reinforced the importance of agent-based simulation models to quickly and accurately provide information about the disease spread that would be otherwise hard or risky to obtain, and how this information can be used to support infectious disease control decisions. Due to the trade-offs between complexity, time, and accuracy, many assumptions are frequently made in epidemiological models. With respect to vector-borne diseases, these assumptions lead to epidemiological models that are usually bounded to single-strain and single-vector scenarios, where human behavior is modeled in a simplistic manner or ignored, and where data quality is usually not evaluated. In order to leverage these models from theoretical tools to decision-making support tools, it is important to understand how information quality, human behavior, multi-vector, and multi-strain affect the results. For this, an agent-based simulation model with different parameter values and different scenarios was considered. Its results were compared with the results of a traditional compartmental model with respect to three outputs: total number of infected individuals, duration of the epidemic, and number of epidemic waves. Paired t-test showed that, in most cases, data quality, human behavior, multi-vector, and multi-strain were characteristics that lead to statistically different results, while the computational costs to consider them were not high. Therefore, these characteristics should be investigated in more detail and be accounted for in epidemiological models in order to obtain more reliable results that can assist the decision-making process during epidemics.

scholarly journals Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

2021 ◽  
Vol 71 ◽  
pp. 953-992
Author(s):  
Roberto Capobianco ◽  
Varun Kompella ◽  
James Ault ◽  
Guni Sharon ◽  
Stacy Jong ◽  
...  
Keyword(s):  
Markov Modeling ◽  
Test Results ◽  
Epidemiological Models ◽  
Partial Observations ◽  
Agent Based ◽  
Hospital Capacity ◽  
Fine Grained ◽  
Economy Running ◽  
Insight Into ◽  
Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.

A bitstring approach for implementing agent-based epidemiological models

2017 ◽  
Vol 13 (4) ◽  
pp. 353-371 ◽  
Author(s):  
Rogério L. Rizzi ◽  
Wesley L. Kaizer ◽  
Claudia B. Rizzi ◽  
Guilherme Galante ◽  
Flávio C. Coelho
Keyword(s):  
Epidemiological Models ◽  
Agent Based

scholarly journals Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

2020 ◽  
Author(s):  
Leslie Ann Goldberg ◽  
Joost Jorritsma ◽  
Júlia Komjáthy ◽  
John Lapinskas
Keyword(s):  
Mobile Phone ◽  
Random Graphs ◽  
Contact Tracing ◽  
Epidemiological Models ◽  
Agent Based ◽  
Inhomogeneous Random Graphs ◽  
Travel Restrictions ◽  
Geometric Space ◽  
Phone Contact

AbstractWe study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs — (1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the yearly quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.

scholarly journals Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250435
Author(s):  
Leslie Ann Goldberg ◽  
Joost Jorritsma ◽  
Júlia Komjáthy ◽  
John Lapinskas
Keyword(s):  
Mobile Phone ◽  
Random Graphs ◽  
Contact Tracing ◽  
Epidemiological Models ◽  
Agent Based ◽  
Inhomogeneous Random Graphs ◽  
Travel Restrictions ◽  
Geometric Space ◽  
Phone Contact

We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs—(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.

scholarly journals Supporting Austria through the COVID-19 Epidemics with a Forecast-Based Early Warning System

2020 ◽  
Author(s):  
Martin Bicher ◽  
Martin Zuba ◽  
Lukas Rainer ◽  
Florian Bachner ◽  
Claire Rippinger ◽  
...  
Keyword(s):  
Monitoring System ◽  
Warning System ◽  
Epidemiological Models ◽  
Short Term ◽  
Worst Case ◽  
Policy Makers ◽  
Agent Based ◽  
The Public ◽  
Hospital Beds ◽  
The Media

BackgroundThe corona crisis hit Austria at the end of February 2020 with one of the first European superspreading events. In response, the governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds.MethodsWe consolidated the output of three independent epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds.FindingsHere, we report om four key contributions by which our forecasting and reporting system has helped shaping Austria’s policy to navigate the crisis and re-open the country step-wise, namely (i) when and where case numbers are expected to peak during the first wave, (ii) how to safely re-open the country after passing this peak, (iii) how to evaluate the effects of non-pharmaceutical interventions and (iv) provide hospital managers guidance to plan health-care capacities.InterpretationComplex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, provided they are used as a monitoring system to detect epidemiological change points. For policy-makers, the media and the public, it might be problematic to distinguish short-term forecasts from worst-case scenarios with undefined levels of certainty, creating distrust in the legitimacy and accuracy of such models. However, when used as a short-term forecast-based monitoring system, the models can inform decisions to ease or strengthen governmental responses.

scholarly journals Epidemiological models and COVID-19: a comparative view

2021 ◽  
Vol 43 (3) ◽  
Author(s):  
Valeriano Iranzo ◽  
Saúl Pérez-González
Keyword(s):  
Policy Making ◽  
Contextual Factors ◽  
Target Population ◽  
Compartmental Models ◽  
Epidemiological Models ◽  
Agent Based Models ◽  
Policy Makers ◽  
Agent Based

AbstractEpidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

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