Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling

Leonenko, Vasiliy N.; Arzamastsev, S.A.; Bobashev, Georgiy

doi:10.1016/j.jocs.2020.101156

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…In Table 1 and Figure 3, Figure 4, we present the distribution of households in Saint-Petersburg on the city map. We depict households built before 2011 and used in 2010 synthetic population [17] as green points and households reconstructed by the demographic model according to data [15] as blue ones.…”

Section: Resultsmentioning

confidence: 99%

A Demographic Microsimulation Model for the Long-Term Evolutionof Synthetic Populations in Saint-Petersburg

Arzamastsev¹,

Leonenko²

2020

Proceedings of the International Conference "Mathematical Biology and Bioinformatics"

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In this paper, we present a microsimulation model (MSM) that allows to simulate demographic processes in agent populations. As its input, the model uses a synthetic population, which consists of independent agents with defined dwellings and workplaces. The model allows not only to monitor the changes in demographics of the population over time but also to consider the geospatial distribution of individuals and their activities in a regarded urban setting. Using open-access 2010-2018 demographic data for St. Petersburg, we have assessed the changes in the states of the agents associated with aging, birth, emigration, immigration, and formation of new households. The modelling algorithm is implemented in Python programming language. To demonstrate the capabilities of the model, we derived a synthetic population of Saint-Petersburg for 2018 from the synthetic population of 2010. The results of the modelling are aligned with the available aggregated statistical data. The synthetic populations created with the help of the model allow fine-scale simulations of the outbreaks of influenza and COVID-19 in Russian cities.

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Section: Resultsmentioning

confidence: 99%

A Demographic Microsimulation Model for the Long-Term Evolutionof Synthetic Populations in Saint-Petersburg

Arzamastsev¹,

Leonenko²

2020

Proceedings of the International Conference "Mathematical Biology and Bioinformatics"

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“…In the field of agent simulation and complex systems, Leonenko, Arzamastsev, and Bobashev [ 23 ] study an agent-based model for the influenza propagation. The work commits to assess the applicability of the model over a previous influenza incidence and to demonstrate the influence of human contact patterns on the simulated influenza dynamics.…”

Section: Overview Of the Virtual Special Issuementioning

confidence: 99%

Computational Science in the Interconnected World: Selected papers from 2019 International Conference on Computational Science

Cardoso

Rodrigues

Monteiro

et al. 2020

Journal of Computational Science

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“…Agent-Based Models (ABMs) support researchers in this direction. ABMs are a modeling tool able to easily incorporate features related to population and society and are widely adopted to simulate human behaviors under specific conditions [48,62], in particular in epidemiological studies [45]. They allow researchers to naturally include human mobility data to model humans interactions between the environment or other individuals.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

et al. 2021

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Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.

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Contact patterns and influenza outbreaks in Russian cities: A proof-of-concept study via agent-based modeling

Cited by 12 publications

References 24 publications

A Demographic Microsimulation Model for the Long-Term Evolutionof Synthetic Populations in Saint-Petersburg

A Demographic Microsimulation Model for the Long-Term Evolutionof Synthetic Populations in Saint-Petersburg

Computational Science in the Interconnected World: Selected papers from 2019 International Conference on Computational Science

Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

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