Agent-based epidemiological modeling of COVID-19 in localized environments

Ciunkiewicz, Philip; Brooke, W.; Rogers, Morgan; Yanushkevich, Svetlana

doi:10.1016/j.compbiomed.2022.105396

Cited by 10 publications

(11 citation statements)

References 25 publications

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“…Of the 24 surfaces sampled, 46% were SARS-CoV-2 positive at the time of sampling. Ciunkiewicz et.al [15]. proposed an agent-based simulation, which uses an agent-based epidemiological model to simulate the spread of COVID-19 within an elderly care facility.…”

Section: Related Workmentioning

confidence: 99%

A tipping point of spreading viruses: Estimating the risk of household contact transmission of COVID-19

et al. 2023

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COVID-19 infection has been reported to be caused by droplet and contact infection. This paper proposes a model that visualizes the risk of contact infection to family members when viruses spread to various items at home. Behavior data after returning home are extracted from a questionnaire-based survey of home behavior to design the agent-based model. The data tables of contact behavior are created, including the room-to-room transfer probability table, the conditional probability table, and the contact probability table. The material transfer efficiency table is also created by measuring the virus transmission rate after contact with droplets in a virus experiment laboratory. In the experiment, the synthetic agent created from the acquired data probabilistically performs movement and contact behavior after returning home and reproduces the state in which the virus attached to the hand or belongings, when going out, propagates to objects at home. Next, we examine the risk of a second family member returning home. As a result, virus-attached contacts within around 30 minutes after returning home are widely confirmed around the entrance and kitchen, suggesting the effectiveness of early hand-washing behavior. And the experiment shows that even if the first person returning home disinfects their hands inside the entrance, the virus remains in a part of the entrance, and the virus is spread inside the room by the second person returning home.

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Section: Related Workmentioning

confidence: 99%

A tipping point of spreading viruses: Estimating the risk of household contact transmission of COVID-19

et al. 2023

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“…Note that models of COVID-19 have not yet accounted for the many drivers of individual vaccination choices, instead opting to either use a population-wide percentage of vaccinated individuals [ 35 ] or account for age [ 33 , 52 ]. This occurred even in highly configurable frameworks where agents execute detailed plans based on their health, demographic, and organizational roles [ 74 ]. Two models accounted for the level of caution (rising with case numbers), sense of safety (rising with vaccination), or perceived vaccination risk, but at the population-level rather than via individuals [ 75 , 76 ].…”

Section: Vaccinal Choicementioning

confidence: 99%

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

2022

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The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.

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“…For example, Dehning et al [6] combined the susceptible-infectedrecovered (SIR) model with Bayesian parameter inference to detect change points of disease spreading rate using COVID-19 case numbers measured in Germany. On the other hand, agentbased models consider the individual differences, microscale policies and detailed actions, which can provide a comprehensive understanding of the transmission dynamics as well as a flexible framework for implementing various intervention rules [12][13][14][15][16][17][18][19][21][22][23][24][25]. The agent-based models have been used to study the spread of COVID-19 in Australia [12], Singapore [13] and France [14] under various interventions [25].…”

Section: Introductionmentioning

confidence: 99%

“…[14]. In addition, some agent-based models consider individual movements in a continuous physical space [18,19,21,22,24]. For example, Cuevas [18] studied how the combination of local random walks and long directed movements of individuals influences COVID-19 transmission.…”

Section: Introductionmentioning

confidence: 99%

“…Though the commute and contact networks can partially reflect the characteristics of human mobility, these networks are usually biased by small population samples used to estimate the network topology and weights of edges [26]. Furthermore, the commute networks only reflect travels in a limited number of areas in the same spatial scale, either the microscopic scale containing home, workplace, school and hospital [12,13,[15][16][17]24], or in the macroscopic scale containing cities and countries [27][28][29]. The social contact networks are naturally formed in a long period of time.…”

Section: Introductionmentioning

confidence: 99%

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A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

Chen

Jiang

et al. 2022

Preprint

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The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. To forecast the transmission of COVID-19, a major challenge is the accurate assessment of the multi-scale human mobility and how they impact the infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of places in geography, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behaviour and individual health conditions on the disease outbreak and the probability of zero COVID in the population. Specifically, individuals perform power-law type of local movements within a container and global transport between different-level containers. Frequent long movements inside a small-level container (e.g. a road or a county) and a small population size reduce the local crowdedness of people and the disease infection and transmission. In contrast, travels between large-level containers (e.g. cities and nations) facilitate global disease spread and outbreak. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing total population and local people accumulation as well as restricting global travels help achieve zero-COVID. In summary, the Mob-Cov model considers more realistic human mobility in a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to investigate the pandemic dynamics and plan actions against the disease.

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Agent-based epidemiological modeling of COVID-19 in localized environments

Cited by 10 publications

References 25 publications

A tipping point of spreading viruses: Estimating the risk of household contact transmission of COVID-19

A tipping point of spreading viruses: Estimating the risk of household contact transmission of COVID-19

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

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