A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

Xi, Guo; Gupta, Abhineet; Sampat, Anand; Zhai, Chengwei

doi:10.1371/journal.pone.0262316

Cited by 5 publications

(3 citation statements)

References 68 publications

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“…In the event of a positive decision, the request Therefore, many authors set the contact rate using the function of a probability distribution. Various studies have used normal distribution [52], lognormal [53], Bernoulli distribution [54], geometric distribution [55], and binomial distribution [56] in modeling. We defined the probability of contact as follows:…”

Section: Data Collectionmentioning

confidence: 99%

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

Koichubekov,

Takuadina,

Korshukov

et al. 2023

Healthcare

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Background: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. Materials and Methods: We utilized a stochastic agent-based model for COVID-19’s spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. Results: Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection’s spread was highest within families, with most COVID-19 cases occurring in the 26–59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a “no intervention” scenario, yielding an estimated economic benefit of 40%. Conclusion: The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.

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Section: Data Collectionmentioning

confidence: 99%

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

Koichubekov,

Takuadina,

Korshukov

et al. 2023

Healthcare

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show abstract

“…Therefore, many authors set the contact rate using the function of probability distribution. In various studies, normal distribution [48], lognormal [49], Bernoulli distribution [50], Geometric distribution [51], binomial distribution [52] were used in modeling. We defined probability of contacts as…”

Section: Model Parametersmentioning

confidence: 99%

The Epidemiological and Economic Impact of COVID‑19 in Kazakhstan. Agent-Based Modelling

Koichubekov,

Korshukov,

Takuadina

et al. 2023

Preprint

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Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

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“…Müller et al [ 6 ] simulate the spread in larger populations on the scale of regions or countries over several months. As shown in [ 7 ], network models can also operate on a local level such as single facilities and small populations for short periods up to two weeks. However, the network approach is relatively abstract.…”

Section: Introductionmentioning

confidence: 99%

Modelling airborne transmission of SARS-CoV-2 at a local scale

et al. 2022

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The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a ‘high risk’ benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.

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A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

Cited by 5 publications

References 68 publications

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

The Epidemiological and Economic Impact of COVID‑19 in Kazakhstan. Agent-Based Modelling

Modelling airborne transmission of SARS-CoV-2 at a local scale

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