Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling.
COVID-19 risk assessment
Disease Spreading Modeling
Microenvironment approach
Probability of infection
Spatial probability of transmission
Temporal probability of transmission
Journal
Journal of exposure science & environmental epidemiology
ISSN: 1559-064X
Titre abrégé: J Expo Sci Environ Epidemiol
Pays: United States
ID NLM: 101262796
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
12
04
2021
accepted:
11
01
2022
revised:
06
01
2022
pubmed:
1
2
2022
medline:
21
9
2022
entrez:
31
1
2022
Statut:
ppublish
Résumé
The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. This paper describes a novel model for COVID-19 infection risks and policy evaluations. The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. The application of the model is demonstrated for a typical office environment and for a real-world case. The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
Sections du résumé
BACKGROUND
The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks.
OBJECTIVE
This paper describes a novel model for COVID-19 infection risks and policy evaluations.
METHODS
The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies.
RESULTS
The application of the model is demonstrated for a typical office environment and for a real-world case.
CONCLUSION
The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
Identifiants
pubmed: 35095095
doi: 10.1038/s41370-022-00411-2
pii: 10.1038/s41370-022-00411-2
pmc: PMC8801387
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
712-719Informations de copyright
© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
Références
Haleem A, Javaid M, Vaishya R. Effects of COVID 19 pandemic in daily life. Curr Med Res. Pract. 2020;10:78–9.
doi: 10.1016/j.cmrp.2020.03.011
pubmed: 32292804
pmcid: 7147210
Fernandes N. Economic effects of coronavirus outbreak (COVID-19) on the world economy. SSRN Electron J. 2020;1:1–33.
Kano T, Yasui K, Mikami T, Asally M, Ishiguro A. An agent-based model of the interrelation between the COVID-19 outbreak and economic activities. Proc R Soc A Math Phys Eng Sci. 2021;477:20200604.
Atalan A. Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective. Ann Med Surg. 2020;56:38–42.
doi: 10.1016/j.amsu.2020.06.010
Linkov I, Keenan JM, Trump BD COVID-19: systemic risk and resilience. Springer; New York 2021.
Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond Ser A Containing Pap A Math Phys Character. 1927;115:700–21.
Kermack WO, McKendrick AG. Contributions to the mathematical theory of epidemics—II. The problem of endemicity. Bull Math Biol. 1991;53:57–87.
pubmed: 2059742
Kermack WO, McKendrick AG. Contributions to the mathematical theory of epidemics—III. Further studies of the problem of endemicity. Bull Math Biol. 1991;53:89–118.
pubmed: 2059743
Calvetti D, Hoover A, Rose J, Somersalo E. Bayesian dynamical estimation of the parameters of an SE (A) IR COVID-19 spread model. 2020. https://arxiv.org/abs/2005.04365 .
Weissman GE, Crane-Droesch A, Chivers C, Luong T, Hanish A, Levy MZ, et al. Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Ann Intern Med. 2020;173:21–8.
doi: 10.7326/M20-1260
pubmed: 32259197
Clancy D, O’Neill PD. Bayesian estimation of the basic reproduction number in stochastic epidemic models. Bayesian Anal. 2008;3:737–57.
doi: 10.1214/08-BA328
Chen Y-C, Lu P-E, Chang C-S A Time-dependent SIR model for COVID-19. 2020. https://arxiv.org/abs/2003.00122 .
Tolles J, Luong T. Modeling epidemics with compartmental models. JAMA. 2020;323:2515–6.
doi: 10.1001/jama.2020.8420
pubmed: 32459319
Cuevas E. An agent-based model to evaluate the COVID-19 transmission risks in facilities. Comput Biol Med. 2020;121:103827.
doi: 10.1016/j.compbiomed.2020.103827
pubmed: 32568667
pmcid: 7237380
Silva PCL, Batista PVC, Lima HS, Alves MA, Guimarães FG, Silva RCP. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos Solitons Fractals. 2020;139:110088.
doi: 10.1016/j.chaos.2020.110088
pubmed: 32834624
pmcid: 7340090
Manzo G, Matthews T. Potentialities and limitations of agent-based simulations. Rev française sociol. 2014;55:653–88.
doi: 10.3917/rfs.554.0653
Troitzsch KG. Analysing Simulation Results Statistically: Does Significance Matter? In: Interdisciplinary applications of agent-based social simulation and modeling, edited by Diana Francisca Adamatti, et al., Hershey, PA, IGI Global; 2014, p. 88–105.
Hussein T, Löndahl J, Thuresson S, Alsved M, Al-Hunaiti A, Saksela K, et al. Indoor model simulation for COVID-19 transport and exposure. Int J Environ Res Public Health. 2021;18:2927.
doi: 10.3390/ijerph18062927
pubmed: 33809366
pmcid: 7999367
Lelieveld J, Helleis F, Borrmann S, Cheng Y, Drewnick F, Haug G, et al. Model calculations of aerosol transmission and infection risk of COVID-19 in indoor environments. Int. J. Environ. Res. Public Health; 2020. p. 1–18.
Vuorinen V, Aarnio M, Alava M, Alopaeus V, Atanasova N, Auvinen M, et al. Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors. Saf Sci. 2020;130:104866.
doi: 10.1016/j.ssci.2020.104866
pubmed: 32834511
pmcid: 7428778
Lewis D. Is the coronavirus airborne? Experts can’t agree. Nature 2020;580:175.
doi: 10.1038/d41586-020-00974-w
pubmed: 32242113
Price PS, Curry CL, Goodrum PE, Gray MN, McCrodden JI, Harrington NW, et al. Monte Carlo modeling of time‐dependent exposures using a microexposure event approach. Risk Anal. 1996;16:339–48.
doi: 10.1111/j.1539-6924.1996.tb01468.x
Price PS, Chaisson CF. A conceptual framework for modeling aggregate and cumulative exposures to chemicals. J Expo Sci Environ Epidemiol. 2005;15:473–81.
doi: 10.1038/sj.jea.7500425
McCarthy JE, Dumas BA, McCarthy MT, Dewitt BD. A deterministic linear infection model to inform Risk-Cost-Benefit Analysis of activities during the SARS-CoV-2 pandemic. 2020. https://www.medrxiv.org/content/10.1101/2020.08.23.20180349v1 .
Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020;395:1973–87.
doi: 10.1016/S0140-6736(20)31142-9
pubmed: 32497510
pmcid: 7263814
Mueller AV, Eden MJ, Oakes JM, Bellini C, Fernandez LA. Quantitative method for comparative assessment of particle removal efficiency of fabric masks as alternatives to standard surgical masks for PPE. Matter. 2020;3:950–62.
doi: 10.1016/j.matt.2020.07.006
pubmed: 32838296
pmcid: 7346791
Bhagat RK, Wykes MD, Dalziel SB, Linden P. Effects of ventilation on the indoor spread of COVID-19. J Fluid Mech. 2020;903:F1.
Sze To GN, Chao CYH. Review and comparison between the Wells–Riley and dose‐response approaches to risk assessment of infectious respiratory diseases. Indoor Air. 2010;20:2–16.
doi: 10.1111/j.1600-0668.2009.00621.x
pubmed: 19874402
@CDC. COVID Data Tracker - Centers for Disease Control and Prevention: @CDC; 2021. https://covid.cdc.gov/covid-data-tracker/#datatracker-h .
@Census. U.S. and World Population Clock - The United States Census Bureau: @Census; 2021. https://www.census.gov/popclock .
Nishiura H, Kobayashi T, Miyama T, Suzuki A, Jung SM, Hayashi K, et al. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). Int J Infect Dis. 2020;94:154.
doi: 10.1016/j.ijid.2020.03.020
pubmed: 32179137
pmcid: 7270890
Byambasuren O, Cardona M, Bell K, Clark J, McLaws M-L, Glasziou P. Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. Off J Assoc Med Microbiol Infect Dis Can. 2020;5:223–34.
Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance. 2020;25:2000180.
doi: 10.2807/1560-7917.ES.2020.25.10.2000180
pmcid: 7078829
Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med. 2020;172:577–82.
doi: 10.7326/M20-0504
pubmed: 32150748
Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–20.
doi: 10.1056/NEJMoa2002032
pubmed: 32109013
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. 2020;382:1199–207.
doi: 10.1056/NEJMoa2001316
pubmed: 31995857
pmcid: 7121484
@JohnsHopkins. Mortality analyses - Johns Hopkins Coronavirus Resource Center: @JohnsHopkins; 2021. https://coronavirus.jhu.edu/data/mortality .
Park SY, Kim Y-M, Yi S, Lee S, Na B-J, Kim CB, et al. Coronavirus disease outbreak in call center, South Korea. Emerg Infect Dis. 2020;26:1666.
doi: 10.3201/eid2608.201274
pubmed: 32324530
pmcid: 7392450