Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study | Scientific Reports
Dong, E., Hongru, D. & Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20(5), 533–534 (2020).
Covid-19 dashboard by the center for systems science and engineering (csse) at johns hopkins university (jhu). https://coronavirus.jhu.edu/map.html. Accessed from 10 Feb 2021.
Covid data tracker. https://covid.cdc.gov/covid-data-tracker. Accessed from 10 Feb 2021.
Price-Haywood, E. G., Burton, J., Fort, D. & Seoane, L. Hospitalization and mortality among black patients and white patients with covid-19. N. Engl. J. Med. 382(26), 2534–2543 (2020).
Millett, G. A. et al. Assessing differential impacts of covid-19 on black communities. Ann. Epidemiol. 47, 37–44 (2020).
O’Driscoll, M. et al. Age-specific mortality and immunity patterns of sars-cov-2. Nature 590(7844), 140–145 (2021).
Estimated influenza illnesses, medical visits, hospitalizations, and deaths in the united states—2019–2020 influenza season. https://www.cdc.gov/flu/about/burden/2019-2020.html. Accessed from 10 Feb 2021.
Noorimotlagh, Z., Jaafarzadeh, N., Martínez, S. S. & Mirzaee, S. A. A systematic review of possible airborne transmission of the covid-19 virus (sars-cov-2) in the indoor air environment. Environm. Res. 193, 110612–110612 (2021).
Morawska, L. et al. How can airborne transmission of covid-19 indoors be minimised?. Environ. Int. 142, 105832 (2020).
Anderson, E. L., Turnham, P., Griffin, J. R. & Clarke, C. C. Consideration of the aerosol transmission for covid-19 and public health. Risk Anal. 40(5), 902–907 (2020).
Hoseinzadeh, E. et al. An updated mini-review on environmental route of the sars-cov-2 transmission. Ecotoxicol. Environ. Saf. 202, 111015–111015 (2020).
Al Huraimel, K., Alhosani, M., Kunhabdulla, S. & Stietiya, M. H. Sars-cov-2 in the environment: modes of transmission, early detection and potential role of pollutions. Sci. Total Environ. 744, 140946 (2020).
Ferretti, L. et al. Quantifying sars-cov-2 transmission suggests epidemic control with digital contact tracing. Science 368(6491), eabb6936 (2020).
Chen, C.-M. et al. Containing covid-19 among 627,386 persons in contact with the diamond princess cruise ship passengers who disembarked in Taiwan: Big data analytics. J. Med. Internet Res. 22(5), e19540 (2020).
Pung, R. et al. Investigation of three clusters of covid-19 in Singapore: Implications for surveillance and response measures. The Lancet 395(10229), 1039–1046 (2020).
Alo, U. R., Nkwo, F. O., Nweke, H. F., Achi, I. I. & Okemiri, H. A. Non-pharmaceutical interventions against covid-19 pandemic: Review of contact tracing and social distancing technologies, protocols, apps, security and open research directions. Sensors (Basel, Switzerland) 22(1), 280, (2021).
Park, S., Choi, G. J. & Ko, H. Information Technology-based tracing strategy in response to COVID-19 in South Korea-privacy controversies. JAMA 323(21), 2129–2130 (2020).
Abar, S., Theodoropoulos, G. K., Lemarinier, P. & O’Hare, G. M. P. Agent based modelling and simulation tools: A review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017).
Hunter, E., Mac Namee, B. & Kelleher, J. D. A taxonomy for agent-based models in human infectious disease epidemiology. J. Artif. Soc. Soc. Simul. 20(3), 2 (2017).
Tracy, M., Cerdá, M. & Keyes, K. M. Agent-based modeling in public health: Current applications and future directions. Ann. Rev. Public Health 39(1), 77–94 (2018).
Berger, C. & Mahdavi, A. Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Build Environ. 173, 106726 (2020).
Parunak, H. D. V., Savit, R. & Riolo, R. L. Multi-agent systems and agent-based simulation. In Proceedings of the First International Workshop of Multi-Agent Systems and Agent-Based Simulation, 4–6 (Springer-Verlag Berlin; Heidelberg, 1998).
Truszkowska, A., Behring, B., Hasanyan, J., Zino, L., Butail, S., Caroppo, E., Jiang, Z.P., Rizzo, A. Porfiri, M., High-resolution agent-based modeling of covid-19 spreading in a small town. Adv. Theory Simul., 4: 2000277 (2021).
Reiner, R. C., Barber, R. M., Collins, J. K., Zheng, P., Adolph, C., Albright, J., Antony, C. M., Aravkin, A. Y., Bachmeier, S. D., Bang-Jensen, B., Bannick, M. S., Bloom, S., Carter, A., Castro, E., Causey, K., Chakrabarti, S., Charlson, F. J., Cogen, R. M., Combs, E., Dai, X., Dangel, W. J., Earl, L., Ewald, S. B., Ezalarab, M., Ferrari, A. J., Flaxman, A., Frostad, J. J., Fullman, N., Gakidou, E., Gallagher, J., Glenn, S. D., Goosmann, E. A., He, J., Henry, N. J., Hulland, E. N., Hurst, B., Johanns, C., Kendrick, P. J., Khemani, A., Larson, S. L., Lazzar-Atwood, A., LeGrand, K. E., Lescinsky, H., Lindstrom, A., Linebarger, E., Lozano, R., Ma, R., Månsson, J., Magistro, B., Mantilla H., Ana M., Marczak, L. B., Miller-Petrie, M. K., Mokdad, A. H., Morgan, J. D., Naik, P., Odell, C. M., O’Halloran, J. K., Osgood-Zimmerman, A. E., Ostroff, S. M., Pasovic, M., Penberthy, L., Phipps, G., Pigott, D. M., Pollock, I., Ramshaw, R. E., Redford, S. B., Reinke, G., Rolfe, S., Santomauro, D. F., Shackleton, J. R., Shaw, D. H., Sheena, B. S., Sholokhov, A., Sorensen, R. J. D., Sparks, G., Spurlock, E. E., Subart, M. L., Syailendrawati, R., Torre, A. E., Troeger, C. E., Vos, T., Watson, A., Watson, S., Wiens, K. E., Woyczynski, L., Xu, L., Zhang, J., Hay, S. I., Lim, S. S., Murray, C. J. L., & IHME COVID-19 Forecasting Team. Modeling covid-19 scenarios for the United States. Nat. Med. 27(1), 94–105 (2021).
IHME COVID-19 health service utilization forecasting team and Christopher JL Murray. Forecasting covid-19 impact on hospital bed-days, icu-days, ventilator-days and deaths by us state in the next 4 months. medRxiv (2020).
Friedman, J., Liu, P. & Gakidou, E. Predictive performance of international covid-19 mortality forecasting models. medRxiv (2020).
Petropoulos, F., Makridakis, S., & Stylianou, N. Covid-19: Forecasting confirmed cases and deaths with a simple time series model. Int. J. Forecast. 38(2), 439-452 (2022).
Silva, P. C. L. et al. Covid-abs: An agent-based model of covid-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos Solitons Fractals 139, 110088–110088 (2020).
Hoertel, N. et al. A stochastic agent-based model of the sars-cov-2 epidemic in france. Nat. Med. 26(9), 1417–1421 (2020).
Gaudou, B. et al. Comokit: A modeling kit to understand, analyze, and compare the impacts of mitigation policies against the covid-19 epidemic at the scale of a city. Front. Public Health 8, 587 (2020).
Wallentin, G., Kaziyeva, D. & Reibersdorfer-Adelsberger, E. Covid-19 intervention scenarios for a long-term disease management. Int. J. Health Policy Manag. 9(12), 508–516 (2020).
Maziarz, M. & Zach, M. Agent-based modelling for sars-cov-2 epidemic prediction and intervention assessment: A methodological appraisal. J. Eval. Clin. Pract. 26(5), 1352–1360 (2020).
Kerr, C. C. et al. Covasim: An agent-based model of covid-19 dynamics and interventions. PLOS Comput. Biol. 17(7), 1–32 (2021).
Cuevas, E. An agent-based model to evaluate the covid-19 transmission risks in facilities. Comput. Biol. Med. 121, 103827–103827 (2020).
Vivek Shastry, D., Cale Reeves, N. W. & Rai, V. Policy and behavioral response to shock events: An agent-based model of the effectiveness and equity of policy design features. PLOS ONE 17(1), 1–21 (2022).
Reguly, I. Z. et al. Microsimulation based quantitative analysis of covid-19 management strategies. PLOS Comput. Biol. 18(1), 1–14 (2022).
Ozik, J., Wozniak, J. M., Collier, N., Macal, C. M. & Binois, M. A population data-driven workflow for covid-19 modeling and learning. Int. J. High Perform. Comput. Appl. 35(5), 483–499 (2021).
Lee, B., Lee, M., Mogk, J., Goldstein, R., Bibliowicz, J., Brudy, F., & Tessier, A. Designing a multi-agent occupant simulation system to support facility planning and analysis for covid-19. In Designing Interactive Systems Conference 2021, DIS ’21, 15–30 (Association for Computing Machinery, New York, NY, USA, 2021).
Balachandar, S., Zaleski, S., Soldati, A., Ahmadi, G. & Bourouiba, L. Host-to-host airborne transmission as a multiphase flow problem for science-based social distance guidelines. Int. J. Multiphase Flow 132, 103439 (2020).
Chaudhuri, S., Basu, S. & Saha, A. Analyzing the dominant sars-cov-2 transmission routes toward an ab initio disease spread model. Phys. Fluids (Woodbury, N.Y. : 1994) 32(12), 123306–123306 (2020).
Busco, G., Yang, S. R., Seo, J. & Hassan, Y. A. Sneezing and asymptomatic virus transmission. Phys. Fluids (Woodbury, N.Y. : 1994) 32(7), 073309–073309 (2020).
Dbouk, T. & Drikakis, D. On coughing and airborne droplet transmission to humans. Phys. Fluids (Woodbury, N.Y. : 1994) 32(5), 053310–053310 (2020).
Mittal, R., Meneveau, C. & Wen, W. A mathematical framework for estimating risk of airborne transmission of covid-19 with application to face mask use and social distancing. Phys. Fluids (Woodbury, N.Y. : 1994) 32(10), 101903–101903 (2020).
Nishiura, H. et al. Estimation of the asymptomatic ratio of novel coronavirus infections (covid-19). Int. J. Infect. Dis. IJID Off. Publ. Int. Soc. Infect. Dis. 94, 154–155 (2020).
Oran, D. P. & Topol, E. J. Prevalence of asymptomatic sars-cov-2 infection: A narrative review. Ann. Internal Med. 173(5), 362–367 (2020).
The proportion of sars-cov-2 infections that are asymptomatic. Ann. Internal Med. 174(5), 655-662, (2021).
Bi, Q. et al. Epidemiology and transmission of covid-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: A retrospective cohort study. Lancet Infect. Dis. 20(8), 911–919 (2020).
Davies, N. G. et al. Effects of non-pharmaceutical interventions on covid-19 cases, deaths, and demand for hospital services in the UK: A modelling study. Lancet Public Health 5(7), e375–e385 (2020).
Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., Pearson, C. A. B., Quilty, B. J., Kucharski, A. J., Gibbs, H., Clifford, S., Gimma, A., van Zandvoort, K., Munday, J. D., Diamond, C., Edmunds, W. J., Houben, R. M. G. J., Hellewell, J., Russell, T. W., Abbott, S., Funk, B., Nikos I., Sun, Y. F., Flasche, S., Rosello, A., Jarvis, C. I., Eggo, R. M. & CMMID COVID-19 working group. Age-dependent effects in the transmission and control of covid-19 epidemics. Na. Med.26(8), 1205–1211 (2020).
Zhai, P. et al. The epidemiology, diagnosis and treatment of covid-19. Int. J. Antimicrob. Agents 55(5), 105955 (2020).
Adam, D. C. et al. Clustering and superspreading potential of sars-cov-2 infections in Hong Kong. Nat. Med. 26(11), 1714–1719 (2020).
Sugawara, H. On the effectiveness of the search and find method to suppress spread of sars-cov-2. Proc. Jpn. Acad. Ser. B 97(1), 22–49 (2021).
Shewmaker, P., Chrysanthopoulou, S. A., Iskandar, R. , Lake, D. & Jutkowitz, E. Microsimulation model calibration with approximate bayesian computation in r: A tutorial. Med. Decis. Mak. 42(5), 557-570 (2022).
