References
- Anastassopoulou, C., Russo, L., Tsakris, A., & Siettos, C. (2020). Data-based analysis, modelling and forecasting of the covid-19 outbreak. PloS One, 15(3), e0230405. https://doi.org/https://doi.org/10.1371/journal.pone.0230405
- Arabnejad, H., Groen, D., & Mahmood, I. (2020). Fabcovid19: A fabsim3 plugin for flu and coronavirus simulator (flacs). https://github.com/djgroen/FabCovid19. GitHub.
- Bagni, R., Berchi, R., & Cariello, P. (2002). A comparison of simulation models applied to epidemics. Journal of Artificial Societies and Social Simulation, 5(3). http://jasss.soc.surrey.ac.uk/5/3/5.html
- Balcan, D., Gonc¸alves, B., Hu, H., Ramasco, J. J., Colizza, V., & Vespignani, A. (2010). Modeling the spatial spread of infectious diseases: The global epidemic and mobility computational model. Journal of Computational Science, 1(3), 132–145. https://doi.org/https://doi.org/10.1016/j.jocs.2010.07.002
- Bar-Yam, Y. (2002). General features of complex systems. Encyclopedia of Life Support Systems (EOLSS), UNESCO, EOLSS Publishers.
- Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., Wei, L., Truelove, S. A., Zhang, T., Gao, W., Cheng, C., Tang, X., Wu, X., Wu, Y., Sun, B., Huang, S., Sun, Y., Zhang, J., Ma, T., Feng, T., & Liu, X. (2020). Epidemiology and transmission of covid-19 in 391 cases and 1286 of their close contacts in shenzhen, china: A retrospective cohort study. The Lancet Infectious Diseases, 20(3), 911–919. https://doi.org/https://doi.org/10.1016/S1473-3099(20)30287-5
- Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280–7287. https://doi.org/https://doi.org/10.1073/pnas.082080899
- Bordehore, C., Navarro, M., Herrador, Z., & Fonfria, E. S. (2020). Understanding covid-19 spreading through simulation modeling and scenarios comparison: Preliminary results. medRxiv.
- Candido, P. (2020). Agent based simulation of covid-19 health and economical effects. GitHub. https://github.com/petroniocandido
- Chan, M., Yeung, & Xu, R.-H. (2003). Sars: Epidemiology. Respirology, 8(s1), S9–S14. https://doi.org/https://doi.org/10.1046/j.1440-1843.2003.00518.x
- Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M., & Prokopenko, M. (2020). Modelling transmission and control of the covid-19 pandemic in australia. arXiv preprint arXiv:2003.10218.
- Chen, T.-M., Rui, J., Wang, Q.-P., Zhao, Z.-Y., Cui, J.-A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious Diseases of Poverty, 9(1), 1–8. https://doi.org/https://doi.org/10.1186/s40249-020-00640-3
- Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore Y Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini, I. M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science, 368(6489), 395–400. https://doi.org/https://doi.org/10.1126/science.aba9757
- CSSE, J. (2020, February). Covid-19 dashboard by the center for systems science and engineering (csse) at johns hopkins university. JHU. https://gisanddata.maps .arcgis.com/apps/opsdashboard/index.html
- Dandekar, R., & Barbastathis, G. (2020). Quantifying the effect of quarantine control in covid-19 infectious spread using machine learning. medRxiv. https:// www.medrxiv.org/content/early/2020/04/06/2020.04.03.20052084
- ECDC. (2020, June). Covid-19 situation update worldwide, as of 7 june 2020. https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
- Ferguson, N., Laydon, D., Nedjati Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., & Cuomo- Dannenburg, G. (2020). Report 9: Impact of non-pharmaceutical interventions (npis) to reduce covid19 mortality and healthcare demand. The Royal Society Medical Research Council (MRC).
- Forrester, J. W. (1994). System dynamics, systems thinking, and soft or. System Dynamics Review, 10(2–3), 245–256. https://doi.org/https://doi.org/10.1002/sdr.4260100211
- Groen, D., & Arabnejad, H. (2014). Fabsim3. GitHub. https://github.com/djgroen/FabSim3
- Groen, D., & Arabnejad, H. (2015). Flee. GitHub. https://github.com/djgroen/flee
- Groen, D., Bhati, A. P., Suter, J., Hetherington, J., Zasada, S. J., & Coveney, P. V. (2016). Fabsim: Facilitating computational research through automation on large-scale and distributed e-infrastructures. Computer Physics Communications, 207(Suppl. C), 375–385. https://doi.org/https://doi.org/10.1016/j.cpc.2016.05.020
- Groen, D., Mahmood, I., & Arabnejad, H. (2020). Flacs: Flu and coronavirus simulator. GitHub. https://github.com/djgroen/flacs
- Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., Kucharski, A. J., Edmunds, W. J., Funk, S., Eggo, R. M., Sun, F., Flasche, S., Quilty, B. J., Davies, N., Liu, Y., Clifford, S., Klepac, P., Jit, M., Diamond, C., van Zandvoort, K., & Munday, J. D. (2020). Feasibility of controlling covid-19 outbreaks by isolation of cases and contacts. The Lancet Global Health, 8(4), e488–e496. https://doi.org/https://doi.org/10.1016/S2214-109X(20)30074-7
- Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Gao, H., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in wuhan, china. The Lancet, 395(10223), 497–506. https://doi.org/https://doi.org/10.1016/S0140-6736(20)30183-5
- Jacobson, S. H., Hall, S. N., & Swisher, J. R. (2006). Discrete-event simulation of health care systems. In Sheldon H. J., Shane N. H., James R. S. (Eds.), Patient flow: Reducing delay in healthcare delivery (pp. 211–252). Springer.
- Klepac, P., Kucharski, A. J., Conlan, A. J., Kissler, S., Tang, M., Fry, H., & Gog, J. R. (2020). Contacts in context: Large-scale setting-specific social mixing matrices from the bbc pandemic project. medRxiv. https://www.medrxiv.org/content/ early/2020/03/05/2020.02.16.20023754
- Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Sun, F., Jit, M., Munday, J. D., Davies, N., Gimma, A., van Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C. I., Clifford, S., Quilty, B. J., Bosse, N. I., Abbott, S., Flasche, S., & Eggo, R. M. (2020). Early dynamics of transmission and control of covid-19: A mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553–558. https://doi.org/https://doi.org/10.1016/S1473-3099(20)30144-4
- Li, M., Yu, W., Tian, W., Ge, Y., Liu, Y., Ding, T., & Zhang, L. (2019). system dynamics modeling of public health services provided by china cdc to control infectious and endemic diseases in china. Infection and Drug Resistance, 12, 613–625. https://doi.org/https://doi.org/10.2147/IDR.S185177
- Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., Xing, X., Xiang, N., Wu, Y., Li, C., Chen, Q., Li, D., Liu, T., Zhao, J., Liu, M., Tu, W., Feng, Z., & Ren, R. (2020). Early transmission dynamics in wuhan, china, of novel coronavirus–infected pneumonia. New England Journal of Medicine, 382(13), 1199–1207. https://doi.org/https://doi.org/10.1056/NEJMoa2001316
- Li, W., Zhang, B., Lu, J., Liu, S., Chang, Z., Peng, C., Liu, X., Zhang, P., Ling, Y., Tao, K., & Chen, J. (2020, April). Characteristics of Household Transmission of COVID-19. Clinical Infectious Diseases, 382, 1199–1207. https://doi.org/https://doi.org/10.1093/cid/ciaa450
- Liu, T., Hu, J., Kang, M., Lin, L., Zhong, H., Xiao, J., He, G., Song, T., Huang, Q., Rong, Z. & Deng, A. (2020). Transmission dynamics of 2019 novel coronavirus (2019-ncov). Cold Spring Harbor Laboratory.
- Lorch, L., Trouleau, W., Tsirtsis, S., Szanto, A., Schölkopf, B., & Gomez-Rodriguez, M. (2020). A spatiotemporal epidemic model to quantify the effects of contact tracing, testing, and containment. arXiv preprint arXiv:2004.07641.
- Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modeling and simulation. Journal of Simulation, 4(3), 151–162. https://doi.org/https://doi.org/10.1057/jos.2010.3
- Mahmood, I., Anagnostou, A., Taylor, S., Bell, D., & Groen, D. (2020, June). Facs framework stress report. Brunel University London. Retrieved from https://brunel.figshare .com/articles/online resource/FACS Framework STRESS Report pdf/12520763/1
- Mahmood, I., Jahan, M., Groen, D., Javed, A., & Shafait, F. (2020). An agent-based simulation of the spread of dengue fever. In International conference on computational science. Brunel University London.
- Miksch, F., Urach, C., Einzinger, P., & Zauner, G. (2014). A flexible agent-based framework for infectious disease modeling. In M. S. Linawati, E. J. Mahendra, A. M. T. Neuhold, & I. You (Eds.), Information and communication technology (pp. 36–45). Springer Berlin Heidelberg.
- Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: An introduction to computational models of social life. Princeton university press.
- Mizumoto, K., & Chowell, G. (2020). Transmission potential of the novel coronavirus (covid- 19) onboard the diamond princess cruises ship, 2020. Infectious Disease Modelling, 5, 264–270. https://doi.org/https://doi.org/10.1016/j.idm.2020.02.003
- Moghadas, S. M., Shoukat, A., Espindola, A. L., Pereira, R. S., Abdirizak, F., Laskowski, M., Viboud, C., & Chowell, G. (2017). Asymptomatic transmission and the dynamics of zika infection. Scientific Reports, 7(1), 1–8. https://doi.org/https://doi.org/10.1038/s41598-017-05013-9
- Monks, T., Currie, C. S. M., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. E. (2019). Strengthening the reporting of empirical simulation studies: Introducing the stress guide- lines. Journal of Simulation, 13(1), 55–67. https://doi.org/https://doi.org/10.1080/17477778.2018.1442155
- Murray, C. J. (2020). Forecasting the impact of the first wave of the covid-19 pandemic on hospital demand and deaths for the USA and European Economic Area Countries. medRxiv.
- NCID. (2020). Position statement from the national centre for infectious diseases and the chapter of infectious disease physicians, academy of medicine, singapore. National Center for Infectious Diseases. https://www.ncid .sg/Documents/Period%20of%20Infectivity%20Position%20Statementv2.pdf.
- O’Neill, P. D., Balding, D. J., Becker, N. G., Eerola, M., & Mollison, D. (2000). Analyses of infectious disease data from household outbreaks by markov chain monte carlo methods. Journal of the Royal Statistical Society: Series C, Applied Statistics, 49(4), 517–542. https://doi.org/https://doi.org/10.1111/1467-9876.00210
- Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of covid-19 in china by dynamical modeling. arXiv preprint arXiv:2002.06563.
- Perumalla, K. (2020). Exacorona. https://github.com/perumallaks/ExaCorona. GitHub.
- Prompetchara, E., Ketloy, C., & Palaga, T. (2020). Immune responses in covid-19 and potential vaccines: Lessons learned from sars and mers epidemic. Asian Pacific Journal Of Allergy And Immunology / launched by the Allergy and Immunology Society of Thailand, 38(1), 1–9. https://doi.org/https://doi.org/10.12932/AP-200220-0772
- Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction. Princeton university press.
- Suleimenova, D., Bell, D., & Groen, D. (2017). A generalized simulation development approach for predicting refugee destinations. Scientific Reports, 7(1), 1–13. https://doi.org/https://doi.org/10.1038/s41598-017-13828-9
- Taylor, S. (2014). Agent-based modeling and simulation. Springer.
- Tuomisto, J. T., Yrjölä, J., Kolehmainen, M., Bonsdorff, J., Pekkanen, J., & Tikkanen, T. (2020). An agent-based epidemic model reina for covid-19 to identify destructive policies. medRxiv. https://www.medrxiv.org/content/early/2020/04/ 17/2020.04.09.20047498
- Venkatramanan, S., Lewis, B., Chen, J., Higdon, D., Vullikanti, A., & Marathe, M. (2018). Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics, 22, 43–49. https://doi.org/https://doi.org/10.1016/j.epidem.2017.02.010
- Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., Fu, H., Walker, P. G. T., Fu, H., Dighe, A., Griffin, J. T., Baguelin, M., Bhatia, S., Boonyasiri, A., Cori, A., Cucunubá, Z., FitzJohn, R., Gaythorpe, K., Green, W., Ferguson, N. M., & Cuomo-Dannenburg, G. (2020). Estimates of the severity of coronavirus disease 2019: A model-based analysis. The Lancet Infectious Diseases, 20(6), 669–677. https://doi.org/https://doi.org/10.1016/S1473-3099(20)30243-7
- Volz, E., & Meyers, L. A. (2007). Susceptible–infected–recovered epidemics in dynamic contact networks. Proceedings of the Royal Society B: Biological Sciences, 274(1628), 2925–2934. https://doi.org/https://doi.org/10.1098/rspb.2007.1159
- Vynnycky, E., & White, R. (2010). An introduction to infectious disease modelling. OUP oxford.
- WHO. (2020, February). Rolling updates on coronavirus disease (covid-19). World Health Organization. Retrieved from https://www.who.int/emergencies/diseases/novel -coronavirus-2019/events-as-they-happen
- Wynants, L., Van Calster, B., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D. & Schuit, E. (2020). Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. BMJ, 369. https://www.bmj.com/ content/369/bmj.m1328
- Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., Cao, B., & Xiang, J. (2020). Clinical course and risk factors for mortality of adult inpatients with covid-19 in wuhan, china: A retrospective cohort study. The Lancet, 395(10229), 1054–1062. https://doi.org/https://doi.org/10.1016/S0140-6736(20)30566-3