{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:55:51Z","timestamp":1760237751401,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand\/model potential second waves of contagions.<\/jats:p>","DOI":"10.3390\/systems8020021","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T11:00:56Z","timestamp":1592478056000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Effectiveness of the Early Response to COVID-19: Data Analysis and Modelling"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9980-5268","authenticated-orcid":false,"given":"Edoardo","family":"Bertone","sequence":"first","affiliation":[{"name":"School of Engineering and Built Environment, Griffith University, Southport, QLD 4222, Australia"},{"name":"Cities Research Institute, Griffith University, Southport, QLD 4222, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0580-6375","authenticated-orcid":false,"given":"Martin Jason","family":"Luna Juncal","sequence":"additional","affiliation":[{"name":"School of Engineering and Built Environment, Griffith University, Southport, QLD 4222, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafaela Keiko","family":"Prado Umeno","sequence":"additional","affiliation":[{"name":"KODE Consulting, Parkwood, QLD 4214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas Alves","family":"Peixoto","sequence":"additional","affiliation":[{"name":"KODE Consulting, Parkwood, QLD 4214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khoi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Cities Research Institute, Griffith University, Southport, QLD 4222, Australia"},{"name":"KODE Consulting, Parkwood, QLD 4214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1914-5379","authenticated-orcid":false,"given":"Oz","family":"Sahin","sequence":"additional","affiliation":[{"name":"Cities Research Institute, Griffith University, Southport, QLD 4222, Australia"},{"name":"KODE Consulting, Parkwood, QLD 4214, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1056\/NEJMp2003762","article-title":"Responding to Covid-19\u2014A once-in-a-century pandemic?","volume":"382","author":"Gates","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1056\/NEJMc2001899","article-title":"Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China","volume":"382","author":"Hoehl","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C.S., and Ho, R.C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17051729"},{"key":"ref_4","first-page":"115","article-title":"The COVID-19 Pandemic: Government vs. Community Action across the United States","volume":"7","author":"Brzezinski","year":"2020","journal-title":"Covid Econ. Vetted Real-Time P."},{"key":"ref_5","unstructured":"Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World, The McGraw-Hill Companies."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.jclepro.2016.06.158","article-title":"Extreme events, water quality and health: A participatory Bayesian risk assessment tool for managers of reservoirs","volume":"135","author":"Bertone","year":"2016","journal-title":"J. Clean. Prod."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bertone, E., Sahin, O., Stewart, R.A., Zou, P.X., Alam, M., Hampson, K., and Blair, E. (2018). Role of financial mechanisms for accelerating the rate of water and energy efficiency retrofits in Australian public buildings: Hybrid Bayesian Network and System Dynamics modelling approach. Appl. Energy, 210.","DOI":"10.1016\/j.apenergy.2017.08.054"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sahin, O., Suprun, E., Richards, R., Salim, H., MacAskill, S., Heilgeist, S., Stewart, R., Rutherford, S., and Beal, C. (2020). Navigating the Wicked Complexity of the COVID-19 Pandemic. Syst. Commun., accepted for publication.","DOI":"10.3390\/systems8020020"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fenton, N., and Neil, M. (2013). Risk Assessment and Decision Analysis with Bayesian Networks, CRC Press.","DOI":"10.1201\/9780367803018"},{"key":"ref_10","unstructured":"Hale, T., Webster, S., Petherick, A., Phillips, T., and Kira, B. (2020, June 05). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government, Available online: https:\/\/www.bsg.ox.ac.uk\/research\/research-projects\/coronavirus-government-response-tracker."},{"key":"ref_11","unstructured":"Hale, T., Petherick, A., Kira, B., Angrist, N., and Phillips, T. (2020). Variation in Government Responses to COVID-19, Blavatnik School of Government."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wilson, N., Kvalsvig, A., Barnard, L.T., and Baker, M. (2020). Estimating the Case Fatality Risk of COVID-19 using Cases from Outside China. medRxiv.","DOI":"10.1101\/2020.02.15.20023499"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bendavid, E., Mulaney, B., Sood, N., Shah, S., Ling, E., Bromley-Dulfano, R., Lai, C., Weissberg, Z., Saavedra, R., and Tedrow, J. (2020). COVID-19 Antibody Seroprevalence in Santa Clara County, California. medRxiv.","DOI":"10.1101\/2020.04.14.20062463"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Volpato, S., Landi, F., and Incalzi, R.A. (2020). Frail Health Care System for an Old Population: Lesson form the COVID-19 Outbreak in Italy. J. Gerontol. Ser. A.","DOI":"10.1093\/gerona\/glaa087"},{"key":"ref_15","unstructured":"Pueyo, T. (2020, May 05). Coronavirus: The Hammer and the Dance. Available online: https:\/\/medium.com\/@tomaspueyo\/coronavirus-the-hammer-and-the-dance-be9337092b56."},{"key":"ref_16","unstructured":"Hensvik, L., and Skans, O. (2020). COVID-19 Crisis Response Monitoring, IZA Institute of Labor Economics."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Tang, K., Feng, K., and Lv, W. (2020). High temperature and high humidity reduce the transmission of COVID-19. SSRN.","DOI":"10.2139\/ssrn.3551767"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rockl\u00f6v, J., and Sj\u00f6din, H. (2020). High population densities catalyse the spread of COVID-19. J. Travel Med., 27.","DOI":"10.1093\/jtm\/taaa038"},{"key":"ref_19","unstructured":"YouGov (2020, June 05). YouGov International COVID-19 Tracker, Available online: https:\/\/yougov.co.uk\/topics\/international\/articles-reports\/2020\/03\/17\/YouGov-international-COVID-19-tracker."},{"key":"ref_20","unstructured":"Acaps (2020, June 05). #COVID19 Government Measures Dataset, Available online: https:\/\/www.acaps.org\/covid19-government-measures-dataset."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1098\/rstl.1763.0053","article-title":"An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S","volume":"53","author":"Bayes","year":"1763","journal-title":"Philos. Trans."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.envsoft.2012.03.012","article-title":"Good practice in Bayesian network modelling","volume":"37","author":"Chen","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.ecolmodel.2006.11.033","article-title":"Advantages and challenges of Bayesian networks in environmental modelling","volume":"203","author":"Uusitalo","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"364","DOI":"10.5455\/aim.2016.24.364-369","article-title":"Applying naive bayesian networks to disease prediction: A systematic review","volume":"24","author":"Langarizadeh","year":"2016","journal-title":"Acta Inform. Med."},{"key":"ref_25","unstructured":"Solares, C., and Sanz, A.M. (2007, January 16\u201319). Different Bayesian network models in the classification of remote sensing images. Proceedings of the Intelligent Data Engineering and Automated Learning-IDEAL 2007, Birmingham, UK."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_27","unstructured":"Norsys (2020, March 06). Netica APIs (Application Programmer Interfaces). Available online: https:\/\/www.norsys.com\/netica_api.html."},{"key":"ref_28","unstructured":"Neapolitan, R. (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/8\/2\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:32Z","timestamp":1760175632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/8\/2\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["systems8020021"],"URL":"https:\/\/doi.org\/10.3390\/systems8020021","relation":{},"ISSN":["2079-8954"],"issn-type":[{"type":"electronic","value":"2079-8954"}],"subject":[],"published":{"date-parts":[[2020,6,18]]}}}