Blog
About

3
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time

      Scientific Reports

      Nature Publishing Group UK

      Diseases, Health care, Medical research, Risk factors

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The coronavirus disease 2019 (COVID-19) has now spread throughout most countries in the world causing heavy life losses and damaging social-economic impacts. Following a stochastic point process modelling approach, a Monte Carlo simulation model was developed to represent the COVID-19 spread dynamics. First, we examined various expected performances (theoretical properties) of the simulation model assuming a number of arbitrarily defined scenarios. Simulation studies were then performed on the real COVID-19 data reported (over the period of 1 March to 1 May) for Australia and United Kingdom (UK). Given the initial number of COVID-19 infection active cases were around 10 for both countries, the model estimated that the number of active cases would peak around 29 March in Australia (≈ 1,700 cases) and around 22 April in UK (≈ 22,860 cases); ultimately the total confirmed cases could sum to 6,790 for Australia in about 75 days and 206,480 for UK in about 105 days. The results of the estimated COVID-19 reproduction numbers were consistent with what was reported in the literature. This simulation model was considered an effective and adaptable decision making/what-if analysis tool in battling COVID-19 in the immediate need, and for modelling any other infectious diseases in the future.

          Related collections

          Most cited references 3

          • Record: found
          • Abstract: found
          • Article: not found

          Modelling microbial infection to address global health challenges

          The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Temporal dynamics in viral shedding and transmissibility of COVID-19

             X HE,  EHY Lau,  P Wu (2020)
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study

               J Zhang (2019)
                Bookmark

                Author and article information

                Contributors
                gxie@csu.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 August 2020
                4 August 2020
                2020
                : 10
                Affiliations
                ISNI 0000 0004 0368 0777, GRID grid.1037.5, Research Office, , Charles Sturt University, ; Wagga Wagga, NSW Australia
                Article
                70091
                10.1038/s41598-020-70091-1
                7403316
                32753639
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized

                risk factors, medical research, health care, diseases

                Comments

                Comment on this article