Decision-making algorithm for determining predictive model to assess the impact of the COVID-19 pandemic on the onco-epidemiological process in Ukraine
Michailovich Yu., Gorokh Ye., Ryzhov A.
Summary. Previsioning the potential outcomes of implementing health programs for COVID-19 control requires thorough study the epidemic process characteristics of this disease, both overall and over specific time periods, followed by the construction of an optimal predictive model. COVID-19 is a new disease, and there is no exact epidemiological profile for it in the world. The spread of the infectious disease COVID-19 cannot be studied experimentally in Ukraine, so the use of mathematical and simulation models will definitely be able to provide a real alternative for studying impact by the COVID-19 pandemic on the onco-epidemiological situation. For this purpose, the search for best predictive models was conducted. We developed multi-agent models of epidemic processes in developed countries, as well as analysed studies simulating the spread of the COVID-19 pandemic for different government strategies, regimes and approaches. This allow will assess the government-introduced restrictions and determine their impact on the onco-epidemiological situation in Ukraine. Materials and methods. The literature search was carried out by analysing the databases: Web of Science, PubMed, Embase, Cochrane Library, National Comprehensive Cancer Network (NCCN), China National Knowledge Infrastructure, Wanfang, and the National Cancer Registry of Ukraine. The meta-analysis included international retrospective cohort studies.
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