ArticleBMC infectious diseases2020
The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.
Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An approach to forecast impact of Covid-19 using supervised machine learning model.Software: practice & experience · 2022Article
- Antiviral drugs suppress infection of 2019-nCoV spike pseudotyped virus by interacting with ACE2 protein.Journal of biochemical and molecular toxicology · 2022Article
- Prediction of global spread of COVID-19 pandemic: a review and research challenges.Artificial intelligence review · 2022Article
- Trend Analysis and Predictions of Coronavirus Disease 2019 in Ethiopia.Journal of research in health sciences · 2021Article
- Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.Journal of medical systems · 2021Review
- Non-pharmaceutical interventions during the COVID-19 pandemic: A review.Physics reports · 2021Review
- Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models.Iranian journal of public health · 2020Article
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6 authors.
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Abstract
backgroundSince pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China.
methodsData from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model.
resultsThe optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above.
conclusionThe end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.
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