2021
DOI: 10.31579/2690-1897/021
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Mathematical Modeling and Epidemic Prediction of Covid-19 and Its Significance to Epidemic Prevention and Control Measures

Abstract: Background: Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainlan… Show more

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Cited by 5 publications
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“… Insightful Policy Guidance:  Ethical Considerations: Time series analysis in public health research raises ethical considerations related to data privacy, informed consent, and potential biases in data collection and It is imperative to develop statistical techniques with high forecasting accuracy and reliability for analyzing and predicting the prevalence and mortality time series of infectious diseases [13].  Unpredictability of Rare Events: Time series analysis may struggle to accurately predict rare events, such as novel disease outbreaks or large-scale pandemics, due to the lack of historical data for such events, the most recent example being the COVID 19 pandemic [14].  Accounting for confounding variables: Propensity score matching helps control for confounding variables, reducing the potential bias in estimating the effects of interventions.…”
mentioning
confidence: 99%
“… Insightful Policy Guidance:  Ethical Considerations: Time series analysis in public health research raises ethical considerations related to data privacy, informed consent, and potential biases in data collection and It is imperative to develop statistical techniques with high forecasting accuracy and reliability for analyzing and predicting the prevalence and mortality time series of infectious diseases [13].  Unpredictability of Rare Events: Time series analysis may struggle to accurately predict rare events, such as novel disease outbreaks or large-scale pandemics, due to the lack of historical data for such events, the most recent example being the COVID 19 pandemic [14].  Accounting for confounding variables: Propensity score matching helps control for confounding variables, reducing the potential bias in estimating the effects of interventions.…”
mentioning
confidence: 99%