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Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

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TLDR
This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data, exhibiting that the number of the cumulative 2019- nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives occurring in late February to early March.
Abstract
The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.

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Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.

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Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

TL;DR: A data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up is proposed and the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
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A fractional-order model for the novel coronavirus (COVID-19) outbreak.

TL;DR: This paper proposes fractional-order susceptible individuals, asymptomatic infected, symptomaticinfected, recovered, and deceased (SEIRD) model for the spread of COVID-19, and shows that the fractional model provides a closer forecast to the real data.
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