Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China

Abstract

Background. The epidemic outbreak cased by coronavirus 2019-nCoV is of great interest to researches because of the high rate of spread of the infection and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the duration of the epidemic, the expected number of patients and deaths. For long time predictions, the complicated mathematical models are necessary which need many efforts for unknown parameters identification and calculations. In this article, some preliminary estimates will be presented.Objective. Since the reliable long time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the most reliable dependences for victim numbers, infected and removed persons versus time.Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988-1989. Results.The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted.Conclusions. Simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus 2019-nCoV in mainland China. The further research should focus on All rights reserved. No reuse allowed without permission. perpetuity. : medRxiv preprint updating the predictions with the use of fresh data and using more complicated mathematical models.

Mathematical Predictions for Covid-19 as a Global Pandemic

“…A recent study by Nesteruk (2020) and Ming and Zhang (2020) focuses on the epidemic outbreak cased by COVID-19 coronavirus due to the global trend of the pandemic with its origin from mainland China. In his study, Nesteruk (2020) used the popular SIR (Susceptible-Infectious-Removed) model to obtain optimal values for the model parameters with the use of statistical approach and hence predicated the number of infected, susceptible and removed persons versus time. This model approach by Nesteruk (2020) has been a major breakthrough in modelling disease control as used by several authors (Ming andZhang, 2020 andKimbir (2018) among others).…”

“…In his study, Nesteruk (2020) used the popular SIR (Susceptible-Infectious-Removed) model to obtain optimal values for the model parameters with the use of statistical approach and hence predicated the number of infected, susceptible and removed persons versus time. This model approach by Nesteruk (2020) has been a major breakthrough in modelling disease control as used by several authors (Ming andZhang, 2020 andKimbir (2018) among others). However, although there exist a global interest in knowing the rate of infection that will occur over time globally, it is of great interest to propose a mathematical model for the end in the spread and subsequent elimination of the virus.…”

Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China

“…Since the optimal curve was obtained only with the use of Wj and the difference between Wj and Vj is very big (e.g., it was 12 289 persons on February 12, 2020), the predictions shown in Fig. 2 and reported in [11] are no longer relevant. To have better predictions, it is necessary to have exact Qjdata for the period before February 12.…”

Estimation of the probability of reinfection with COVID-19 coronavirus by the SEIRUS model

“…A recent study by Victor (2020), Nesteruk (2020) and Ming and Zhang (2020) focuses on the epidemic outbreak cased by COVID-19 coronavirus due to the global trend of the pandemic with its origin from mainland China. In his study, Nesteruk (2020) used the popular SIR (Susceptible-Infectious-Removed) model to obtain optimal values for the model parameters with the use of statistical approach and hence predicated the number of infected, susceptible and removed persons versus time. This model approach by Nesteruk (2020) has been a major breakthrough in modelling disease control as used by several authors (Ming and Zhang, (2020) and Victor (2020) among others).…”

“…In his study, Nesteruk (2020) used the popular SIR (Susceptible-Infectious-Removed) model to obtain optimal values for the model parameters with the use of statistical approach and hence predicated the number of infected, susceptible and removed persons versus time. This model approach by Nesteruk (2020) has been a major breakthrough in modelling disease control as used by several authors (Ming and Zhang, (2020) and Victor (2020) among others). However, although there exist a global interest in knowing the rate of infection that will occur over time globally, in this study we adopt solutions from Victor (2020) and Victor and Oduwole (2020) for a new deterministic endemic model (Susceptible -Exposed -Infectious -Removed -Undetectable -Susceptible: SEIRUS) originally developed for the control of the prevalence of HIV/AIDS in Africa.…”