Nonlinear Time Series Models to Analysis and Predicting COVID- 19 Cases in Holy Kerbala- Iraq

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JassimNasirHussain Mohammed Saleh Hashem

Abstract

The emerging coronavirus (Covid-19) represents the last detected strain of the coronavirus.
This virus appeared for the first time in the Chinese city of Wuhan in December / 2019. While in Iraq,
it appeared for the first time on February 24, 2020. The Covid-19 virus is a global health problem that
includes all the parts of the world without any exception, especially in Iraq and the governorate of holy
Kerbala. The number of infected people from the beginning of the pandemic until the date of (5-8-
2020) reached about (1108558) injuries and about (15741) deaths. This represents a very high rate, and
it certainly represents a major economic, social, and health problem.
This instigated our interest in researching and studying this phenomenon, as well as predicting
the number of infected people in the future. So, this study aims to predict the numbers of people
infected with the Covid-19 virus in the holy city of Kerbala via utilizing the nonlinear time series
models. In addition to choosing the best prediction model by utilizing some statistical criteria such as
(AIC, BIC, H-Q).
Data for the numbers of people infected by the Covid-19 virus in the governorate of holy
Kerbala were obtained from the official website of the Iraqi Ministry of Health for the period from (1-
6-2020) to (30-9-2020).
Three nonlinear models have been utilized in order to predict this series (Exponential model,
Logistic model, Gompertz model). Furthermore, the statistical criteria were utilized in order to
compare these models and choose the best model that represents these data.
The results showed that the logistic model is the best model representing COVID-19 data, which
gives the lowest values for all three criteria. Then the Gompertz model and the exponential model are
coming after it.

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