Comparison of Active COVID-19 Cases per Population Using Time-Series Models

Comparison of Active COVID-19 Cases per Population Using Time-Series Models

Sakinat Oluwabukonla Folorunso, Joseph Bamidele Awotunde, Oluwatobi Oluwaseyi Banjo, Ezekiel Adebayo Ogundepo, Nureni Olawale Adeboye
Copyright: © 2022 |Pages: 21
DOI: 10.4018/IJEHMC.20220701.oa6
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This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.
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1. Introduction

The novel severe contagious respiratory syndrome coronavirus called (COVID-19) has cause greatest global challenge and public health, after the pandemic of influenza outbreak of 1918. The spread of COronaVirus Disease 2019 (COVID-19) has caused significant changes in the style of living of communities around the world. The onset of the epidemic was discovered in Wuhan, China in December, 2019 and was triggered due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2) virus (Ogundokun, Lukman, Kibria, Awotunde, & Aladeitan, 2020). As stated by World Health Organization (WHO) there have been 30, 949,804 confirmed cases and 959,116 deaths of COVID-19 Globally, as of 4:30pm CEST, 21 September 2020 and affecting more than 180 countries, and due to the exponential level of spread, WHO stated COVID-19 as an epidemic. These make governments of different countries to take immediate control actions like separating profoundly influenced regions, stopping the cross-border traffic between nations, shutting down schools, working environments, regular spots, limiting resident’s movements by encouraging them to stay at as much as possible. The social life and economics had significantly affected due to these measures put in place. The societies face different challenges ranges from education, healthcare, manufacturing, supply chain management travel, tourism and service delivery under the prevailing conditions and in a post COVID-19 world. As an example, the overcrowded of hospitals and other healthcare facilities due to exponential increase of COVID-19 patients and the inability to provide medical assistance to normal patients due to limited movement are significant barriers to the fight against COVID-19 in the healthcare sector. Similarly, delays and the increased resource demand for manual contact tracing, and unavailability of effective and automated contact tracing software impede the actions for controlling the spread. Hence, it is the duty of different parties to act with their fullest potential to control the prevailing COVID-19 situation, such parties are healthcare workers, government authorities, students, researchers, engineers, technology managers and the general public. In order to not only safeguard, but also handle the post-COVID-19 environment, digitalization and the implementation of information and communication technologies will be imperative. Technologies like Big data, 5G communication, Internet of Things (IoT), cloud computing, Artificial Intelligence (AI), and blockchain play vital role to assist the environment adopting to different protection and improvement of people and economies. The technologist and engineers will have to tackle important challenges to implement these promising solutions and realize their benefits, and carry out elaborate findings regard to risk management, resources, cost, scope, and quality.

In order to predict the prevalence of coronavirus in Nigeria, some researchers recently used the Linear Regression (LR) and curve estimation model (Ogundokun, Lukman, Kibria, Awotunde, & Aladeitan, 2020; Lukman, et al., 2020). In India (Pandey, Chaudhary, Gupta, & Pal, 2020) used the SEIR and Regression model to predict COVID 19 and LR to estimate the count of deaths in India due to coronavirus. (Ghosal, Sengupta, Majumder, & Sinha, 2020) proposed AutoRegressive Integrated Moving Average (ARIMA) model is among the numerous statistical methods that are used to forecast disease events. In multiple disease outbreaks, many researchers have used the ARIMA model for disease prevalence / incidence prediction. These include (Guan, Huang, & Zhou, 2004; Earnest, Chen, Ng, & Sin, 2005; Gaudart, et al., 2009; Liu, Liu, Jiang, & Yang, 2011; Nsoesie, Beckman, Shashaani, Nagaraj, & Marathe, 2013; Zheng, Zhang, Zhang, Wang, & Zheng, 2015; Fang, et al., 2020; Polwiang, 2020; Cao, et al., 2020)

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