A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

Abstract

Background: Iraq is among the countries affected by the COVID-19 pandemic. As of 2 August 2020, 129,151 COVID-19 cases were confrmed, including 91,949 recovered cases and 4,867 deaths. After the announcement of lockdown in early April 2020, situation in Iraq was getting steady until late May 2020, when daily COVID-19 infections have raised suddenly due to gradual easing of lockdown restrictions. In this context, it is important to develop a forecasting model to evaluate the COVID-19 outbreak in Iraq and so to guide future health policy. Methods: COVID-19 lag data were made available by the University of Anbar through their online analytical platform (https://www.uoanbar.edu.iq/covid/), engaged with the day-to-day fgures form the Iraqi health authorities. 154 days of patient data were provided covering the period from 2 March 2020 to 2 August 2020. An ensemble of feed-forward neural networks has been adopted to forecast COVID-19 outbreak in Iraq. Also, this study highlights some key questions about this pandemic using data analytics. Results: Forecasting were achieved with accuracy of 87.6% for daily infections, 82.4% for daily recovered cases, and 84.3% for daily deaths. It is anticipated that COVID-19 infections in Iraq will reach about 308,996 cases by the end of September 2020, including 228,551 to recover and 9,477 deaths. Conclusion: The applications of artifcial neural networks supported by advanced data analytics represent a promising solution through which to realise intelligent solutions, enabling the space of analytical operations to drive a national health policy to contain COVID-19 pandemic.