Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan

By Muhammad Yousaf, Samiha Zahir, Muhammad Riaz, S. Hussain, Kamal Shah. Chaos, Solitons & Fractals, 2020

158 citations recorded by the source corpus.

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

Subfields: Economics and Econometrics, Management Science and Operations Research, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/81e266c27540d8e3fdb6b1d5ad1aa3019c9830a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Fouzi Harrou
2. Ying Sun
3. Abdelkader Dairi
4. Abdelhafid Zeroual

Citations in the corpus

• Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study (2020, background): “Accurately modeling and forecasting the spread of confirmed and recovered COVID-19 cases is vital to 19 19 understand and help decision-makers to slowdown or arrest its spreading [1, 2, 3].”