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

Abstract

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.

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“…In this study, a combined linear regression analysis and data-fitting model is used. To deal with data fluctuation, this model adopted the hypothesis that was successfully used in other published studies of a short time span of one month maximum for forecasting, [10][11][12][13]. That hypothesis is logical and rational because the world knows that the virus spread in unpredictable; thus, longer time spans may encompass inaccurate data.…”

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Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

“…For public health officials, such an estimate plays an important role in allocating limited health resources rationally and in directing when and which health interventions should be adopted to alleviate the disease outbreak [4,6,11,12]. Recently, a great number of mathematical and statistical techniques have been deemed as policy-supportive tools to model the prevalence, morbidity and mortality of COVID-19 in different countries [10,13,14]. For example, Saba et al used an autoregressive integrated moving average (ARIMA) model and a nonlinear autoregressive artificial neural networks (NARNN) to forecast the prevalence of the COVID-19 outbreak in Egypt [11].…”

“…Given the advantage of α-Sutte Indicator and the current epidemic status of COVID-19 in the mentioned countries, this study aims to describe the epidemic situation of COVID-19 and to forecast the epidemiological trends of the COVID-19 prevalence and mortality in the abovementioned countries and worldwide using this advanced α-Sutte Indicator. In the meantime, the predictive ability of the α-Sutte Indicator was also compared with that of the most common use of ARIMA model in the COVID-19 outbreak forecasting [4,11,14,[27][28][29][30].…”