Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Abstract

<jats:title>Abstract</jats:title> <jats:p>The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.</jats:p>

Mathematical Modeling Based Study and Prediction of COVID-19 Epidemic Dissemination Under the Impact of Lockdown in India

“…Further, we have assumed that β has been changed to β/k, (0.2 ≤ k ≤ 1) under the impact of lockdown in India [29] and projected the number of total confirmed cases for the estimated β. Out of these estimated values of β, the most suitable value i.e., β = 0.4809 is used to obtain the best fit for predicted number of cases with reported number of cases.…”

“…Recently, SIR model has also been applied for prediction of COVID-19 trajectory and its epidemic peak in all over the world [15][16][17][18][19]. Numerous relevant studies have been carried out in India as well [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. A COVID-19 simulation models i.e., IndiaSIM have been proposed by Center for Disease Dynamics, Economics & Policy (CDDEP) to predict COVID-19 infected cases under different lockdown periods in India [25].…”

“…Pai et al investigated the COVID-19 transmission dynamics under the effect of lockdown. This study predicts that the peak of active infected cases around 43,000 will occur in the middle of May, 2020 [29]. In another study, a compartmental mathematical model to predict COVID-19 dynamics has been proposed by analyzing data up to April 30, 2020.…”

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series

“…Since the beginning of this pandemic in late 2019 or early 2020, forecast models were developed for forecasting various COVID-19 counts including cases and deaths [1-5, 11-16, 18, 19, 21, 24, 25, 28]. Some of these authors have used Box-Jenkins methodologies to develop autoregressive integrated moving average (ARIMA) models for COVID-19 counts of cases and deaths [2,4,5,12,13,15,18,21,24,25]. Although ARIMA models for both COVID-19 cases and deaths are modeled separately, a detailed exploratory analysis reveals that a short-term forecast model for deaths may take advantage of case counts as inputs.…”

COVID-19 and lockdown in India: Evaluation using analysis of covariance

“…[4] To combat such scenarios and for optimum allocation of resources a number of mathematical models were suggested to forecast evolution of COVID-19 cases , mortality and length of hospital stays. [5] [6] [7] [8] In addition to provision of health services, coordination between various agencies including transport departments, police , food supply departments play a key role. [9].…”