Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models

(چکیده مقاله) :
Abstract :

Background: The short term forecasts regarding different parameters of the COVID-19 are
very important to make informed decisions. However, majority of the earlier contributions
have used classical time series models, such as auto regressive integrated moving average
(ARIMA) models, to obtain the said forecasts for Iran and its neighbors. In addition, the
impacts of lifting the lockdowns in the said countries have not been studied. The aim of
this paper is to propose more flexible Bayesian structural time series (BSTS) models for
forecasting the future trends of the COVID-19 in Iran and its neighbors, and to compare the
predictive power of the BSTS models with frequently used ARIMA models. The paper also
aims to investigate the casual impacts of lifting the lockdown in the targeted countries
using proposed models.
Methods: We have proposed BSTS models to forecast the patterns of this pandemic in Iran
and its neighbors. The predictive power of the proposed models has been compared with
ARIMA models using different forecast accuracy criteria. We have also studied the causal
impacts of resuming commercial/social activities in these countries using intervention
analysis under BSTS models. The forecasts for next thirty days were obtained by using the
data from March 16 to July 22, 2020. These data have been obtained from Our World in
Data and Humanitarian Data Exchange (HDX). All the numerical results have been obtained
using R software.
Results: Different measures of forecast accuracy advocated that forecasts under BSTS
models were better than those under ARIMA models. Our forecasts suggested that the
active numbers of cases are expected to decrease in Iran and its neighbors, except
Afghanistan. However, the death toll is expected to increase at more pace in majority of
these countries. The resuming of commercial/social activities in these countries has
accelerated the surges in number of positive cases.

(توضیحات تکمیلی) :

(توضیحات تکمیلی) :
Description :

مقاله ISI انگلیسی اصلی
سال انتشار: 2021
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 8 صفحه

Authors / Descriptions(نویسندگان/توضیحات): مقاله ISI سال انتشار: 2021 / نویسندگان: Navid Feroze
Sent date(تاریخ ارسال) : 1400/01/12  |   4/1/2021
Number of visits(تعداد بازدید): 561
Key words (کلمات کلیدی): Infectious disease modeling , Bayesian time series models , ARIMA models
Number of pages(تعداد صفحات) : 8
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