Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the
State-of-the-Art
#MMPMID33063048
Shinde GR
; Kalamkar AB
; Mahalle PN
; Dey N
; Chaki J
; Hassanien AE
SN Comput Sci
2020[]; 1
(4
): 197
PMID33063048
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COVID-19 is a pandemic that has affected over 170 countries around the world. The
number of infected and deceased patients has been increasing at an alarming rate
in almost all the affected nations. Forecasting techniques can be inculcated
thereby assisting in designing better strategies and in taking productive
decisions. These techniques assess the situations of the past thereby enabling
better predictions about the situation to occur in the future. These predictions
might help to prepare against possible threats and consequences. Forecasting
techniques play a very important role in yielding accurate predictions. This
study categorizes forecasting techniques into two types, namely, stochastic
theory mathematical models and data science/machine learning techniques. Data
collected from various platforms also play a vital role in forecasting. In this
study, two categories of datasets have been discussed, i.e., big data accessed
from World Health Organization/National databases and data from a social media
communication. Forecasting of a pandemic can be done based on various parameters
such as the impact of environmental factors, incubation period, the impact of
quarantine, age, gender and many more. These techniques and parameters used for
forecasting are extensively studied in this work. However, forecasting techniques
come with their own set of challenges (technical and generic). This study
discusses these challenges and also provides a set of recommendations for the
people who are currently fighting the global COVID-19 pandemic.
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