Open AccessCovid-19 Decision Making Intelligence during Disasters Management
Author(s) -
Amin Ismail,
Rosmah Mat Isa,
Farah Laili Muda Ismail,
Ainulashikin Marzuki,
Nurzi Juana Mohd Zaizi,
Nur Fatin Nabila Mohd Rafei Heng,
Sakinah Ahmad
Publication year - 2021
Publication title -
ulum islamiyyah
Language(s) - English
Resource type - Journals
eISSN - 2289-4799
pISSN - 1675-5936
DOI - 10.33102/uij.vol33nos4.433
Subject(s) - big data , emergency management , covid-19 , natural disaster , business , nowcasting , public health , pandemic , china , facility management , computer science , medical emergency , operations research , environmental planning , risk analysis (engineering) , political science , engineering , medicine , geography , marketing , infectious disease (medical specialty) , disease , pathology , meteorology , law , operating system , nursing
As the coronavirus (COVID-19) spreads from China to neighbouring areas and beyond, increased national and international efforts are underway to contain the epidemic. Humanity is increasingly confronted with a diverse array of man-made and natural disasters. While emergency circumstances cannot be avoided, they may be managed more efficiently. Effective emergency management requires thorough planning, informed reaction, and well-coordinated actions throughout the emergency management life cycle. According to the literature, data-driven emergency management information systems that are well-integrated help in disaster management operations. Recent advances in molecular and computational techniques, as well as in information and communication technologies (ICTs), artificial intelligence (AI), and Big Data, can assist in managing the massive, unprecedented amount of data generated by public health surveillance, real-time epidemic outbreak monitoring, trend nowcasting/forecasting, routine situation briefing and updating from governmental institutions and organisms, and health facility utilisation. This study could be tailored to assist organisations in adapting to their new normal.