A Review on Predictive Systems and Data Models for COVID-19

Fatima Nazish Khan, Ayesha Ayubi Khanam, Ayyagari Ramlal, Shaban Ahmad

Research output: Other contributionpeer-review

Abstract

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or novel Coronavirus, responsible for the transmission of Coronavirus disease (COVID-19), represents the causative agent of a conceivably deadly sickness, and a global public health concern. In December 2019 in China (Wuhan), the spread of SARS-CoV-2 has taken the shape of a pandemic and affects the respiratory system and manifests as pneumonia in humans, influencing more than 216 nations so far. On January 12th, 2020, the World Health Organization (WHO) gave the name “2019-nCoV” for 2019 novel Coronavirus, and the infection further on February 11th, 2020, is authoritatively named as COVID-19. Instead of using various predictive systems and data models, the prevalence of COVID-19 is continuously increasing, affecting millions of individuals. This chapter focuses on predictive systems and data models utilized from the beginning of COVID-19 outbreak that helped in predicting the cases and deaths qualities of COVID-19 in the desire for giving a reference to future investigations and help in controlling the spread of further epidemics. And also suggest how these data models can help and enable policymakers to plan the regional and national healthcare systems required and design monitored active plans.
Original languageEnglish
DOIs
StatePublished - 1 Jan 2021

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