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Implementation of a real-time, data-driven online Epidemic Calculator for tracking the spread of COVID-19 in Singapore and other countries.
Yap, Fook Fah; Yong, Minglee.
  • Yap FF; Nanyang Technological University, School of Mechanical and Aerospace Engineering, 50 Nanyang Ave, Singapore, 639798, Singapore.
  • Yong M; Nanyang Technological University, National Institute of Education, 1 Nanyang Walk, Singapore, 637616, Singapore.
Infect Dis Model ; 6: 1159-1172, 2021.
Article in English | MEDLINE | ID: covidwho-1466377
ABSTRACT
While there are many online data dashboards on COVID-19, there are few analytics available to the public and non-epidemiologists to help them gain a deeper insight into the COVID-19 pandemic and evaluate the effectiveness of social intervention measures. To address the issue, this study describes the methods underlying the development of a real-time, data-driven online Epidemic Calculator for tracking COVID-19 growth parameters. From publicly available infection case and death data, the calculator is used to estimate the effective reproduction number, final epidemic size, and death toll. As a case study, we analyzed the results for Singapore during the "Circuit Breaker" period from April 7, 2020 to the end of May 2020. The calculator shows that the stringent measures imposed have an immediate effect of rapidly slowing down the spread of the coronavirus. After about two weeks, the effective reproduction number reduced to about 1.0. Since then, the number has been fluctuating around 1.0 for more than a month. The COVID-19 Epidemic Calculator is available in the form of an online Google Sheet and the results are presented as Tableau Public dashboards at www.cv19.one. By making the calculator readily accessible online, the public can have a tool to assess the effectiveness of measures to control the pandemic meaningfully.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: Infect Dis Model Year: 2021 Document Type: Article Affiliation country: J.idm.2021.10.002

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: Infect Dis Model Year: 2021 Document Type: Article Affiliation country: J.idm.2021.10.002