Article: Estimation of infection density and epidemic size of COVID-19 using the back-calculation algorithm.
Health information science and systems
2020 Volume 8, Issue 1, Page(s) 28
Abstract: ... In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID ... 19 epidemic size in South Korea and find a similar effect of its "test, trace, isolate, and treat ... if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown ...
| Abstract | The novel coronavirus (COVID-19) is continuing its spread across the world, claiming more than 160,000 lives and sickening more than 2,400,000 people as of April 21, 2020. Early research has reported a basic reproduction number (R0) between 2.2 to 3.6, implying that the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China in and outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. We also investigate the COVID-19 epidemic size in South Korea and find a similar effect of its "test, trace, isolate, and treat" strategy. Our findings are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world. |
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| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-09-28 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2697647-X |
| ISSN | 2047-2501 |
| ISSN | 2047-2501 |
| DOI | 10.1007/s13755-020-00122-8 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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