Influence of COVID-19 transmission stages and demographics on length of in-hospital stay in Singapore for the first 1000 patients
Creators: name: Sreevalsan-Nair, Jaya
name: Reddy Rani Vangimalla
name: Pritesh Rajesh Ghogale
Publisher: name: F1000 Research Limited
Year: 2020

description: COVID-19 is a viral contagion caused by the novel coronavirus, called SARS-CoV2, and the contagion has been declared as a global pandemic by the World Health Organization (WHO) on March 11, 2020<sup><sup>1</sup></sup>. The outbreak started in December 2019 in Wuhan, China, which as of mid-May 2020, has infected ~4.5 million people<sup><sup>2</sup></sup> from 213 countries, of which ~1.7 million patients have recovered. Each country has taken into consideration their specific socio-economic priorities to announce national efforts in containing the pandemic. The lessons learned from epicenters of the disease in Asia and Europe, and experiences from the SARS-COV1 outbreak in 2003 in China, Singapore, South Korea, etc. have helped in strategizing handling of the current pandemic. We discuss a retrospective study of the first 1000 patients in Singapore to analyze government strategies. While mathematical epidemiological models have been proposed to understand the trends of transmission of the pandemic, we study the factors that influence the length of in-hospital stay (LoS) of the discharged patients. Analyzing recovery helps us to understand how effective the efforts taken by the government have been as well as how the hospital load is affected in different transmission stages.<br><br>The case study of Singapore [1] has been carefully chosen owing to the reliable, accessible, and available data from the official press releases of the Ministry of Health (MoH), Government of Singapore. The healthcare system of Singapore has handled the widespread contagion in terms of imposing strict lockdown, quarantine, and isolation, and aggressive, large-scale contact tracing and testing. 1000 individuals have been confirmed positive for COVID-19 from January 23-April 01, 2020, of which 245 have been discharged after clinical recovery. The positive confirmation has been made using the real-time reverse transcription-polymerase chain reaction (RT-PCR) tests on respiratory samples (sputum or nasal/throat/nasopharyngeal swabs), based on experiential learning from the outbreak of SARS in 2003<sup><sup>3</sup></sup>. Similarly, the protocol for clinical recovery or hospital discharge has been based on the results on the RT-PCR tests of two consecutive samples being negative over two days [2]. Singapore has opened its dedicated facility, the National Center for Infectious Diseases (NCID), and has made logistical preparations to handle pandemics. NCID is a centralized facility, which also enabled efficient and reliable case-wise data dissemination by the government on the MoH website<sup><sup>4</sup></sup>. The case-wise data give insights into the influence of demographic factors in recovery. Additionally, we consider the COVID-19 transmission stages, as defined by the WHO, which are Stage 0 of no infection, Stage 1 of imported cases, Stage 2 of local transmission, and Stage 3 of large-scale transmission<sup><sup>5</sup></sup>. Thus, in the case of Singapore, we periodize January 23-April 01, into three periods, i.e. January 23-February 03, February 04-March 16, and March 17-April 01, respectively. We thus study the first 16 days of Stage 3. We present our 3-step analysis of LoS.<br><br>Firstly, we perform a descriptive statistical analysis to understand the age-gender distribution of the infected and recovered patients. We observe that in Stage 3, the age group 20-29 has the most infected number of patients, and the age group 30-39 has the most recovered patients. Secondly, we consider the trendline analysis of the length of in-hospital stay (LoS) using local regression model, loess. We find that there is a reduction in the LoS. Thirdly, we estimate the most likely LoS using regression with a generalized linear model (GLM). Since we find that the age group-wise median monotonously increases in the age group 20-59, and monotonously decrease in the age-group 60-89, we perform two different piecewise linear regression models. We compare the performance of negative binomial and Poisson distribution functions for LoS [3] in the GLM, using age, gender, and transmission stage as independent variables, and LoS as the dependent one. Gender does not indicate any influence.<br><br>We observe that the LoS for COVID-19 can be estimated to be 9 days using the better-fitting negative binomial distribution function for GLM. This is an improvement from earlier studies indicating 11 days. We conclude that the governmental efforts have enabled the reduction in LoS during the transmission stages and, the age distribution shift has influenced the LoS. We find that the government measurements of restricting movement and exposure of the vulnerable groups, namely the senior citizens and children, have protected these groups.<br><br><b>References:</b><br>[1] Rachael Pung, Calvin J. Chiew, Barnaby E. Young, Sarah Chin, Mark I-C Chen, Hannah E. Clapham, et al.. Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures. The Lancet, 395:1039–46, 2020.<br>[2] John E. L. Wong, Yee Sin Leo, and Chorh Chuan Tan. COVID-19 in Singapore—Current Experience: Critical Global Issues That Require Attention and Action. JAMA, 02 2020.<br>[3] Evelene M Carter and Henry WW Potts. Predicting length of stay from an electronic patient record system: a primary total knee replacement example. BMC medical informatics and decision making, 14(1):26, 2014.<br>1<sup> </sup> https://www.who.int/dg/speeches<br>2<sup> </sup> https://www.worldometers.info/coronavirus/<br>3<sup> </sup> https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5218a1.htm<br>4<sup> </sup> https://www.moh.gov.sg/news-highlights/<br>5<sup> </sup> https://iris.wpro.who.int/bitstream/handle/10665.1/14512/Holistic-response-strategies-COVID-19-eng.pdf?sequence=1