ESTIMATION OF SHEDDING TIME IN LABORATORY-CONFIRMED COVID-19 CASES IN SOUTH AFRICA: A POPULATION-BASED RECORD LINKAGE STUDY, MARCH-DECEMBER 2020 By Carroll Tshabane Student No: 2374447 A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Epidemiology (Field epidemiology) This research work was supervised by: Dr Lazarus Kuonza Dr Nkengafac Villyen Motaze Mrs Hetani Mdose SEPTEMBER 2022 i DECLARATION I, Carroll Tshabane, declare that this Research Report is my own unaided work. It is being submitted for the Degree of Master of Science in Epidemiology (Field Epidemiology) at the University of the Witwatersrand, Johannesburg. It has not been submitted before for any degree or examination to any other University. (Signature of candidate) 2nd day of August 2022 in Johannesburg, South Africa ii DEDICATION In loving memory of my grandmother Shalati N’wa Hlupheka Xikoxa 1921- 2021 iii CONFERENCE PRESENTATION An abstract for this research was submitted to the 1st International Conference on Public Health in Africa (CPHIA 2021) and was accepted for an oral presentation (Appendix 2). The conference was held virtually from 14-16 December 2021. The abstract was titled: Estimation of shedding time in laboratory-confirmed COVID-19 cases in South Africa, 2020. iv ABSTRACT Background Since the announcement of the first confirmed COVID-19 case in March 2020, South Africa has seen an increase in cases. Determining shedding time is important to inform public health interventions. We aimed to estimate viral shedding time among laboratory-confirmed COVID-19 cases in South Africa. Methods A cross-sectional analytic study was conducted using COVID-19 data from 5 March-31 December 2020, obtained from the NMClist and the DATCOV system. These platforms report on laboratory- confirmed and hospitalized COVID-19 patients, respectively. The study consisted of COVID-19 patients with repeat positive PCR tests (at least two positive PCR test results) and who subsequently tested negative. To determine the association between shedding time and predictor variables, multiple linear regression analysis was conducted and a p-value <0.05 was considered statistically significant. Results We included 2752 cases that met the inclusion criteria. The 2752 included participants had a total of 8 642 PCR positive test results with a median of 3 positive tests per participant (range: 2 – 11). The majority (74.7%: 2,057/2752) of the participants had 3 consecutive positive tests. About 39.9% (1099/2752) of participants were inpatients and 60.1% (1653/2752) were outpatients. The median shedding time was 17 days (range: 1–128). No significant difference in shedding time was found between males (median:16 days, range:1-128) and females (median:17 days, range:1–94) and between inpatients (median: 16 days, range 1–108) and outpatients (median:17 days, range:1– 128). Individuals aged 0-4 years had the lowest shedding time (median:14 days, range:1–72). The shortest shedding time was observed in Eastern Cape with a median of 15 days (range: 1-56) and the longest shedding time was observed in Limpopo province with a median of 21 days (range: 1- 39). After adjusting for age, sex and province, shedding time was shorter for hospitalized patients compared to outpatients (coefficient: -0.14, CI: -0.24 ─ -0.03, P-value: 0.014). Conclusion v Shedding time varies with admission status (inpatients versus outpatients). We found a significant association between the hospital admission status of COVID-19 patients and shedding time. Further studies are required to explore the association between other comorbidities (such as heart disease, obesity and diabetes mellitus), socio-economic status, level of education, vaccination status and SARS-CoV-2 shedding. In addition, different approaches that can address transmission risk effectively in asymptomatic patients are required to control SARS-CoV-2 infection. Keywords: COVID-19, South Africa, viral shedding time, laboratory-confirmed, population- based, SARS-COV-2 vi ACKNOWLEDGEMENTS I would like to thank my supervisors Dr Villyen Motaze, Dr Lazarus Kuonza and Mrs Hetani Mdose for their supervision, support and guidance during my Master’s degree. Special thanks to Dr Alex De Voux and the South African Field Epidemiology Training Program team for the immerse support and motivation. I would also like to acknowledge the COVID-19 Response Team at the National Institute for Communicable Diseases (NICD) for working tirelessly to respond to the COVID-19 pandemic in South Africa. My appreciation also goes to my family and friends for their support, encouragement, prayers and belief in me. Lastly, I would like to send my gratitude to Godwin Kalu and Ephordia Thabane for walking this journey with me and for their insightful comments and suggestions in this research. vii TABLE OF CONTENTS DECLARATION ............................................................................................................................. i DEDICATION ................................................................................................................................ ii CONFERENCE PRESENTATION ............................................................................................... iii ABSTRACT ................................................................................................................................... iv ACKNOWLEDGEMENTS ............................................................................................................ v LIST OF FIGURES ........................................................................................................................ ix LIST OF TABLES .......................................................................................................................... x CHAPTER 1: INTRODUCTION ................................................................................................... 1 1.1. Background ...................................................................................................................... 1 1.1.1. Epidemiology of COVID-19 ..................................................................................... 1 1.1.2. COVID-19 in South Africa ....................................................................................... 2 1.1.3. COVID-19 Viral shedding time ................................................................................ 3 1.2. Literature Review ............................................................................................................. 4 1.2.1. COVID-19 viral shedding time ................................................................................. 5 1.2.2. Factors affecting viral shedding time ........................................................................ 5 1.2.3. Importance of estimating COVID-19 shedding time ................................................ 6 1.2.4. Laboratory diagnosis ................................................................................................. 6 1.3. Problem Statement ........................................................................................................... 7 1.4. Justification ...................................................................................................................... 7 1.5. Research Question ............................................................................................................ 7 1.6. Aim ................................................................................................................................... 8 1.7. Objectives ......................................................................................................................... 8 References ................................................................................................................................... 9 CHAPTER 2 – MANUSCRIPT .................................................................................................... 14 viii ABSTRACT .................................................................................................................................. 17 INTRODUCTION ........................................................................................................................ 19 METHODS AND MATERIALS .................................................................................................. 20 RESULTS ..................................................................................................................................... 23 DISCUSSION ............................................................................................................................... 24 CONCLUSION ............................................................................................................................. 28 REFERENCES ............................................................................................................................. 30 TABLES ....................................................................................................................................... 36 FIGURE LEGENDS ..................................................................................................................... 41 CHAPTER 3: EXTENDED METHODS ...................................................................................... 44 3.1 Record linkage ............................................................................................................... 44 3.2 Ethical Considerations ................................................................................................... 44 CHAPTER 4: EXTENDED RESULTS ........................................................................................ 45 CHAPTER 5: OVERALL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ... 46 5.1 DISCUSSION ................................................................................................................. 46 5.2 LIMITATIONS ............................................................................................................... 47 5.3 CONCLUSION ............................................................................................................... 47 5.4 RECOMMENDATIONS ................................................................................................ 48 REFERENCES ............................................................................................................................. 49 APPENDICES .............................................................................................................................. 50 Appendix 1: Plagiarism declaration .......................................................................................... 50 Appendix 2: Conference Abstract .............................................................................................. 51 Appendix 3: Conference Attendance certificate ........................................................................ 53 Appendix 4: Turnitin Report ..................................................................................................... 54 Appendix 5: Ethics clearance certificates ................................................................................. 55 ix Appendix 6: Clinical Infectious Diseases Guidelines .............................................................. 57 Appendix 7: Declaration- Candidate’s contribution to manuscript .......................................... 70 x LIST OF FIGURES CHAPTER 2 Figure 1: Flow chart showing inclusion and exclusion criteria of COVID-19 cases in South Africa, 2020 .................................................................................................................................. 40 Figure 2: Distribution of shedding time of COVID-19 cases in South Africa by sex, 2020 ....... 40 Figure 3: Distribution of shedding time by age group (years) in South Africa by sex, 2020 ...... 40 CHAPTER 4 Figure 4.1: Number of positive test results by COVID-19 admission status (hospitalized patients and Outpatients) in South Africa, 2020 ........................................................................................ 44 xi LIST OF TABLES CHAPTER 2 Table 1: Baseline characteristics of COVID-19 patients in South Africa, 2020 ......................... 35 Table 2: Shedding time of laboratory-confirmed COVID-19 cases in South Africa, 2020 ......... 37 Table 3: Univariate and multivariate analysis of factors affecting viral shedding time in COVID- 19 patients in South Africa, 2020.................................................................................................. 38 xii LIST OF ABBREVIATIONS CDW Corporate Data Warehouse CDC Centers for Disease Control and Prevention COVID-19 Coronavirus disease 2019 HIV/AIDS Human immunodeficiency virus/acquired immune deficiency syndrome NICD National Institute for Communicable Diseases NMC Notifiable Medical Condition Public Health Emergency of International Concern PHEIC POCT point-of-care tests RDT Rapid Diagnostic Test RNA Ribonucleic acid RT-PCR Real-time reverse transcription-polymerase chain reaction SARS‐CoV‐2 Severe acute respiratory syndrome coronavirus 2 SAFETP South African Field Epidemiology Training Programme WHO World Health Organization 1 CHAPTER 1: INTRODUCTION This research report presents findings from a quantitative study that was conducted on coronavirus disease 2019 (COVID-19) patients in South Africa (SA). It provides an overview of COVID-19 viral shedding time and factors associated with shedding time in laboratory-confirmed COVID-19 cases. The introductory chapter will provide background information on COVID-19, describing the burden of disease, disease patterns and its epidemiology. We will further provide a literature review, providing context on shedding time on COVID-19 patients globally. The importance of estimating shedding time and gaps in knowledge will be discussed to justify the research study. To summarize the chapter, the research question, aim and objectives of the study will be listed. 1.1. Background Wuhan, a city in Hubei province of China recorded its first COVID-19 cases in December 2019. COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus1. In early January 2020, COVID-19 cases started to show a gradual increase and by mid-January 2020, the disease had spread to other countries such as Japan, Thailand, and the United States of America. Following the spread of SARS-CoV-2 infections in many countries globally, the disease was announced as a public health emergency of international concern (PHEIC) in January 2020 and was later declared a pandemic in March 20202,3. As of 5 January 2021, there were 84,233,579 reported COVID-19 cases and 1,843,293 reported deaths globally4. To date, there is no cure for COVID-19, however, it can be prevented and controlled through vaccinations, social distancing, wearing of masks and washing or sanitizing hands. As of 05 January 2021, the African region reported 2,852,491 confirmed COVID-19 cases and 67 943 deaths, with a case fatality ratio of 2.4%3. The highest number of SARS-CoV-2 infections in the African region were reported in South Africa. 1.1.1. Epidemiology of COVID-19 COVID-19 spreads through contact with a person infected with SARS-CoV-2, via larger respiratory droplets, through touching contaminated surfaces, and by inhaling small airborne droplets5,6. COVID-19 can stay in the air for some time and can spread through the airborne route however, the contribution of air transmission to the pandemic is unknown7,8,9. Studies have shown that poorly ventilated spaces can act as a potential for airborne transmission10,11. SARS-CoV-2 2 have also been discovered in specimens such as sperms, blood and stools. Although the virus has been identified in these non-respiratory sites, the role these sites play in COVID-19 transmission is unknown. Different transmission patterns have been reported across Africa, with the majority of the countries reporting community transmissions and some observing clusters and sporadic transmissions. Compared to other continents, Africa is one of the least affected, representing 3.4% of the total reported global cases12. COVID-19 affects people of all ages. However, individuals with comorbidities (such as heart disease, obesity and diabetes mellitus) and older people (above 60 years of age) are more vulnerable to infection and have a higher risk of developing severe illness13. Signs and symptoms of COVID-19 include body aches, coughing, sore throat, shortness of breath, chills, fever, headache, nausea, diarrhoea, and loss of smell or taste14. Some people remain asymptomatic after acquiring the infection. However, most people develop mild symptoms of the disease and recover from the illness without medical intervention15. One in six people have presented with more severe symptoms such as shortness of breath13. Nevertheless, the virus can effectively be prevented by wearing a face mask, hand sanitizing, vaccinating and social distancing. 1.1.2. COVID-19 in South Africa South Africa ranks 15th globally with COVID-19 cases and is the most affected country in the African region even though it has relatively low numbers of deaths12,16. The first COVID-19 case in South Africa was confirmed on 5 March 2020, and on 17 March 2020, the president declared a national state of disaster17. Since then, lockdown control measures were put in place. These measures included compulsory wearing of face masks, closure of businesses except businesses providing essential services, travel ban, closure of schools and religious centers. Quarantine and isolation measures were also put in place. Asymptomatic people who may have been infected with COVID-19 are placed in quarantine. While isolation is for people who have been infected with SARS-CoV-2 but are not hospitalized18. Although these control measures were in place, the virus continued to spread throughout the country. By 05 January 2021, there were 1,127,759 cases of COVID-19 and 30 524 deaths in SA. Gauteng province had reported the highest number of cases, representing 27% of total cases, followed by Western Cape at 19.9% and KwaZulu-Natal at 19.8%19. 3 COVID-19 continues to have severe economic impacts on the healthcare system20. Healthcare workers have a key role in fighting the COVID-19 pandemic however, they are at a very high risk of being infected by the virus. There is a gradual increase of infections amongst health workers throughout the African continent. Since the beginning of the outbreak, there had been 44 055 reported cases of healthcare workers by 20 October 2020. The most affected healthcare workers were from South Africa, with 27 360 infections12. SARS-CoV-2 has been changing over time through mutations causing new variants of the virus. Multiple SARS-CoV-2 variants have been discovered in different countries globally. The variants reported include the Alpha variant (B.1.1.7) which was identified in the United Kingdom (UK); Beta (B.1.351) which was discovered in SA, Delta (B.1.617.2) which was discovered in India and the Gamma variant (P.1) which emerged in Brazil21,22. The Delta variant has been reported to spread faster than other variants and may cause more severe cases compared to other variants23. The Beta variant was identified in October 2020 in the Eastern Cape, South Africa. Since then, cases caused by this variant has been reported in other countries. Due to significant genetic changes, which is associated with an increased transmission, the Beta variant is a variant of concern24. Variants could be the leading cause of an increase in COVID-19 cases in South Africa which led to a resurgence and affected younger populations during the second wave. The development of SAR-CoV-2 variants has caused an increased risk to public health globally. COVID-19 vaccines have been developed and rolled out in response to the pandemic to offer protection against the virus to the masses. The Johnson and Johnson vaccine was first administered to healthcare professionals in South Africa in 202025. A COVID-19 national vaccination program started in February 2021 and the rollout was done in phases covering people aged 12 years and older. Johnson and Johnson, Pfizer-BioNTech and Oxford-AstraZeneca are the only vaccines being administered because they have been reported to provide protection against the Beta variant and other variants26. As of 02 November 2021, 21.1% of the population was fully vaccinated. Studies show that vaccines may reduce the risk of infection and reduce the severity of COVID-19 illness among those that get infected7,27. 1.1.3. COVID-19 Viral shedding time Various molecular techniques including point-of-care tests (POCT) and central laboratory tests have been made available to diagnose and manage COVID-19 patients. This includes viral and 4 antibody tests. The World Health Organization (WHO) has recommended that COVID-19 be diagnosed using real-time reverse transcription-polymerase chain reaction (RT-PCR) test28. The PCR test is a molecular test used to detect viral Ribonucleic acid (RNA) which in people infected with SARS-CoV-2 can be detected before, during and after a person presents with symptoms. Studies have used the PCR test to study viral shedding in COVID-19 patients29,30. Viral shedding happens when a virus in an infected person’s body is replicated and released into the environment31. An individual who is infected with a viral disease is infectious if they are shedding the virus. Therefore, the time and rate at which an infected person sheds the virus are important. For SARS-CoV-2, people shed the virus when they sneeze, talk, cough and through their stools. During the period of infection, viral shedding time or its duration is used to determine the suitable period of isolation. Viral shedding time is a useful guide for infectivity since it estimates the period of infectiousness. Furthermore, it can be used to establish guidelines for the duration of the isolation period for COVID-19 positive patients, and quarantine for those exposed to COVID-1932. As at the time of reporting, COVID-19 guidelines require asymptomatic patients to isolate for 10 days after testing whereas people with mild disease are required to isolate for 10 days after symptoms onset. Also, people with severe COVID-19 should isolate for 10 days after showing clinical stability33. Research shows that SARS-CoV-2 transmission begins before symptom onset and peaks early as the disease develops and the risk of transmission reduces thereafter34,7. During the early period of the pandemic, patients infected with COVID-19 were assumed to remain infectious no longer than 14 days after symptom onset. Research shows that the virus can begin shedding before a person starts to show symptoms and shedding increases during or after the onset of symptoms34. Understanding viral shedding time in detail can assist in decreasing the rate of SARS-COV-2 transmission. Therefore, there is a need to estimate shedding time in laboratory- confirmed COVID-19 cases in South Africa. 1.2. Literature Review This literature review examines various studies that have assessed COVID-19 viral shedding time. It reviews studies that have examined the contributing factors that affect viral shedding time. It also evaluates studies on the importance of estimating COVID-19 shedding time and laboratory diagnosis. 5 1.2.1. COVID-19 viral shedding time Viral shedding time is the period during which the virus is released from the body of a person with an infectious disease34. The shedding time of COVID-19 in the population of South Africa has not been studied35. In China, RT-PCR was used to diagnose COVID-19 among hospitalized patients to determine the duration of shedding time. They defined viral shedding time as “the number of days from the onset of the symptoms until the successive negative detection of SARS-CoV-2 RNA 36.” To determine shedding time among COVID-19 patients with mild disease in South Korea, viral shedding time was defined as “the duration from the day of diagnosis to the day when a patient displayed negative results for two series of RT-PCR tests, performed at least at a 24-hour interval apart37.” With COVID-19 patients, viral shedding could start before the onset of symptoms34. The median shedding time amongst patients with COVID-19 in China was 11 days38. This was different from Japan where the median viral shedding time of COVID-19 patients was found to be 19 days, ranging between 6-37 days after the first viral detection39. The inconsistency in viral shedding time from different studies across the world may be due to differences in the definition of the RT-PCR test results (negative or positive)40. Studies show that both symptomatic and asymptomatic patients have the potential of transmitting COVID-19 since they can possess similar viral loads41,42. This is evident from a study conducted in Saudi Arabia which found that the mean shedding time of COVID-19 asymptomatic patients was 13.6 days and 16.9 days for symptomatic patients43. Age and comorbidities have been reported to have an impact on symptom severity and clinical outcome44. However, pre-symptomatic and asymptomatic cases have been reported to transmit the virus to close contacts and have led to secondary cases45. Asymptomatic cases make it difficult to control the transmission of the virus since the duration of their infectiousness is hard to measure. 1.2.2. Factors affecting viral shedding time Different factors affect viral shedding time in COVID-19 patients. These factors could either slow or increase viral shedding in an individual. Prolonged viral shedding is associated with severe COVID-19 disease which may require ventilation. Studies have reported that shedding time among COVID-19 patients is affected by corticosteroid treatment, fever, long hospital stay and time from onset to hospitalization36,46. Evidence shows that early interventions such as early treatment of COVID-19 patients with symptoms can reduce viral shedding time46. Although COVID-19 can 6 infect anyone, younger age was associated with shorter shedding time whereas older age was associated with longer shedding time30. A study conducted in South Korea reported that comorbidities delay shedding time, also, females shed the virus more than males, although the difference was found to be marginal37. 1.2.3. Importance of estimating COVID-19 shedding time Several studies have shown that viral shedding time is significant in determining disease transmission39,46. Acquiring more knowledge on the importance of shedding time is important for public health planning, clinical purposes and could benefit society. Considering South Africa’s unique epidemiological profile, the population has high HIV/AIDS prevalence, high Tuberculosis incidence and high death rates from non-communicable diseases such as diabetes, making it of study interest. As identified in this literature review, there are some noticeable gaps around shedding time in different contexts and in a large population32. Guidelines for isolation and the duration of quarantine in different countries are made to reflect viral shedding duration. 1.2.4. Laboratory diagnosis It is fundamental to have rapid and accurate detection of SARS-CoV-2 for treatment and control of the virus. Different diagnostic tests are used to diagnose COVID-19, including molecular tests (such as RT-PCR tests), rapid diagnostic tests (RDT) and antigen tests. The signs and symptoms of the COVID-19 infection are similar to some other infectious respiratory diseases such as influenza, therefore performing laboratory tests on symptomatic people is crucial to identify SAR- CoV-2 infections. Moreover, it is estimated that up to 40% of infected individuals may have no symptoms (subclinical infection) or mild symptoms, yet are still capable of transmitting the virus to others47. This means that individuals without obvious signs or symptoms of SARS-CoV-2 infection also require testing. COVID-19 testing can be done in different ways, including detection of viral RNA, detection of viral antigen and detection of antibodies. The PCR test is the standard test for COVID-19 however, it is costly and may take time to receive results. The antigen test is cheaper compared to the PCR test, however, the test is not sensitive and may provide inaccurate results. Antibody tests can identify people who previously tested positive for COVID-19 but were asymptomatic, however, it can miss individuals whose IgM or IgG antibodies are not detectable. The WHO has recommended that laboratories use RT-PCR assays for COVID-19 diagnosis, as it is considered the best test at the moment48. 7 1.3. Problem Statement To inform infection control, prevention measures and public health policies, it is vital to understand the transmission dynamics of SARS-CoV-2. Population-level information on shedding time and factors affecting the duration of SARS-CoV-2 infection is lacking. This information could be used to understand the association between viral shedding and infectivity and the effectiveness of using a PCR test for infectiousness or re-infection. Estimating viral shedding time is vital since shedding can be utilized as a proxy for infectiousness46. Control measures such as quarantine and wearing of masks have been put in place in South Africa. However, the average shedding time in the South African population is unknown and there is a lack of thorough research on the duration of infectiousness for SARS-CoV-235. Also, there is no routine test available to measure infectiousness. This means that determining viral shedding time and understanding the associated predictors will play a significant role in informing control and prevention measures in COVID-19 patients. 1.4. Justification Numerous studies have been conducted globally to estimate the duration of SARS-CoV-2 viral shedding however, no study has estimated viral shedding time for laboratory-confirmed COVID- 19 cases in the South African population. Since SA reported the highest numbers of COVID-19 infections on the African continent, it is important to better understand the transmission dynamics of the virus in the population. The results from this study will assist in refining infection control strategies such as contact tracing and isolation or quarantine, and assist in understanding the potential difference of positive PCR tests over time. The results may further be utilized in facilitating early treatment and intervention, leading to a decrease in the incidence and mortality due to coronavirus disease. 1.5. Research Question What is the viral shedding time of SARS-CoV-2 among laboratory-confirmed COVID-19 cases in South Africa? 8 1.6. Aim To estimate the viral shedding time among laboratory-confirmed COVID-19 cases in South Africa. 1.7. Objectives Objective 1: To describe the clinical and epidemiological characteristics of patients with COVID- 19 in South Africa, 2020. 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The formatting of the journal article may differ from the rest of the research report to comply with the author guidelines of the journal to which we intend to submit the article. The journal guidelines are included in the appendices (Appendix 6). 15 Estimation of shedding time in laboratory-confirmed COVID-19 cases in South Africa: A population-based record linkage study, March-December 2020 Carroll Tshabane1,2, Villyen Motaze1,3, Hetani Mdose1,2,4, Alfred Musekiwa1,4, Lazarus Kuonza1,2,4 1National Institute for Communicable Diseases, a Division of the National Health Laboratory Service, Johannesburg, South Africa 2 School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa 3Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa 4School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa Correspondence to: Carroll Tshabane, National Institute for Communicable Diseases, 1 Modderfontein Road, Sandringham 2131, Johannesburg, South Africa. Email: ctshabane@gmail.com; Tel: +2711 386 6400 16 Summary: This cross-sectional analytic study reports shedding time among PCR-confirmed COVID-19 cases in South Africa from March to December 2020. The median shedding time was 17 days (range: 1–128). Disease severity (admission status) was associated with shedding time. 17 ABSTRACT Background In South Africa, COVID-19 cases are notified through the NMCList platform while hospitalized cases are reported on the DATCOV platform. It is crucial to estimate the duration of SARS- CoV-2 shedding to inform public health interventions. We aimed to estimate viral shedding time among laboratory-confirmed COVID-19 cases in South Africa. Methods We analyzed COVID-19 PCR results reported on the NMCList and DATCOV platforms from 5 March to 31 December 2020. We included cases with at least 2 consecutive positive PCR tests and a subsequent negative test. We performed multiple linear regression to determine the association between shedding time and predictor variables (age, sex, admission status and province). A p-value below 0.05 was considered to be statistically significant. Results We included 2752 cases that met the inclusion criteria. About 39.9% (1099/2752) of participants were inpatients and 60.1% (1653/2752) were outpatients. The median shedding time was 17 days (range: 1–128). There was no significant difference in shedding time between males (median:16 days, range:1-128) and females (median:17 days, range:1–94) and between hospitalized patients (median: 16 days, range 1–108) and outpatients (median:17 days, range:1–128). Individuals aged 0-4 years had the lowest shedding time (median:14 days, range:1–72). After adjusting for age, sex and province, shedding time was shorter for hospitalized patients compared to outpatients (coefficient: -0.14, CI: -0.24 ─ -0.03, P-value: 0.014). 18 Conclusion Shedding time differs between hospitalized and outpatients. Further studies are required to explore the association between comorbidities and SARS-CoV-2 shedding time. Keywords: COVID-19, SARS-CoV-2, viral shedding time, South Africa, population-based 19 INTRODUCTION Coronavirus disease 2019 (COVID-19) is an illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The virus was first identified in Wuhan, China in December 2019. COVID-19 has spread globally, causing a global pandemic [2]. On 5 March 2020, South Africa reported its first confirmed COVID-19 case [3]. Despite strict measures such as lockdown and compulsory wearing of masks put in place to control the epidemic, the virus has continued to spread. Viral shedding time is a useful guide for infectivity and can be used to establish the duration of isolation, quarantine and inform contact tracing guidelines [4]. With the polymerase chain reaction (PCR) test being the best test available for COVD-19 diagnosis, several studies have used it to estimate viral shedding time [5,6,7]. The Republic of Korea reported a median shedding time of 15 days among COVID-19 patients [8]. This was different from Japan where the median viral shedding time of COVID-19 patients was reported to be 19 days [9]. Studies show that both symptomatic and asymptomatic patients have the potential of transmitting the COVID-19 [8,10]. This is evident from a study conducted in Saudi Arabia which found that the mean shedding time of COVID-19 asymptomatic patients was 13.6 days and 16.9 days for symptomatic patients [11]. Factors affecting shedding time include the severity of disease, age, sex, comorbidity, timeliness of treatment and COVID-19 symptoms such as fever [6,7,12,13]. Age and comorbidities have been reported to have an impact on symptom severity and clinical outcome [14]. South Africa has reported the highest number of SAR-CoV-2 infections in Africa, therefore determining viral shedding time is important to understand the transmission dynamics of the disease within the population. This study aimed to estimate viral shedding time among laboratory-confirmed COVID-19 cases in South Africa. 20 METHODS AND MATERIALS Study design and procedures We performed a cross-sectional analytic study using COVID-19 data obtained from the COVID- 19 Notifiable Condition List (NMCList). The study included laboratory results from public and private clinical laboratories in South Africa that reported COVID-19 test results to the NMCList database. The study population consisted of all laboratory-confirmed COVID-19 patients reported to the NMCList database from March to December 2020. We included laboratory-confirmed COVID-19 patients with repeat positive PCR tests (at least two positive PCR test results) and subsequently tested negative. A person should have done consecutive testing no longer than 14 days between the repeat positive PCR tests. We excluded positive cases that were tested using antigen tests and cases that took a repeat test after 14 days of continuing positivity. Data sources COVID-19 test results are reported to the NMCList database hosted by the National Institute for Communicable Diseases (NICD). COVID-19 is a category 1 Notifiable Medical Condition (NMC) with a requirement of immediate reporting upon clinical suspicion or laboratory diagnosis followed by a written notification within 24 hours. Clinical data of patients tested for COVID-19 are collected using the NMC reporting platform, which is a national reporting system in South Africa. Both RT-PCR and antigen COVID-19 test results are stored in the NMCList database and reported to the National Department of Health (NDoH). Individuals who have multiple test results are identified using a unique identifier (case_id). Other variables stored in the NMCList include personal identifiers (name, surname, date of birth, sex, national identity number), demographic information (physical address), and health facility information. Data from the NMCList is stored in the NICD surveillance data warehouse. 21 The DATCOV system is an active, prospective sentinel surveillance programme for COVID-19 in South Africa. This online platform enables private and public healthcare facilities to submit hospital admissions data for COVID-19 patients. COVID-19 admissions trends are monitored and the epidemiology of the disease in hospitalized patients within the country is described. Admission status (hospitalized or outpatient) was used as a proxy to determine disease severity. Hospitalized patients were defined as patients with severe disease and outpatients as patients with mild disease. Data collection We extracted data from the NMCList and DATCOV databases. It was assumed that COVID-19 cases that were not in the DATCOV database were not hospitalized. The patients’ national identity number was used to link cases between the NMCList and DATCOV databases. Case Definitions COVID-19 case: “A confirmed case of COVID-19 is a person with laboratory confirmation of SARS-CoV-2 infection (using an RT-PCR assay), irrespective of clinical signs and symptoms between March and December 2020” [15]. Negative result: A person with a negative SARS-CoV-2 laboratory test using an RT-PCR assay. Variables Outcome Variable: The outcome variable is COVID-19 viral shedding time measured in days. Viral shedding time (days) was defined as the period from the date of specimen collection for the first positive PCR test to the date of specimen collection for the last positive test prior to a first negative PCR test. The date of specimen collection for the first positive test was subtracted from 22 the date of specimen collection for the last positive test to obtain the shedding time (in days). The date of onset of viral shedding was defined as the day the specimen of a case was collected since laboratory data had no variable for the date of symptom onset. This approach catered for individuals who would have been symptomatic or asymptomatic. Explanatory variables included sex, age, province, number of tests done and the severity of COVID-19 (hospitalized patients versus outpatient). Statistical analysis Data were analyzed using STATA 15 (Version 15, StataCorp, College Station, Texas). Characteristics of study participants were reported using descriptive statistics. Categorical variables (age groups, sex, admission status and province) were presented using absolute numbers and percentages. Normally distributed continuous variables (age) were reported using means and standard deviations while medians and ranges were used when continuous variables were not normally distributed (shedding time). Initially, simple linear regression was done to assess the association between the dependent variable (shedding time) and each predictor variable. Predictor variables with a p-value of <0.1 from the simple linear regression were added to the multiple linear regression model. We determined differences in shedding time by age, sex, hospital admission status and province. We reported effect estimates and 95% confidence intervals. We considered a p-value of less than 0.05 as statistically significant. Ethical Considerations We received ethics approval from the Human Research Ethics Committee at the University of the Witwatersrand (Clearance Certificate No. M210443). 23 RESULTS Characteristics of the study population There were 1 057 161 COVID-19 cases reported from 5 March to 31 December 2020 in South Africa. Out of these cases, 2752 (0.26%) met the inclusion criteria. Figure 1 shows details of the participant selection process. The 2752 included participants had a total of 8 642 PCR positive test results with a median of 3 positive tests per participant (range: 2 – 11). The majority (74.7%: 2,057/2752) of the participants had 3 consecutive positive tests. Table 1 describes the characteristics of the study population. There were more males than females (53% versus 47%). Participants ages ranged from 0-99 years, with a mean of 46.4 years (SD: 17.4). Individuals aged 35-44 years were the most frequently represented age group (22.9%) while 0- 14 year-olds were the least frequent age group (1.2%). Of all the participants, 39.9% were inpatients and 60.1% were outpatients. Gauteng province had the highest number of participants with 987 (35.9%) cases and Northern Cape had the lowest with 39 (1.4%) cases. Shedding time in COVID-19 patients Table 2 summarizes the distribution of shedding time by age, sex, province, and admission status. The median duration of viral shedding was 17 days, ranging from 1 to 128 days from the date of specimen collection or symptoms onset date. There was no significant difference in shedding time between males (median:16 days, range:1-128) and females (median:17 days, range:1-94) (Figure 2). Viral shedding time between inpatients (median: 16 days, range 1-108) and outpatients (median:17 days, range:1-128) did not differ much either. Also, there was no significant difference in shedding time between age groups. Individuals aged 0-14 years had the lowest shedding time (median:14 days, range:1-72) while those aged 25-34 years had the highest shedding time (median:18 days, range:1-128) (Figure 3). The shortest shedding time was 24 observed in Eastern Cape with a median of 15 days (range: 1-56) and the longest shedding time was observed in Limpopo with a median of 21 days (range: 1-39). Factors associated with COVID-19 shedding time In Table 3, we assessed factors associated with shedding time in COVID-19 patients in South Africa. We applied simple and multiple linear regression to compare the association between predictor variables (age, sex, province and hospital admission status) and SARS-CoV-2 shedding time. Following unadjusted analysis, shedding time did not vary with sex and age group. Regarding province of residence, Eastern Cape was considered the baseline for comparisons. Shedding time in Free State and Northern Cape did not differ from shedding time in Eastern Cape. However, shedding time was longer in KwaZulu-Natal (coefficient: 0.3, CI: 0.1 ─ 0.5, P- value: <0.001), Limpopo (coefficient: 0.6, CI: 0.3 ─ 0.9, P-value: <0.001), Mpumalanga (coefficient: 0.5, CI: 0.2 ─ 0.8, P-value: 0.002), North West (coefficient:0.4, CI: 0.1 ─ 0.7, P- value: 0.012) and Western Cape (coefficient: 0.3, CI: 0.1 ─ 0.5, P-value: 0.001). Shedding time was shorter among hospitalized patients compared to outpatients (coefficient: -0.1: -0.2 ─ -0.04, P-value: 0.007). After adjusting for age, sex and province, shedding time remained shorter for hospitalized patients compared to outpatients (coefficient: -0.14, CI: -0.24 ─ -0.03, P-value: 0.014). Province, sex and age, were not associated with shedding time. DISCUSSION This study aimed to estimate the viral shedding time among laboratory-confirmed COVID-19 cases in South Africa. We found that the median shedding time our sample was 17 days from symptoms onset date or date of specimen collection. A study conducted on the Canadian population [16] found that the median shedding time was 19 days which is 2 days more than 25 our findings among the South African population. Another study conducted in the United States of America reported a median shedding time of 21 days within their population, which is higher than the shedding time we found within our population [17]. The difference in shedding time between South Africa, Canada and the United States could be due to the difference in population characteristics in these geographic locations. Our findings are similar to those reported in a study conducted in China which found that the median shedding time among adults was 17 days [6]. Although our study shows a similar median shedding time to that of China, our study included both children and adults and covered a wider population whereas the study conducted in China only included adults. Shedding time ranged from 1 to 128 days from the date of specimen collection or symptoms onset date. Our study did not show a significant difference in the median shedding time between males and females and between hospitalized and outpatients. Our study is supported by another study that found no significant difference in the median shedding time between males and females and further reported that prolonged shedding was not affected by signs and symptoms of COVID-19 [18]. Another study conducted on outpatients and inpatients in the United States of America found that the viral shedding time of inpatients was similar to that of outpatients [17]. Our study also shows no significant difference in shedding time between age groups. Age group 0-14 years had a median shedding time of 14 days which was the lowest among other age groups. Age group 25-34 reported the highest shedding time with a median shedding time of 18 days. The slight difference in shedding time between age groups might be due to the fact that people live in families and communities where people of different age groups live together, allowing for transmission to happen within different age groups. Our study found that South 26 African children had a lower shedding time compared to children in South Korea who were reported to have an average shedding time of 17.6 days [19]. Another study found that children less than 18 years had a shorter shedding time compared to other age groups [17]. The study further highlights that viral shedding can happen in children presenting with symptoms and those without symptoms, with a shedding time of up to 3 weeks (21 days). Children and young adults are more likely to have mild disease and less likely to have severe or fatal COVID-19 compared to adults [18]. The age-related difference in severity of COVID-19 in children and adults could be due to the fact that children have lower intensity of exposure to SARS-CoV-2. Furthermore, it is thought that children have a stronger innate immune system, which acts as the first line of defense against SARS-CoV-2 [19]. Older adults usually have a higher prevalence of comorbidities, such as obesity, hypertension and heart disease, which are associated with severe COVID-19 compared to children [20]. Although children can also experience severe clinical disease, they are less affected by COVID-19 than adults. In general, testing of is patterns in children are different from those in adults and children are most often tested when they are symptomatic or a contact of a case. SARS-CoV-2 is shed when talking, coughing, sneezing, or even exhaling. Studies also show that the virus can be shed via feces and urine and can continue for several weeks [23,24]. This makes it possible for the virus to spread in schools where they share classrooms and objects and in pre-schools where children are taken care of by the same teacher. Viral shedding can continue for longer periods in some patients. Two cases had a shedding time greater than 100 days. The cases were a 77-years-old inpatient male who shed the virus for 108 days and a 34-year-old outpatient male who shed the virus for up to 128 days. A study of 38 COVID-19 patients with prolonged shedding reported that the longest shedding time they observed was 118 days, and was a 77-year-old male [25]. It is difficult to determine why 27 certain individuals have such prolonged shedding and further research is needed to better understand this. A study conducted on six COVID-19 patients shows that prolonged viral shedding can be observed in both asymptomatic and severe patients [25]. Severe disease, comorbidities, socioeconomic status, old age and male sex have been reported as factors associated with prolonged SARS-CoV-2 viral shedding [13,16,21,26]. Other studies have reported prolonged shedding, however, prolonged shedding was mainly reported in patients with severe disease [18]. The Centers for Disease Control and Prevention (CDC) recommends that people with possible COVID-19 exposure should quarantine for 14 days and people who test positive should isolate for 10 days from the date of symptoms onset [27]. South Africa however recommends 10 days of isolation or quarantine for asymptomatic patients, and patients with mild, moderate or severe disease [28]. Of people infected with COVID-19, only 20% experience moderate or severe disease and require hospitalization. Eighty per cent experience mild COVID- 19 and isolate from home. Also, it has been reported that asymptomatic or pre- symptomatic cases are the main drivers of COVID-19, accounting for about 50% of new COVID-19 infections [26]. Therefore, it is important to monitor viral shedding in patients with mild disease because they can shed the virus longer and could potentially be a source of future COVID-19 outbreaks. Our findings indicate that infection control strategies provided by CDC and the NDoH should take into account prolonged shedding and its impact on disease transmission. Our study found a significant association between hospitalized patients and shedding time. Inpatients had 1 day less shedding time compared to hospitalized patients. Studies show that COVID-19 patients who are hospitalized usually have COVID-19 symptoms, comorbidities and 28 are more likely to be older [30,31,32,34]. In line with another study, our study found no association between age and shedding time [35]. However, some studies have reported that older age is associated with prolonged shedding [36,37]. Furthermore, male sex has been reported to have an association with prolonged shedding however, our study found no association between sex and viral shedding time [38]. Several studies have reported no association between viral shedding time and sex [37, 39,40]. Although the variable province was not statistically significant, the association between shedding time and province varied. KwaZulu-Natal, Gauteng, Limpopo, North West, Mpumalanga, and Western Cape had a significant association with shedding time. The differences in shedding time between different regions may be due to differences in comorbidities, socioeconomic status, type of test used, sample size and disease severity. Our study had some limitations. Firstly, data received from the NMCList did not include clinical information about the cases. Secondly, due to the secondary nature of the study, we could not get certain variables that could have assisted in determining other factors affecting shedding time within the population. Lastly, viral shedding before symptom onset and before testing was not measurable using the data we obtained. It is likely that viral shedding occurs beyond the time points we have described. It is also possible that the shedding time presented in our study is likely to be an underestimate of the true shedding time in the population. CONCLUSION The duration of viral shedding within the population of South Africa varies from 1 to 128 days. Hospitalized patients are associated with SARS-CoV-2 shedding time. Our findings indicate that infection control strategies should take into account factors affecting shedding time such as disease severity. 29 NOTES Conflict of interest No conflict of interest declared Acknowledgements We would like to acknowledge the NICD COVID-19 Outbreak Response Team and the South African Field Epidemiology Training Programme for their support and guidance. We would also like to acknowledge Stanford Kwenda, Ndivhuwo Munava, Lincoln Darwin, Tsumbedzo Mukange and Trevor Bell. Author contributions Carroll Tshabane and NV Motaze conceived the study question. All authors reviewed and approved the study protocol. Carroll Tshabane conducted the study, analyzed the data and drafted the manuscript. Co-Authors were responsible for study supervision and reviewing the manuscript. 30 REFERENCES 1. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. 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Johansson MA, Quandelacy TM, Kada S, et al. SARS-CoV-2 Transmission from People without COVID-19 Symptoms. JAMA Netw Open 2021; 4:1–8. 30. Weinbergerova B, Mayer J, Hrabovsky S, et al. COVID-19’s natural course among ambulatory monitored outpatients. Sci Rep 2021; 11:1–16. 31. Pandita A, Gillani FS, Shi Y, et al. Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island. PLoS One 2021; 16:1–15. 32. Pouw N, de Maat J van, Veerman K, et al. Clinical characteristics and outcomes of 952 hospitalized COVID-19 patients in the Netherlands: A retrospective cohort study. PLoS One 2021; 16:1–15. 33. Sandoval M, Nguyen DT, Vahidy FS, Graviss EA. Risk factors for severity of COVID-19 in hospital patients age 18-29 years. PLoS One 2021; 16:1–22. 34. Abrahim SA, Tessema M, Defar A, et al. Time to recovery and its predictors among adults hospitalized with COVID-19: A prospective cohort study in Ethiopia. PLoS One 2020; 15:e0244269. 35. Yan D, Liu XY, Zhu YN, et al. Factors associated with prolonged viral shedding and impact of lopinavir/ritonavir treatment in hospitalised non-critically ill patients with SARS-CoV-2 infection. Eur Respir J 2020; 56. 36. Zhou C, Zhang T, Ren H, et al. Impact of age on duration of viral RNA shedding in patients with COVID-19. Aging (Albany NY) 2020; 12:22399–22404. 37. Xu K, Chen Y, Yuan J, et al. Factors associated with prolonged viral RNA shedding in 35 patients with COVID-19 Kaijin. 2020; :1–27. 38. Stehlik P, Alcorn K, Jones A, Schlebusch S, Wattiaux A, Henry DA. Repeat testing for SARS-CoV-2: persistence of viral RNA is common, and clearance is slower in older people. Med J Aust 2021; 214:468–470. 39. Ji J, Zhang J, Shao Z, Xie Q, Zhong L, Liu Z. Glucocorticoid therapy does not delay viral clearance in COVID-19 patients. Crit Care 2020; 24:2–5. 40. Chen X, Zhang Y, Zhu B, et al. Associations of Clinical Characteristics and Antiviral Drugs with Viral RNA Clearance in Patients with COVID-19 in Guangzhou, China. SSRN Electron J 2020; 36 TABLES Table 1: Baseline characteristics of COVID-19 patients in South Africa, 2020 Number of cases (n) Percentage (%) Age Group (years) 0-14 15-24 25-34 35-44 45-54 55-64 65+ 46 172 516 630 547 418 419 1.7 6.3 18.8 22.9 19.9 15.2 15.3 Sex Male Female Unknown 1,456 1,291 5 52.9 46.9 0.2 Admission status Inpatient Outpatient 1,099 1,653 39.9 60.1 Province Eastern Cape Free State Gauteng KwaZulu-Natal Limpopo Mpumalanga North West Northern Cape 283 70 987 603 94 95 94 39 10.3 2.5 35.9 21.9 3.4 3.5 3.4 1.4 37 Western Cape 487 17.7 Number of positive tests 2 3 4 5 6 7 8 9 11 255 2,057 314 86 20 12 3 4 1 9.3 74.7 11.4 3.1 0.7 0.4 0.1 0.2 0.04 38 Table 2: Shedding time of laboratory-confirmed COVID-19 cases in South Africa, 2020 Number of cases n(%) Median (Range) Overall shedding time 2 752 (100) 17 (1-128) Age Group (years) 0-14 15-24 25-34 35-44 45-54 55-64 65+ 46 (1.7) 172 (6.3) 516 (18.8) 630 (22.9) 547 (19.9) 418(15.2) 419 (15.3) 14 (1-72) 16 (1-56) 18 (1-128) 17 (1-78) 17 (1-84) 15 (1-94) 16 (1-108) Sex Male Female 1,456 (53) 1,291 (47) 16 (1-128) 17 (1-94) Admission status Inpatient Outpatient 1,099 (39.9) 1,653 (60.1) 16 (1-108) 17 (1-128) Province Eastern Cape Free State Gauteng KwaZulu-Natal Limpopo Mpumalanga North West Northern Cape Western Cape 283 (10.3) 70 (2.5) 987 (35.9) 603 (21.9) 94 (3.4) 95 (3.5) 94 (3.4) 39 (1.4) 487 (17.7) 15 (1-56) 16 (1-67) 17 (1-128) 17 (1-84) 21 (1-39) 20 (1-78) 16 (1-54) 16 (1-51) 17 (1-84) 39 Table 3: Univariate and multivariate analysis of factors affecting viral shedding time in COVID- 19 patients in South Africa, 2020 Bivariate Multivariate Coefficient 95%CI P-value Coefficient 95%CI P-value Age Groups (years) 0-14 15-24 25-34 35-44 45-54 55-64 65+ -0.2 -0.01 0.1 0.03 -0.1 -0.1 Ref* -0.6 - 0.2 -0.4 - 0.4 -0.3- 0.5 -0.4 - 0.4 -0.5- 0.3 - 0.5 – 0.3 0.376 0.952 0.696 0.870 0.488 0.506 - Sex Male Female -0.3 -0.1 - 0.07 Ref* 0.576 - Admission status Inpatient Outpatient -0.1 -0.2 - -0.04 Ref* 0.007 -0.1 -0.2- -0.03 0.014 Province Eastern Cape Free State Gauteng KwaZulu-Natal Limpopo Mpumalanga North West Northern Cape 0.2 0.4 0.3 0.6 0.5 0.4 0.04 Ref* -0.2 - 0.5 0.2 - 0.6 0.1 - 0.5 0.3 - 0.9 0.2 - 0.8 0.1 - 0.7 -0.4 - 0.5 0.339 <0.001 <0.001 <0.001 0.002 0.012 0.857 - 40 Western Cape 0.3 0.1 - 0.5 0.001 *p-values – overall p-values for each exposure variable in the model, CI – Confidence Intervals Bivariate analysis, Adjusted - Multivariable analysis, Ref* - Reference category - Not included in the final model because it is not significant (p>0.005) 41 FIGURE LEGENDS Figure 1: Flow chart showing the participant selection process. Figure 2: Distribution of shedding time of COVID-19 cases in South Africa by sex, 2020 Figure 3: Distribution of shedding time by age group (years) in South Africa by sex, 2020 42 Figure 1 Figure 2 43 Figure 3 44 CHAPTER 3: EXTENDED METHODS This chapter presents additional research materials and methods that were not included in the manuscript due to the manuscript word count. These include record linkage and ethical considerations. 3.1 Record linkage We applied a deterministic method to identify repeat test results from the same individuals and to link patients between the NMCList and DATCOV databases. The national identity or passport number was used as the linking variable. The linkage helped us determine patients who were admitted and who were not. 3.2 Ethical Considerations For further permission to use COVID-19 surveillance data, we obtained ethical approval for the NMC surveillance system from the Human Research Ethics Committee at the University of the Witwatersrand (Approval number: M160667). Privacy of participants was ensured by removing personal identifiers (name, surname, ID number, telephone number, address) and using the unique identifier in the NMCSS line list database. The study data was stored on a password-protected computer and data was only accessible to the study team. 45 CHAPTER 4: EXTENDED RESULTS This chapter presents additional findings of the study. The results are presented in percentages and a chart. We provide analysis and interpretation of findings based on the results of the statistical analysis performed. In the manuscript, we reported that the median number of tests done by COVID-19 patients in our study was 3. Patients who had tested 3 times accounted for 75% (2057/ 2752) of the study population. The highest number of tests done was 11 and were done by 1 patient who was an inpatient. Inpatients had the highest numbers of tests conducted compared to outpatients, with 6 tests done by 65%, 7 tests (91.7%), 8 tests (66.6%), 9 and 11 tests done by inpatients only (Figure 4.1). Figure 4.1: Number of positive test results by COVID-19 admission status (inpatients patients and Outpatients) in South Africa, 2020 100% 90% 80% 70% 60% 50% Inpatient 40% Outpatient 30% 20% 10% 0% 2 3 4 5 6 7 8 9 11 Number of tests N u m b e r o f c a s e s ( % ) 46 CHAPTER 5: OVERALL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS This chapter presents the main findings of the study. The results are presented in percentages, charts and tables. We provide analysis and interpretation of findings based on the results of the statistical analysis performed. 5.1 DISCUSSION Recent guidelines in South Africa states that COVID-19 retesting after the isolation period is not needed1. These guidelines were set on the basis that the presence of a detectable virus on a positive case does not necessarily indicate infectiousness. Even though continuous positivity from a PCR- test is not used to indicate continuous infectivity as assumed at the beginning of the pandemic, an RT-PCR test is still the most reliable for SARS-CoV-2 detection and diagnosis2. For infection control, symptom and test-based strategies are used in different settings. Symptoms based strategy is done by calculating time passed from symptom onset and test-based strategy makes the use of a negative SARS-CoV-2 RT-PCR result. Furthermore, other factors are used to determine virus transmissibility positivity besides the use of viral load. Viral shedding time varied within provinces across the country. Eastern Cape observed the shortest shedding time with a median of 15 days. Free State, North West and Northern Cape had a shedding time with a median of 16 days. They were followed by Gauteng, KwaZulu-Natal and Western Cape with a median of 17 days. Mpumalanga and Limpopo observed the highest shedding times with a median of 20 and 21 days, respectively. From the literature explored, factors associated with shedding time may differ according to the setting. This is evident in studies that were done in other countries and regions where we observed varying shedding durations. For example, the median shedding time in Changsha was (17 days)3, Singapore (12 days)4, Ethiopia (19 days)5, Tianjin (17 days)6 and Netherlands (8 days)7. This finding is similar to a study conducted in Germany that reported an association of viral shedding with disease severity which was defined by whether a patient was on mechanical ventilation or not8. Whereas Free State and Northern Cape were not statistically significant. Eastern Cape was used as a reference group. The difference in shedding time among provinces could be due to varying socioeconomic status and age distribution. 47 5.2 LIMITATIONS In the manuscript, we mentioned the lack of clinical information from the data obtained from the NMCList and the DATCOV system. Although our data contained inpatients and outpatients, we could not report disease severity due to the lack of supporting information such as treatment given to patients. Our study contained a small sample of children, making it hard to generalize the findings of shedding time among children. Since we could not measure viral shedding before testing or before symptoms onset, we assumed that the viral shedding began on the date of symptom onset or date of specimen collection. This could have led to an underestimation of viral shedding time. This study only included cases that tested using an RT-PCR test and excluded cases that were tested using antigen tests. As a result, it is possible that our study might have underestimated shedding time within the population. Furthermore, we only addressed viral shedding time from the upper respiratory specimen sampled from the oropharyngeal and nasopharyngeal. With studies showing that cases also shed the virus using other channels such as feces and urine, we may need to compare shedding time from our study with shedding time from other studies that estimated shedding time using other routes. Since our study used the RT-PCR test, we need to consider the fact that there is a possibility that the positive SAR-CoV-2 results detected on the test may not have a correlation with infectivity. Although our study had limitations, the study design used enabled us to obtain data across all provinces within the country and enabled us to obtain a bigger sample size that is representative of the population. Our study enabled us to explore multiple settings which reduces selection bias. 5.3 CONCLUSION This study provided details on viral shedding time within the population of South Africa. Our study found a significant association between the hospital admission status of COVID-19 patients and shedding time. There was a 1-day difference in shedding time between hospitalized and outpatients. Therefore, there is a need to strengthen prevention and control strategies such as contact tracing as both hospitalized and outpatients can shed the virus for a similar duration. We discovered that factors such as province, age and sex were not associated with shedding time. The study showed that some people can shed the virus longer than others. Although prolonged shedding might not necessarily indicate infectiousness, individual patient monitoring and management is needed for patients with prolonged shedding. Furthermore, to control SARS-CoV-2 infection, 48 different approaches that can address transmission risk effectively in asymptomatic patients are required. 5.4 RECOMMENDATIONS Our study only looked at four predictor variables (sex, admission status, age and province). We recommend that further studies be conducted, including more variables such as comorbidities, socio-economic status, level of education and vaccination status. Since continuous positivity might not necessarily mean infectivity, we recommend that future studies consider evaluating live viruses within the population and also do a following up on cases with prolonged shedding to assess their contacts. This can assist in figuring out infectivity and can be used to inform isolation and quarantine requirements. We also recommend that studies evaluate the impacts of possible symptom recrudescence on shedding time and its impacts on SARS-COV-2 transmission. 49 REFERENCES 1. DEPARTMENT OF HEALTH. Government Gazette Staatskoerant. Vol. 583, Government Gazette. 2020. 2. Centers for Disease Control and Prevention (CDC). SARS | Guidance | Lab | Diagnostic Assays in Community Preparedness and Response | CDC [Internet]. [cited 2021 Oct 26]. Available from: https://www.cdc.gov/sars/guidance/f-lab/assays.html 3. Qi L, Yang Y, Jiang D, Tu C, Wan L, Chen X, et al. Factors associated with the duration of viral shedding in adults with COVID-19 outside of Wuhan, China: a retrospective cohort study. Int J Infect Dis. 2020;96:531–7. 4. Young BE, Ong SWX, Kalimuddin S, Low JG, Tan SY, Loh J, et al. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA. 2020 Apr 21;323(15):1488–94. 5. Abrahim SA, Tessema M, Defar A, Hussen A, Ejeta E, Demoz G, et al. Time to recovery and its predictors among adults hospitalized with COVID-19: A prospective cohort study in Ethiopia. PLoS One. 2020 Dec 1;15(12):e0244269. 6. Han J, Shi LX, Xie Y, Zhang YJ, Huang SP, Li JG, et al. Analysis of factors affecting the prognosis of COVID-19 patients and viral shedding duration. Epidemiol Infect. 2020;148. 7. van Kampen JJA, van de Vijver DAMC, Fraaij PLA, Haagmans BL, Lamers MM, Okba N, et al. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19). Nat Commun 2021 121. 2021 Jan 11;12(1):1–6. 8. Weinbergerova B, Mayer J, Hrabovsky S, Novakova Z, Pospisil Z, Martykanova L, et al. COVID-19’s natural course among ambulatory monitored outpatients. Sci Rep. 2021;11(1):1–16. http://www.cdc.gov/sars/guidance/f-lab/assays.html 50 APPENDICES Appendix 1: Plagiarism declaration 51 Appendix 2: Conference Abstract 52 53 Appendix 3: Conference Attendance certificate 54 Appendix 4: Turnitin Report 55 Appendix 5: Ethics clearance certificates 56 57 Appendix 6: Clinical Infectious Diseases Guidelines Available from: https://academic.oup.com/cid/pages/Manuscript_Preparation#Manuscript%20format%20and%20 structure https://academic.oup.com/cid/pages/article_types#Major%20Articles https://academic.oup.com/cid/issue-pdf/40/1/859089 MANUSCRIPT FORMAT AND STRUCTURE Major Articles Report clinically relevant investigations or observations within CID s scope of interests. 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Recommendations for the use of antiretroviral drugs in pregnant HIV-1 infected women for maternal health and interventions to reduce perinatal HIV-1 transmission in the United States. Available at: http://www.aidsinfo.nih.org.