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Associations between wearing masks, washing hands, and social distancing practices, and
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risk of COVID-19 infection in public: a cohort-based case-control study in Thailand
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Pawinee Doung-ngern,1 Repeepong Suphanchaimat,1,2 Apinya Panjangampatthana,1 Chawisar
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Janekrongtham,1 Duangrat Ruampoom,1 Nawaporn Daochaeng,1 Napatchakorn Eungkanit,1
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Nichakul Pisitpayat,1 Nuengruethai Srisong,1 Oiythip Yasopa,1 Patchanee Plernprom,1 Pitiphon
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Promduangsi,1 Panita Kumphon,1 Paphanij Suangtho,1 Peeriya Watakulsin,1 Sarinya Chaiya,1
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Somkid Kripattanapong,1 Thanawadee Chantian,1 Chawetsan Namwat,1,2 Direk
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Limmathurotsakul,3,4,5
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1 Department of Disease Control, Ministry of Public Health, Tiwanon Road, Nonthaburi, 11000,
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Thailand.
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2 International Health Policy Program (IHPP), Ministry of Public Health, Tiwanon Road,
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Nonthaburi, 11000, Thailand.
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3 Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol
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University, Rajvithi Road, Bangkok, 10400, Thailand
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4 Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Rajvithi
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Road, Bangkok, 10400, Thailand
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5 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University
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of Oxford, Old Road Campus, Oxford, OX3 7LG, United Kingdom
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Corresponding author: Direk Limmathurotsakul, 420/6 Mahidol-Oxford Tropical Medicine
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Research Unit, Faculty of Tropical Medicine, Rajvithi Road, Bangkok, Thailand, 10400. Tel.
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+66-2-203-6333 Email: direk@tropmedres.ac
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Alternative corresponding author: Pawinee Doung-ngern, Department of Disease Control,
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Ministry of Public Health, Nonthaburi, 11000, Thailand. Email: pawind@gmail.com
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Word count: Abstract 368, Main Text 3,930
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Keywords: COVID-19, SARS-CoV-2, mask, medical mask, non-medical mask, hand washing,
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social distancing, contact tracing
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Abstract
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Objective. To investigate whether wearing masks, washing hands and social distancing practices
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are associated with lower risk of COVID-19 infection.
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Design. A retrospective cohort-based case-control study. All participants were retrospectively
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interviewed by phone about their preventive measures against COVID-19 infection.
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Setting. Thailand, using the data from contact tracing of COVID-19 patients associated with
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nightclub, boxing stadium and state enterprise office clusters from the Surveillance Rapid
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Response Team, Department of Disease Control, Ministry of Public Health. Contacts were tested
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for COVID-19 using PCR assays per national contact tracing guidelines.
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Participants. A cohort of 1,050 asymptomatic contacts of COVID-19 patients between 1 and 31
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March 2020.
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Main outcome measures. Diagnosis of COVID-19 by 21 April 2020. Odds ratios for COVID-19
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infection and population attributable fraction were calculated.
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Exposure. The study team retrospectively asked about wearing masks, washing hands, and social
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distancing practices during the contact period through telephone interviews.
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Results. Overall, 211 (20%) were diagnosed with COVID-19 by 21 Apr 2020 (case group) while
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839 (80%) were not (control group). Fourteen percent of cases (29/210) and 24% of controls
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(198/823) reported wearing either non-medical or medical masks all the time during the contact
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period. Wearing masks all the time (adjusted odds ratio [aOR] 0.23; 95%CI 0.09-0.60) was
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associated with lower risk of COVID-19 infections compared to not wearing masks, while wearing
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masks sometimes (aOR 0.87; 95%CI 0.41-1.84) was not. Shortest distance of contact >1 meter
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(aOR 0.15; 95%CI 0.04-0.63), duration of close contact ≤15 minutes (aOR 0.24; 95%CI 0.07-
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0.90) and washing hands often (aOR 0.33; 95%CI 0.13-0.87) were significantly associated with
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lower risk of infection. Sharing a cigarette (aOR 3.47; 95%CI 1.09-11.02) was associated with
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higher risk of infection. Type of mask was not independently associated with risk of infection.
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Those who wore masks all the time were more likely to wash hands and practice social distancing.
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We estimated that if everyone wore a mask all the time, washed hands often, did not share a dish,
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cup or cigarette, had shortest distance of contact >1 meter and had duration of close contact ≤15
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minutes, cases would have been reduced by 84%.
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Conclusions. Our findings support consistently wearing non-medical masks, washing hands, and
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social distancing in public to prevent COVID-19 infections.
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Introduction
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There is an urgent need to evaluate the effectiveness of wearing masks by healthy persons in the
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general public against COVID-19 infections.1 2 During the early stages of the outbreak of COVID-
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19, the World Health Organization (WHO) announced on 27 February 2020 that, “For
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asymptomatic individuals, wearing a mask of any type is not recommended”.3 The rationale, at
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that time, was to avoid unnecessary cost, procurement burden, and a false sense of security.3 4 A
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number of systematic reviews also found no conclusive evidence to support the widespread use of
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masks in public against respiratory infectious diseases such as influenza, SARS and COVID-19.5-
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8 However, China and many countries in Asia including South Korea, Japan and Thailand have
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recommended the use of face mask among the general public since early in the outbreak.9 There
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is also increasing evidence that COVID-19 patients can have a “pre-symptomatic” period, during
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which infected persons can be contagious and, therefore, transmit the virus to others before
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symptoms develop.2 This led to the change of the recommendation of the US Centers for Disease
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Control and Prevention, updated on 4 April 2020, from warning the public against wearing face
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masks to advising everyone to wear a cloth face covering when in public.10 On 6 April and 5 June
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2020, WHO updated their advice on the use of masks for the general public, and encouraged
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countries that issue the recommendations to conduct research on this topic.2
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Thailand has been implementing multiple measures against transmission of COVID-19 since the
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beginning of the outbreak. The country has established thermal screening at airports since 3
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January 2020, and detected the first case of COVID-19 outside China, a traveler from Wuhan
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arriving at Bangkok Suvarnabhumi airport, on 8 January 2020.11 The country utilized the
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Surveillance and Rapid Response Team (SRRT), together with Village Health Volunteers, to
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perform contact tracing, educate the public about the disease and monitor the close contacts of
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COVID-19 patients in quarantine. The SRRT is an epidemiologic investigation team trained to
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conduct surveillance, investigations and initial controls of communicable diseases; including
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H5N1, SARS and MERS.12 13 Currently, there are more than 1,000 SRRTs established at district,
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provincial and regional levels in the country,12 working on contact tracing for COVID-19. In
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February 2020, public pressure to wear masks was high, medical masks were difficult to procure
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by the public, and the government categorized medical masks as price-controlled goods and
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announced COVID-19 as a dangerous communicable disease according to the Communicable
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Disease Act 2015 in order to empower officials to quarantine contacts and close venues.14 15 On 3
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March, the Ministry of Public Health (MoPH) announced the recommendation of cloth mask for
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the public.16 On 18 March, schools, universities, bars, nightclubs and entertainment venues were
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closed.17 On 26 March, while the country was reporting approximately 100-150 new COVID-19
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patients per day, the government declared a national state of emergency, prohibited public
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gatherings, and enforced everyone to wear a face mask on public transport.18 19 On 21 April, 19
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new PCR-confirmed COVID-19 patients were announced by the Ministry of Public Health
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(MoPH), Thailand, bringing the total number of patients to 2,811 patients.20
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Given the lack of currently available evidence, we evaluated the effectiveness of mask wearing,
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hand washing, social distancing and other preventive measures against COVID-19 infection in
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public in Thailand
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Methods
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Study design.
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We conducted a retrospective case-control study in which both cases and controls were drawn
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from a cohort of contact tracing records of the central SRRT team, Department of Disease Control
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(DDC), MoPH, Thailand (Figure 1). Contacts were defined by the DDC MoPH as individuals who
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had activities together with or were in the same location(s) as a COVID-19 patient.21 22 Contacts
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were classified by MoPH as high-risk contacts if they were family members or lived in the same
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household as a COVID-19 patient, if they were within 1-meter distance longer than 5 minutes of
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a COVID-19 patient, if they were exposed to cough, sneeze or secretions of a COVID-19 patient
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and were not wearing a protective gear, such as mask, or if they were in the same closed
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environment (e.g. room, nightclub, stadium, vehicle) within 1-meter distance longer than 15
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minutes of a COVID-19 patient and they were not wearing a protective gear, such as mask.21 22
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Contacts were classified as low-risk contacts if they had activities together with or were in the
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same locations as a COVID-19 patient, but did not fulfil the criteria of a high-risk contact.21 22 All
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high-risk contacts with any symptoms were tested with a PCR assay and quarantined in a hospital
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or a quarantine site.21 22 All high-risk contacts without any symptoms were self-quarantined at
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home.21 22 Before 23 March 2020, all high-risk contacts without any symptoms were tested using
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PCR assays on day 5 after the last date of exposure to a case.21 As of 23 March 2020, all household
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contacts were tested using PCR assays regardless of their symptoms. Other high-risk contacts were
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tested only if they developed any COVID-19 symptoms.22 All low-risk contacts were
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recommended to perform self-monitoring for 14 days, and visit healthcare facilities immediately
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for PCR-assays if they develop any symptoms of COVID-19.21 22 Hence, the main aim of the
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contact tracing was to identify and evaluate contacts, perform PCR diagnostic tests, and quarantine
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high-risk contacts. All PCR tests were performed at laboratories certified for COVID-19 testing
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by the National Institute of Health of Thailand. Data of risk factors associated with COVID-19
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infection, such as type of contact and use of mask, were recorded during the contact investigation,
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but not complete.
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The central SRRT team was tasked to perform contact investigations for any cluster with at least
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five PCR-confirmed COVID-19 patients from the same location(s) within a one-week period.21
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We primarily used these data to identify asymptomatic contacts of COVID-19 patients between 1
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and 31 March 2020. To reduce the bias of the selection of asymptomatic contacts, all contact
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tracing records of the central SRRT team were used in the study.
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We then conducted telephone calls and asked details about their contacts with COVID-19 patients
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(e.g. date, location, duration and distance of contacts), whether they wore masks, washed their
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hands and performed social distancing during the contact period, and whether the COVID-19
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patient, if known, wore a mask. We also asked, and checked using records of the DDC, whether
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and when they were sick and diagnosed with COVID-19. To include only asymptomatic contacts
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in the study, we excluded people from the analysis who already had any symptoms of COVID-19;
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including cough, fever, fatigue, diarrhoea, abdominal pain, loss of appetite, and loss of smell and
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taste,23 24 on the first day of contact. We also excluded contacts whose contact locations were
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healthcare facilities because this study aimed to focus on infection in the public.
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Asymptomatic contacts, cases, controls, index patients, primary index patients and COVID-19
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patients were defined as described in Table 1. The reporting of this study follows the STROBE
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guidelines.
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Selection of cases and controls
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We defined asymptomatic contacts who were later diagnosed as COVID-19 patients using PCR
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assays by 21 Apr 2020 as cases (Table 1). All asymptomatic contacts who were not diagnosed as
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COVID-19 patients using PCR assays by 21 Apr 2020 were controls. We arbitrarily used 21 days
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after 31 March as the cutoff based on the evidence that most COVID-19 patients would likely
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develop symptoms within 14 days25 and it should take less than another 7 days for symptomatic
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patients, under contact investigations, to present at healthcare facilities and be tested for COVID-
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19 with PCR assays.
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Statistical analysis
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Odds ratios and 95% confidence intervals were estimated for associations between development
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of COVID-19 and baseline covariates, such as wearing masks, washing hands and social distancing
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using logistic regression with a random effect for location and a random effect for index patient
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nested within the same location. The interviewer identified the index patient, the symptomatic
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COVID-19 patient who had the closet contact, if an asymptomatic contact contacted more than
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one symptomatic COVID-19 patient. The percentage of missing values in the variable whether the
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COVID-19 patients wore a mask was 27%, and the variable was not included in the analyses. We
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assumed that missing values were missing at random and used imputation by chained equations.
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We created 10 imputed datasets and the imputation model included all listed confounders and the
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case-control indicator. We developed the final multilevel mixed-effect logistic regression models
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on the basis of previous knowledge and a purposeful selection method.26
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We also estimated odds ratios and 95% confidence intervals for associations between compliance
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of mask wearing and other practices; including washing hands and social distancing using
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multinomial logistic regression models and the imputed data set. Logistic regression was also used
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to estimate p value for pairwise comparisons. Bonferroni correction was not performed. We
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estimated secondary attack rate using definitions as described in Table 1, to allow for comparison
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with other studies.
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Sensitivity analyses
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We conducted a sensitivity analysis by including type of mask in the multilevel mixed-effects
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logistic regression model for COVID-19 infection. We also tested a pre-defined interaction
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between type of mask and compliance of wearing masks.
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Additional analyses
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To respond to the national policy, we estimated population attributable fraction (PAF) using the
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imputed dataset and a direct method based on logistic regression as described previously (details
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in Supplementary Text).27 28 In short, the final multivariable model was modified by considering
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each risk factor dichotomously, and PAF was calculated by subtraction of the total number of
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predicted cases from total number of observed cases, divided by the total number of observed cases.
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STATA version 14.2 and R version 4.0.0 were used for all analyses.
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Participants and public involvement
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No participants were involved in setting the research question or the outcome measures, nor were
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they involved in developing plans for design or implementation of the study. However, the study,
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as part of the outbreak investigation of the DDC, MoPH, was developed to respond to concerns by
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the public about risks and effectiveness of preventive measures of COVID-19 in different settings,
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and which preventive measures should be implemented when public gathering places, including
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restaurants, nightclubs, stadiums, workplaces, etc., were re-opening. No participants were asked
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to aid in interpreting or disseminated the results. There are plans to disseminate the results of the
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research to the public.
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RESULTS
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Characteristics of the cohort data
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The contact tracing of the central SRRT team consisted of 1,716 individuals who had contact with
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or were in the same location as a COVID-19 patient who were associated with three large clusters
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in nightclubs, boxing stadiums and a state enterprise office in Thailand (Figure 1). Overall, we
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considered 18 individuals as primary index patients because they were the first who had symptoms
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at those places, had had symptoms since the first day of visiting those places, or were considered
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to be the origin of infection of cases based on the contact investigations; 11 from the nightclub
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cluster, 5 from the boxing stadium cluster and 2 from the state enterprise office cluster. Timelines
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of primary index patients from nightclub, boxing stadium and state enterprise clusters are
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described in details in Supplementary Text and Supplementary Figure 1-3. All 18 primary index
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patients were excluded from the analysis of the case-control study.
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Selection of cases and controls
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After retrospectively interviewing each contact by phone and applying the exclusion criteria
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(Figure 1), we included 1,050 asymptomatic contacts who had contact with or were in the same
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location as a symptomatic COVID-19 patient between 1 and 31 March 2020 in the analysis. The
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median age of individuals was 38 years (IQR 28-51) and 55% were male (Table 1). Most
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asymptomatic contacts included in the study were associated with the boxing stadium cluster (61%,
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n=645), with 36% (n=374) with the nightclub cluster, and 3% (n=31) with the state enterprise
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office cluster.
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Overall, 211 (20%) asymptomatic contacts were later diagnosed with COVID-19 by 21 Apr 2020
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(case group) and 839 (80%) were not (control group). Of the 211 cases, 150 (71%) had symptoms
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prior to the diagnosis of COVID-19 using PCR assays. The last date that a COVID-19 case
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diagnosed was 9 April 2020. Of 839 controls, 719 (86%) were tested with PCR assays at least once.
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Figure 2 illustrates contacts (and possible transmission of COVID-19 infections) between index
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patients to asymptomatic contacts included in the study. A total of 228, 144 and 20 asymptomatic
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contacts contacted with index patients at nightclubs, boxing stadiums and the state enterprise office,
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respectively. For simplicity, Figure 2 is shown as all of them were contacted with the primary
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index patients in the clusters. The others then contacted with cases associated with nightclubs,
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boxing stadiums and the state enterprise office at workplaces (n=277), households (n=230) and
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other places (n=151).
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Primary analysis
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Table 2 shows that there was a negative association between risk of COVID-19 infection and
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shortest distance of contact >1 meter (adjusted odds ratio [aOR] 0.15, 95% confidence interval
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[CI] 0.04-0.63), duration of contact within 1 meter ≤15 minutes (aOR 0.24, 95%CI 0.07-0.90),
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washing hands often (aOR 0.33, 95%CI 0.13-0.87) and wearing masks all the time (aOR 0.23,
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95%CI 0.09-0.60). Wearing masks sometimes was not significantly associated with lower risk of
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infection (aOR 0.87, 95%CI 0.41-1.84). Sharing cigarettes was associated with higher risk of
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COVID-19 infection (aOR 3.47, 1.09-11.02). Type of masks was not independently associated
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with the risk of infection, and was not included in the final multivariable model.
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Association between compliance of mask wearing and other social distancing practices.
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Since wearing masks all the time was found to be negatively associated with COVID-19 infection,
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we wanted to explore characteristics of those patients because of a potential false sense of security
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caused by wearing masks. We found that those who wore masks all the time were more likely to
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have shortest distance of contact >1 meter (25% vs. 18%, pairwise p=0.03), have duration of
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contact within 1 meter ≤15 minutes (26% vs 13%, pairwise p<0.001) and wash their hands often
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(79% vs. 26%, pairwise p<0.001) compared with those who did not wear masks (Table 3). We
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found that those who wore masks sometimes were more likely to wash their hands often (43% vs.
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26%, pairwise p<0.001) compared with those who did not wear masks. However, they were more
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likely to had physical contact (50% vs. 42%, pairwise p=0.03) and duration of contact within 1
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meter >60 minutes (75% vs. 67%, pairwise p=0.04) compared with those who did not wear masks.
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Secondary attack rate
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Overall, 982 (94%) were contacts with high-risk exposure. All 68 asymptomatic contacts without
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high-risk exposure were controls. Among asymptomatic contacts with high-risk exposure included
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in the study, the nightclub secondary attack rate was 16% (35/213), the boxing stadium secondary
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attack rate was 87% (125/144), the workplace secondary attack rate was 4% (11/250), the
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household secondary attack rate was 17% (38/230), and the secondary attack rate at other places
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was 1% (2/145).
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Sensitivity analyses
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Since aOR of type of mask could be useful for future studies, we modified the final multivariable
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model and presented those aOR in the Supplementary Table 1. Interaction between type of mask
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and compliance of mask wearing was not observed.
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Population attributable fraction (PAF)
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Using the direct method to calculate PAF, we estimated that the proportional reduction in cases
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that would occur if everyone wore a mask all the time during contact with index patients (PAF of
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not wearing masks all the time) was 0.28 (Table 4). Among modifiable risk factors evaluated, PAF
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of shortest distance of contact <1 meter was highest at 0.40. If everyone wore a mask all the time,
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washed hands often, did not share a dish, cup or cigarette, had shortest distance of contact >1 meter
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and had duration of close contact ≤15 min, cases would have been reduced by 84%.
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DISCUSSIONS
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Statement of principal findings
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This cohort-based case-control study provides a supporting evidence that wearing masks, washing
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hands and social distancing are independently associated with lower risk of COVID-19 infection
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in the general public. We observed that wearing masks all the time when expose to someone with
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COVID-19 was associated with lower risk of infection, while wearing masks sometimes was not.
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This supports the recommendation that people should be wearing their masks correctly at all times
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in public and at home when there is an increased risk.2 4 9 10
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We also quantified the effectiveness of different measures that could be implemented to prevent
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transmission in nightclubs, stadiums, workplaces and other public gathering places. We found that
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those who wore masks all the time were also more likely to wash hands and perform social
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distancing. We estimated that adopting all recommendations (wear masks all the time, wash hands
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often, not sharing dishes, cups or cigarettes, maintain a distance of <1 meter and, if needed, have
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less than 15 minutes contact) could result in controlling 86% of the burden of COVID-19 infections
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in our setting during the study period. We recommend that all public gathering places consider
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multiple measures to prevent transmission of COVID-19 and new pandemic diseases in the future.
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Public messaging on how to wear masks correctly needs to be consistently delivered, particularly
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among those who wear masks sometimes or incorrectly (e.g. not covering both nose and mouth).
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This is because, based on our findings, those who wear masks intermittently could be a group that
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did not practice social distancing adequately.
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Comparison with other studies
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The effectiveness of wearing masks observed in this study is consistent with previous studies;
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including a randomized-controlled trial (RCT) showing that adherent use of a face mask reduce
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the risk of influenza-like illness29 and case-control studies which found that wearing masks is
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associated with lower risk of SARS infection.30-32 While previous studies found use of surgical
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masks or 12–16-layer cotton masks demonstrated protection against coronavirus infection in the
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community,30-32 we did not observe a difference between wearing non-medical and medical masks
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in the general population. Therefore, we strongly support wearing non-medical masks in public to
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prevent COVID-19 infections. Even though the risk perception of COVID-19 threat can increase
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the likelihood of wearing medical masks in other settings,33 we maintain that medical masks should
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be reserved for healthcare workers.
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This study found a negative association between risk of COVID-19 infection and social distancing
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(i.e. distance and duration of contact), which is consistent with previous studies which found that
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at least 1-meter physical distancing was strongly associated with a large protective effect, and
328
distances of 2 meters could be more effective.32 Effectiveness of hand hygiene is consistent with
329
the previous studies.34 Although sharing dishes or cups was not independently associated with the
330
infection in our study, based on previous studies,35 we still recommend not sharing dishes or cups.
331
332
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17
The household secondary attack rate in our study (17%) is comparable with those reported ranging
333
from 11% to 19%,35 36 and relatively high compared to workplaces and other places. While
334
challenging and sometimes impractical, household members should immediately separate a person
335
who develops any possible symptoms of COVID-19 from other household members (i.e. a sick
336
person should stay in a specific room, use a separate bathroom, if possible, and do not share dishes,
337
cups and other utensils in the households).37 All household members should be encouraged to wear
338
masks, keep washing hands and perform social distancing to the extent possible.38
339
340
The high number of COVID-19 patients associated with nightclubs in Bangkok is comparable to
341
COVID-19 outbreak associated with Itaewon nightclub cluster in Seoul, Korea, in May 2020.39
342
Similarly, we also found individuals who visited several nightclubs in the same area during the
343
short period of time. The high number of COVID-19 patient cluster associated with boxing
344
stadiums in Bangkok is similar to COVID-19 case cluster probably associated with a football
345
match in Italy in February 2020.40 The secondary attack rate of COVID-19 at a chore practice in
346
the U.S. was reported to be as high as 53%,41 and the secondary attack rates in public gathering
347
places with high density of people shouting and cheering, such as football and boxing stadiums,
348
are still largely unknown.
349
350
It is likely that clear and consistent public messaging from policy makers prevents a false sense of
351
security and promotes compliance with social distancing in Thailand. It is recommended that both
352
mainstream and social media should support public health responses by teaming with government
353
in providing consistent, simple and clear messages.42 Both positive and negative messages can
354
influence the public.42 In Thailand, daily briefings of Thailand's Centre for COVID-19 Situation
355
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18
Administration (CCSA) gave clear and consistent messages on social distancing every day, as well
356
as how to put on a mask and wash hands. The situation reports and advices by CCSA on daily
357
basis have greatly improved the confidence in the public and compliance with the
358
recommendations. Those are shown by the official online surveys of the DDC,43 of which results
359
are reported during the daily briefings regularly.
360
361
Strength and limitations of the study
362
To our knowledge,32 this is the first epidemiological study to quantitatively assess the protective
363
effect of wearing masks against COVID-19 infections in the general population. Studying
364
asymptomatic contacts covering the period when multiple measures (including wearing masks)
365
were recommended but not compulsory, allowed us to evaluate the potential effectiveness of each
366
measure.
367
368
There are several limitations of the study. First, our finding might not be generalizable to all
369
settings, since findings were based on contacts associated with three major COVID-19 clusters in
370
Thailand during March 2020. Second, the estimated odds ratios were based on a condition that the
371
contact with index patients occurred. Our study did not evaluate or take into account the probability
372
of contacting index patients in public. Third, our findings were based on PCR testing per national
373
contact tracing guideline,21 22 and as such the estimated odds ratios might not take account of all
374
asymptomatic infections. Fourth, it is impossible to identify every potential contact an individual
375
has and some individuals may have been contacts to more than one COVID-19 patient. Hence, our
376
estimated secondary attack rates among contacts with high-risk exposure could be over or under-
377
estimated. Fifth, findings were subject to common biases of retrospective case-control studies;
378
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19
including memory bias, observer bias and information bias. Nonetheless, we used structured
379
interviews, whereby each participant was asked the same set of defined questions, to reduce
380
potential biases.
381
382
Considerations for further research
383
Evaluating effectiveness of wearing masks, washing hands and social distancing during an
384
outbreak of COVID-19 is difficult. Prospective RCTs could give the best estimate of the
385
effectiveness of each measure; however, setting up an RCT in an area or a country where a measure
386
of interest is strongly recommended or compulsory is probably impractical. Nonetheless, we
387
suggest that RCT of wearing masks should be conducted when and where possible because
388
findings of RCTs will give a higher level of evidence to the public and policy makers. Other types
389
of studies; including natural experiment,44 cross-sectional, case-control and cohort studies should
390
also be conducted to evaluate effectiveness of wearing masks against COVID-19 and other
391
respiratory infections in different settings. In addition, social and behavioural studies are needed
392
to understand how people could perceive and adopt the recommendations of wearing masks,
393
washing hands and social distancing in different settings.45
394
395
Conclusions and future implications
396
As measures against COVID-19 are being implemented or relaxed in many countries worldwide,
397
it is important that we continue to expand our understanding about the effectiveness of each
398
measure. Wearing masks, washing hands and social distancing are strongly associated with lower
399
risk of COVID-19 infections. We strongly support wearing non-medical masks in public to prevent
400
COVID-19 infections. We also suggest that medical masks should be reserved for healthcare
401
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20
workers. Everyone should also wash their hands frequently and comply with recommendations of
402
social distancing.
403
404
405
Acknowledgement:
406
We thank all participants and all COVID-19 patients involved in providing information. We thank
407
all SRRT members at the central, regional, provincial and district levels, as well as all Village
408
Health Volunteers in Thailand. We thank Pattraporn Klanjatturat and Inthira Yamabhai for the
409
technical assistance. We thank Dr. Suwannachai Wattanayingcharoenchai, Dr. Sombat
410
Thanprasertkul, Dr. Panithee Thammawijaya and Dr. Walairat Chaifoo of the DDC, MoPH, and
411
Dr. Virsasakdi Chongsuvivatwong from Prince of Songkhla University for their advices and
412
direction.
413
414
Contributors
415
PD, RS, CN and DL contributed to design of the study. PD, RS and DL contributed to setting up
416
the database and quality control. AP, CJ, DR, ND, NE, NP, NS, OY, PaP, PiP, PK, PS, PW, SC,
417
SK and TC contributed to data collection. DL carried out the main statistical analysis. PD and RS
418
coordinated the study and contributed to the statistical analyses. PD, RS and DL contributed to
419
interpretation of the results and drafted the manuscript. All authors commented on drafts and read
420
and approved the final manuscript. The corresponding author attests that all listed authors meet
421
authorship criteria and that no others meeting the criteria have been omitted. PD is the guarantor.
422
423
Competing interests:
424
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 12, 2020. .https://doi.org/10.1101/2020.06.11.20128900doi: medRxiv preprint
21
The authors declare that they have no completing interests.
425
426
Ethical approval
427
As this study is part of the routine situation analysis and outbreak investigation of the DDC MoPH
428
Thailand, it was not required to obtain ethics approval and no written informed consent was
429
collected. However, the study team strictly followed ethical standards in research, that is, all
430
individual information was strictly kept confidential and not reported in the paper. The DDC
431
MoPH Thailand approved the analysis and reporting of data in aggregate.
432
433
Funding
434
The study was supported by the DDC, MoPH, Thailand. DL is supported by the Wellcome Trust
435
(106698/Z/14/Z).
436
437
Data sharing
438
All data in aggregate are reported in the manuscript.
439
440
441
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28
Table 1. Definitions used in the study
572
Classification
Definition
Asymptomatic contacts
Individuals who had contact with or were in the same location as a
symptomatic COVID-19 patient, and had no symptoms of COVID-
19 on the first day of contact.
Cases
Asymptomatic contacts of COVID-19 patients who were later
diagnosed and officially reported as COVID-19 patients by 21 Apr
2020.
Controls
Asymptomatic contacts of COVID-19 patients who were never
diagnosed as COVID-19 patients by 21 Apr 2020.
Index patients
The COVID-19 patients identified from the contract tracing data as
the potential source of infection. Cases (as defined above) could
also be included as index patients.
Primary index patients
The earliest COVID-19 patients whose probable sources of infection
were prior to the study period (1 to 31 March 2020), whom we were
not able to identify the source of infection from, or whose probable
sources of infection were outside the contract tracing data included
in the study
COVID-19 patients
Individuals who had PCR positive for SARS-CoV-2, officially
confirmed and reported by Department of Disease Control (DDC),
Ministry of Public Health (MoPH), Thailand
Secondary attack rate
The percentage of new cases among asymptomatic contacts with
high-risk exposure
High-risk exposure
Individuals who lived in the same household as a COVID-19 patient,
had a direct physical contact with a COVID-19 case, had face-to-
face contact with a COVID-19 case within 1 meter and longer than
15 minutes, or were in a closed environment with a COVID-19
patient at a distance of within 1 meter and longer than 15 minutes.
Household contact
Individuals who lived in the same household as a COVID-19 patient
573
574
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29
Table 2. Factors associated with COVID-19 infections
Factors
Cases
(n=211)
Controls
(n=839)
Crude odds ratio
(95% CI) a
P
Adjusted odds
ratio (95% CI) a
P
Male gender
146/211 (69%)
434/838 (52%)
0.83 (0.47-1.46)
0.52
0.76 (0.41-1.41)
0.38
Age group
≤15 years old
6/211 (3%)
49/829 (6%)
0.65 (0.17-2.48)
0.28
0.57 (0.15-2.21)
0.20
>15 – 40 years old
94/211 (45%)
435/829 (52%)
1.0
1.0
>40 – 65 years old
98/211 (46%)
302/829 (36%)
1.66 (0.92-2.99)
1.77 (0.94-3.32)
>65 years old
13/211 (6%)
43/829 (5%)
1.27 (0.32-4.97)
0.97 (0.22-4.24)
Contact place b
Nightclub
35 (17%)
193 (23%)
Not applicable c
-
Not applicable c
-
Boxing stadium
125 (59%)
19 (2%)
Workplace
11 (5%)
286 (34%)
Household
38 (18%)
192 (23%)
Others
2 (1%)
149 (18%)
Shortest distance of contact
Physical contact
132/197 (67%)
292/809 (36%)
1.0
0.001
1.0
0.02
≤1 meter without physical contact
61/197 (31%)
335/809 (41%)
0.76 (0.43-1.36)
1.09 (0.58-2.07)
>1 meter
4/197 (2%)
182/809 (22%)
0.08 (0.02-0.30)
0.15 (0.04-0.63)
Duration of contact within 1 meter
>60 minutes
180/199 (90%)
487/801 (61%)
1.0
0.003
1.0
0.09
>15 – 60 minutes
14/199 (7%)
162/801 (20%)
0.52 (0.23-1.16)
0.67 (0.29-1.55)
≤15 minutes
5/199 (3%)
152/801 (19%)
0.13 (0.04-0.46)
0.24 (0.07-0.90)
Sharing dishes or cups d,e
None
125/210 (60%)
576/837 (69%)
1.0
0.001
1.0
0.38
Yes
85/210 (40%)
261/837 (31%)
2.72 (1.49-4.97)
1.33 (0.70-2.54)
Sharing cigarettes d,f
None
196/209 (94%)
824/836 (99%)
1.0
0.001
1.0
0.04
Yes
13/209 (6%)
12/836 (1%)
6.19 (2.13-17.95)
3.47 (1.09-11.02)
Washing hands d,g
None
44/210 (21%)
121/826 (15%)
1.0
<0.001
1.0
0.04
Sometimes
114/210 (54%)
333/826 (40%)
0.40 (0.18-0.89)
0.34 (0.14-0.81)
Often
52/210 (25%)
372/826 (45%)
0.19 (0.08-0.44)
0.33 (0.13-0.87)
Wearing masks d,h
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Not wearing masks
102/211 (48%)
500/834 (60%)
1.0
0.003
-
-
Wearing non-medical masks
25/211 (12%)
77/834 (9%)
0.78 (0.32-1.90)
Wearing non-medical and medical
masks alternately
12/211 (6%)
48/834 (6%)
0.46 (0.13-1.64)
Wearing medical masks
72/211 (34%)
209/834 (25%)
0.25 (0.12-0.53)
Compliance with mask wearing d,h
Not wearing a mask
102/210 (49%)
500/823 (61%)
1.0
<0.001
1.0
0.007
Sometimes
79/210 (38%)
125/823 (15%)
0.75 (0.37-1.52)
0.87 (0.41-1.84)
All the time
29/210 (14%)
198/823 (24%)
0.15 (0.07-0.36)
0.23 (0.09-0.60)
Footnote of Table 2. a Both crude and adjusted odds ratios were estimated using logistic regression with a random effect for location
and a random effect for index patient nested within the same location. b The state enterprise office was considered and included as a
workplace. Others included restaurants, markets, malls, religious places, households of index patients or other people but not living
together, etc. c Location was included in the model as a random effect variable. d During the contact period. e Sharing dishes but using
communal spoons all the time was considered as not sharing dishes. f Included sharing electronic cigarettes and any vaping devices. g
Included washing with soap and water, and with alcohol-based solutions. h Wearing masks incorrectly (i.e. not covering both nose and
mouth) was considered as not wearing.
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Table 3. Factors associated with compliance of mask wearing
Factors
Not wearing
masks
(n=602)
Wearing masks
sometimes
(n=204)
Wearing masks
all the time
(n=227)
P
Male gender
324/601 (54%)
129/204 (63%)
115/227 (51%)
0.03
Age group
≤15 years old
45/594 (8%)
5/204 (2%)
3/225 (1%)
<0.001
>15 – 40 years old
269/594 (45%)
117/204 (57%)
132/225 (59%)
>40 – 65 years old
236/594 (40%)
76/204 (37%)
84/225 (37%)
>65 years old
44/594 (7%)
6/204 (3%)
6/225 (3%)
Contact places
Nightclub
84 (14%)
51 (25%)
91 (40%)
<0.001
Boxing stadium
48 (8%)
66 (32%)
29 (13%)
Workplace a
178 (30%)
46 (23%)
64 (28%)
Household
167 (28%)
27 (13%)
33 (15%)
Others b
125 (21%)
14 (7%)
10 (4%)
Shortest distance of contact
Physical contact
246/588 (42%)
96/191 (50%)
76/212 (36%)
0.005
≤1 meter without physical
contact
238/588 (40%)
70/191 (37%)
83/212 (39%)
>1 meter
104/588 (18%)
25/191 (13%)
53/212 (25%)
Duration of contact within 1
meter
>60 minutes
396/590 (67%)
143/190 (75%)
121/205 (59%)
<0.001
>15 – 60 minutes
120/590 (20%)
23/190 (12%)
30/205 (15%)
≤15 minutes
74/590 (13%)
24/190 (13%)
54/205 (26%)
Sharing dishes or cups c,d
None
361/601 (60%)
130/203 (64%)
200/226 (88%)
<0.001
Yes
240/601 (40%)
73/203 (36%)
26/226 (12%)
Sharing cigarettes c,e
None
586/600 (98%)
194/202 (96%)
223/226 (99%)
0.26
Yes
14/600 (2%)
8/202 (4%)
3/226 (1%)
Washing hands c,f
None
142/594 (24%)
16/203 (8%)
6/224 (3%)
<0.001
Sometimes
298/594 (50%)
99/203 (49%)
42/224 (19%)
Often
154/594 (26%)
88/203 (43%)
176/224 (79%)
Footnote of Table 3. P values were estimated using univariable multinomial logistic regression
models. Missing values were imputed using the imputation model. Wearing masks incorrectly (i.e.
not covering both nose and mouth) was considered as not wearing. a The state enterprise office was
considered and included as a workplace. b Included restaurants, markets, malls, religious places,
public places, households of index patients or other people but not living together, etc. c During
the contact period. d Sharing dishes but using communal spoons all the time was considered as not
sharing dishes. e Included sharing electronic cigarettes and any vaping devices. f Included washing
with soap and water, and with alcohol-based solutions.
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32
Table 4. Population attributable fraction (PAF) of risk factors for COVID-19 infection
Risk factors
Nightclub
Boxing
stadium
Workplace
Household
Other places
Overall
Prev a
PAF b
Prev a
PAF b
Prev a
PAF b
Prev b
PAF b
Prev a
PAF b
Prev a
PAF b
Non-modifiable
Female gender
0.51
0.08
0.13
0.002
0.40
0.03
0.68
0.09
0.40
0.08
0.45
0.03
Age group >15 years old
1.00
0.32
0.98
0.05
0.99
0.37
0.82
0.26
0.96
0.37
0.95
0.15
Modifiable
Distance of contact <1 m c
0.88
0.71
0.98
0.19
0.65
0.72
0.87
0.68
0.85
0.76
0.82
0.40
Duration of contact within 1 m
>15 min c
0.86
0.55
0.99
0.11
0.70
0.57
0.91
0.53
0.91
0.64
0.85
0.29
Sharing dishes or a cups c,d
0.34
0.10
0.30
0.01
0.19
0.06
0.57
0.11
0.26
0.13
0.33
0.04
Sharing cigarettes c,e
0.08
0.13
0.02
0.001
0.01
0.07
0
0
0.01
0.009
0.02
0.03
Not washing hands c,f
0.05
0.06
0.21
0.01
0.20
0.17
0.10
0.08
0.28
0.29
0.16
0.04
Not wearing masks all the time c,g
0.60
0.52
0.80
0.08
0.78
0.65
0.86
0.55
0.94
0.68
0.78
0.28
Sum of all modifiable risk
factors i
0.98
0.75
0.98
0.97
0.99
0.84
Footnote of Table 4. a Prevalence (Prev) was estimated using the imputed data set. b PAF was estimated using the direct method
(Supplementary Text). c During the contact period. d Sharing a dish but using communal spoons all the time was considered as not
sharing a dish. e Included sharing an electronic cigarette and any vaping device. f Washing hands included washing with soap and water,
and with alcohol-based solutions. g Wearing masks incorrectly (i.e. not covering both nose and mouth) was considered as not wearing. i
Age and gender were considered as non-modifiable risk factors, while other risk factors were considered as modifiable. Total PAF was
directly estimated using logistic regression in the form of natural logarithm; therefore, total PAF was not equal to the direct summation
of PAF of each risk factor.
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Figure 1. Study flow diagram
Footnote of Figure 1. SRRT= Surveillance and Rapid Response Team (SRRT), Ministry of Public
Health (MoPH), Thailand
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Figure 2. Development and transmission of COVID-19 among asymptomatic contacts
included in the study
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Footnote of Figure 2. A, B and C represent the nightclub cluster, boxing stadium cluster and state
enterprise office cluster, respectively. Black nodes represent primary index patients, red dots
represent cases, and green dots represent controls. Orange dots represent index patients (confirmed
COVID-19 patients) who could not be contacted by the study team. Black lines represent
household contacts, purple lines represent contacts at workplaces and gray lines represent contacts
at other locations. Definition of index patients, cases and controls are listed in Table 1.
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Supplementary Text
Supplementary Methods
To respond to the national policy, we estimated direct population attributable fraction (PAF) using
the imputed dataset and the direct method as previously described.27 28 Direct PAF can be obtained
by calculating PAFs directly from individuals’ data using logistic regression.27 28 First, we had to
modify our final logistic regression model by considering each risk factor dichotomously. Then,
irrespective of exposure to each risk factor for each individual, that factor was removed from the
population by calculating probability based on all observations as unexposed. The predicted
probability of developing COVID-19 infection for each asymptomatic contact, with the
assumption that there was no exposure to a certain risk factor, is:
Pki is representative of predicted probability of COVID-19 infection in individual asymptomatic
contact k, assuming no exposure to a specific risk factor (xi); indicates the regression coefficient
of risk factor (xj), except risk factor number i (xi). Subsequently, the sum of all predicted
probabilities for all individuals in the study would be equal to adjusted estimate of total cases,
which is anticipated in the absence of that specific risk factor (xi).
Then, PAF was estimated by subtraction of the total number of predicted cases from total number
of observed cases, divided by the total number of observed cases:
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Supplementary Results
For the pub cluster, we identified 11 primary index patients who started having symptoms from 4
to 8 March and were diagnosed (and isolated) from 3 to 10 March (Supplementary Figure 1). Those
primary index patients visited multiple nightclubs included in the analysis during the study period,
and 35 of 228 (15%) asymptomatic contacts at nightclubs had PCR-confirmed COVID-19
infections after the contact (Figure 2, Cluster A).
For the boxing stadium cluster, we identified 5 primary index patients who started having
symptoms from 6 to 12 March and were diagnosed (and isolated) from 11 to 21 March
(Supplementary Figure 2). Those primary index patients visited multiple boxing stadiums included
in the analysis during the study period, and 125 of 144 (87%) asymptomatic contacts at the boxing
stadiums had PCR-confirmed COVID-19 infections after the contact (Figure 2, Cluster B).
Of the two primary index patients for the office cluster; one had had symptoms since 15 March
2020 (Primary index patient C1 in Supplementary Figure 3) and was considered as the source of
infection to one new case in the office during the study period. The other primary index patient
(Primary index patient C2 in Supplementary Figure 3) was a household member of a staff at the
office, and was considered as the source of infection to that staff via household contact.
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Supplementary Table 1. Factors associated with COVID-19 infections in a multivariable
model including type of mask
Factors
Adjusted odds
ratio (95% CI) a
P
Male gender
0.75 (0.40-1.38)
0.35
Age group
≤15 years old
0.55 (0.14-2.15)
>15 – 40 years old
1.0
>40 – 65 years old
1.76 (0.93-3.31)
>65 years old
1.00 (0.23-4.34)
Contact place b
Nightclub
Not applicable c
-
Boxing stadium
Workplace
Household
Others
Shortest distance of contact
Physical contact
1.0
0.02
≤1 meter without physical contact
1.07 (0.56-2.01)
>1 meter
0.15 (0.04-0.63)
Duration of contact within 1 meter
>60 minutes
1.0
0.09
>15 – 60 minutes
0.66 (0.28-1.52)
≤15 minutes
0.24 (0.06-0.91)
Sharing dishes or a cups d,e
None
1.0
0.39
Yes
1.32 (0.69-2.52)
Sharing cigarettes d,f
None
1.0
0.03
Yes
3.46 (1.09-10.98)
Washing hands d,g
None
1.0
0.04
Sometimes
0.33 (0.14-0.79)
Often
0.33 (0.13-0.88)
Wearing masks d,h
Not wearing masks
1.0
0.55
Wearing Non-medical masks
1.30 (0.48-3.47)
Wearing Non-medical and medical mask alternately
1.04 (0.26-4.14)
Wearing Medical masks
0.62 (0.25-1.52)
Wearing masks all the time d,h
No
1.0
0.006
Yes
0.31 (0.12-0.80)
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39
Footnote of Supplementary Table 1. a Both crude and adjusted odds ratios were estimated using
logistic regression with a random effect for location and a random effect for index patient nested
within the same location. Missing values were imputed using the imputation model. b The state
enterprise office was considered and included as workplaces. Others included restaurants, markets,
malls, religious places, households of index patients or other people but not living together, etc. c
Location was included in the model as a random effect variable. d During the contact period. e
Sharing dishes but using communal spoons all the time was considered as not sharing dishes. f
Included sharing electronic cigarettes and any vaping devices. g Included washing with soap and
water, and with alcohol-based solutions. h Wearing masks incorrectly (i.e. not covering both nose
and mouth) was considered as not wearing.
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Supplementary Figure 1. Timeline and possible transmission of primary index patients of the pub cluster
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Supplementary Figure 2. Timeline and possible transmission of primary index patients of the boxing stadium cluster
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Supplementary Figure 3. Timeline and possible transmission of primary index patients of the state enterprise office cluster
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