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Supplemental material, sj-docx-1-dhj-10.1177_20552076231178418 for Use of Bluetooth contact tracing technology to model COVID-19 quarantine policies in high-risk closed populations by Yinxiaohe Sun, Joel Ruihan Koo, Minah Park, Huso Yi, Borame L Dickens and Alex R Cook in DIGITAL HEALTH

Background: A pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions. Methods: Here, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 – 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors. Results: Across provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors. Conclusions: By developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale. Ministry of Education (MOE) Published version This research / project is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RS04/22).

Additional file 1.

Supplemental material, sj-docx-2-dhj-10.1177_20552076231178418 for Use of Bluetooth contact tracing technology to model COVID-19 quarantine policies in high-risk closed populations by Yinxiaohe Sun, Joel Ruihan Koo, Minah Park, Huso Yi, Borame L Dickens and Alex R Cook in DIGITAL HEALTH

Research on how socioeconomic status interacts with neighbourhood characteristics to influence disparities in obesity outcomes is currently limited by residential segregation-induced structural confounding, a lack of empirical studies outside the U.S. and other 'Western' contexts, and an over-reliance on cross-sectional analyses. This study addresses these challenges by examining how socioeconomic status modifies the effect of accumulated exposures to obesogenic neighbourhood environments on children and mothers' BMI, drawing from a longitudinal mother-child birth cohort study in Singapore, an Asian city-state with relatively little residential segregation. We find that increased access to park connectors was associated with a decrease in BMI outcomes for mothers with higher socioeconomic status, but an increase for those with lower socioeconomic status. We also find that increased access to bus stops was associated with an increase in BMIz of children with lower socioeconomic status, but with a decrease in BMIz of children with higher socioeconomic status, while increased access to rail stations was associated with a decrease in BMIz of children with lower socioeconomic status only. Our results suggest that urban interventions might have heterogeneous effects by socioeconomic status.

Additional file 1: Figures.

The evidence base for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is nascent. We sought to characterize SARS-CoV-2 transmission within US households and estimate the household secondary infection rate (SIR) to inform strategies to reduce transmission.We recruited patients with laboratory-confirmed SARS-CoV-2 infection and their household contacts in Utah and Wisconsin during 22 March 2020-25 April 2020. We interviewed patients and all household contacts to obtain demographics and medical histories. At the initial household visit, 14 days later, and when a household contact became newly symptomatic, we collected respiratory swabs from patients and household contacts for testing by SARS-CoV-2 real-time reverse-transcription polymerase chain reaction (rRT-PCR) and sera for SARS-CoV-2 antibodies testing by enzyme-linked immunosorbent assay (ELISA). We estimated SIR and odds ratios (ORs) to assess risk factors for secondary infection, defined by a positive rRT-PCR or ELISA test.Thirty-two (55%) of 58 households secondary infection among household contacts. The SIR was 29% (n = 55/188; 95% confidence interval [CI], 23%-36%) overall, 42% among children (aged18 years) of the COVID-19 patient and 33% among spouses/partners. Household contacts to COVID-19 patients with immunocompromised conditions and household contacts who themselves had diabetes mellitus had increased odds of infection with ORs 15.9 (95% CI, 2.4-106.9) and 7.1 (95% CI: 1.2-42.5), respectively.We found substantial evidence of secondary infections among household contacts. People with COVID-19, particularly those with immunocompromising conditions or those with household contacts with diabetes, should take care to promptly self-isolate to prevent household transmission.

Yinxiaohe Sun,1 Hirono Otomaru,1,2,* Sharon Esi Duoduwa Quaye,1 Jyoti Somani,3 Natasha Bagdasarian,3 Darius LL Beh,3 Dale A Fisher,3,4 Alex R Cook,1 Borame L Dickens1,* 1Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; 2Department of Virology, Tohoku University Graduate School of Medicine, Sendai, Japan; 3Division of Infectious Diseases, National University Hospital, Singapore; 4Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore*These authors contributed equally to this workCorrespondence: Hirono Otomaru Email otomaruh@med.tohoku.ac.jpBackground: By estimating N95 respirator demand based on simulated epidemics, we aim to assist planning efforts requiring estimations of respirator demand for the healthcare system to continue operating safely in the coming months.Methods: We assess respiratory needs over the course of mild, moderate and severe epidemic scenarios within Singapore as a case study using a transmission dynamic model. The number of respirators required within the respiratory isolation wards and intensive care units was estimated over the course of the epidemic. We also considered single-use, extended-use and prolonged-use strategies for N95 respirators for use by healthcare workers treating suspected but negative (misclassified) or confirmed COVID-19 patients.Results: Depending on the confirmed to misclassified case ratio, from 1:0 to 1:10, a range of 117.1 million to 1.1 billion masks are required for single-use. This decreases to 71.6– 784.4 million for extended-use and 12.8– 148.2 million for prolonged-use, representing a 31.8– 38.9% and 86.5– 89.1% reduction, respectively.Conclusion: An extended-use policy should be considered when short-term supply chains are strained but planning measures are in place to ensure long-term availability. With severe shortage expectations from a severe epidemic, as some European countries have experienced, prolonged use is necessary to prolong supply.Keywords: COVID-19 pandemic, mathematical modelling, policy, healthcare resources, N95 respirators