There is limited evidence of work-related transmission in the emerging coronaviral pandemic. We aimed to identify high-risk occupations for early coronavirus disease 2019 (COVID-19) local transmission. Methods In this observational study, we extracted confirmed COVID-19 cases from governmental investigation reports in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam. We followed each country/area for 40 days after its first locally transmitted case, and excluded all imported cases. We defined a possible work-related case as a worker with evidence of close contact with another confirmed case due to work, or an unknown contact history but likely to be infected in the working environment (e.g. an airport taxi driver). We calculated the case number for each occupation, and illustrated the temporal distribution of all possible work-related cases and healthcare worker (HCW) cases. The temporal distribution was further defined as early outbreak (the earliest 10 days of the following period) and late outbreak (11 th to 40 th days of the following period). Results We identified 103 possible work-related cases (14.9%) among a total of 690 local transmissions. The five occupation groups with the most cases were healthcare workers (HCWs) (22%), drivers and transport workers (18%), services and sales workers (18%), cleaning and domestic workers (9%) and public safety workers (7%). Possible work-related transmission played a substantial role in early outbreak (47.7% of early cases). Occupations at risk varied from early outbreak (predominantly services and sales workers, drivers, construction laborers, and religious professionals) to late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, police officers, and religious professionals).

The biodistribution of the drug analogue [(18)F]gefitinib suggests that it may be used to assess noninvasively the pharmacokinetics of gefitinib in patients by PET imaging. This is of clinical relevance, as insufficient intratumoral drug concentrations are considered to be a factor for resistance to gefitinib therapy. However, the highly nonspecific cellular binding of [(18)F]gefitinib may preclude the use of this imaging probe for noninvasive assessment of EGFR receptor status in patients.

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Background Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

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Epidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distancing. A 1 °C increase in ambient temperature was associated with 6% lower COVID-19 mortality at 30 days following the first reported death (multivariate-adjusted mortality rate ratio: 0.94, 95% CI 0.90, 0.99, p = 0.016). The results were robust for COVID-19 mortality at 25, 35 and 40 days after the first death, as well as other sensitivity analyses. The results provide consistent evidence across various models of an inverse association between higher average temperatures and subsequent COVID-19 mortality rates after accounting for other meteorological variables and predictors of SARS-CoV-2 infection or death. This suggests potentially decreased viral transmission in warmer regions and during the summer season.

1 Importance: Our study helps fill the knowledge gap related to work-related 2 transmission in the emerging coronaviral pandemic. 3 Objective: To demonstrate high-risk occupations for early coronavirus 4 disease 2019 (Covid-19) local transmission. 5 Methods: In this observational study, we extracted confirmed Covid-19 cases 6 from governmental investigation reports in Hong Kong, Japan, Singapore, 7Taiwan, Thailand, and Vietnam. We followed each country/area for 40 days 8 after its first locally transmitted case, and excluded all imported cases. We 9 defined a possible work-related case as a worker with evidence of close 10 contact with another confirmed case due to work, or an unknown contact 11 history but likely to be infected in the working environment (e.g. an airport taxi 12 driver). We calculated the case number for each occupation, and illustrated 13 the temporal distribution of all possible work-related cases and healthcare 14 worker (HCW) cases. The temporal distribution was further defined as early 15 outbreak (the earliest 10 days of the following period) and late outbreak (11 th 16 to 40 th days of the following period). 17 Results: We identified 103 possible work-related cases (14.9%) among a 18 total of 690 local transmissions. The five occupation groups with the most 19 cases were healthcare workers (HCWs) (22%), drivers and transport workers 20 (18%), services and sales workers (18%), cleaning and domestic workers 21 (9%) and public safety workers (7%). Possible work-related transmission 22 late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, 1 police officers, and religious professionals).