The novel coronavirus (SARS-CoV-2/ 2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were : medRxiv preprint all over China, 21 cities/ provinces in Italy, 21 cities/ provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r 2 >0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

BackgroundAcute respiratory distress syndrome (ARDS) is a critical condition characterized by bilateral pulmonary infiltrates and severe hypoxemia. This study aimed to evaluate the diagnostic and prognostic values of Club cell protein 16 (CC16) in critical care patients with ARDS.MethodsIn this retrospective observational study, 83 patients with ARDS and 129 non‐ARDS patients on ICU admission were enrolled. The differences in serum CC16 and other laboratory indicators between two groups were analyzed. The sensitivity, specificity, positive and negative predictive values, and accuracy of CC16 as a diagnostic marker on ICU admission were determined by receiver operating characteristic (ROC) curve analysis. The correlation between serum CC16 levels and the severity of ARDS as quantified by PaO2/FiO2 ratio were further assessed. CC16 levels were compared between survivors and non‐survivors. The relationships between CC16 levels and duration of ICU and hospitalization were evaluated.ResultsThe serum CC16 levels in ARDS patients were significantly higher than that in non‐ARDS patients (54.44±19.62 vs 24.13±12.32 ng/mL, P=.001). ROC analysis showed that the sensitivity, specificity, positive predictive value, and negative predictive value were 90.4%, 79.8%, 74.2%, and 92.8%, respectively, when the cut‐off value was set at 33.3 ng/mL. CC16 levels were correlated with the severity of ARDS. The serum CC16 levels were significantly greater in non‐survivors than in survivors from the ARDS group. CC16 levels were associated with ICU stay but not hospital stay.Conclusions CC16 may serve as a diagnostic and stratification marker for ARDS. However, it provided limited prognostic information for ARDS.

ObjectiveWe aimed to investigate the effect of a Health Belief Model (HBM)-based education program on the perception scores of 5 HBM domains, physical activity, and serum uric acid (SUA) among asymptomatic hyperuricemia (AHU) patients in a randomized controlled trial.MethodsOne hundred and ninety-three AHU patients were involved in this prospective experimental interventional study in Shanghai, China. Subjects were randomly divided into interventional or control group. The educational program was designed based on HBM component for the improvement of knowledge and promotion of lifestyle adherence in terms of low-purine diet and physical activity among AHU patients. This program included educational booklets and educational classes. Data were collected from interventional and control group members both before and after the intervention, using a questionnaire covering sociodemographic characteristics, HBM variables, physical activity from the Health-Promoting Lifestyle Profile II, and a check list for recording the subject’s SUA values, as well as the body mass index, waist–hip ratio, systolic blood pressure, and diastolic blood pressure.ResultsIn the interventional group, the mean scores of the HBM variables (perceived susceptibility, perceived severity, perceived benefit, perceived barriers, and self-efficacy), SUA values, physical activity, body mass index, and waist–hip ratio were improved significantly after the intervention (p<0.05), whereas no significant differences were detected in the control group between baseline and follow-up measures.ConclusionThis study showed the importance of the educational program based on the HBM in improving the model constructs and physical activity, as well as in decreasing the SUA values in AHU patients.

ObjectivesThis study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors.DesignA retrospective, qualitative study.Methods and analysisWe collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak.ResultsSignificant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, ‘restricted’, ‘controlled’, ‘natural’, ‘tropical’ and ‘southern’. This model’s prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control.ConclusionsThis model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area.