scholarly journals Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 187
Author(s):  
Olympia E. Anastasiou ◽  
Anika Hüsing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Cloud Cover ◽  
Detection Rate ◽  
Logistic Regression Model ◽  
Meteorological Factors ◽  
Seasonal Pattern ◽  
Weather Factors ◽  
Immunosuppressed Patients ◽  
Immunocompetent Patients

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

scholarly journals Seasonality of non-SARS, non-MERS Corona viruses and the impact of meteorological factors

2020 ◽  
Author(s):  
Olympia E Anastasiou ◽  
Anika Huesing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Cloud Cover ◽  
Detection Rate ◽  
Meteorological Factors ◽  
Seasonal Pattern ◽  
Virus Detection ◽  
Virus Infections ◽  
Weather Factors ◽  
Corona Virus ◽  
Corona Viruses

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS Corona Virus detection by PCR. Methods: We performed a retrospective analysis of 12763 respiratory tract sample results (288 positive and 12475 negative) for non-SARS, non-MERS Corona viruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the Corona virus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Corona virus infections followed a seasonal pattern peaking from December to March and plunging from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent. Different automatic variable selection processes agreed to select the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased Corona virus detection rates. Conclusions: Corona virus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed. Several meteorological factors were associated with the Corona virus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the Corona virus detection rate.

scholarly journals Development of a Fire Weather Index Using Meteorological Observations within the Northeast United States

2016 ◽  
Vol 55 (2) ◽  
pp. 389-402 ◽  
Author(s):  
Michael J. Erickson ◽  
Joseph J. Charney ◽  
Brian A. Colle
Keyword(s):  
Logistic Regression ◽  
Wind Speed ◽  
Relative Humidity ◽  
Logistic Regression Model ◽  
Daily Maximum ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Wildfire Occurrence ◽  
Binomial Logistic Regression

AbstractA fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts

2016 ◽  
Vol 144 (7) ◽  
pp. 2565-2577 ◽  
Author(s):  
Stephan Hemri ◽  
Thomas Haiden ◽  
Florian Pappenberger
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Cloud Cover ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Total Cloud Cover ◽  
Proportional Odds ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range

Abstract This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

scholarly journals The Impact of Sedation on Adenoma Detection Rate and Cecal Intubation Rate in Colonoscopy

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qiongmei Zhang ◽  
Zhiyu Dong ◽  
Yuanxi Jiang ◽  
Tingting Zhan ◽  
Junwen Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Detection Rate ◽  
Logistic Regression Model ◽  
Independent Factor ◽  
Intubation Rate ◽  
Cecal Intubation ◽  
Adenoma Detection ◽  
Cecal Intubation Rate

Purpose. To explore the effect of sedation on the quality of colonoscopy. Methods. The data collected from the Digestive Endoscopy Center of Shanghai Tongji Hospital from March 2012 to June 2019 were retrospectively analyzed. The rate of sedation and quality metrics of colonoscopy such as adenoma detection rate (ADR) and cecal intubation rate (CIR) were calculated. The logistic regression model was used to explore the relationship between sedation and quality metrics of colonoscopy. The interaction effects between experience of endoscopists and sedation on quality of colonoscopy was also investigated in subgroups stratified by total number of colonoscopies during career using the logistic regression model. Results. A total of 63,417 colonoscopies including 11,417 colonoscopies without sedation and 52,000 colonoscopies with sedation were enrolled in our study. The proportion of colonoscopy with sedation was 82.0%. The ADR and CIR were all significantly higher in cases with sedation compared with cases without sedation (ADR, 22.5% vs. 17.0%, p < 0.001 ; CIR, 94.7% vs. 91.2%, p < 0.001 ). Multivariate analysis showed that the sedation was an independent factor associated with adenoma detection ( OR = 1.448 , 95% CI: 1.372~1.529, p < 0.001 ) and cecal intubation ( OR = 1.560 , 95% CI: 1.446~1.683, p < 0.001 ). A total of 14 endoscopists with complete colonoscopy data in our database and corresponding 20,949 colonoscopies data were enrolled for further analysis. The logistic regression model yielded a similar result that sedation was an independent factor on adenoma detection and cecal intubation when the factor, experience of endoscopists, was also entered into the model as a confounder (adenoma detection, OR = 1.408 , 95% CI: 1.333~1.487, p < 0.001 ; cecal intubation, OR = 1.601 , 95% CI: 1.482-1.729, p < 0.001 ). Conclusion. Colonoscopy with sedation has a positive effect on ADR and CIR in all endoscopists with different experience of colonoscopy, which makes the quality of colonoscopy better.

scholarly journals Incidence rate and clinical characteristics of seasonal affective disorders in senior medical students

2021 ◽  
Vol 20 (3) ◽  
pp. 112-119
Author(s):  
I. I. Ukraintsev ◽  
E. D. Schastnyy ◽  
N. A. Bokhan
Keyword(s):  
Logistic Regression ◽  
Medical Students ◽  
Regression Model ◽  
Incidence Rate ◽  
Affective Disorders ◽  
Logistic Regression Model ◽  
Seasonal Affective Disorder ◽  
Seasonal Pattern ◽  
Undergraduate Medical Students ◽  
Binary Logistic Regression Model

Aim. To study the incidence rate, clinical features, and prognosis of seasonal affective disorder (SAD) in senior (6th-year) medical students.Materials and methods. SAD screening using the Seasonal Pattern Assessment Questionnaire (SPAQ, 1987) included 119 undergraduate medical students. 78 students were females (65.5%) and 41 – males (34.5%) (p = 0.001). The average age of women was 23 (22; 23) years, the average age of men – 23 (22; 24) years. Statistical processing was performed using the Mann – Whitney U-test, Pearson’s χ2 test, and Spearman’s rank correlation coefficient (rs).Results. The data on the prevalence of affective disorders with a seasonal pattern in medical students were obtained: SAD – 9.2%, sub-SAD – 13.5%, psychological undulation of season perception (PUSP) – 16.8%. The number of students who did not exhibit seasonal undulation of the six main characteristics recorded by the SPAQ was 72 (60.5%) (p = 0.001). There were statistically significant differences in the higher median Global Seasonality Score of the SPAQ for SAD compared with PUSP, both with and without account of the gender factor (p = 0.001). The use of a binary logistic regression model made it possible to identify groups of students with or without SAD according to the SPAQ. The data obtained determined the contribution of the following factors: gender, seasonality, body weight, and the number of sleep hours per day in spring.Conclusion. The study made it possible to obtain a logistic regression model that allowed to predict the greatest likelihood of developing SAD.

The Effect of Sanctions on Cooperative Behavior: A Study on Medium Trust Individuals in the Context of Corruption

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Dewi Rosiana ◽  
Achmad Djunaidi ◽  
Indun Lestari Setyono ◽  
Wilis Srisayekti
Keyword(s):  
Logistic Regression ◽  
Public Interest ◽  
Logistic Regression Model ◽  
Cooperative Behavior ◽  
Research Subjects ◽  
Chi Square ◽  
Public Interests ◽  
The Public ◽  
Chi Square Analysis ◽  
Personal Interests

This study aims to describe the effect of sanctions (individual sanctions, collective sanctions, and absence of sanctions) on cooperative behavior of individuals with medium trust in the context of corruption. Both collective sanctions and individual sanctions, are systemic, which means sanctioning behavior is exercised not by each individual but by the system. Cooperative behavior in this context means choosing to obey rules, to reject acts of corruption and to prioritize public interests rather than the personal interests. Conversely, corruption is an uncooperative behavior to the rules, and ignores the public interest and prioritizes personal interests. Research subjects were 62 students. The Chi-Square Analysis was used to see the association between the variables and the logistic regression model was applied to describe the structure of this association. Individual sanction is recommended as punishment to medium trust individuals to promote cooperative behavior in the context of corruption. The results showed that individuals with medium trust had more cooperative behavior.

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