daily mortality
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2022 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze

The lockdown introduced in Georgia on November 28, 2020 contributed to positive trends in the spread of COVID-19 until February - the first half of March 2021. Then, in April-May 2021, the epidemiological situation worsened significantly, and from June to the end of December COVID - situation in Georgia was very difficult. In this work results of the next statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2021 to December 31, 2021 are presented. It also presents the results of the analysis of monthly forecasting of the values of C, D and I. As earlier, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/. The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained. Georgia's ranking in the world for Covid-19 monthly mean values of infection and deaths cases in investigation period (per 1 million population) was determined. Among 157 countries with population ≥ 1 million inhabitants in October 2021 Georgia was in the 4 place on new infection cases, and in September - in the 1 place on death. Georgia took the best place in terms of confirmed cases of diseases (thirteenth) in December, and in mortality (fifth) - in October. A comparison between the daily mortality from Covid-19 in Georgia from September 01, 2021 to December 31, 2021with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 76.8 % (September 03, 2021), the smallest 18.7 % (November 10, 2021). As in previous work [9,10] the statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 6024 (November 3, 2021), R = 6017 (November 15, 2021), D = 86 (September 3, 2021), I = 12.04 % (November 24, 2021). Maximum mean decade values of investigation parameters are following: C = 4757 (1 Decade of November 2021), R = 4427 (3 Decade of November 2021), D = 76 (2 Decade of November 2021), I = 10.55 % (1 Decade of November 2021). It was found that as in spring and summer 2021 [9,10], from September to December 2021 the regression equations for the time variability of the daily values of C, R, D and I have the form of a tenth order polynomial. Mean values of speed of change of confirmed -V(C), recovered - V(R), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months for the indicated period of time were determined. Maximum mean decade values of investigation parameters are following: V(C) = +139 cases/day (1 Decade of October 2021), V(R) = +124 cases/day (3 Decade of October 2021), V(D) = +1.7 cases/day (3 Decade of October 2021), V(I) = + 0.20 %/ day (1 decades of October 2021). Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that from September 1, 2021 to November 30, 2021 the maximum effect of recovery is observed on 12 and 14 days after infection (CR=0.77 and 0.78 respectively), and deaths - after 7, 9, 11, 13 and 14 days (0.70≤CR≤0.72); from October 1, 2021 to December 31, 2021 - the maximum effect of recovery is observed on 14 days after infection (RC=0.71), and deaths - after 9 days (CR=0.43). In Georgia from September 1, 2021 to November 30, 2021 the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to 28 and 35 days respectively; from October 1, 2021 to December 31, 2021 - up to 21 and 29 days respectively. Comparison of daily real and calculated monthly predictions data of C, D and I in Georgia are carried out. It was found that in investigation period of time daily and mean monthly real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values. Traditionally, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.


The Auk ◽  
2022 ◽  
Author(s):  
Ana Morales ◽  
Barbara Frei ◽  
Greg W Mitchell ◽  
Camille Bégin-Marchand ◽  
Kyle H Elliott

Abstract Migration consists of a sequence of small- to large-scale flights often separated by stopovers for refueling. Tradeoffs between minimizing migration time (more flights, shorter stopovers) and maximizing energy gain (fewer flights, longer stopovers) will affect overall migration timing. For example, some individuals make long-term stopovers in high-quality habitat that maximize energy gain (e.g., molt-migration), but movement to those habitats likely costs time. We used radio telemetry and blood plasma metabolite levels to examine physiological and behavioral tradeoffs between molt-migrant (birds molting at the molt stopover; n = 59) and post-molt (birds that presumably completed their molt elsewhere; n = 19) migrant Swainson’s Thrushes (Catharus ustulatus) near Montreal, Canada. Molt-migration was a large time investment as the average stopover duration for molt-migrants was of 47 ± 9 days (~13% of the entire annual cycle), almost twice as long as previously assumed from banding records, and far longer than stopovers of post-molting individuals (7 ± 2 days). Daily mortality rate during the molt stopover was similar to the average annual daily mortality rate. Molt-migrants’ circadian rhythms closely matched light levels, whereas post-molting birds had irregular rhythms and averaged 1 hr greater activity per day than molt-migrants. Despite being less active, molt-migrants had similar refueling rates based on metabolite profiles. As compared with migrants that completed molt earlier, molt-migrants at this stopover site had slower subsequent migration rates. Thus, birds using long-term stopovers appeared to tradeoff energy (efficient refueling) for time (slower subsequent migration).


Author(s):  
Sonia Villamizar Cancelado

Introduction: Daily and outbursts mortality composting have been identified as one of the finest methods for final disposal of animal corpses, but the probable threat of pathogens transmission truly limits its use. Materials and Methods:  In this study we evaluated the quality and microbiological biosafety of a compost produced in daily mortality experimental unit composting at the Universidade Estadual Paulista in the state of Sao Paulo, Brazil. Settled compost sample was evaluated in order to determine the presence and counting of coliforms and Salmonella sp. and the pathotypes of E. coli STEC, EPEC and EHEC using culture and molecular techniques.  The occurrence of frequent soil borne phytopathogenic fungi was also estimated using selective and differential microbiological culture media. Results and Discussion: The occurrence of pathogenic E. coli, Salmonella sp and phytopathogenic fungi were negative. Coliforms level was 3.05 log10/g. Concussions: The results showed that daily mortality composting method is effective to reduce pathogenic microorganisms, however, in order to add the product on crops or plants such as vegetables that are for direct human consumption, additional tests must be performed to assess the presence of viral pathogens and endospores forming bacteria.


Pathogens ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1498
Author(s):  
Armin R. W. Elbers ◽  
José L. Gonzales

(1) Background: Highly pathogenic avian influenza (HPAI) is a viral infection characterized by inducing severe disease and high levels of mortality in gallinaceous poultry. Increased mortality, drop in egg production or decreased feed or water intake are used as indicators for notification of suspicions of HPAI outbreaks. However, infections in commercial duck flocks may result in mild disease with low mortality levels, thereby compromising notifications. (2) Methods: Data on daily mortality, egg production, feed intake and water intake from broiler and breeder duck flocks not infected (n = 56 and n = 11, respectively) and infected with HPAIV (n = 13, n = 4) were used for analyses. Data from negative flocks were used to assess the baseline (daily) levels of mortality and production parameters and to identify potential threshold values for triggering suspicions of HPAI infections and assess the specificity (Sp) of these thresholds. Data from infected flocks were used to assess the effect of infection on daily mortality and production and to evaluate the sensitivity (Se) of the thresholds for early detection of outbreaks. (3) Results: For broiler flocks, daily mortality > 0.3% (after the first week of production) or using a regression model for aberration detection would indicate infection with Se and Sp higher than 80%. Drops in mean daily feed or water intake larger than 7 g or 14 mL (after the first week of production), respectively, are sensitive indicators of infection but have poor Sp. For breeders, mortality thresholds are poor indicators of infection (low Se and Sp). However, a consecutive drop in egg production larger than 9% is an effective indicator of a HPAI outbreak. For both broiler and breeder duck flocks, cumulative average methods were also assessed, which had high Se but generated many false alarms (poor Sp). (4) Conclusions: The identified reporting thresholds can be used to update legislation and provide guidelines to farmers and veterinarians to notify suspicions of HPAI outbreaks in commercial duck flocks.


Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1481
Author(s):  
Henrik Olstrup ◽  
Christer Johansson ◽  
Bertil Forsberg ◽  
Christofer Åström ◽  
Hans Orru

Urban air pollutant emissions and concentrations vary throughout the year due to various factors, e.g., meteorological conditions and human activities. In this study, seasonal variations in daily mortality associated with increases in the concentrations of PM10 (particulate matter), PM2.5–10 (coarse particles), BC (black carbon), NO2 (nitrogen dioxide), and O3 (ozone) were calculated for Stockholm during the period from 2000 to 2016. The excess risks in daily mortality are presented in single and multi-pollutant models during the whole year and divided into four different seasons, i.e., winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). The excess risks in the single-pollutant models associated with an interquartile range (IQR) increase for a lag 02 during the whole year were 0.8% (95% CI: 0.1–1.4) for PM10, 1.1% (95% CI: 0.4–1.8) for PM2.5–10, 0.5% (95% CI: −0.5–1.5) for BC, −1.5% (95% CI: −0.5–−2.5) for NO2, and 1.9% (95% CI: 1.0–2.9) for O3. When divided into different seasons, the excess risks for PM10 and PM2.5–10 showed a clear pattern, with the strongest associations during spring and autumn, but with weaker associations during summer and winter, indicating increased risks associated with road dust particles during these seasons. For BC, which represents combustion-generated particles, the pattern was not very clear, but the strongest positive excess risks were found during autumn. The excess risks for NO2 were negative during all seasons, and in several cases even statistically significantly negative, indicating that NO2 in itself was not harmful at the concentrations prevailing during the measurement period (mean values < 20 µg m−3). For O3, the excess risks were statistically significantly positive during “all year” in both the single and the multi-pollutant models. The excess risks for O3 in the single-pollutant models were also statistically significantly positive during all seasons.


2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze

AbstractThe lockdown introduced in Georgia on November 28, 2020 brought positive results. There were clearly positive tendencies in the spread of COVID-19 to February - first half of March 2021. However, in April-May 2021 there was a significant deterioration in the epidemiological situation. From June to August 2021, the epidemiological situation with Covid-19 in Georgia became very difficult.In this work results of the next statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from June 01, 2021 to August 31, 2021 are presented. It also presents the results of the analysis of two-week forecasting of the values of C, D and I. As earlier, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/.The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained.Georgia’s ranking in the world for Covid-19 monthly mean values of infection and deaths cases in summer 2021 (per 1 million population) was determined. Among 159 countries with population ≥ 1 million inhabitants in August 2021 Georgia was in the 1 place on new infection cases and on Death.A comparison between the daily mortality from Covid-19 in Georgia in summer 2021 with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 66.0 % (26.08.2021 and 31.08.2021), the smallest 6.0 % (09.07.2021).The statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 6208 (17.08.2021), R = 6177 (29.08.2021), D = 79 (26.08.2021 and 31.08.2021), I = 13.0 % (17.08.2021). Maximum mean decade values of investigation parameters are following: C = 5019 (2 Decade of August 2021), R = 4822 (3 Decade of August 2021), D = 69 (3 Decade of August 2021), I = 10.88 % (2 Decade of August 2021).It was found that as with September 2020 to February 2021 and in spring 2021 [7,8], from June to August 2021 the regression equations for the time variability of the daily values of C, R and D have the form of a tenth order polynomial.Mean values of speed of change of confirmed -V(C), recovered - V(R), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months in the summer 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +134 cases/day (1 Decade of August 2021), V(R) = +134 cases/day (2 Decade of August 2021), V(D) = +2.4 cases/day (3 Decade of August 2021), V(I) = + 0.25 %/ day (1 decades of August 2021).Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that the maximum effect of recovery is observed 19 days after infection (RC=0.95), and deaths - after 16 and 18 days (RC=0.94). In Georgia in the summer 2021, the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to two months.Comparison of real and calculated predictions data of C, D and I in Georgia are carried out. It was found that in summer 2021 two-week daily and mean two-week real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values.With September 1, 2021, it is started monthly forecasting of C, D and I values.As earlier, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.


Author(s):  
Qin Shao ◽  
Hanh Nguyen

This paper studies several key metrics for COVID-19 using a public surveillance system data set. It compares the difference between two case fatality rates: the naive case fatality rate, which has been frequently mentioned in media outlets, and one which is the sample estimate for the mortality rate. A logistic regression model is applied to modeling the daily mortality rate. The conclusion is that time, gender, age and some of their interactions, appear to have a significant impact on the mortality rate; the daily mortality rate has been decreasing since the outbreak; males older than 60 has been the most vulnerable group. The receiver operating characteristics curve and the curve under the area show that the proposed logistic model is capable of predicting the outcome of a reported case with accuracy as high as 89%. These findings are helpful in assessing the magnitude of the risk posed by the COVID-19 virus to certain groups, predicting outcome severity, and optimally allocating medical resources such as intensive care units and ventilators.


2021 ◽  
Vol 4 (2) ◽  
pp. 49-53
Author(s):  
Casey Mace Firebaugh ◽  
Tishra Beeson ◽  
Amie Wojtyna ◽  
Ryan Arboleda

Yakima County, Washington was subject to the extrordinary Washington Wildfire Season of 2020 in which unhealty air (PM2.5) persisted for a 14-day period. This remarkable fire and smoke season began in tandem with the COVID-19 pandemic. SARS-CoV-2 virus, like inhaled particulate matter is known to cause respiratory illness or injury. This study sought to determine through publicly available data whether increased levels of PM2.5 were associated with increased cases of COVID-19. Using a 12-day lag analysis, Pearson product correlations were performed between PM2.5 24-hour averages in Yakima County Washington and daily confirmed cases of COVID-19 for data available on March 1, 2020-October 15, 2020. In addition, total running cases of confirmed COVID-19, daily mortality and total running mortality rates were compared in the lag analyses. All days (PM2.5) in the lag analysis were found to have a statistically significant positive correlation with COVID-19 case counts and total running counts of COVID-19 (p<.001) with correlation coefficients ranging from 0.24-0.28. The total running mortality rates were also significantly associated with daily PM2.5 (p<.001); however, the daily mortality rates were not found to be statistically significantly related to PM2.5. This simple analysis provides preliminary evidence that increased air pollution (PM2.5) is associated with higher rates of confirmed COVID-19 cases. However, further research is required to determine the potentially confounding factors in this relationship.


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