scholarly journals A statistical forecast of LOW mortality (< 400,000 deaths) due to COVID-19, for the whole WORLD

2020 ◽  
Author(s):  
Cesar A. Barbero
Keyword(s):  
Time Series ◽  
Virus Transmission ◽  
Imperial College ◽  
World Population ◽  
Worst Case ◽  
Actual Mortality ◽  
Death Toll ◽  
Total Death ◽  
The Mean ◽  
Calculated Number

AbstractOBJECTIVETo forecast the death toll of COVID-19 in the whole world by fitting the time series of reported deaths with a parametric equation (integrated Gaussian equation) related to Farr’s law.DATAThe time series of cumulative deaths due to COVID-19 produced by John Hopkins University and stored in a github repository.RESULTSThe projected total death toll will be 261680 (392520 – 183176) which represents the 0.003 % of world population. This number amounts to 0.054 deaths per 1000, while the mean in the world (all causes) is 7.7. The daily peak of deaths (7270 (+/-500)) happened the 15 (+/- 3) of April, meaning that we are in descending curve of the pandemic. The outbreak will end completely the 23th (+/-3) of June. However, already on 9th (+/- 3) of May, 2 σ (95.45%) of the deaths will have be occured. The projected death toll is much lower (5-10 times) than those forecasted by the Imperial College Group (ICG) even considering the best scenario of total suppression of virus transmission. Using actual mortality rates it is possible to back calculate which number of infected individuals would produce such mortality. The death toll arises from a number of infected individuals between 53 (worst case) and 3.3 million. The calculated number of infected individuals is significantly lower than that calculated by ICG (227.5 millions) with suppression.

scholarly journals A statistical forecast of LOW mortality and morbidity due to COVID-19, in ARGENTINA and other Southern Hemisphere countries

2020 ◽  
Author(s):  
Cesar A. Barbero
Keyword(s):  
Southern Hemisphere ◽  
Virus Transmission ◽  
Disease Outbreaks ◽  
Process Data ◽  
Case Scenario ◽  
Worst Case ◽  
Mortality And Morbidity ◽  
Death Toll ◽  
Total Death ◽  
Using Data

AbstractA set of open source programs in Python is devised to fit a parametric integrated Gaussian equation to cumulative deaths due to COVID-19 in Southern Hemisphere countries. The programs were successfully tested using data from advanced outbreak trajectories (Italy and Spain). The procedure was applied to data reported by Argentina. The projected total death toll will be 182 (277-182) with a peak of deaths (6(+/-2)) the 14 of April. The outbreak begins the 9thof March and end completely the 20thof May. However, already on 1stof May, 2 s (95.45%) of the deaths have occurred. The death toll arises from a number of infected individuals between 36412 and 2275. Then, they were to use to process data from several Southern Hemisphere countries: Argentina, Brazil, Mexico, Peru, Colombia, Ecuador, Cuba, Chile, Panama, Australia, Bolivia, Honduras, New Zealand, Paraguay, Guatemala, Venezuela, Uruguay, El Salvador, Jamaica, Haiti, Costa Rica and Nicaragua. The trend is to show low number of total deaths compared with other disease outbreaks. A total projected number of deaths between 15148 and 9939 deaths for a total population of ca. 664 M inhabitants. The projected death toll is much lower (5-10 times) than those forecasted by the Imperial College Group (ICG) even considering the best scenario of total suppression of virus transmission. Using actual mortality rates it is possible to back calculate which number of infected individuals would produce such mortality. The calculated number of infected individuals (worst case scenario) is below 2.5 million. This is significantly lower than that calculated by ICG (> 45 millions). In most countries the outbreak will end in May or early June. The dynamics of the outbreaks seems to do not saturate the health services (hospital beds) but only Peru, Ecuador and Panama should have not enough ICU beds for grave COVID-19 patients.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

scholarly journals The influence of solar wind on extratropical cyclones – Part 1: Wilcox effect revisited

2009 ◽  
Vol 27 (1) ◽  
pp. 1-30 ◽  
Author(s):  
P. Prikryl ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Northern Hemisphere ◽  
High Speed ◽  
Coronal Holes ◽  
Extratropical Cyclones ◽  
Sector Boundary ◽  
Speed Solar Wind ◽  
The Mean ◽  
High Speed Solar Wind

Abstract. A sun-weather correlation, namely the link between solar magnetic sector boundary passage (SBP) by the Earth and upper-level tropospheric vorticity area index (VAI), that was found by Wilcox et al. (1974) and shown to be statistically significant by Hines and Halevy (1977) is revisited. A minimum in the VAI one day after SBP followed by an increase a few days later was observed. Using the ECMWF ERA-40 re-analysis dataset for the original period from 1963 to 1973 and extending it to 2002, we have verified what has become known as the "Wilcox effect" for the Northern as well as the Southern Hemisphere winters. The effect persists through years of high and low volcanic aerosol loading except for the Northern Hemisphere at 500 mb, when the VAI minimum is weak during the low aerosol years after 1973, particularly for sector boundaries associated with south-to-north reversals of the interplanetary magnetic field (IMF) BZ component. The "disappearance" of the Wilcox effect was found previously by Tinsley et al. (1994) who suggested that enhanced stratospheric volcanic aerosols and changes in air-earth current density are necessary conditions for the effect. The present results indicate that the Wilcox effect does not require high aerosol loading to be detected. The results are corroborated by a correlation with coronal holes where the fast solar wind originates. Ground-based measurements of the green coronal emission line (Fe XIV, 530.3 nm) are used in the superposed epoch analysis keyed by the times of sector boundary passage to show a one-to-one correspondence between the mean VAI variations and coronal holes. The VAI is modulated by high-speed solar wind streams with a delay of 1–2 days. The Fourier spectra of VAI time series show peaks at periods similar to those found in the solar corona and solar wind time series. In the modulation of VAI by solar wind the IMF BZ seems to control the phase of the Wilcox effect and the depth of the VAI minimum. The mean VAI response to SBP associated with the north-to-south reversal of BZ is leading by up to 2 days the mean VAI response to SBP associated with the south-to-north reversal of BZ. For the latter, less geoeffective events, the VAI minimum deepens (with the above exception of the Northern Hemisphere low-aerosol 500-mb VAI) and the VAI maximum is delayed. The phase shift between the mean VAI responses obtained for these two subsets of SBP events may explain the reduced amplitude of the overall Wilcox effect. In a companion paper, Prikryl et al. (2009) propose a new mechanism to explain the Wilcox effect, namely that solar-wind-generated auroral atmospheric gravity waves (AGWs) influence the growth of extratropical cyclones. It is also observed that severe extratropical storms, explosive cyclogenesis and significant sea level pressure deepenings of extratropical storms tend to occur within a few days of the arrival of high-speed solar wind. These observations are discussed in the context of the proposed AGW mechanism as well as the previously suggested atmospheric electrical current (AEC) model (Tinsley et al., 1994), which requires the presence of stratospheric aerosols for a significant (Wilcox) effect.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Hepatic steatosis: a risk factor for increased COVID-19 prevalence and severity—a computed tomography study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Asmaa Ali ◽  
Mona Hasan ◽  
Shaimaa Hamed ◽  
Amir Elhamy
Keyword(s):  
Hepatic Steatosis ◽  
Severe Disease ◽  
Severe Pneumonia ◽  
Liver Disorder ◽  
Control Group ◽  
Case Group ◽  
World Population ◽  
Positive Class ◽  
The Mean ◽  
And Control

Abstract Background Around 25% of the world population was affected by the metabolic-related fatty liver disorder. Hepatic steatosis is frequently observed in conjunction with hypertension, obesity comorbidities, and diabetes. We evaluate the hepatic steatosis frequency found in chest CT exams of COVID-19-positive cases compared to non-infected controls and evaluate the related increased prevalence and severity of COVID. Results Our research includes 355 subjects, 158 with positive PCR for COVID-19 (case group) and 197 with negative PCR and negative CT chest (control group). The mean age in the positive group was 50.6 ± 16 years, and in the control, it was 41.3 ± 16 years (p < 0.001). Our study consists of 321 men (90.5%) and 34 women (9.5%). The number of males in both cases and control groups was greater. In the case group, 93% men vs. 6.9% women, while in controls, 88.3% men vs.11.6% women, p < 0.001. CT revealed normal results in 55.5% of individuals (i.e., CORADs 1) and abnormal findings in 45.5% of participants (i.e., CORADs 2–5). In abnormal scan, CO-RADs 2 was 13.92%, while CO-RADs 3–4 were 20.89% of cases. CO-RADs 5 comprised 65.19% of all cases. Approximately 42.6% of cases had severe disease (CT score ≥ 20), all of them were CO-RADs 5. The PCR-positive class had a greater prevalence of hepatic steatosis than controls (28.5% vs.12.2%, p < 0.001). CO-RADs 2 represented 11.1%, CO-RADs 3–4 represented 15.6%, and CO-RADs 5 represented 73.3% in the hepatic steatosis cases. The mean hepatic attenuation value in the case group was 46.79 ± 12.68 and in the control group 53.34 ± 10.28 (p < 0.001). When comparing patients with a higher severity score (CT score ≥ 20) to those with non-severe pneumonia, it was discovered that hepatic steatosis is more prevalent (73.2% vs. 26.8%). Conclusions Steatosis was shown to be substantially more prevalent in COVID-19-positive individuals. There is a relation among metabolic syndrome, steatosis of the liver, and obesity, as well as the COVID-19 severity.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

Trend of Hospital Admission and Outcome Study of Patients Admitted in a Neurology Unit at a Tertiary Care Neuroscience Hospital in Bangladesh

2018 ◽  
Vol 4 (2) ◽  
pp. 69-74
Author(s):  
Md Tauhidul Islam Chowdhury ◽  
Mohammad Shah Jahirul Hoque Choudhury ◽  
KM Ahasan Ahmed ◽  
Mohammad Sadekur Rahman Sarkar ◽  
Md Abdullah Yusuf ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Neurological Diseases ◽  
Tertiary Care ◽  
Length Of Hospital Stay ◽  
Retrospective Chart Review ◽  
Total Death ◽  
Neurological Patients ◽  
Study Population ◽  
The Mean ◽  
Day By Day

Background: Neurological disorders is becoming a growing concern both for developed and developing countries. Magnitude of the problem is increasing day by day. Among all neurological disorders, stroke is the leading cause of morbidity and mortality globally.Objectives: The purpose of the study was to see the trend of admission of patients with neurological diseases and to study the outcome of patients at referral neurology hospital in Bangladesh.Methodology: This retrospective chart review was conducted in the blue unit of the Department of Neurology at National Institute of Neurosciences and Hospital, Dhaka, Bangladesh from 1st January to 31st December 2016 for a period of one (01) year. All the admitted patients with both sexes were selected as study population. The outcome was observed among the study population.Result: A total number of 1044 patients were admitted during the study period. Majority of the patients were in the age group of the 41 to 50 years which was 417(39.9%) cases. Both male and female were in highest number in the month of May which was 63 and 48 cases respectively. The total death of the study population was 146(14.0%) cases. The mean length of hospital stay was 8.4±2.31 days.Conclusion: Middle aged male is the main bulk of the neurological patients, admitted in a referral neurology hospital in Bangladesh. Highest admission and mortality was observed in stroke patients.Journal of National Institute of Neurosciences Bangladesh, 2018;4(2): 69-74

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

Global Earthquake Fatalities and Population

2013 ◽  
Vol 29 (1) ◽  
pp. 155-175 ◽  
Author(s):  
Thomas L. Holzer ◽  
James C. Savage
Keyword(s):  
Population Growth ◽  
20Th Century ◽  
Poisson Distribution ◽  
21St Century ◽  
World Population ◽  
Background Rate ◽  
Death Toll ◽  
World Population Growth ◽  
Human Death ◽  
Earthquake Fatalities

Modern global earthquake fatalities can be separated into two components: (1) fatalities from an approximately constant annual background rate that is independent of world population growth and (2) fatalities caused by earthquakes with large human death tolls, the frequency of which is dependent on world population. Earthquakes with death tolls greater than 100,000 (and 50,000) have increased with world population and obey a nonstationary Poisson distribution with rate proportional to population. We predict that the number of earthquakes with death tolls greater than 100,000 (50,000) will increase in the 21st century to 8.7±3.3 (20.5±4.3) from 4 (7) observed in the 20th century if world population reaches 10.1 billion in 2100. Combining fatalities caused by the background rate with fatalities caused by catastrophic earthquakes ( >100,000 fatalities) indicates global fatalities in the 21st century will be 2.57±0.64 million if the average post-1900 death toll for catastrophic earthquakes (193,000) is assumed.

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