scholarly journals NATURAL REPRODUCTION OF THE POPULATION SIBERIAN FEDERAL DISTRICT AT THE BEGINNING OF THE SECOND WAVE OF DEPOPULATION (PECULIARITIES AND PREDICTION)

2019 ◽  
Vol 63 (3) ◽  
pp. 116-121
Author(s):  
S. N. Filimonov ◽  
Olga I. Baran ◽  
V. A. Ryabov
Keyword(s):  
Mortality Rate ◽  
Birth Rate ◽  
Federal District ◽  
Natural Increase ◽  
Siberian Federal District ◽  
Short Term ◽  
Term Prediction ◽  
Natural Reproduction ◽  
Short Term Prediction ◽  
Second Wave

Since 2017, a new stage of depopulation has begun in Russia related to a reduction in the birth rate. The consequences of the “demographic gap” of the 1990s have reached the present time and there has been a sharp decrease in the number of women of active reproductive age, especially from 25 to 29 years old. Objective. The aim is the analysis of the dynamics and prediction of natural reproduction of the population in the Siberian Federal District and its individual administrative territories at the beginning of the second wave of depopulation. Material and methods. Data on the birth rate, mortality and natural increase (decrease) per 1000 population of the Russian Federation, the Siberian Federal District and of individual territories of the Siberian Federal District are obtained on the website of the Federal Service of State Statistics. To analyze the dynamics of natural reproduction of the population of the Siberian Federal District for 2000-2018 and for a short-term prediction of indices, the capabilities of the Microsoft Office application (MS Excel) were used and several variants of approximation of birth rate, mortality rate and natural increase (decrease) in the population were considered using the following trends: linear, logarithmic, and degree (third degree polynomial). Results. A short-term prediction based on the trends indicates a continuation of the emerging trend: the birth rate in the Siberian Federal District will decrease, and the natural decline in the population will increase. With this approximation, the mortality rate of the population will increase. Conclusion. In the coming years, the containment of depopulation in the Siberian Federal District is possible due to the favorable ratio of birth rate and mortality rate in the Republics of Tyva, Altai, Buryatia, and Khakassia. Of particular concern is the significant natural decline in the population in the Kemerovo Region and the Altai Territory. The problem of reducing mortality and, accordingly, increasing life expectancy can be solved only with an increase in the level of culture, education, medical care and with a wide spread of healthy lifestyles.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1111-1126
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1507-1523
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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