scholarly journals CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1727
Author(s):  
Victor Zakharov ◽  
Yulia Balykina ◽  
Ovanes Petrosian ◽  
Hongwei Gao
Keyword(s):  
Case Based Reasoning ◽  
Short Term ◽  
Infected People ◽  
The United Kingdom ◽  
Spread Dynamics ◽  
The Usa ◽  
Spread Parameters ◽  
Increasing Demand ◽  
Short Term Forecasting ◽  
Case Based

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.

scholarly journals Inductive Learning: Does Interleaving Exemplars Affect Long-Term Retention?

2013 ◽  
Author(s):  
Norehan Zulkiply ◽  
Jennifer S Burt
Keyword(s):  
Inductive Learning ◽  
Study Phase ◽  
Case Based Reasoning ◽  
Short Term ◽  
Actual Performance ◽  
Term Retention ◽  
Mixed Factorial Design ◽  
Long Term Retention ◽  
Case Based

Purpose – The present study investigated whether or not the benefits of interleaving of exemplars from several categories vary with retention interval in inductive learning.   Methodology – Two experiments were conducted using paintings (Experiment 1) and textual materials (Experiment 2), and the experiments used a mixed factorial design. Forty students participated in each experiment for course credit. In each experiment, participants studied a series of exemplars from several categories which were presented massed and interleaved, and later their induction was tested either shortly after the study phase (short-term retention) or after a week’s delay (long- term retention).   Findings – Consistent with findings from previous studies, the interleaving effect was found in the short-term retention condition, and crucially, the present study provided the initial evidence that interleaving of exemplars also affected long-term retention. Interestingly, massing was judged to be more effective than spacing (interleaving) in most groups, even when actual performance showed the opposite.   Significance – The present study shows that interleaved exemplars have considerable potential in improving inductive learning in the long term. For example, induction is used in case-based reasoning which requires one to start with learning from specifi c cases, and then form generalizations of these cases by identifying the commonalities between them. In order to enhance long-term retention, educators may want to consider using interleaved presentation rather than massed presentation in teaching examples or cases from a particular category or concept.

scholarly journals Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Firdos Khan ◽  
Mohamed Lounis
Keyword(s):  
Confidence Intervals ◽  
Statistical Models ◽  
African Country ◽  
Multivariate Time Series ◽  
Viral Disease ◽  
Future Scenarios ◽  
Short Term ◽  
Policy Makers ◽  
Infected People ◽  
Short Term Forecasting

Abstract Background A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. Results The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of − 89 to 214 and 108–186, respectively. Deaths’ forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1–17 and 4–20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of − 106 to 185 and 44–239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. Conclusion The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

scholarly journals Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Karol Bot ◽  
Antonio Ruano ◽  
Maria da Graça Ruano
Keyword(s):  
Energy Management ◽  
Management Systems ◽  
Percentage Error ◽  
Average Error ◽  
Short Term ◽  
Energy Management Systems ◽  
Multi Objective Genetic Algorithm ◽  
Home Energy Management ◽  
The Usa ◽  
Short Term Forecasting

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

Short-term solar flare prediction using image-case-based reasoning

2017 ◽  
Vol 17 (11) ◽  
pp. 116 ◽  
Author(s):  
Jin-Fu Liu ◽  
Fei Li ◽  
Huai-Peng Zhang ◽  
Da-Ren Yu
Keyword(s):  
Solar Flare ◽  
Case Based Reasoning ◽  
Short Term ◽  
Case Based
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