scholarly journals Growth Models for Covid-19 Death Figures of Turkey

2020 ◽  
pp. 1-11
Author(s):  
Muzaffer Balaban
Keyword(s):  
Growth Models ◽  
Exponential Model ◽  
Prediction Errors ◽  
Short Term ◽  
Von Bertalanffy Model ◽  
Peak Point ◽  
Term Forecast ◽  
Reported Data ◽  
Second Wave ◽  
Better Than

This paper presents modeling of the COVID-19 pandemic deaths to understand behavior of it, predict the peak point of the deaths and cases and produces a short-term forecast using the growth models for the reported data of Turkey. The data which is used in this study are gathered of daily announced by Minister of Health. Von Bertalanffy model has outperformed to the other models considered in this study. However, exponential model has predicted the total deaths and total cases better than the others. And, exponential model has given the best prediction errors among them regarding to the death and positive case figures for last months. Observed data have tended to increase since the last days of August. This could mean that the COVİD-19 threat has reached to a critical stage to crack down on prevention of pandemics spread. Or it could sign the beginning of a second wave of epidemics. More studies must be realized to learn more about the pandemic when the new data are available.

scholarly journals Progress in Forecast Skill at Three Leading Global Operational NWP Centers during 2015–17 as Seen in Summary Assessment Metrics (SAMs)

2018 ◽  
Vol 33 (6) ◽  
pp. 1661-1679 ◽  
Author(s):  
Ross N. Hoffman ◽  
V. Krishna Kumar ◽  
Sid-Ahmed Boukabara ◽  
Kayo Ide ◽  
Fanglin Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Short Term ◽  
Absolute Value ◽  
Term Forecast ◽  
Assessment Metrics ◽  
Primary Assessment ◽  
Better Than

Abstract The summary assessment metric (SAM) method is applied to an array of primary assessment metrics (PAMs) for the deterministic forecasts of three leading numerical weather prediction (NWP) centers for the years 2015–17. The PAMs include anomaly correlation, RMSE, and absolute mean error (i.e., the absolute value of bias) for different forecast times, vertical levels, geographic domains, and variables. SAMs indicate that in terms of forecast skill ECMWF is better than NCEP, which is better than but approximately the same as UKMO. The use of SAMs allows a number of interesting features of the evolution of forecast skill to be observed. All three centers improve over the 3-yr period. NCEP short-term forecast skill substantially increases during the period. Quantitatively, the effect of the 11 May 2016 NCEP upgrade to the four-dimensional ensemble variational data assimilation (4DEnVar) system is a 7.37% increase in the probability of improved skill relative to a randomly chosen forecast metric from 2015 to 2017. This is the largest SAM impact during the study period. However, the observed impacts are within the context of slowly improving forecast skill for operational global NWP as compared to earlier years. Clearly, the systems lagging ECMWF can improve, and there is evidence from SAMs in addition to the 4DEnVar example that improvements in forecast and data assimilation systems are still leading to forecast skill improvements.

scholarly journals Evaluating Short-term Forecast among Different Epidemiological Models under a Bayesian Framework

2020 ◽  
Author(s):  
Qiwei Li ◽  
Tejasv Bedi ◽  
Guanghua Xiao ◽  
Yang Xie
Keyword(s):  
Public Health ◽  
Predictive Modeling ◽  
Growth Models ◽  
Sir Model ◽  
Global Scale ◽  
Bayesian Framework ◽  
Epidemiological Models ◽  
Short Term ◽  
Predictive Capacity ◽  
Term Forecast

AbstractForecasting of COVID-19 daily confirmed cases has been one of the several challenges posed on the governments and health sectors on a global scale. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard SIR model into one Bayesian framework to evaluate their short-term forecasts. In summary, it was noted that none of the models proved to be golden standards across all the regions in their entirety, while all outperformed ARIMA in a predictive capacity as well as in terms of interpretability.

scholarly journals Transmission Dynamics and Short-Term Forecasts of COVID-19: Nepal 2020/2021

Epidemiologia ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 639-659
Author(s):  
Sushma Dahal ◽  
Ruiyan Luo ◽  
Raj Kumar Subedi ◽  
Meghnath Dhimal ◽  
Gerardo Chowell
Keyword(s):  
Performance Metrics ◽  
Growth Models ◽  
National Level ◽  
Growth Parameter ◽  
Transmission Dynamics ◽  
Short Term ◽  
Linear Pattern ◽  
Mobility Data ◽  
Using Data ◽  
Second Wave

Nepal was hard hit by a second wave of COVID-19 from April–May 2021. We investigated the transmission dynamics of COVID-19 at the national and provincial levels by using data on laboratory-confirmed RT-PCR positive cases from the official national situation reports. We performed 8 week-to-week sequential forecasts of 10-days and 20-days at national level using three dynamic phenomenological growth models from 5 March 2021–22 May 2021. We also estimated effective and instantaneous reproduction numbers at national and provincial levels using established methods and evaluated the mobility trends using Google’s mobility data. Our forecast estimates indicated a declining trend of COVID-19 cases in Nepal as of June 2021. Sub-epidemic and Richards models provided reasonable short-term projections of COVID-19 cases based on standard performance metrics. There was a linear pattern in the trajectory of COVID-19 incidence during the first wave (deceleration of growth parameter (p) = 0.41–0.43, reproduction number (Rt) at 1.1 (95% CI: 1.1, 1.2)), and a sub-exponential growth pattern in the second wave (p = 0.61 (95% CI: 0.58, 0.64)) and Rt at 1.3 (95% CI: 1.3, 1.3)). Across provinces, Rt ranged from 1.2 to 1.5 during the early growth phase of the second wave. The instantaneous Rt fluctuated around 1.0 since January 2021 indicating well sustained transmission. The peak in mobility across different areas coincided with an increasing incidence trend of COVID-19. In conclusion, we found that the sub-epidemic and Richards models yielded reasonable short-terms projections of the COVID-19 trajectory in Nepal, which are useful for healthcare utilization planning.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Chapter I. The British Economy to End-1972

1973 ◽  
Vol 63 ◽  
pp. 9-33
Keyword(s):  
Full Employment ◽  
Annual Review ◽  
Short Term ◽  
Term Forecast ◽  
Recent Developments ◽  
Detailed Assessment ◽  
Employment Potential ◽  
Economic Developments

Chapter I presents a general review of economic developments in 1972 including an attempt to assess the position of the economy in relation to its full employment potential. Chapter II includes the usual short-term forecast of likely developments over the next eighteen months together with a less detailed assessment of prospects over the rather longer term. Recent developments in and short-term prospects for various industries within the industrial production index are dealt with in some detail in Chapter III, while the final chapter contains our annual review and forecasts for the World Economy.

scholarly journals Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

2021 ◽  
Author(s):  
Nino Antulov-Fantulin ◽  
Tian Guo ◽  
Fabrizio Lillo
Keyword(s):  
Econometric Models ◽  
Gradient Boosting ◽  
Point Estimate ◽  
Order Book ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Ensemble Models ◽  
Gradient Boosting Machine ◽  
Different Sources ◽  
Better Than

AbstractWe study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

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