The growth of COVID-19 in Spain. A view based on time-series forecasting methods

Carrión-García, Andrés; Jabaloyes, José; Grisales, Angela

doi:10.1016/b978-0-12-824536-1.00020-4

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The temperatures recorded in the fractures by thermologgers located near the surface (Up) are correlated to the temperatures of the national station installed at the summit, the cross-correlation coefficients [68,69], ranging between 0.65 and 0.77 for 1600 measures during the summer. A cross correlation between the Up and Down temperatures at the same location is also performed, showing the maximum correlation for a time delay equal to zero, with values ranging from 0.61 (Th3) to 0.85 (Th2).…”

Section: Temperatures Analysis Resultsmentioning

confidence: 99%

Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques

Wolff,

Jaboyedoff,

Fei

et al. 2023

Remote Sensing

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Foreseeing the failure of important unstable volumes is a major concern in the Alps, especially due to the presence of people and infrastructures in the valleys. The use of monitoring and remote sensing techniques is aimed at detecting potential instabilities and the combination of several techniques permits the cross-validation of the detected movements. Supplemented with field mapping and structural analysis, it is possible to define possible scenarios of rupture in terms of volume, mechanisms of failure and susceptibility. A combined observation strategy was applied to the study of major instability located in the Ticinese Alps (Switzerland), Cima del Simano, where the monitoring started in 2006 with the measurement of opened cracks with extensometers. Since 2021, the monitoring has been completed by LiDAR, satellite and GB-InSAR observations and structural analysis. Here, slow but constant movements of about 7 mm/yr were detected along with rockfall activities near the Simano summit. Eight failure scenarios of various sizes ranging from 2.3 × 105 m3 to 51 × 106 m3, various mechanisms (toppling, planar, wedge and circular sliding) and various occurrence probabilities were defined based on the topography and the monitoring results and by applying a Slope Local Base Level (SLBL) algorithm. Weather acquisition campaigns by means of thermologgers were also conducted to suggest possible causes that lead to the observed movements and to suggest the evolution of the instabilities with actual and future climate changes.

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Section: Temperatures Analysis Resultsmentioning

confidence: 99%

Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques

Wolff,

Jaboyedoff,

Fei

et al. 2023

Remote Sensing

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“…Regarding the COVID-19 forecasts, studies have used traditional models to forecast COVID-19 data. For example, García et al [13] used ARIMA-based models for forecasting the number of COVID-19-related death in Spain. They tested their model for a short period of time (10 days in total) and reported an average accuracy of 95% over those 10 days.…”

Section: A Traditional Timeseries Forecasting Methodsmentioning

confidence: 99%

DSA-BEATS: Dual Self-Attention N-BEATS Model for Forecasting COVID-19 Hospitalization

Motavali,

Yow,

Hansmeier

et al. 2023

IEEE Access

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The high number of hospitalization cases of COVID-19 made public health providers overloaded. Forecasting the number of hospitalized patients related to COVID-19 can help public health providers to make informed decisions for controlling the spread. In this study, we present the Dual Self-Attention NBEATS (DSA-BEATS) model, a novel approach that effectively combines the self-attention mechanism of transformers with the proficiency of the N-BEATS model in dealing with multivariate forecasting problems. We expanded the dataset to a multivariate one by including data from Canadian transportation hub cities and SARS-CoV-2 RNA load in wastewater, which allowed for a more comprehensive modeling of the complex relationships impacting COVID-19 hospitalizations. These transportation hub cities were the major ports of entry for international travelers coming to the country. The DSA-BEATS model was tested on a 55-day test set with a 12-day horizon, resulting in a Mean Absolute Percentage Error (MAPE) of 14.23%, which implies an accuracy of 85.77%. These results demonstrate substantial improvements over state-of-the-art models such as N-BEATS and Informer, validating the efficacy of the DSA-BEATS model in accurately predicting COVID-19 hospitalizations. The study provides a significant contribution to the ongoing development of enhanced timeseries forecasting methods, particularly in the context of public health crises. The DSA-BEATS model's ability to capture complex temporal relationships and effectively handle multivariate data inputs underscores its potential in a wide range of forecasting tasks beyond the COVID-19 pandemic.

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“…Over time, different predictive models have been developed and utilized to forecast and anticipate the trends and patterns of COVID-19. For instance, researchers in Spain used ARIMA (Autoregressive Integrated Moving Average), a mathematical statistical model, to forecast the number of hospitalizations and deaths due to COVID-19 and found that their models had good predictive performance [6]. An additional illustration could be provided by the implementation of machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Using logistic function to predict epidemic trend of COVID-19 in China

2023

TNS

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As the novel coronavirus continues to spread and mutate, there has been growing concern over public health. Multiple measures have been enacted to mitigate the transmission of the disease, resulting in varying infection scenarios across different countries. To achieve timely and effective control of the epidemic, we note that predicting the future course of an epidemic plays an important role. The logistic function, a continuous-time demographic model, may be a suitable mathematical tool for estimating the trend of the epidemic. This paper aims to evaluate the accuracy of the logistic map in estimating the future trend of the COVID-19. We collect the most recent COVID-19 epidemiological data prior to January 30, 2023, and subsequently integrate figures into the curve fitting tool in MATLAB to generate an epidemic curve. By comparing the actual numbers and the predicted figures, the accuracy of logistic map can be properly assessed.

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The growth of COVID-19 in Spain. A view based on time-series forecasting methods

Cited by 4 publications

References 23 publications

Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques

Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques

DSA-BEATS: Dual Self-Attention N-BEATS Model for Forecasting COVID-19 Hospitalization

Using logistic function to predict epidemic trend of COVID-19 in China

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