Related Experiment Video
Updated: Oct 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
COVID-19 cases prediction in multiple areas via shapelet learning
1Computer Engineering College, Jimei University, Yinjiang Road 185, Xiamen, 361021 China.
Abstract:
Predicting the number of COVID-19 cases in a geographical area is important for the management of health resources and decision making. Several methods have been proposed for COVID-19 case predictions but they have important limitations in terms of model interpretability, related to COVID-19's incubation period and major trends of disease transmission. To be able to explain prediction results in terms of incubation period and transmission trends, this paper presents the Multivariate Shapelet Learning (MSL) model to learn shapelets from historical observations in multiple areas. An experimental evaluation was done to compare the prediction performance of eleven algorithms, using the data collected from 50 US provinces/states. Results show that the proposed method is effective and efficient. The learned shapelets explain increasing and decreasing trends of new confirmed cases, and reveal that the COVID-19 incubation period in the USA is around 28 days.
Related Concept Videos
Steps in Outbreak Investigation
Pie Chart
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Pareto Chart
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...

