A novel spatiotemporal method for predicting covid-19 cases

Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sucient supply of testing kits, hospital beds and medical personnel. This paper experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid- 19 pandemic cases. These ten methods include three types of neural networks and extrapola- tion methods based on best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method proposed by the second author. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining temporal and spatial prediction methods. The experiments show that among these ten prediction methods, the spatiotemporal method has the smallest root mean square error and mean absolute error on Covid-19 cumulative data for counties in New York State between May and July, 2020. © This article is published under the terms of the Creative Commons Attribution License 4.0 https://creativecommons.org/licenses/by/4.0/deed.en_US

A novel spatio-Temporal interpolation algorithm and its application to the COVID-19 pandemic

This paper describes several interpolation methods for predicting the number of cases of the COVID-19 pandemic. The interpolation methods include some well-known temporal interpolation algorithms including Lagrange interpolation, cubic spline interpolation, and exponential decay interpolation. These temporal interpolation algorithms enable the interpolation of the COVID-19 cases at locations where measures on prior days are available. However, pandemics are not purely temporal but spatio-Temporal phenomena. Therefore, the neighboring locations need to be considered too in order to derive accurate interpolation values for future days. This paper introduces a novel spatio-Temporal interpolation algorithm that is shown to be better than any purely temporal interpolation algorithm in predicting the COVID-19 cases in the continental United States. In particular, the novel spatio-Temporal interpolation method achieves a mean absolute error of 8.44 cases over a million people when predicting two days ahead the number of cases of the COVID-19 pandemic. © 2020 ACM.