A Novel Spatiotemporal Prediction Method of Cumulative Covid-19 Cases

Authors

Junzhe Cai, University of Nebraska - LincolnFollow

First Advisor

Peter Z. Revesz

Second Advisor

Leen-Kiat Soh

Third Advisor

Ashok Samal

Date of this Version

Winter 12-4-2020

Citation

Junzhe Cai. A Novel Spatiotemporal Prediction Method of Cumulative Covid-19 Cases. University of Nebraska-Lincoln. Master’s thesis. 2020.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Peter Z. Revesz. Lincoln, Nebraska: December, 2020

Copyright 2020 Junzhe Cai

Abstract

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 sufficient supply of testing kits, hospital beds and medical personnel. This thesis experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid-19 pandemic cases. These ten methods include two types of neural networks and extrapolation methods based on best fit linear, best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method from Revesz. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining the Best fit linear and IDW. 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 June and July, 2020.

Adviser: Peter Z. Revesz