A Geotemporal Clustering Model for COVID-19 Projection

13 Pages Posted: 11 Sep 2020

See all articles by N. Bora Keskin

N. Bora Keskin

Duke University - Fuqua School of Business

Xu Min

Tsinghua University - Tsinghua University School of Economics and Management

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business

Date Written: August 31, 2020

Abstract

We propose a geotemporal clustering based algorithm to predict the state-level COVID-19 cases in the United States, using the state-level population and historical COVID-19 case data as input. Our algorithm has two novel features. First, we treat a (state, date) pair as one observation in the COVID-19 case data, summarize features from the data, and classify similar observations using k-means clustering. Second, we use the similarity between the observations in the same cluster to capture the similarity of future trajectory of cases. Thus, when predicting the number of cases in a state in the future, we first map the pair of this state and the current date to a corresponding cluster, then take the observable future of older observations in this cluster as potential samples. Using mean absolute percentage error (MAPE) as the performance metric, we demonstrate that our algorithm provides reliable results for prediction periods ranging from 1 to 20 days. Our algorithm achieves the highest 7-day prediction accuracy both at the state and the national levels compared to three existing models and one intuitive baseline model. Our results indicate that in the next 20 days, states may be in starkly different situations if there are no interventions. While some states are getting better, the cases in others are still trending upward.

Note: Funding: None to declare

Declaration of Interest: None to declare

Keywords: COVID-19 cases, prediction, k-means clustering, sample average approximation

Suggested Citation

Keskin, N. Bora and Min, Xu and Song, Jing-Sheng Jeannette, A Geotemporal Clustering Model for COVID-19 Projection (August 31, 2020). Available at SSRN: https://ssrn.com/abstract=3686506 or http://dx.doi.org/10.2139/ssrn.3686506

N. Bora Keskin (Contact Author)

Duke University - Fuqua School of Business ( email )

100 Fuqua Drive
Durham, NC 27708-0120
United States

HOME PAGE: http://faculty.fuqua.duke.edu/~nk145/

Xu Min

Tsinghua University - Tsinghua University School of Economics and Management ( email )

Beijing
China

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business ( email )

100 Fuqua Drive
Duke University
Durham, NC 27708
United States

HOME PAGE: http://people.duke.edu/~jssong/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
95
Abstract Views
1,119
rank
322,893
PlumX Metrics