Full Citation

Title: Spatiotemporal Prediction of COVID-19 Cases Using Inter- and Intra-County Proxies of Human Interactions

Citation Type: Journal Article

Publication Year: 2021

ISBN:

ISSN: 2041-1723

DOI: 10.1038/s41467-021-26742-6

NSFID:

PMCID:

PMID: 34750353

Abstract: Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

Url: https://www.nature.com/articles/s41467-021-26742-6

User Submitted?: No

Authors: Vahedi, Behzad; Karimzadeh, Morteza; Zoraghein, Hamidreza

Periodical (Full): Nature Communications

Issue: 1

Volume: 12

Pages: 1-15

Data Collections: IPUMS NHGIS

Topics: Health, Population Data Science

Countries: