Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.
Nat Commun
; 12(1): 6440, 2021 11 08.
Article
in English
| MEDLINE | ID: covidwho-1506955
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Cell Phone Use
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2021
Document Type:
Article
Affiliation country:
S41467-021-26742-6
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