Author: Wang, Yifei; Peng, Hao; Sha, Long; Liu, Zheyuan; Hong, Pengyu
Title: State-level COVID-19 Trend Forecasting Using Mobility and Policy Data Cord-id: oyntyd4g Document date: 2021_1_1
ID: oyntyd4g
Snippet: AO_SCPLOWBSTRACTC_SCPLOWThe importance of pandemic forecast cannot be overemphasized. We propose an interpretable machine learning approach for forecasting pandemic transmission rates by utilizing local mobility statistics and government policies. A calibration step is introduced to deal with time-varying relationships between transmission rates and predictors. Experimental results demonstrate that our approach is able to make accurate two-week ahead predictions of the state-level COVID-19 infec
Document: AO_SCPLOWBSTRACTC_SCPLOWThe importance of pandemic forecast cannot be overemphasized. We propose an interpretable machine learning approach for forecasting pandemic transmission rates by utilizing local mobility statistics and government policies. A calibration step is introduced to deal with time-varying relationships between transmission rates and predictors. Experimental results demonstrate that our approach is able to make accurate two-week ahead predictions of the state-level COVID-19 infection trends in the US. Moreover, the models trained by our approach offer insights into the spread of COVID-19, such as the association between the baseline transmission rate and the state-level demographics, the effectiveness of local policies in reducing COVID-19 infections, and so on. This work provides a good understanding of COVID-19 evolution with respect to state-level characteristics and can potentially inform local policymakers in devising customized response strategies.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date