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Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests

Zhaowei She, Zilong Wang, Turgay Ayer, Asmae Toumi, Jagpreet Chhatwal

arXiv 31 Oct 2020 · Machine Learning · 3 citations (OpenAlex)

arXiv:2011.01219 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accuracy vs. speed. In particular, while estimation accuracy improves with longer fitting windows, speed degrades. This paper presents a machine learning framework to balance this tradeoff using generalized random forests (GRF), and applies it to detect county level COVID-19 outbreaks. This algorithm chooses an adaptive fitting window size for each county based on relevant features affecting the disease spread, such as changes in social distancing policies. Experiment results show that our method outperforms any non-adaptive window size choices in 7-day ahead COVID-19 outbreak case number predictions.

Citation extraction

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Susan Athey, Julie Tibshirani, Stefan Wager, et al (2019) Generalized random forests0.64422100%
2Junling Ma, Jonathan Dushoff, Benjamin M Bolker, and David JD Earn (2014) Estimating initial epidemic growth rates0.64422100%
3J Raifman, K Nocka, D Jones, J Bor, S Lipson, J Jay, and P Chan (2020) Covid-19 us state policy database0.5112250%
4The University of Melbourne (2020) Coronavirus 10-day forecast0.5112250%
5Jeffrey M Wooldridge (2010) Econometric analysis of cross section and panel data0.51121100%
6The New York Times (2020) Coronavirus (Covid-19) Data in the United States0.40511100%
7Alberto Abadie (2003) Semiparametric instrumental variable estimation of treatment response models0.40511100%
8Joshua D Angrist and Guido W Imbens (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity0.40511100%
9Gerardo Chowell, Paul W Fenimore, Melissa A Castillo-Garsow, and Car… (2003) Sars outbreaks in ontario, hong kong and singapore: the role of diagnosis and isolation as a control mechanism0.40511100%
10Gerardo Chowell, Hiroshi Nishiura, and Luis MA Bettencourt (2007) Comparative estimation of the reproduction number for pandemic influenza from daily case notification data0.40511100%

Showing the top 10 of 17 scored citations.