An Open Repository of Real-Time COVID-19 Indicators

Reinhart, Alex; Brooks, Logan; Jahja, Maria; Rumack, Aaron; Tang, Jingjing; Agrawal, Sumit; Saeed, Wael Al; Arnold, Taylor; Basu, Amartya; Bien, Jacob; Cabrera, Ángel Alexander; Chin, Andrew I.; Chua, Eu Jing; Clark, Brian; Colquhoun, Sarah; DeFries, Nat; Farrow, David; Forlizzi, Jodi; Grabman, Jed; Gratzl, Samuel; Green, Alden; Haff, George; Han, Robin; Harwood, Kate; Hu, Addison J.; Hyde, Raphael; Hyun, Sangwon; Joshi, Ananya; Kim, Jimi; Kuznetsov, Andrew; Motte‐Kerr, Wichada La; Lee, Yeonjin; Lee, Kenneth; Liu, Michael X.; Mackey, Lester; Mazaitis, Kathryn; McDonald, Daniel J.; McGuinness, Phillip; Narasimhan, Balasubramanian; O’Brien, Michael P.; Oliveira, Natalia L.; Patil, Pratik; Perer, Adam; Politsch, Collin A.; Rajanala, Samyak; Rucker, Dawn; Scott, Chris; Shah, Nigam H.; Shankar, Viswanathan; Sharpnack, James; Shemetov, Dmitry; Simon, Noah; Smith, Benjamin Y.; Srivastava, Vishakha; Tan, Shuyi; Tibshirani, Robert; Tuzhilina, Elena; Nortwick, Ana Karina Van; Ventura, Valérie; Wasserman, Larry; Weaver, B. A.; Weiss, Jeremy C.; Whitman, Spencer; Williams, Kristin; Rosenfeld, Roni; Tibshirani, Ryan J.

doi:10.1101/2021.07.12.21259660

Cited by 9 publications

(8 citation statements)

References 24 publications

(29 reference statements)

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“…State-level, daily COVID-19 hospitalization reports are obtained from the HHS, accessed via the Delphi Epidata API (Farrow et al, 2015; Reinhart et al, 2021). We use Y ℓ , s to denote the 7-day trailing average of finalized reported new COVID-19 hospitalization counts corresponding to location ℓ and time s .…”

Section: Methodsmentioning

confidence: 99%

Nowcasting Reported COVID-19 Hospitalizations Using De-Identified, Aggregated Medical Insurance Claims Data

Shen,

Rumack,

Wilder

et al. 2023

Preprint

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We propose, implement, and evaluate a method for nowcasting the daily number of new COVID-19 hospitalizations, at the level of individual US states, based on de-identified, aggregated medical insurance claims data. Our analysis proceeds under a hypothetical scenario in which, during the Delta wave, states only report data on the first day of each month, and on this day, report COVID-19 hospitalization counts for each day in the previous month. In this hypothetical scenario (just as in reality), medical insurance claims data continues to be available daily. At the beginning of each month, we train a regression model, using all data available thus far, to predict hospitalization counts from medical insurance claims. We then use this model to nowcast the (unseen) values of COVID-19 hospitalization counts from medical insurance claims, at each day in the following month. Our analysis uses properly-versioned data, which would have been available in real-time, at the time predictions are produced. In spite of the difficulties inherent to real-time estimation (e.g., latency and backfill) and the complex dynamics behind COVID-19 hospitalizations themselves, we find overall that medical insurance claims can be an accurate predictor of hospitalization reports, with mean absolute errors typically around 0.4 hospitalizations per 100,000 people, i.e., proportion of variance explained around 75%. Perhaps more importantly, we find that nowcasts made using medical insurance claims can qualitatively capture the dynamics (upswings and downswings) of hospitalization waves, which are key features that inform public health decision-making.

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Section: Methodsmentioning

confidence: 99%

Nowcasting Reported COVID-19 Hospitalizations Using De-Identified, Aggregated Medical Insurance Claims Data

Shen,

Rumack,

Wilder

et al. 2023

Preprint

Self Cite

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“…We use COVID‐19 incidence rates from Johns Hopkins University, collected via the COVIDcast API (COVID‐19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University 2022; Reinhart et al. 2021). To alleviate the impact of (likely erroneous) outliers in COVID‐19 case reporting, we compute a 7‐day running robust median for every county (Hodges and Lehmann 1963).…”

Section: Data and Analysesmentioning

confidence: 99%

From fear to hate: Sources of anti‐Asian sentiment during COVID‐19

Dai,

Gao,

Radford

2024

Social Science Quarterly

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ObjectiveThis paper seeks to explain and empirically test how a public health crisis, such as the COVID‐19 pandemic, might lead to discriminatory attitudes and behaviors against marginalized groups.MethodWe identify four causal mechanisms that may account for the increase in anti‐Asian racism during COVID‐19. Using a large data set of geolocated COVID‐19‐related tweets in the U.S., we examine the spatiotemporal pattern of anti‐Asian sentiment on Twitter and test all four mechanisms that link the crisis to the behaviors of “othering” and “scapegoating.”ResultsWe find evidence consistent with the elite cueing, perceived threat, and grievance mechanisms. The president's scapegoating rhetoric significantly increased anti‐Asian hate tweets across all counties. In addition, high COVID‐19 infection rates and grievances from containment policies are also associated with greater numbers of anti‐Asian hate tweets. On the other hand, counties with larger vulnerable populations do not seem to have more anti‐Asian hate tweets.ConclusionsWe demonstrate that a crisis alone does not necessarily lead to othering behavior, while elite rhetoric can significantly influence the public's opinion and behavior during a crisis.

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“…Now we apply the multi-period forecasting approaches on the real data obtained from the Delphi COVIDcast API [10]. This open-source data set, which is updated daily, tracks multiple signals related to the spread and impact of the COVID-19 pandemic across the United States on both county and state levels.…”

Section: Covidcast Data Experimentsmentioning

confidence: 99%

“…We extend the methodology to the case that some of the response signals are unobserved in Section 5 and propose an analogue based on quantile-regression in Section 8. Sections 6, 7 and 9 illustrate the MPF technique on a small simulation example as well as real COVID-19 case incidence data obtained from the Delphi Epidata CovidCAST API [10]. We conclude the paper with a Discussion where we suggest some future research directions.…”

Section: Introductionmentioning

confidence: 97%

Smooth multi-period forecasting with application to prediction of COVID-19 cases

Tuzhilina¹,

Hastie²,

McDonald³

et al. 2022

Preprint

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Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the CovidCast dataset as well as a small simulation example. * J. Kenneth Tay was a part of Department of Statistics at Stanford when the project was completed.

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An Open Repository of Real-Time COVID-19 Indicators

Cited by 9 publications

References 24 publications

Nowcasting Reported COVID-19 Hospitalizations Using De-Identified, Aggregated Medical Insurance Claims Data

Nowcasting Reported COVID-19 Hospitalizations Using De-Identified, Aggregated Medical Insurance Claims Data

From fear to hate: Sources of anti‐Asian sentiment during COVID‐19

Smooth multi-period forecasting with application to prediction of COVID-19 cases

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