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We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 week ahead of the current time, at the county level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (1) temporal auto- and pairwise correlation of the two local time series (confirmed cases and deaths from the COVID-19), (2) correlation between locations (propagation between counties), and (3) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model’s high dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top 10 metropolitan areas in the nation, which we refer to (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multivariate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability.
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A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Several "data-for-good" projects [1, 5, 12] initiated by major companies (e.g., Meta, Google) release to the public spatio-temporal datasets to benefit COVID-19 spread modeling [17, 47, 64] and understand human mobility [14, 24]. Most often, spatio-temporal data are provided in the form of snapshot high resolution population density information, where the released statistics capture population counts in small areas for short time periods. Since high resolution is required for utility (e.g., in modeling COVID hotspots) privacy risks are elevated. To prevent malicious actors from using the data to infer sensitive details about individuals, the released datasets must be first sanitized. Typically, [1, 5, 7, 12], differential privacy (DP) is employed as protection model, due to its formal protection guarantees that prevent an adversary to learn whether a particular individual's data has been included in the release or not.
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- PAR ID:
- 10431849
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3588701
