COVID-Philadelphia

@article{milkman2022citywide,

title = {A citywide experiment testing the impact of geographically targeted, high-pay-off vaccine lotteries},

author = {Katherine L. Milkman and Linnea Gandhi and Sean F. Ellis and Heather N. Graci and Dena M. Gromet and Rayyan S. Mobarak and Alison M. Buttenheim and Angela L. Duckworth and Devin Pope and Ala Stanford and Richard Thaler and Kevin G. Volpp },

url = {https://www.nature.com/articles/s41562-022-01437-0},

doi = {10.1038/s41562-022-01437-0},

issn = {2397-3374},

year  = {2022},

date = {2022-09-01},

journal = {Nature Human Behavior},

abstract = {Lotteries have been shown to motivate behaviour change in many settings, but their value as a policy tool is relatively untested. We implemented a pre-registered, citywide experiment to test the effects of three high-pay-off, geographically targeted lotteries designed to motivate adult Philadelphians to get their COVID-19 vaccine. In each drawing, the residents of a randomly selected ‘treatment’ zip code received half the lottery prizes, boosting their chances of winning to 50×–100× those of other Philadelphians. The first treated zip code, which drew considerable media attention, may have experienced a small bump in vaccinations compared with the control zip codes: average weekly vaccinations rose by an estimated 61 per 100,000 people per week (+11%). After pooling the results from all three zip codes treated during our six-week experiment, however, we do not detect evidence of any overall benefits. Furthermore, our 95% confidence interval provides a 9% upper bound on the net benefits of treatment in our study.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{barreras2021autoekf,

title = {AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data},

author = {Francisco Barreras and Mikhail Hayhoe and Hamed Hassani and Victor M. Preciado},

url = {https://arxiv.org/abs/2106.14357},

year  = {2021},

date = {2021-06-28},

journal = {arXiv Preprint},

pages = {6},

abstract = {We present an Extended Kalman Filter framework for system identification and control of a stochastic high-dimensional epidemic model. The scale and severity of the COVID-19 emergency have highlighted the need for accurate forecasts of the state of the pandemic at a high resolution. Mechanistic compartmental models are widely used to produce such forecasts and assist in the design of control and relief policies. Unfortunately, the scale and stochastic nature of many of these models often makes the estimation of their parameters difficult. With the goal of calibrating a high dimensional COVID-19 model using low-level mobility data, we introduce a method for tractable maximum likelihood estimation that combines tools from Bayesian inference with scalable optimization techniques from machine learning. The proposed approach uses automatic backward-differentiation to directly compute the gradient of the likelihood of COVID-19 incidence and death data. The likelihood of the observations is estimated recursively using an Extended Kalman Filter and can be easily optimized using gradient-based methods to compute maximum likelihood estimators. Our compartmental model is trained using GPS mobility data that measures the mobility patterns of millions of mobile phones across the United States. We show that, after calibrating against incidence and deaths data from the city of Philadelphia, our model is able to produce an accurate 30-day forecast of the evolution of the pandemic.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}