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Public Sector Analytics |
COVID-19 Pandemic
Closer to Home: Partnering to Distribute Vaccinations under Spatially Heterogeneous Demand
Fernanda Bravo, Jingyuan Hu, Elisa F. Long
Working Paper, 2023
featured in UCLA Anderson Review
Fernanda Bravo, Jingyuan Hu, Elisa F. Long
Working Paper, 2023
featured in UCLA Anderson Review
Nursing Home Staff Networks and COVID-19 [Appendix]
M. Keith Chen, Judith A. Chevalier, Elisa F. Long
Proceedings of the National Academy of Sciences, 2021
featured in NPR, New York Times, UCLA Anderson Review, The New Yorker, How the World Works Podcast
M. Keith Chen, Judith A. Chevalier, Elisa F. Long
Proceedings of the National Academy of Sciences, 2021
featured in NPR, New York Times, UCLA Anderson Review, The New Yorker, How the World Works Podcast
Partisan Responses to COVID-19 Government Restrictions: Evidence from Individual Smartphone Mobility Data
M. Keith Chen, Yilin Zhuo, Malena de la Fuente, Elisa F. Long
Working Paper, 2021
featured in UCLA Anderson Review
M. Keith Chen, Yilin Zhuo, Malena de la Fuente, Elisa F. Long
Working Paper, 2021
featured in UCLA Anderson Review
Hurricane Evacuations
Using a novel smartphone GPS dataset comprising more than 2.7 million residents in Florida and Texas, we empirically examine drivers of hurricane evacuations, focusing on Hurricane Matthew in 2016 and Hurricanes Harvey and Irma in 2017. We observe that residents of Republican-leaning districts evacuate at much lower rates than similar Democrat areas, with an estimated 10-11 percentage-point lower evacuation rate among 2016 Trump voters versus Clinton voters. This effect occurs during Hurricane Irma, immediately following public comments by Rush Limbaugh that cast doubt on the veracity of hurricane advisories.
Political Storms: Emergent Partisan Skepticism of Hurricane Risks [Appendix]
Elisa F. Long, M. Keith Chen, Ryne Rohla
Science Advances, 2020
featured in Scientific American, Popular Science, Newsweek, Axios, UCLA Anderson Review
Elisa F. Long, M. Keith Chen, Ryne Rohla
Science Advances, 2020
featured in Scientific American, Popular Science, Newsweek, Axios, UCLA Anderson Review
Ebola Resource Allocation
In early 2014, the World Health Organization declared the Ebola outbreak in West Africa "relatively small" and medical teams were prematurely evacuated from crisis regions. Months later, studies predicted a devastating epidemic, projecting up to 1.4 million cases, and the international community mobilized response efforts. By 2016, nearly 29,000 people had contracted Ebola, fortunately far fewer than initially projected. These miscalculations, compounded by a fragmented international health community, shortages of medical personnel and supplies, and public fear, all contributed to an inadequate response. Public health experts have highlighted the need for drastic reforms to ensure better preparedness for the inevitable next outbreak.
We develop an epidemic model with 2 novel components: dynamic behavior adaptation and an adjacency matrix to adjust transmission based on geographic distance. Calibrating the model to daily Ebola cases results in an aggregate error <10%, even when using only 4-8 weeks of data. We develop an estimation-optimization technique to iteratively estimate parameters and solve for the optimal resource allocation across regions, to minimize future infections. We compare this to a solution obtained via approximate dynamic programming and a heuristic based on the basic reproduction number, R0. Our study demonstrates that an enhanced model of geospatial disease spread improves forecasts and can identify the locations most vulnerable to transmission from neighboring regions, which could help policymakers efficiently target relief efforts.
We develop an epidemic model with 2 novel components: dynamic behavior adaptation and an adjacency matrix to adjust transmission based on geographic distance. Calibrating the model to daily Ebola cases results in an aggregate error <10%, even when using only 4-8 weeks of data. We develop an estimation-optimization technique to iteratively estimate parameters and solve for the optimal resource allocation across regions, to minimize future infections. We compare this to a solution obtained via approximate dynamic programming and a heuristic based on the basic reproduction number, R0. Our study demonstrates that an enhanced model of geospatial disease spread improves forecasts and can identify the locations most vulnerable to transmission from neighboring regions, which could help policymakers efficiently target relief efforts.
Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic Policies [Appendix]
Elisa F. Long, Eike Nohdurft, Stefan Spinler
Manufacturing & Service Operations Management, 2018
Elisa F. Long, Eike Nohdurft, Stefan Spinler
Manufacturing & Service Operations Management, 2018
- Second place, 2015 INFORMS Public Sector OR Paper Competition