Data-Driven Modeling to Facilitate Policymaking in Fighting to Contain the COVID-19 Pandemic

Qiu, Robin G.; Wang, Yuanxun Ethan; Gong, Iris

doi:10.1016/j.procs.2021.05.034

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Some were designed for surveillance purposes, e.g. visualizing infection rates and trends at different spatial resolutions (Dong et al 2020;Peddireddy et al 2020;Wissel et al 2020), identifying anomalous hotspots (Hohl et al 2020), and informing policymakers about the necessary levels of restriction in a timely fashion (Qiu 2021). Another set of systems was developed to help policymakers observe the effects of different non-pharmaceutical interventions in order to help them make informed decisions (Barrett et al 2007;Beckman et al 2014;Chang et al 2021b).…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

Mehrab

Wilson

Chang

et al. 2022

AAAI

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The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. We also validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups.

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Section: Related Workmentioning

confidence: 99%

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

Mehrab

Wilson

Chang

et al. 2022

AAAI

View full text Add to dashboard Cite

show abstract

“…Scientific studies of mitigation policy responses to COVID-19 have been done in many countries and regions, revealing that these mitigation measures have effectively slowed the community spread of COVID-19 (Qiu, Wang, & Gong, 2021). In fact, wearing face masks in public becomes one of scientific basics in helping fight the virus spreading (Eikenberry et al , 2020).…”

mentioning

confidence: 99%

“…We started with an overall study on how data-driven modeling could be applied to facilitating public health intervention policymaking when a public health crisis arises (Qiu et al , 2021). We then implemented an agent-based simulation model to demonstrate that different mitigation measures would have different consequences in terms of containing virus spreading using the COIVD-19 as an example.…”

mentioning

confidence: 99%

Editorial: Agent-based simulation to transform a society with good sense of adopting mitigation measures in containing virus spreading

Qiu

Gong

2023

DTS

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Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

Mehrab

Wilson

Chang

et al. 2021

Preprint

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The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.

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Data-Driven Modeling to Facilitate Policymaking in Fighting to Contain the COVID-19 Pandemic

Cited by 4 publications

References 15 publications

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

Editorial: Agent-based simulation to transform a society with good sense of adopting mitigation measures in containing virus spreading

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

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