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Monitoring Local Responses to COVID-19 Pandemic Using Real-time Big Data


Associate Name: Qingfeng Li

Funding Source/Period of the Grant: HPC Pilot Research Program 04/14/2020 - 12/31/2020

Project Description

Specific Aims
Evidence on the effectiveness of different containment measures is urgently needed to inform
subsequent policy responses. The heterogeneous pandemic situations and differentiated policy
responses across the country presents an opportunity for a natural experiment. We propose to
systematically document the implementation of containment actions at local levels, and
conduct statistical analyses to evaluate the responses.

Aim 1 Develop algorithms to extract, validate and manage big data on responses to COVID-19
at local levels. The whole process will be automated to prevent potential errors and reduce
future maintenance costs.

Aim 2 Conduct statistical analyses to associate the big data measures obtained from Aim 1 to
pandemic trajectories at local levels. The finding may be able to identify the most effective
response actions for each pandemic stage, accounting for demographic and social-economic

Aim 3 Build an online platform for rapid result dissemination. The platform will be based on R
Shiny from RStudio, which has the capacity for both visualization and real-time computation