Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis
Original Paper
[키워드] activity
Analysis
approach
Area
Arellano-Bond estimator
Atlanta
average
Baltimore
Boston
calculated
Charlotte
Chicago
COVID-19
COVID-19 7-day lag
COVID-19 cities
COVID-19 metropolitan areas
COVID-19 pandemic
COVID-19 transmission
COVID-19 transmission deceleration
COVID-19 transmission jerk
Dallas
Denver
Detroit
dynamic
dynamic panel data
Effect
effective
Effects
extreme
First wave
generalized method of moments
generalized method of the moments
global COVID-19 surveillance
GMM
Health
highest
Houston
Impact
increase in
Local
lockdowns
longitudinal
Los Angeles
measure
Metrics
Miami
Minneapolis
modeling
New York City
objective
Orlando
pandemic
Panel
persistence
Philadelphia
Phoenix
Portland
positive result
public health
Regulation
Result
risk
Riverside
San Antonio
San Diego
San Francisco
SARS-CoV-2
SARS-CoV-2 surveillance
Seattle
second wave
spike
St Louis
statistically
Study design
Surveillance
Tampa
the United State
The United States
Transmission
trend
turn
US COVID-19
US COVID-19 surveillance system
US COVID-19 transmission acceleration
US COVID-19 transmission speed
US econometrics
US public health surveillance
US SARS-CoV-2
US surveillance metrics
Vaccine
variable
virus
Washington, DC
wave 2
wave two
[DOI] 10.2196/26081 PMC 바로가기 [Article Type] Original Paper
[DOI] 10.2196/26081 PMC 바로가기 [Article Type] Original Paper