scholarly journals COVID-19 Surveillance Updates in U.S. Metropolitan Areas—A Dynamic Panel Data Modeling Approach: Is the ‘Delta wave’ over? (Preprint)

10.2196/28737 ◽  
2021 ◽  
Author(s):  
Theresa B Oehmke ◽  
Charles B Moss ◽  
James F Oehmke
Keyword(s):  
Panel Data ◽  
Metropolitan Areas ◽  
Data Modeling ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Delta Wave ◽  
Modeling Approach

Dynamic Panel Data Modeling and COVID-19 Surveillence Updates in US Metropolitan Areas: Houston Shows Warning Signs (Preprint)

2021 ◽  
Author(s):  
Theresa B Oehmke ◽  
Sarah B Welch ◽  
Charles B Moss ◽  
Lori Post ◽  
James F Oehmke
Keyword(s):  
Panel Data ◽  
Metropolitan Areas ◽  
Face Mask ◽  
Dynamic Panel Data ◽  
Warning Signs ◽  
Cumulative Number ◽  
The Novel ◽  
Visual Interpretation ◽  
Dynamic Panel ◽  
Recommended Guidelines

BACKGROUND COVID-19 cases in U.S. metropolitan areas fell dramatically in January and early February, but flattened out in the last two weeks of February and first week of March. The reopening of states and municipalities coupled with the emergence of SARS-Cov-2 variants raises the specter of a re-ignition of explosive growth. Vigilant surveillance can help identify any re-ignition and validate an early and strong public health policy response. OBJECTIVE This surveillance reports aimed to provide up to date information about a potential re-ignition of the pandemic using the novel metrics of speed, acceleration, jerk, and 7-day persistence. METHODS COVID-19 pandemic dynamics for the 25 largest U.S. metropolitan areas were analyzed through 3/7/2021 using the novel metrics calculated on the basis of observed data on the cumulative number of cases as reported in usafacts.org. Statistical analysis was conducted using dynamic panel data models estimated with Arellano-Bond regression techniques. Results are presented in tabular and graphic forms for visual interpretation. RESULTS On average, speed in the 25 largest U.S. metropolitan areas declined from 68 new cases per day per 100,000 population during the week of 1/4-1/10/21 to 20 during the week of 3/1-3/7/2021. However, the decline stagnated and speed dropped only one case per day over the past two weeks from a value of 21 during the week of 2/15-2/21/2021. This stagnating decline is confirmed by acceleration and jerk data. Houston exhibited a smaller than average overall decline in speed and a bounce off its low speed of 17 during the week of 2/15-2/21/2021 to 27 during the week of 2/22-2/28-2021 and 29 during the week of 3/1/-3/7/2011. CONCLUSIONS The stagnation is evidence of the persistence of the pandemic and the possibility of a surge in new cases and possibly explosive growth as states reopen and if people choose not to follow recommended guidelines including social distancing and face mask wearing. Our evidence that Houston showed signs of a bounce during the last week of February and the first week of March coupled with the presence of all the major SARS-Cov-2 variants in the metropolitan area strongly suggests that reopening will lead to an upsurge in Houston’s COVID-19 cases with the potential for re-igniting exponential growth.

scholarly journals Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis

10.2196/26081 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e26081
Author(s):  
Theresa B Oehmke ◽  
Lori A Post ◽  
Charles B Moss ◽  
Tariq Z Issa ◽  
Michael J Boctor ◽  
...  
Keyword(s):  
United States ◽  
Panel Data ◽  
Data Model ◽  
Metropolitan Areas ◽  
The United States ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Extreme Behavior ◽  
Second Wave

Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

scholarly journals Dynamic Panel Data Modeling and Surveillance of COVID-19 in U.S. Metropolitan Areas (Preprint)

2020 ◽  
Author(s):  
Theresa B Oehmke ◽  
Lori A Post ◽  
Charles B Moss ◽  
Tariq Z Issa ◽  
Michael J Boctor ◽  
...  
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Metropolitan Areas ◽  
The United States ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
The Us ◽  
Extreme Behavior ◽  
Second Wave

BACKGROUND The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late Spring. For other areas that kept businesses open the ‘first wave’ in the US hit in mid-summer. As the weather turns colder, Universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. OBJECTIVE The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk at the metropolitan area scale. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide metropolitan area values for novel indicators to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 260 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the 25 largest U.S. metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. RESULTS Most recently, Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population, which we call speed. Extreme behavior in Minneapolis showed speed jumping from 17 to 30 (67%) in one week. The jerk and acceleration calculated for these areas also showed extreme behavior. The Dynamic Panel Data Model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases one week are statistically attributable to new cases from the prior week. CONCLUSIONS Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the US at the beginning of their cold weather season. With these persistence effects, and indoor activities becoming more popular as weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Bayesian estimation of dynamic panel data gravity model

2021 ◽  
Vol 40 (7) ◽  
pp. 607-634
Author(s):  
Moonhee Cho ◽  
Xiaoyong Zheng
Keyword(s):  
Panel Data ◽  
Bayesian Estimation ◽  
Gravity Model ◽  
Dynamic Panel Data ◽  
Dynamic Panel
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