Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis
Background: The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths globally. Without a coordinated national public health plan, informed by surveillance with actionable...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
JMIR Publications
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/142736 |
| _version_ | 1855527615512707072 |
|---|---|
| author | Post, Lori Ann Issa, Tariq Ziad Boctor, Michael J. Moss, Charles B. Murphy, Robert Leo Resnick, Danielle |
| author_browse | Boctor, Michael J. Issa, Tariq Ziad Moss, Charles B. Murphy, Robert Leo Post, Lori Ann Resnick, Danielle |
| author_facet | Post, Lori Ann Issa, Tariq Ziad Boctor, Michael J. Moss, Charles B. Murphy, Robert Leo Resnick, Danielle |
| author_sort | Post, Lori Ann |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background: The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths globally. Without a coordinated national public health plan, informed by surveillance with actionable metrics, the U.S. is ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance suffers from incomplete ascertainment and is limited by the use of standard surveillance metrics. While many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic. Objective: The objective of this study is to develop dynamic metrics for public health surveillance that can inform world-wide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision-making and to identify where and when corrective action is required to prevent outbreaks. Methods: Using a longitudinal trend analysis study design, we extracted COVID data from global public health registries. We use an empirical difference equation to measure daily case numbers for our use case in 50 U.S. states and Washington D.C. as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. Results: Examination of U.S. and state data demonstrate most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk and persistence. Larger U.S. states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic while other states have alarming rates of speed, acceleration, jerk, and 7-Day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the U.S. enters its third wave of COVID-19, all 50 states and D.C. have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota) and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) per new infections per 100,000. Conclusions: Standard surveillance techniques such as daily and cumulative infections and deaths are helpful, but only provide a static view of what has already occurred in the pandemic, and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as to signal when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allows us to zero in on where transmissions are increasing meaning corrective action can be applied with razor precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary, while preserving the economy in other U.S. areas. |
| format | Journal Article |
| id | CGSpace142736 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | JMIR Publications |
| publisherStr | JMIR Publications |
| record_format | dspace |
| spelling | CGSpace1427362025-02-24T06:49:38Z Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis Post, Lori Ann Issa, Tariq Ziad Boctor, Michael J. Moss, Charles B. Murphy, Robert Leo Resnick, Danielle data covid-19 health monitoring disease management surveillance systems disease control persistence disease surveillance disease transmission public health Background: The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths globally. Without a coordinated national public health plan, informed by surveillance with actionable metrics, the U.S. is ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance suffers from incomplete ascertainment and is limited by the use of standard surveillance metrics. While many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic. Objective: The objective of this study is to develop dynamic metrics for public health surveillance that can inform world-wide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision-making and to identify where and when corrective action is required to prevent outbreaks. Methods: Using a longitudinal trend analysis study design, we extracted COVID data from global public health registries. We use an empirical difference equation to measure daily case numbers for our use case in 50 U.S. states and Washington D.C. as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments (GMM) approach by implementing the Arellano-Bond estimator in R. Results: Examination of U.S. and state data demonstrate most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk and persistence. Larger U.S. states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic while other states have alarming rates of speed, acceleration, jerk, and 7-Day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the U.S. enters its third wave of COVID-19, all 50 states and D.C. have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota) and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) per new infections per 100,000. Conclusions: Standard surveillance techniques such as daily and cumulative infections and deaths are helpful, but only provide a static view of what has already occurred in the pandemic, and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as to signal when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allows us to zero in on where transmissions are increasing meaning corrective action can be applied with razor precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary, while preserving the economy in other U.S. areas. 2020-11-01 2024-05-22T12:10:58Z 2024-05-22T12:10:58Z Journal Article https://hdl.handle.net/10568/142736 en https://doi.org/10.2196/24248 Open Access JMIR Publications Post, Lori Ann; Issa, Tariq Ziad; Boctor, Michael J.; Moss, Charles B.; Murphy, Robert L.; Resnick, Danielle; et al. 2020. Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis. Journal of Medical Internet Research 22(12): e24286. https://doi.org/10.2196/24286 |
| spellingShingle | data covid-19 health monitoring disease management surveillance systems disease control persistence disease surveillance disease transmission public health Post, Lori Ann Issa, Tariq Ziad Boctor, Michael J. Moss, Charles B. Murphy, Robert Leo Resnick, Danielle Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title | Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title_full | Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title_fullStr | Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title_full_unstemmed | Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title_short | Dynamic metrics for public health surveillance are imperative to gain control of the COVID-19 pandemic in America: Longitudinal trend analysis |
| title_sort | dynamic metrics for public health surveillance are imperative to gain control of the covid 19 pandemic in america longitudinal trend analysis |
| topic | data covid-19 health monitoring disease management surveillance systems disease control persistence disease surveillance disease transmission public health |
| url | https://hdl.handle.net/10568/142736 |
| work_keys_str_mv | AT postloriann dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis AT issatariqziad dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis AT boctormichaelj dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis AT mosscharlesb dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis AT murphyrobertleo dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis AT resnickdanielle dynamicmetricsforpublichealthsurveillanceareimperativetogaincontrolofthecovid19pandemicinamericalongitudinaltrendanalysis |