scholarly journals Middle East and North Africa SARS-CoV-2 Surveillance: A Longitudinal Trend Analysis (Preprint)

2020 ◽  
Author(s):  
Lori Post ◽  
Emily Marogi ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Michael G Ison ◽  
...  
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Middle East ◽  
Trend Analysis ◽  
North Africa ◽  
Public Health Surveillance ◽  
Generalized Method Of Moments ◽  
Health Surveillance ◽  
Infection Rates ◽  
Longitudinal Trend

BACKGROUND The COVID-19 global pandemic has disrupted the lives of millions and forced countries to devise public health policies to reduce the pace of transmission. In the Middle East and North Africa, falling oil prices, disparities in wealth and public health infrastructure, and large refugee populations have significantly increased the COVID-19 disease burden. In light of these exacerbating factors, public health surveillance is particularly necessary to help leaders understand and implement effective disease control policies to reduce Sars-CoV-2 persistence and transmission. OBJECTIVE The goal of this study is to provide advanced surveillance metrics, in combination with traditional surveillance, for COVID-19 transmission that account for weekly shifts in the pandemic speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and to better inform those who are managing the pandemic. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed. METHODS Using a longitudinal trend analysis study design, we extracted 30 days of COVID-19 data from public health registries. We use an empirical difference equation to measure the daily number of cases in the Middle East and North Africa as a function of the prior number of cases, the level of testing, 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 The regression Wald statistic is significant (χ^2 (5)=859.5, P<.001). The Sargan test is not significant, failing to reject the validity of over identifying restrictions (χ^2 (294)= 16 P=.99). Countries with the highest cumulative caseload of the novel coronavirus include Iran, Iraq, Saudi Arabia, and Israel with 530,380, 426,634, 342,202, and 303,109 cases respectively. Many of the smaller countries in MENA have higher infection rates than those countries with the highest caseloads. Oman has 33.3 new infections per 100,000 population while Bahrain has 12.1, Libya has 14, and Lebanon has 14.6. In order of most to least number of cumulative deaths since January 2020, Iran, Iraq, Egypt, and Saudi Arabia have 30,375, 10,254, 6,120, and 5,185 respectively. Israel, Bahrain, Lebanon, and Oman had the highest rates of COVID-19 persistence which are the number of new infections statistically related to new infections 7 days ago. Bahrain had positive speed, acceleration and jerk signaling the potential for explosive growth. CONCLUSIONS Static and dynamic public health surveillance metrics provide a more complete picture of pandemic progression across countries in MENA. Static measures capture data at a given point in time such as infection rates and death rates. By including speed, acceleration, jerk, and 7-day persistence, public health officials may design policy with an eye to the future. Iran, Iraq, Saudi Arabia, and Israel all demonstrated the highest rate of infections, acceleration, jerk, and 7-day persistence rates prompting public health leaders to increase prevention efforts. CLINICALTRIAL

scholarly journals Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis

10.2196/25799 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25799
Author(s):  
Lori Ann Post ◽  
Elana T Benishay ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Chad J Achenbach ◽  
...  
Keyword(s):  
Public Health ◽  
Central Asia ◽  
Trend Analysis ◽  
Infection Rate ◽  
Generalized Method Of Moments ◽  
Infection Rates ◽  
Dynamic Panel Model ◽  
Transmission Rates ◽  
Longitudinal Trend ◽  
Positive Acceleration

Background SARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19. Objective The goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully. Methods Using a longitudinal trend analysis study design, we extracted 60 days of COVID-19–related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results COVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight. Conclusions The rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

scholarly journals Central Asia SARS-CoV-2 Surveillance: A Longitudinal Trend Analysis (Preprint)

2020 ◽  
Author(s):  
Lori Ann Post ◽  
Elana T Benishay ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Chad J Achenbach ◽  
...  
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Central Asia ◽  
Trend Analysis ◽  
Infection Rate ◽  
Population Characteristics ◽  
Infection Rates ◽  
Transmission Rates ◽  
Longitudinal Trend ◽  
Positive Acceleration

BACKGROUND SARS-CoV-2, the virus that caused the COVID-19 global pandemic, has severely impacted Central Asia, resulting in a high caseload and deaths that varied by country in Spring 2020. The varying severity of the pandemic is explained by differences in prevention efforts in the form of public health policy, adherence to those guidelines, as well as socio-cultural, climate, and population characteristics. The second wave of the COVID-19 currently is breaching the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions obscuring shifts in the pandemic, increases in infection rates, and the persistence in the transmission of COVID-19. OBJECTIVE The goal of this study is to provide enhanced surveillance metrics for COVID-19 transmission that account for shifts in the pandemic, week over week, speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and those who are managing the pandemic successfully. Existing surveillance, coupled with our dynamic metrics of transmission, will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed and provides novel metrics to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 60 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the Central Asia region 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 COVID-19 transmission rates were tracked for the weeks of 9/30-10/06 and 10/07 to 10/13 in Central Asia. The region averaged 11,730 new cases per day for the week ending in 10/06 and 14,514 for the week ending in 10/13. Infection rates increased across the region from 4.74 per 100,000 in the population to 5.66. Infection rates varied by country. Russia and Turkey had the highest seven-day moving averages in the region, at 9,836 and 1,469 respectively for the week of 10/06 and 12,501 and 1,603 respectively for the week of 10/13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19, with an infection rate of 13.73 for the week of 10/06 quickly jumping to 25.19, the highest in the region, the following week. The pandemic speed in Armenia, consistent with the infection rate trajectory, increased from 15.4 to 21.7, with an acceleration increase from 0.4 to 1.6 meaning acceleration has increased fourfold. The region overall is experiencing increases in seven-day moving average of new cases, infection, rate and speed, with continued positive acceleration and no sign of a reversal in sight. CONCLUSIONS The rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze pandemic trajectory and control spread. Policymakers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia. Russia already has the fourth highest number of cases in the world and current metrics suggest Russia will continue on that trajectory. CLINICALTRIAL NA

Canada SARS-CoV-2 Surveillance System: Longitudinal Trend Analysis (Preprint)

2020 ◽  
Author(s):  
Lori Post ◽  
Michael J Boctor ◽  
Tariq Z Issa ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
...  
Keyword(s):  
Public Health ◽  
Trend Analysis ◽  
Generalized Method Of Moments ◽  
Effective Vaccine ◽  
Dynamic Panel Model ◽  
Longitudinal Trend ◽  
Global Pandemic ◽  
Positive Acceleration ◽  
National Leaders ◽  
Novel Coronavirus

BACKGROUND The COVID-19 global pandemic has disrupted structures and communities across the globe. Numerous regions of the world have had varying responses in their attempts to contain the spread of the virus. Varying factors such as public health policies, governance, and sociopolitical factors, have led to differential levels of success at controlling the spread of SARS-CoV-2. Ultimately, a more advanced surveillance metric for COVID-19 transmission is necessary to help government systems and national leaders understand which responses have been effective and gauge where outbreaks occur. OBJECTIVE The goal of this study is to provide advanced Canadian surveillance metrics at the Province level for COVID-19 transmission that account for shifts in the pandemic, week over week, speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and those who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed and provides novel metrics to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 52 days of COVID data from public health registries for 14 Provinces and Territories. We use an empirical difference equation to measure the daily number of cases in Canada 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 We compare the week of October 11-17 with the week of October 18-24. Canada, as a whole, had an increase in 7-day average COVID-19 cases from 1965 per 100,000 population during October 11-17 to 2043 per 100,000 population during October 18-24. Evaluating Canada’s COVID-19 at the subnational level is necessary to identify where the novel coronavirus is transmitting to prevent future outbreaks. Alberta, BC, Ontario and Manitoba had positive acceleration of cases for October 11-17 at 2.21, 1.23, .97, and .71 respectively per 100,000 population, however, these same provinces experienced deceleration one week later at -2.06, -1.06, -.71, and -.16.; Moreover, the positive jerk experienced during October 11-17 at 2.18, 1.19, 2.15 and 1.57 reversed course and jerked downward during October 18 to 24 at -4.96, -2.44, -1.39, and -.19 respectively. CONCLUSIONS While Canada maintained good COVID-19 control policies that resulted in fewer transmissions, the first week of this study on October 11-17 resulted in increases in new cases, increased rates of infections, increased acceleration and jerk in infections for the most populated provinces. These same provinces reversed course whereby the number of new cases decreased, the speed of new infections decelerated, and experienced a negative jerk in COVID-19 per 100,000 population during the week of October 12-24. The surge followed by a significant decrease is consistent with Canadians celebrating Thanksgiving on October 12, 2020. While no Province or Territory has exceeded 1000 cases per day, new sources of COVID-19 expected from the pending Wave 2 of COVID-19 transmissions could result in novel outbreaks. It is not time for Canada to declare victory over COVID-19 transmissions or to be complacent just because there were decreases this past week. To that end, Canada must remain vigilant and continue implementing those policies that caused the Canadian outbreak to reverse course and decrease. CLINICALTRIAL NA

scholarly journals Middle East and North Africa SARS-CoV-2 Surveillance: A Longitudinal Trend Analysis (Preprint)

10.2196/25830 ◽  
2020 ◽  
Author(s):  
Lori Post ◽  
Emily Marogi ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Michael G Ison ◽  
...  
Keyword(s):  
Middle East ◽  
Trend Analysis ◽  
North Africa ◽  
Longitudinal Trend

scholarly journals European SARS-CoV-2 Surveillance: Longitudinal Trend Analyses of Wave Two (Preprint)

2020 ◽  
Author(s):  
Lori Post ◽  
Kasen Culler ◽  
Charles B Moss ◽  
Robert L Murphy ◽  
Chad J Achenbach ◽  
...  
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Czech Republic ◽  
The Netherlands ◽  
Generalized Method Of Moments ◽  
Population Characteristics ◽  
Rapid Expansion ◽  
Infection Rates ◽  
Longitudinal Trend ◽  
Second Wave

BACKGROUND The COVID-19 global pandemic has severely impacted Western Europe, resulting in a high caseload and deaths that varied by country in Spring 2020. The varying severity of the pandemic is explained by differences in prevention efforts in the form of public health policy, adherence to those guidelines, as well as socio-cultural, climate, and population characteristics. The second wave of the COVID-19 currently is breaching the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders, however, existing surveillance explains past transmissions obscuring shifts in the pandemic, increases in infection rates, and the persistence in the transmission of COVID-19. OBJECTIVE The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for shifts in the pandemic, week over week, speed, acceleration, jerk and persistence, to better understand country risk for explosive growth and those who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed and provides novel metrics to measure the transmission of disease. METHODS Using a longitudinal trend analysis study design, we extracted 47-50 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in Europe 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 The countries with the largest COVID caseloads and infection rates also had positive acceleration and jerk as well as large 7-day persistence rates. The combination of large populations with an increase in COVID caseload with increases in rates are indicative of large outbreaks. The UK, Spain and Belgium had the highest number of observed cases during the first two weeks of October at 11,993, 9,530, and 4,236 during October 1-7th. During October 8-14th their infection rate increased for Belgium at 102.2, Czech Republic at 78.03, Netherlands at 42.51, UK at 25.78, Spain at 25.43, and Iceland at 23.80. Speed mirrored infection rates in that speed increased between Oct 1 and Oct 14 in Belgium from 29.90 to 60.96, Czech Republic from 29.70 to 53.19, and the Netherlands from 22.60 to 36.12 over two weeks. These increases are consistent with a second wave. Belgium, Czech Republic, and the Netherlands had the largest acceleration during the week of 10/8-10/14, with increases to 7.53, 5.18, and 2.35. Belgium and the Czech Republic had positive jerk during weeks one and two meaning week over week, the acceleration rate was increasing. CONCLUSIONS These dynamic data suggest that the second wave of the COVID-19 pandemic has breached European borders. Belgium, Czech Republic, and the Netherlands, in particular, are at risk for a rapid expansion in the transmission of COVID-19. An examination of the European region suggests that there was an increase in caseload of COVID-19 between October 1 and October 14. Moreover, the rates of jerk which were negative for Europe at the beginning of the month, reversed course and became positive, along with increases of speed and increases of acceleration. Finally, the 7-day persistence rate during the second week was larger than the first week. In combination, these indicators suggest the second wave of the COVID-19 pandemic occurred between weeks 1 and 2 of October. CLINICALTRIAL NA

National Retail Data Monitor for Public Health Surveillance

2004 ◽  
Author(s):  
Michael M. Wagner ◽  
F-C. Tsui ◽  
J. Espino ◽  
W. Hogan ◽  
J. Hutman ◽  
...  
Keyword(s):  
Public Health ◽  
Public Health Surveillance ◽  
Health Surveillance
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