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

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

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 The Infection Rate of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China

2020 ◽  
Author(s):  
Hui-Qi Qu ◽  
Zhangkai J. Cheng ◽  
Zhifeng Duan ◽  
Lifeng Tian ◽  
Hakon Hakonarson
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Mortality Rate ◽  
Infection Rate ◽  
Infection Rates ◽  
Policy And Practice ◽  
Wuhan City ◽  
Infected People ◽  
Health Measures ◽  
New Findings

Summary BoxWhat is already known about this subject?The Wuhan city in China had a much higher mortality rate (Feb 10th statistics: 748 death/18,454 diagnosis =4.05%; Apr 24th statistics: 3,869 death/50,333 diagnosis=7.69%) than the rest of China.What are the new findings?Based on our analysis, the number of infected people in Wuhan is estimated to be 143,000 (88,000 to 242,000) in late January and early February, significantly higher than the published number of diagnosed cases.What are the recommendations for policy and practice?Increased awareness of the original infection rates in Wuhan, China is critically important for proper public health measures at all levels, as well as to eliminate panic caused by overestimated mortality rate that may bias health policy actions by the authorities

scholarly journals The effects of the first and second wave of COVID-19 pandemic on public health

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Crisis Management ◽  
Best Practice ◽  
Summer Season ◽  
Health Policies ◽  
Negative Effects ◽  
Maximum Capacity ◽  
Negative Effect ◽  
Second Wave

Abstract One of the current problems in the presence of COVID-19 pandemic crisis is to analyze the effects of the second wave on public health to design appropriate strategies to reduce negative effects in society. The study here analyzes first and second wave of COVID-19 pandemic in Italy because is one of European countries to experience a rapid increase in confirmed cases and deaths. Results reveal that the first wave of COVID-19 pandemic in Italy had a high negative effect on public health from February to May 2020 that declined with the approaching of summer season and with the health policy of lockdown and quarantine; instead, second wave of the COVID-19 pandemic, from October 2020, has increasing confirmed cases but admission to ICUs is, to date, below maximum capacity and total deaths have a lower level than first wave. This study can support best practice of crisis management to design effective health policies to constrain current and future waves of the COVID-19 pandemic and epidemics of similar viral agents.

scholarly journals SARS-CoV-2 Wave Two Surveillance in East Asia and the Pacific: Longitudinal Trend Analysis

10.2196/25454 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25454
Author(s):  
Lori Ann Post ◽  
Jasmine S Lin ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Michael G Ison ◽  
...  
Keyword(s):  
East Asia ◽  
Trend Analysis ◽  
Generalized Method Of Moments ◽  
French Polynesia ◽  
The Philippines ◽  
High Morbidity ◽  
Negative Consequences ◽  
Longitudinal Trend ◽  
The Pacific ◽  
Second Wave

Background The COVID-19 pandemic has had a profound global impact on governments, health care systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more evident that additional SARS-CoV-2 surveillance is needed to track recent shifts, rates of increase, and persistence associated with the pandemic. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk, persistence, and weekly shifts, to better understand country risk for explosive growth and those countries 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. We provide novel indicators to measure disease transmission. Methods Using a longitudinal trend analysis study design, we extracted 330 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in East Asia and the Pacific 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 approach by implementing the Arellano-Bond estimator in R. Results The standard surveillance metrics for Indonesia, the Philippines, and Myanmar were concerning as they had the largest new caseloads at 4301, 2588, and 1387, respectively. When looking at the acceleration of new COVID-19 infections, we found that French Polynesia, Malaysia, and the Philippines had rates at 3.17, 0.22, and 0.06 per 100,000. These three countries also ranked highest in terms of jerk at 15.45, 0.10, and 0.04, respectively. Conclusions Two of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration, and positive upwards jerk belong to French Polynesia, Malaysia, and the Philippines, and may result in explosive growth. While all countries in East Asia and the Pacific need to be cautious about reopening their countries since outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern is the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions.

scholarly journals East Asia and the Pacific Surveillance of SARS-CoV-2 Wave Two: Longitudinal Trend Analysis (Preprint)

2020 ◽  
Author(s):  
Lori Ann Post ◽  
Jasmine S Lin ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Micahel G Ison ◽  
...  
Keyword(s):  
East Asia ◽  
Trend Analysis ◽  
Generalized Method Of Moments ◽  
French Polynesia ◽  
The Philippines ◽  
High Morbidity ◽  
Negative Consequences ◽  
Longitudinal Trend ◽  
The Pacific ◽  
Second Wave

BACKGROUND The COVID-19 pandemic has had a profound global impact on governments, healthcare systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more obvious that additional SARS-CoV-2 surveillance is needed 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,persistence, and weekly shifts in the pandemic, 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. We provide novel indicators to measure the transmission of disease METHODS Using a longitudinal trend analysis study design, we extracted 330 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in East Asia and the Pacific 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 Based on standard surveillance metrics, Indonesia, Philippines, and Myanmar are concerning because they have the largest new caseloads at 4,301, 2,588, and 1,387 respectively. However, when looking at the acceleration of new COVID-19 infections, we find that French Polynesia, Malaysia, and Philippines have the rates at 3.17, .22, and .06 per 100,000. These three countries also rank highest in jerk at 15.45, .10 and .04 respectively. CONCLUSIONS Two of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration and positive upwards jerk belong to French Polynesia, Malaysia and the Philippines. Positive rates of speed, acceleration and upwards jerk are more likely to result in explosive growth. While all countries in East Asia and Pacific need to be cautious in regards to opening their countries because outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern remains the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions. CLINICALTRIAL NA

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