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)

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 Latin America and the Caribbean SARS-Cov-2 Surveillance: Longitudinal Trend Analysis (Preprint)

2020 ◽  
Author(s):  
Lori Post ◽  
Ramael O Ohiomoba ◽  
Ashley Maras ◽  
Sean J Watts ◽  
Charles B Moss ◽  
...  
Keyword(s):  
Latin America ◽  
Trend Analysis ◽  
Death Rate ◽  
Moving Average ◽  
Generalized Method Of Moments ◽  
Caribbean Region ◽  
The Bahamas ◽  
Longitudinal Trend ◽  
Positive Acceleration ◽  
The Caribbean

BACKGROUND SARS-CoV-19, the virus that causes COVID-19, is a global pandemic that has placed unprecedented stress on national economies, food systems and healthcare resources in Latin America and the Caribbean (LAC). This region has become an epicenter for the coronavirus, with Brazil and Mexico leading the globe in deaths following the U.S. in death count. Existing surveillance provides a proxy on COVID-19 caseload and deaths; however, these measures make it difficult to identify shifts to the pandemic and changes in the speed and acceleration in COVID-19. Accordingly, we provide an enhanced surveillance system to complement static metrics with dynamic ones that inform hen there are shifts and where explosive growth is likely to occur in LAC. OBJECTIVE This study aims to provide additional surveillance metrics for SARS-Cov-2 transmission that more accurately tracks shifts in the pandemic, speed, acceleration, jerk, and persistence in transmission than existing metrics. Enhanced surveillance will inform policy and COVID-19 outbreaks for leaders in LAC. METHODS Using a longitudinal trend analysis study design, we extracted 45 days of COVID data from public health registries. We use an empirical difference equation to measure the daily number of cases in the Latin America and Caribbean 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 transmission rates were tracked for Latin America and the Caribbean during the weeks of 9/30-10/06 and 10/07-10/13. New cases in the region totaled 79,053 on 10/06 and 42,837 on 10/13. The 7-day moving average of new cases for the week of 10/6 was 56,106 and for the week of 10/13 was 47,276. Total infection rate decreased from 12.42 to 6.73 accompanied by a death rate decrease from 0.33 to 0.24. Within the region, on 9/30, Brazil had the largest number of new cases at 41,906 followed by Argentina at 14,740, Colombia at 7,650, and Mexico at 4,828. On 10/07, Argentina had the largest number of new cases in the region at 13,305, followed by Brazil at 10,220, Colombia at 5,014, and Mexico at 4,295. For both weeks, Brazil had the highest 7-day moving average, followed by Argentina. The region as a whole saw a decrease in speed, acceleration, and jerk for the week of 10/13 compared to the week of 10/6, accompanied by a decrease in new cases and 7-day moving average. For the week of 10/6, Belize had the highest acceleration and jerk in the region, at 1.7 and 1.8 respectively, which is particularly concerning given the high death rate in the country. The Bahamas also had a high acceleration at 1.5. 11 countries had a positive acceleration during the week of 10/6 whereas only six countries had a positive acceleration for the week of 10/13. The region overall is trending positively, with a speed of 10.40, an acceleration of 0.27, and a jerk of -0.31 all decreasing the subsequent week to 9.04, -0.81 and -0.03 respectively. CONCLUSIONS 1) Metrics such as new cases, cumulative cases, deaths, and 7-day moving averages provide a static view of the pandemic but fail to identify where and the speed at which SARS-CoV-19 is infecting new persons, the rate at which the speed is accelerating or decelerating and comparing this week to last week, how the rate of acceleration is increasing or decreasing indicate pending explosive growth or control of the pandemic; and 2) Although Latin America and the Caribbean saw an overall decrease in speed, acceleration, and jerk for the week of 10/13 compared to the week of 10/6, accompanied by a decrease in new cases and 7-day moving average, this is largely due to decreases in infections in Brazil and Mexico, the two countries containing over 50% of the population in the region. However, Brazil continues to have the highest 7-day moving average in the region, more than two times that of Argentina, the next highest in the region. CLINICALTRIAL NA

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 Tuberculosis with malaria or HIV co-infection in a large hospital in Luanda, Angola

2013 ◽  
Vol 7 (03) ◽  
pp. 269-272 ◽  
Author(s):  
Emília Valadas ◽  
Augusto Gomes ◽  
Ana Sutre ◽  
Sara Brilha ◽  
Afonso Wete ◽  
...  
Keyword(s):  
Public Health ◽  
Plasmodium Falciparum ◽  
Hospital Stay ◽  
Infection Rate ◽  
Current Data ◽  
Infection Rates ◽  
Large Hospital ◽  
Predominant Species ◽  
Integrated Approaches ◽  
Overall Mortality

Introduction: Three major public health problems, tuberculosis, malaria and HIV/AIDS, are widespread in Angola, often as co-infections in the same individual. In 2009, it was assumed that 44,151 new cases of TB occurred in Angola. Interestingly, interventions such as treatment/prevention of malaria appear to reduce mortality in HIV-infected and possibly TB co-infected patients. However, despite the seriousness of the situation, current data on TB and co-infection rates are scarce. This study aimed to characterize all TB cases seen at the Hospital Sanatório de Luanda, and to determine the co-infection rate with HIV and/or malaria. Methodology: This retrospective study collected demographic, diagnostic and clinical data from all patients admitted during 2007. Results: A total of 4,666 patients were admitted, of whom 1,906 (40.8%) were diagnosed with TB. Overall, 1,111 patients (58.3%) were male and most patients (n=1302, 68.3%) were adults (≥14 years). The rate of HIV co-infection was 37.4% (n=712).  Malaria was diagnosed during admission and hospital stay in 714 patients (37.5%), with Plasmodium falciparum the predominant species. Overall mortality was 15.2% (n=290). Conclusions: Because Luanda does not have the infrastructure to perform culture-based diagnosis of TB, confirmation of TB is problematic. The HIV-co-infection rate is high, with 37.4% of patients requiring integrated approaches to address this problem. With more than 1/3 of the TB patients co-infected with malaria, even during the hospital stay, the prevention of malaria in TB patients appears to be an effective way to reduce overall mortality.

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 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 Projecting the Spread of COVID19 for Germany

2020 ◽  
Author(s):  
Jean Roch Donsimoni ◽  
René Glawion ◽  
Bodo Plachter ◽  
Klaus Wälde
Keyword(s):  
Public Health ◽  
Infection Rate ◽  
Recent Observation ◽  
Individual Risk ◽  
Infection Rates ◽  
Worst Case ◽  
Social Contacts ◽  
Health Measures ◽  
Long Run ◽  
Number Of Individuals

We model the evolution of the number of individuals that are reported to be sick with COVID-19 in Germany. Our theoretical framework builds on a continuous time Markov chain with four states: healthy without infection, sick, healthy after recovery or after infection but without symptoms and dead. Our quantitative solution matches the number of sick individuals up to the most recent observation and ends with a share of sick individuals following from infection rates and sickness probabilities. We employ this framework to study inter alia the expected peak of the number of sick individuals in a scenario without public regulation of social contacts. We also study the effects of public regulations. For all scenarios we report the expected end of the CoV-2 epidemic.We have four general findings: First, current epidemiological thinking implies that the long-run effects of the epidemic only depend on the aggregate long-run infection rate and on the individual risk to turn sick after an infection. Any measures by individuals and the public therefore only influence the dynamics of spread of CoV-2. Second, predictions about the duration and level of the epidemic must strongly distinguish between the officially reported numbers (Robert Koch Institut, RKI) and actual numbers of sick individuals. Third, given the current (scarce) medical knowledge about long-run infection rate and individual risks to turn sick, any prediction on the length (duration in months) and strength (e.g. maximum numbers of sick individuals on a given day) is subject to a lot of uncertainty. Our predictions therefore offer robustness analyses that provide ranges on how long the epidemic will last and how strong it will be. Fourth, public interventions that are already in place and that are being discussed can lead to more and less severe outcomes of the epidemic. If an intervention takes place too early, the epidemic can actually be stronger than with an intervention that starts later. Interventions should therefore be contingent on current infection rates in regions or countries.Concerning predictions about COVID-19 in Germany, we find that the long-run number of sick individuals (that are reported to the RKI), once the epidemic is over, will lie between 500 thousand and 5 million individuals. While this seems to be an absurd large range for a precise projection, this reflects the uncertainty about the long-run infection rate in Germany. If we assume that Germany will follow the good scenario of Hubei (and we are even a bit more conservative given discussions about data quality), we will end up with 500 thousand sick individuals over the entire epidemic. If by contrast we believe (as many argue) that once the epidemic is over 70% of the population will have been infected (and thereby immune), we will end up at 5 million cases.Defining the end of the epidemic by less than 100 newly reported sick individuals per day, we find a large variation depending on the effectiveness of governmental pleas and regulations to reduce social contacts. An epidemic that is not influenced by public health measures would end mid June 2020. With public health measures lasting for few weeks, the end is delayed by around one month or two. The advantage of the delay, however, is to reduce the peak number of individuals that are simultaneously sick. When we believe in long-run infection rates of 70%, this number is equally high for all scenarios we went through and well above 1 million. When we can hope for the Hubei-scenario, the maximum number of sick individuals will be around 200 thousand “only”.Whatever value of the range of long-run infection rates we want to assume, the epidemic will last at least until June, with extensive and potentially future public health measures, it will last until July. In the worst case, it will last until end of August.We emphasize that all projections are subject to uncertainty and permanent monitoring of observed incidences are taken into account to update the projection. The most recent projections are available at https://www.macro.economics.unimainz.de/corona-blog/.

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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