Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis
BackgroundSARS-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 surv...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-02-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2021/2/e25799 |
| _version_ | 1818873796800544768 |
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| author | Post, Lori Ann Benishay, Elana T Moss, Charles B Murphy, Robert Leo Achenbach, Chad J Ison, Michael G Resnick, Danielle Singh, Lauren Nadya White, Janine Chaudhury, Azraa S Boctor, Michael J Welch, Sarah B Oehmke, James Francis |
| author_facet | Post, Lori Ann Benishay, Elana T Moss, Charles B Murphy, Robert Leo Achenbach, Chad J Ison, Michael G Resnick, Danielle Singh, Lauren Nadya White, Janine Chaudhury, Azraa S Boctor, Michael J Welch, Sarah B Oehmke, James Francis |
| author_sort | Post, Lori Ann |
| collection | DOAJ |
| description | BackgroundSARS-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.
ObjectiveThe 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.
MethodsUsing 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.
ResultsCOVID-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.
ConclusionsThe 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. |
| first_indexed | 2024-12-19T13:00:24Z |
| format | Article |
| id | doaj.art-3634219c6ab84159934e3151ffc5422c |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-12-19T13:00:24Z |
| publishDate | 2021-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-3634219c6ab84159934e3151ffc5422c2022-12-21T20:20:15ZengJMIR PublicationsJournal of Medical Internet Research1438-88712021-02-01232e2579910.2196/25799Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend AnalysisPost, Lori AnnBenishay, Elana TMoss, Charles BMurphy, Robert LeoAchenbach, Chad JIson, Michael GResnick, DanielleSingh, Lauren NadyaWhite, JanineChaudhury, Azraa SBoctor, Michael JWelch, Sarah BOehmke, James FrancisBackgroundSARS-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. ObjectiveThe 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. MethodsUsing 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. ResultsCOVID-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. ConclusionsThe 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.https://www.jmir.org/2021/2/e25799 |
| spellingShingle | Post, Lori Ann Benishay, Elana T Moss, Charles B Murphy, Robert Leo Achenbach, Chad J Ison, Michael G Resnick, Danielle Singh, Lauren Nadya White, Janine Chaudhury, Azraa S Boctor, Michael J Welch, Sarah B Oehmke, James Francis Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis Journal of Medical Internet Research |
| title | Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis |
| title_full | Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis |
| title_fullStr | Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis |
| title_full_unstemmed | Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis |
| title_short | Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis |
| title_sort | surveillance metrics of sars cov 2 transmission in central asia longitudinal trend analysis |
| url | https://www.jmir.org/2021/2/e25799 |
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