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1. Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

   https://doi.org/10.1186/s12879-021-06078-8  

   Huo, Xi; Chen, Jing; Ruan, Shigui (December 2021, BMC Infectious Diseases)

   Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6 % (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693 % ,0.814 % ]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs. 

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2. Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2021-2023)

   https://doi.org/10.5281/zenodo.10392322  

   Porter, Nicholas; Gangopadhyay, Avijit; Silver, Adrienne; Gangopadhyay, Avijit; Silver, Adrienne; Porter, Nicholas (January 2024, Zenodo)

   Porter, Nicholas; Gangopadhyay, Avijit (Ed.)

   This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings (WCR) that existed between 2021 and 2023. This work builds upon two previous datasets: (i) Warm Core Ring trajectory information from 2000 to 2010 -- Porter et al. (2022) (https://doi.org/10.5281/zenodo.7406675) (ii) Warm Core Ring trajectory information from 2011 to 2020 -- Silver et al. (2022a) (https://doi.org/10.5281/zenodo.6436380). Combining these three datasets (previous two and this one), a total of 24 years of weekly Warm Core Ring trajectories are now available. An example of how to use such a dataset can be found in Silver et al. (2022b). The format of the dataset is similar to that of Porter et al. (2022) and Silver et al. (2022a), and the following description is adapted from those datasets. This dataset is comprised of individual files containing each ring’s weekly center location and its surface area for 81 WCRs that existed and tracked between January 1, 2021 and December 31, 2023 (5 WCRs formed in 2020 and still existed in 2021; 28 formed in 2021; 30 formed in 2022; 18 formed in 2023). Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddX', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'X' represents the sequential sighting (formation) of the eddy in that particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the previous year and absorbed by the initial alphabets for the next year. For example, the first ring formed in 2022 has a trailing alphabet of 'H', which signifies that a total of seven rings were carried over from 2021 which were still present on January 1, 2022 and were assigned the initial seven alphabets (A, B, C, D, E, F and G). Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables every week, “Lon”- the ring center’s longitude, “Lat”- the ring center’s latitude, “Area” - the rings size in km^2, and “Date” in days – which is the number of days since Jan 01, 0000. Five rings formed in the year 2020 that carried over into the year 2021 were included in this dataset. These rings include ‘WE20200724Q’, ‘WE20200826R’, ‘WE20200911S’, ‘WE20200930T’, and ‘WE20201111W’. The two rings that formed in 2023, and were carried over into the following year were included with their full trajectories going into the year 2024. These rings include ‘WE20231006U’ and ‘WE20231211W’. The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts (Gangopadhyay et al., 2019) used to create this dataset are 2-3 times a week from 2021-2023. Thus, we used approximately 360+ Charts for the 3 years of analysis. All of these charts were reanalyzed between -75° and -55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). 

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3. A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring

   https://doi.org/10.3390/ijerph182010783  

   Manda, Samuel O.; Darikwa, Timotheus; Nkwenika, Tshifhiwa; Bergquist, Robert (October 2021, International Journal of Environmental Research and Public Health)

   The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January–September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county’s social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19. 

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4. Excess mortality in Ukraine during the course of COVID-19 pandemic in 2020–2021

   https://doi.org/10.1038/s41598-023-33113-2  

   Shishkin, Aleksandr; Lhewa, Pema; Yang, Chen; Gankin, Yuriy; Chowell, Gerardo; Norris, Michael; Skums, Pavel; Kirpich, Alexander (December 2023, Scientific Reports)

   Abstract In this work, the COVID-19 pandemic burden in Ukraine is investigated retrospectively using the excess mortality measures during 2020–2021. In particular, the epidemic impact on the Ukrainian population is studied via the standardized both all-cause and cause-specific mortality scores before and during the epidemic. The excess mortality counts during the pandemic were predicted based on historic data using parametric and nonparametric modeling and then compared with the actual reported counts to quantify the excess. The corresponding standardized mortalityP-score metrics were also compared with the neighboring countries. In summary, there were three “waves” of excess all-cause mortality in Ukraine in December 2020, April 2021 and November 2021 with excess of 32%, 43% and 83% above the expected mortality. Each new “wave” of the all-cause mortality was higher than the previous one and the mortality “peaks” corresponded in time to three “waves” of lab-confirmed COVID-19 mortality. The lab-confirmed COVID-19 mortality constituted 9% to 24% of the all-cause mortality during those three peak months. Overall, the mortality trends in Ukraine over time were similar to neighboring countries where vaccination coverage was similar to that in Ukraine. For cause-specific mortality, the excess observed was due to pneumonia as well as circulatory system disease categories that peaked at the same times as the all-cause and lab-confirmed COVID-19 mortality, which was expected. The pneumonias as well as circulatory system disease categories constituted the majority of all cases during those peak times. The seasonality in mortality due to the infectious and parasitic disease category became less pronounced during the pandemic. While the reported numbers were always relatively low, alcohol-related mortality also declined during the pandemic. 

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5. Cold Eddy Trajectories in the Northwest Atlantic Sargasso Sea (2017-2023)

   https://doi.org/10.5281/zenodo.15186223  

   Porter, Nicholas; Gangopadhyay, Avijit; Jensen, Grace G; Porter, Nicholas; Gangopadhyay, Avijit; Jensen, Grace G (January 2025, Zenodo)

   This dataset consists of weekly trajectory information of Gulf Stream Cold Eddies (CE) that existed between 2017 and 2023. The format of this Cold Eddy dataset is similar to the Warm Core Ring (WCR) Trajectory data from Porter et al. (2022, 2024) and Silver et al. (2022), and the following description is adapted from those datasets. This dataset is comprised of individual files containing each eddy’s weekly center location and its surface area for 181 CEs that existed and were tracked between January 1, 2017 and December 31, 2023 (28 CEs formed in 2017; 24 formed in 2018; 25 formed in 2019; 26 formed in 2020; 35 formed in 2021; 23 formed in 2022; and 20 formed in 2023). Each Cold Eddy is identified by a unique alphanumeric code 'CEyyyymmddX', where 'CE' represents a Cold Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'X' represents the sequential sighting (formation) of the eddy in that particular year. Continuity of an eddy which passes from one year to the next is maintained by the same character in the previous year and absorbed by the initial alphabets for the next year. For example, the first eddy formed in 2021 has a trailing alphabet of 'J', which signifies that a total of nine eddies were carried over from 2020 which were still present on January 1, 2021 and were assigned the initial nine alphabets (A, B, C, D, E, F, G, H, and I). Each eddy trajectory has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables every week, “Lon”- the eddy center’s longitude, “Lat”- the eddy center’s latitude, “Area” - the eddies size in km^2, and “Date” in days – which is the number of days since Jan 01, 0000. Note that in this dataset, which ended tracking all eddies up to 2023, there were six eddies that formed in 2023, and were carried over into 2024 were included with their full trajectories going into the year 2024. These eddies are: ‘CE20230515P’, ‘CE20230818U’, 'CE20230925V', 'CE20231030Y', 'CE20231103Z', and 'CE20231106a'. Findings from Jensen et al. (2024) suggest three different cyclonic eddy formation types: pinch-off cyclonic rings, hook-type cyclonic eddies, and Sargasso Sea cyclonic eddies. Pinch-off cyclonic rings form from a Gulf Stream meander trough amplifying, then encircling Slope Sea water and eventually detaching from the Gulf Stream as a cyclonic cold-core ring in the Sargasso Sea. Hook-type eddies form from a southward extending filament of the southern flank of the Gulf Stream establishing as a hook-like entity cyclonically encircling a body of Sargasso Sea water at its core. Sargasso Sea cyclonic eddies are isolated from the Gulf Stream and occur in the Sargasso Sea. A separate file is also created to help identify the cold eddy's formation type. Two files are provided here. These are: (1)  The trajectories of all Gulf Stream Cold Eddies formed from 2017 to 2023. Filename – CE_2017_2023_ncfiles.zip (2)  Information on the formation type of each Cold Eddy. Filename – CE_FormationTypes_2017to2023.doc The process of creating the CE weekly tracking dataset follows the same GIS-based methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts described in Gangopadhyay et al. (2019), and continued through 2023 were used to create this dataset and were available 2-3 times a week from 2017-2023. Thus, we used approximately 840+ Charts for the 7 years of analysis. All of these charts were reanalyzed between 75°W and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). A single eddy trajectory is then obtained following an eddy through all of the available charts during the eddy's lifespan on a weekly basis. This process is repeated for every individual eddy.     

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