This is the daily information that are used in the public CoVID-19 Surveillance, Trends, and Progress and Warnings Dashboards. Each field is updated after 6pm CST Monday through Friday. Weekend data is added on Monday as individual records, along with Monday's reported data. The Surveillance Dashboard is live and available here.Backlog CoVID-19 cases are cases that are reported more than 14-days after the event date (date of Test or date of onset of symptoms). Backlog cases are reported along with the Monday Cumulative Cases, but are not included in in the daily Case Change.This data reflects information provided by the City of San Antonio Metro Health Department, and is released Monday through Friday at 6PM on the City of San Antonio CoVID-19 website.

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTONThis is the weekly information that is used in the public CoVID-19 Surveillance, Trends, and Progress and Warnings Dashboards. Each field is updated weekly since the first date the data was tracked. The Surveillance Dashboard is live and available here.Currently the following fields are being reported weekly:Reported DateCurrent Testing CapacityEstimated Active CasesEstimated Recovered CasesAverage Daily CasesCases per 100,000 population (moving average)Weekly change in cases per 100,000 populationThis data reflects information provided by the City of San Antonio Metro Health Department, and is released weekly by 7 pm on Monday evenings; on the City of San Antonio CoVID-19 website.

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTON

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTONWeekly COVID-19 lab testing of San Antonio residents. Provided by San Antonio Metropolitan Health District.This data reflects information provided by the City of San Antonio Metro Health Department. Table is updated every Monday as of data closed out as of the previous Friday/Weekend. Tests are both molecular (PCR/NAAT) and antigen (FIA) tests, and represent tests on those in Bexar County only.

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTONCoVID-19 Cases and Deaths reported weekly grouped by Age. This data is contains the data reported on Monday going back to March 23rd, the first date available for the data. The Attribute fields are in groups of 10 years with the exception of the first grouping; 0-19. The counts in each record are cumulative up to the date of the record.This data is a product of CoVID-19+ case management, maintained by the San Antonio Metropolitan Health District.

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The City of San Antonio's Open Data page for CoVID-19 surveillance data.

Multivariable odds ratios relating time, baseline demographic, and medical and psychiatric history to COVID-symptoms for 6 months.

Context

Multisystem inflammatory syndrome in children (MIS-C), also known as pediatric inflammatory multisystem syndrome, is a new dangerous childhood disease that is temporally associated with coronavirus disease 2019 (COVID-19). We aimed to describe the typical presentation and outcomes of children diagnosed with this hyperinflammatory condition.

https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30271-6/fulltext#seccesectitle0001

Content

A systematic review to communicate the clinical signs and symptoms, laboratory findings, imaging results, and outcomes of individuals with MIS-C.

Acknowledgements

Authors: Mubbasheer Ahmeda;; , Shailesh Advanib;; Axel Moreira;; , Sarah Zoretic;; , John Martinez;; Kevin Chorath;; , Sebastian Acosta;; , Rija Naqvi;; Finn Burmeister-Morton;; Fiona Burmeister;; Aina Tarriela;; , Matthew Petershack;; , Mary Evans;; , Ansel Hoang;; Karthik Rajasekaran;; , Sunil Ahuja;; Alvaro Moreira

Department of Pediatrics, Texas Children’s Hospital, Houston, TX, USA;; Department of Oncology, Georgetown University, Washington, DC, USA;; Social Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, USA;; Department of Pediatrics, University of Texas Health Science Center San Antonio, San Antonio, TX 78229-3900, USA;; Department of Otorhinolaryngology, The University of Pennsylvania, Philadelphia, PA, USA.

Photo by L N on Unsplash

Inspiration

Covid-19 Pandemic.

The City of San Antonio's Open Data page for CoVID-19 vaccination data.

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Background: The burden of the COVID-19 pandemic in Peru has led to people seeking alternative treatments as preventives and treatment options such as medicinal plants. This study aimed to assess factors associated with the use of medicinal plants as preventive or treatment of respiratory symptom related to COVID-19 during the pandemic in Cusco, Peru.

Method: A web-based cross-sectional study was conducted on general public (20- to 70-year-old) from August 31 to September 20, 2020. Data were collected using a structured questionnaire via Google Forms, it consisted of an 11-item questionnaire that was developed and validated by expert judgment using Aiken's V (Aiken's V > 0.9). Both descriptive statistics and bivariate followed by multivariable logistic regression analyses were conducted to assess factors associated with the use of medicinal plants for COVID-19 prevention and respiratory symptom treatment during the pandemic. Prevalence ratios (PR) with 95% Confidence Interval (CI), and a P-value of 0.05 was used to determine statistical significance.

Results: A total of 1,747 respondents participated in the study, 80.2% reported that they used medicinal plants as preventives, while 71% reported that they used them to treat respiratory symptoms. At least, 24% of respondents used medicinal plants when presenting with two or more respiratory symptoms, while at least 11% used plants for malaise. For treatment or prevention, the multivariate analysis showed that most respondents used eucalyptus (p ​​< 0.001 for both), ginger (p​​ < 0.022 for both), spiked pepper (p < 0.003 for both), garlic (p = 0.023 for prevention), and chamomile (p = 0.011 for treatment). The respondents with COVID-19 (p < 0.001), at older ages (p = 0.046), and with a family member or friend who had COVID-19 (p < 0.001) used more plants for prevention. However, the respondents with technical or higher education used less plants for treatment (p < 0.001).

Conclusion: There was a significant use of medicinal plants for both prevention and treatment, which was associated with several population characteristics and whether respondents had COVID-19.

Methods We conducted an online cross-sectional multicenter survey, which was initially evaluated by 10 expert judges using Aiken's V (40). After including the experts’ observations, a pilot study was performed (from August 16 to 4) with 336 respondents in in five districts of Cusco, Peru. The pilot data was used to calculate the minimal sample size necessary for the actual study. It was determined that a minimum sample size of 1,530 was necessary to achieve a minimum percentage difference of 2.5% (49.0% versus 51.5%), a statistical power of 80%, and a confidence level of 95%. The sample size was calculated using power analysis.

The actual survey consisted of an online questionnaire that was sent via WhatsApp, Messenger, and Facebook. The shared questionnaire was made anonymous ensuring data confidentiality and reliability. The survey was performed from August 31 to September 20, 2020 after approximately 9 months of lockdown and social distancing measures in Peru due to the COVID-19 outbreak. At the beginning of the survey (August 31) the number of COVID-19 confirmed cases was 652,037 and 28,944 deaths, while at the end of the survey (September 20) the confirmed cases increased to 772,896 and the deaths increased to 31,474. We surveyed general public who were adults of both genders aged 20 to 70 years in five districts of Cusco, Peru with high-risk COVID-19 transmission according to the Epidemiological Alert AE-017-2020. The five districts were Cusco, San Jerónimo, San Sebastián, Santiago, and Wanchaq. Participants were recruited by the research team of the Universidad Nacional de San Antonio Abad del Cusco.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1,383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32–1.67]); Black patients (aOR 1.74; 95 CI 1.24–2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70–6.79) and Other (aOR 2.97; 95 CI 1.71–5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83–12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63–3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20–2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66–3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89–22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort were 9% and 37%, respectively; however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient- and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to Non-Hispanic White patients.

They are women who migrated after 2018, considered by UNHCR as Venezuelans displaced abroad, people who are likely to be in need of international protection, requiring protection against forced returns and access to basic services, regardless of age, educational level and migration status. The population is a database of migrant population of which 2,495 are women of legal age who migrated from Venezuela between April and May 2019, through the migratory corridor between Ureña/San Antonio and Villa del Rosario Cúcuta, digesting to different destinations in South America including Colombia. It is a population that has continued to be linked to the High Border Studies Group (ALEF) of the Simon Bolivar University, because it has been consulted for various studies to study the living conditions in the host countries. And in this study we want to make visible the need to understand the conditions of female migration in terms of access to and exercise of SRR. The sampling frame design was non-probabilistic. An online survey was applied to 2495 women during April 1 to June 3, 2021, obtaining the response of 86 women (sample for this study). 3.5%

Baseline demographic characteristics among adults with COVID-19 infection.

The global COVID-19 spread has forced countries to implement non-pharmacological interventions (NPI) to preserve health systems. Spain is one of the most severely impacted countries, both clinically and economically. In an effort to support policy decision-making, Candel et al.(2021) [https://dx.doi.org/10.2139/ssrn.3745801] have developed a modified Susceptible-Exposed-Infectious-Removed (SEIR) epidemiological model to simulate the pandemic evolution. Its output was used to populate an economic model to quantify healthcare costs and GDP variation, through a regression model which correlates NPI and GDP change from 42 countries. The dataset contains information on the main variables used in order to specify and estimate this predictive model.

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