ABSTRACT
The emergence of the COVID-19 pandemic has led to an unprecedented change in the lifestyle routines of millions of people. Beyond the multiple repercussions of the pandemic, we are also facing significant challenges in the population's mental health and health programs. Typical techniques to measure the population's mental health are semiautomatic. Social media allow us to know habits and daily life, making this data a rich silo for understanding emotional and mental well-being. This study aims to build a resilient and flexible system that allows us to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic. We built an extensive data system utilizing modern cloud-based serverless architectures to analyze 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. We provide metrics, advantages, and challenges of developing serverless cloud-based architectures for a natural language processing project of a large magnitude. © 2022, The Author(s).
ABSTRACT
Backgrounds: A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now spread to all countries of the world, including Iran. Although anti-SARS-CoV-2 antibodies may be identified in patients using immunological methods with sufficient sensitivity and specificity, the conclusive diagnosis of the disease is made using the molecular RT-PCR method. A population-based seroepidemiological survey was conducted to quantify the proportion of the exposed population with SARS-CoV-2 antibodies and evaluate whether the antibodies are a marker of total or partial immunity compared to the population that remains susceptible to the virus. Material & Methods: This cross-sectional study was conducted to investigate the seroprevalence of COVID-19 in Valiasr, Sajad, and Ghaem hospitals in Tehran, the capital of Iran, from April to the end of October 2020. Clotted and heparinized blood specimens (2mL) were collected from the patients. The serum and plasma were separated and stored at −80 °C until use. Anti-SARS-CoV-2 IgG and IgM antibodies were examined in the serum samples of 1375 in-patients admitted to the hospitals using ELISA kits. The obtained data were analyzed using SPSS software Ver.22.0 by employing statistical tests such as Chi-square and Fisher’s exact tests. A p-value <.05 was considered as significant. Findings: In total, 1375 participants were enrolled in this study, and SARS‐CoV‐2 antibodies were detected in 291 patients using IgM‐IgG antibody assay. Among the seropositive patients studied, 187 were male (64.3%), and 104 were female (35.7%) (p<.05). The mean age of the patients was 49±8.4 years;the majority of whom (27%) were in the age group of 31-40 years. Also, the lowest frequency of infected cases was related to the age group of 1-10 years (p <.05). The seroprevalence of SARS‐CoV‐2 IgM or IgG antibodies was determined to be 21.2%. Diabetes mellitus was the most common underlying disease among SARS‐CoV‐2 patients [p=.05;Odd Ratio=1.61(0.90-2.91)]. Conclusion: The use of conventional serological assays, such as the enzyme-linked immunoassay (ELISA), for detecting specific IgM and IgG antibodies in SARS‐CoV‐2 patients has a high-throughput advantage while minimizing false-negative results obtained using the RT-PCR method. In this study, the seroprevalence of SARS-CoV-2 antibodies was determined to be 21%. Control of diabetes, among other influential factors, plays an important role in the management and control of COVID-19. © 2021, TMU Press.