swine flu
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Background:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world causing a serious health issue. After the swine flu pandemic (also known as H1N1) of 2009-10, WHO had declared Coronavirus disease 2019 (COVID-19) as a global pandemic. This pandemic caused huge losses to the entire world. Most of the patients with mild symptoms were treated at home but patients with difficulty in breathing and various complications were treated at the hospital. Naturally produced antibodies or vaccination can only offer protection. The objective was to compare the antibody status after SARS-CoV-2 infection in hometreated and hospital-treated patients. Methods: This cross-sectional study was conducted in the Department of Biochemistry and Molecular Biology, BIRDEM General Hospital, from July, 2020 to June, 2021. A total of 77 patients (age 18 – 70 years) infected by SARS-CoV-2 were enrolled for this study. Among them, 37 were home treated and 40 were treated in hospital. The study subjects were non-vaccinated. For this study, serum IgG level was measured by the automated analyser. For statistical analysis, the Mann Whitney U test was done. Results:The median value of serum IgG was significantly higher in hospital-treated patients than in hometreated patients (p < 0.001). Hospital treated patients with a history of comorbidity developed more amount of antibody in comparison to home treated patients. Conclusion:Hospital treated patients develop higher antibodies in comparison to home treated patients. BIRDEM Med J 2022; 12(1): 11-15

The coronavirus disease (COVID-19) pandemic has affected all our lives but did not affect all parts of societies equally. This study uses a systematic literature review approach to examine the experiences of homeless populations during COVID-19. Our literature review identified lessons learned and promising practices from the field at a global level, and summarizes academic studies in order to promote future efforts to prepare homeless populations for potential extreme events in the future. Forty-one of 209 articles were selected to prepare this literature review. Following the academic database search, grey literature from various organizations were also identified to enrich the literature results and analysis. Findings from these articles were grouped under three main themes to better illustrate the results: (1) impact of COVID-19 on people experiencing homelessness (PEH), (2) support mechanisms, and (3) promising practices. A comparative approach also was used to examine how PEH responded during two previous pandemics (severe acute respiratory syndrome [SARS] in 2003 and Swine Flu 2009) compared to the COVID-19 pandemic. Findings showed that there was continuous improvement in the disaster preparedness for PEH during COVID-19 when compared to past pandemics. In addition, promising practices have emerged. However, ongoing issues, such as lack of personal protective equipment (PPE), staff shortages, and communication problems, still persist in the field. More research regarding PEH during pandemics is needed, and their voices should be included.

Novel data science approaches are needed to confront large-scale infectious disease epidemics such as COVID-19, human immunodeficiency viruses, African swine flu and Ebola. Human beings are now equipped with richer data and more advanced data analytics methodologies, many of which have become available only in the last decade. The theme issue Data Science Approaches to Infectious Diseases Surveillance reports the latest interdisciplinary research on developing novel data science methodologies to capitalize on the rich ‘big data’ of human behaviours to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic. Compared to conventional public health research, articles in this issue present innovative data science approaches that were not possible without the growing human behaviour data and the recent advances in information and communications technology. This issue has 12 research papers and one review paper from a strong lineup of contributors from multiple disciplines, including data science, computer science, computational social sciences, applied maths, statistics, physics and public health. This introductory article provides a brief overview of the issue and discusses the future of this emerging field. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

The World Health Organization (WHO) declared the novel Coronavirus (SARS-CoV-2; Covid-19) a pandemic on 11 March 2020. Unlike preceding highly contagious diseases that brought the threat of global instability this century, such as SARS-CoV, Zika virus (ZIKV), Swine flu (H1N1), and Avian flu (H5N1), Covid-19, governments across the world introduced strict measures and interruptions to daily life incomparable in living memory. Overnight, countries closed schools, higher education institutions, workplaces and shut down borders – this left people scrambling to adapt, including those implementing peacebuilding interventions. In this unprecedented situation, peacebuilding organisations have worked, responded, and adapted to the new normal. These new dynamics have created both challenges and opportunities for peacebuilding. This article documents the experiences of peacebuilders during the pandemic, making sense of changing conditions, challenges and opportunities they faced. It explores two key questions. How have peacebuilding organisations adapted during COVID-19? Has COVID-19 contributed to the move to local ownership of peacebuilding or localisation? It addresses these questions by engaging with peacebuilding organisations across different geographical regions through an online survey and key informant interviews. The main results focus on localisation, digital adaptation and funding strategy and administration challenges.

This chapter investigates the pandemic viruses of the twenty-first century, including human immunodeficiency virus (HIV), flu, and SARS-CoV-2. Before we are even a quarter of the way through the twenty-first century, we have experienced three pandemics, with millions of lives lost. The first of these is HIV-1, which began in the twentieth century and is still ongoing, while H1N1/09 swine flu and COVID-19 arose in 2009 and 2019 respectively. All three pandemics are caused by zoonotic RNA viruses that appeared without warning and had spread uncontrollably before the alarm was raised. While all three are entirely new to humans and are therefore classed as emerging infections, other strains of the flu virus have infected us for many centuries. This chapter looks at where and how these viruses jumped to humans, and how and why they spread around the world.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become a novel pandemic event following the swine flu that occurred in 2009, which was caused by the influenza A virus (H1N1 subtype). The accurate identification of the huge number of samples during a pandemic still remains a challenge. In this study, we integrate two technologies, next-generation sequencing and cloud computing, into an optimized workflow version that uses a specific identification algorithm on the designated cloud platform. We use 182 samples (92 for COVID-19 and 90 for swine flu) with short-read sequencing data from two open-access datasets to represent each pandemic and evaluate our workflow performance based on an index specifically created for SARS-CoV-2 or H1N1. Results show that our workflow could differentiate cases between the two pandemics with a higher accuracy depending on the index used, especially when the index that exclusively represented each dataset was used. Our workflow substantially outperforms the original complete identification workflow available on the same platform in terms of time and cost by preserving essential tools internally. Our workflow can serve as a powerful tool for the robust identification of cases and, thus, aid in controlling the current and future pandemics.

The relationship between pandemics and travel is an essential element in understanding health security and global change. Using cross-country COVID-19 confirmed cumulated cases and deaths data for 165 countries as of 3rd February 2021, this study investigates the impact of tourism, the experience of SARS and Swine flu, and several other socio-economic variables on the spread of the COVID-19 pandemic at the global and regional level. The results reveal that (1) the higher the volume of inbound and outbound tourism, the higher the number of reported COVID-19 cases and deaths at the global as well as at the regional level; (2) the impact of tourism on COVID-19 cases and deaths varies across regions, and; (3) countries that have experience in handling SARS or Swine flu pandemics have been able to reduce COVID-19 cases and deaths significantly, in particular, the countries in the Asia Pacific region.