ABSTRACT
Introduction: COVID-19 spread globally, including across the Balkans, resulting in different morbidity and mortality outcomes in different countries. The aim of this study was to review the impact of COVID-19 over 17 months with regards to pandemic progression, implemented mitigation strategies, and COVID-19 vaccination programs across the Balkan countries, while identifying any valuable pieces of information acquired serendipitously throughout the pandemic that can be implemented in future action plans. Methods: A longitudinal ecological study was conducted across the Balkan countries from the onset of COVID-19 in these countries up until 1st August 2021. Epidemiological data was obtained from Our World in Data databases, while Ministry of Health websites for each respective country as well as local newspapers were utilized to review COVID-19-related mitigation and vaccination strategies. Comparisons of vaccination coverage, incident cases and mortality were made across neighboring countries, by converting the respective data to rates per 100,000 population for each country using Microsoft® Excel for mac (Version 16.59). Results: More than 10 million positive COVID-19 cases and 164,470 deaths were observed across the Balkan countries up until 1st August 2021. Trends in COVID morbidity and mortality outcomes were evident across neighbouring countries. A staggered vaccination rollout was observed, with various rollout speeds, although gradual decline in both morbidity and mortality occurred. Conclusion: Results obtained from this study strongly indicate that COVID-19 outcome for a particular country is not only dependent on the country’s own level of viral transmission, mitigations, and vaccination rates but also on neighbouring countries’ COVID-19 situation. Hence, cross-border governance action and recovery plans are recommended along with targeting vaccination hesitance. © 2021 Tehran University of Medical Sciences.
ABSTRACT
OBJECTIVE: Coronavirus disease 2019 (COVID-19) spread globally, including across Europe, resulting in different morbidity and mortality outcomes. The aim of this study was to explore the progression of the COVID-19 pandemic over 18 mo in relation to the effect of COVID-19 vaccination at a population level across 35 nations in Europe, while evaluating the data for cross-border epidemiological trends to identify any pertinent lessons that can be implemented in the future. METHODS: Epidemiological data were obtained from European Centre for Disease Prevention and Control and Our World in Data databases while Ministry of Health websites of each respective country and local newspapers were used for COVID-19-related vaccination strategies. Case, mortality, and vaccination incidence comparative analyses were made across neighboring countries. RESULTS: Similar morbidity and mortality outcomes were evident across neighboring countries over 18 mo, with a bidirectional relationship evident between cumulative fully vaccinated population and case fatality rates. CONCLUSION: Countries' COVID-19 outcome is related on national mitigative measures, vaccination rollouts, and neighboring countries' actions and COVID-19 situations. Mass population vaccination appeared to be effective in reducing COVID-19 case severity and mortality rates. Vaccination equity and pan-European commitment for cross-border governance appear to be the way forward to ensure populations' return to "normality."
ABSTRACT
As of October 10, 2021, the entire planet has reported 219 million COVID-19 cases, with 4.55 million fatalities. Lockdowns and softening measures have been thrown into turmoil throughout the world since the outbreak. Our social life will only return to normal once an appropriate vaccine is produced and proper authorized preventive techniques are implemented. To tackle this pandemic, governments and health experts all around the globe are experimenting with a range of measures and preventative strategies. So far, 22 vaccines have been approved. They are effective against immunocompromised people, pregnant women, and multiple sclerosis patients. Certain nations are considered to be more successful than others in terms of providing safety to their inhabitants and increasing their economic activities. A plethora of vaccinations have been produced, and a research anthology has been published. However, medical personnel are still searching for a viable treatment to limit this pandemic. © Pakistan Academy of Sciences.
ABSTRACT
BACKGROUND: Approximately 80% of those infected with COVID-19 are immune. They are asymptomatic unknown carriers who can still infect those with whom they come into contact. Understanding what makes them immune could inform public health policies as to who needs to be protected and why, and possibly lead to a novel treatment for those who cannot, or will not, be vaccinated once a vaccine is available. OBJECTIVE: The primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary purpose was to learn about the relevance of other immune factors. METHODS: This was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly grouped Chinese COVID-19-positive patients, 37 of whom were symptomatic and 37 asymptomatic. The setting was a single-center primary care hospital in the Wanzhou District of China. Immunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before observations were recorded. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years); 22 were female, 15 were male. For comparison, 37 RT-PCR test-positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex. Machine learning models were trained and compared to understand the pathogen-immune relationship and predict who was immune to COVID-19 and why, using the statistical programming language R. RESULTS: When stem cell growth factor-beta (SCGF-ß) was included in the machine learning analysis, a decision tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptom immunity with 100% accuracy. When SCGF-ß was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8% AUROC (area under the receiver operating characteristic) curve accuracy (95% CI 90.17%-100%). In total, 34 common immune factors have statistically significant associations with COVID-19 symptoms (all c<.05), and 19 immune factors appear to have no statistically significant association. CONCLUSIONS: The primary outcome was that asymptomatic patients with COVID-19 could be identified by three distinct immunological factors and levels: SCGF-ß (>127,637), interleukin-16 (IL-16) (>45), and macrophage colony-stimulating factor (M-CSF) (>57). The secondary study outcome was the suggestion that stem-cell therapy with SCGF-ß may be a novel treatment for COVID-19. Individuals with an SCGF-ß level >127,637, or an IL-16 level >45 and an M-CSF level >57, appear to be predictively immune to COVID-19 100% and 94.8% (AUROC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point of care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-ß and M-CSF appear to be promising novel therapeutics for patients with COVID-19.