Spatial mapping of COVID-19 for Indian states using Principal Component Analysis

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) following COVID-19 vaccines: A systematic review

BACKGROUND

The global COVID-19 pandemic began in March 2019, and given the number of casualties and adverse effects on the economy, society, and all aspects of the health system, efforts have been made to develop vaccines from the beginning of the pandemic. Numerous vaccines against COVID-19 infection have been developed in several technologies and have spread rapidly. There have been reported multiple complications of the COVID-19 vaccines as with other vaccines. A number of studies have reported multiple sclerosis (MS ) and neuromyelitis optica spectrum disorder (NMOSD) as complications of COVID-19 vaccines.

METHODS

First, we found 954 studies from 4 databases (PubMed, Embase, Scopus, and Web of Science) from inception to March 1^st, 2022. Next, duplicate articles were eliminated, and 476 studies remained. Then 412 studies were removed according to inclusion and exclusion criteria. After obtaining the full text of 64 articles, 12 studies were selected finally.

RESULTS

The data were extracted from included studies in a table. Our data includes demographic data, comorbidities, vaccines information and side effects, NMOSD and MS symptoms, laboratory and cerebrospinal fluid (CSF) findings, magnetic resonance imaging (MRI) results, treatment, and outcome of all cases.

CONCLUSION

MS and NMOSD are two neuroinflammatory disorders that arise in the CNS. Cases of MS and NMOSD have been reported following COVID-19 vaccination. Nevertheless, more studies with more subjects are needed to assess any possible relationship between the COVID-19 vaccine and central nervous system demyelination.

COVID-19 Vulnerability Mapping of Asian Countries

OBJECTIVE

The objective of this study is to map vulnerability of Asian countries to the COVID-19 pandemic.

METHOD

According to the Intergovernmental Panel on Climate Change (IPCC) 2007 framework for natural hazards, vulnerability is a function of exposure, sensitivity, and adaptive capacity. From an extensive literature review, we identified 16 socioeconomic, meteorological, environmental, and health factors that influence coronavirus disease 2019 (COVID-19) cases and deaths. The underlying factors of vulnerability were identified using principal component analysis.

RESULTS

Our findings indicate that the percentage of the urban population, obesity rate, air connectivity, and the population aged 65 and over, diabetes prevalence, and PM_2.5 levels all contributed significantly to COVID-19 sensitivity. Subsequently, governance effectiveness, human development index (HDI), vaccination rate, and life expectancy at birth, and gross domestic product (GDP) all had a positive effect on adaptive capacity. The estimated vulnerability was corroborated by a Pearson correlation of 0.615 between death per million population and vulnerability.

CONCLUSION

This study demonstrates the application of universal indicators for assessing pandemic vulnerability for informed policy interventions such as the COVAX vaccine roll-out priority. Despite data limitations and a lack of spatiotemporal analysis, this study's methodological framework allows for ample data incorporation and replication.

Factors that most expose countries to COVID-19: a composite indicators-based approach

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.