How the global health security index and environment factor influence the spread of COVID-19: A country level analysis

Leichtweis, Bruno Grespan; Silva, Letícia de Faria; Silva, Felipe Lopes da; Peternelli, Luíz Alexandre

doi:10.1016/j.onehlt.2021.100235

Cited by 18 publications

(13 citation statements)

References 22 publications

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“…Of note, some clinical factors previously documented to be associated with the increased risk of COVID-19 infection, which was not confirmed in our analysis are as follows BCG vaccination coverage (44)(45)(46)(47); the number of reported malaria cases (48)(49)(50)(51)(52)(53)(54)(55), and deaths due to communicable diseases (56,57). Lastly, regarding the environmental factors, such as rainfall, temperature, and exposure to UV radiation, contrary to some reports, we did not observe any association (23,58).…”

Section: Discussioncontrasting

confidence: 99%

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis

Bouba

Tsinda

Fonkou

et al. 2021

Front. Public Health

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Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

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Section: Discussioncontrasting

confidence: 99%

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis

Bouba

Tsinda

Fonkou

et al. 2021

Front. Public Health

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“…GHS Index was developed after the outbreak of Ebola in 2014, 23 and used to evaluate the countries' capacity to manage the pandemics. 6 In our study, both measures are used as independent study variables.…”

Section: Introductionmentioning

confidence: 99%

Demography, inequalities and Global Health Security Index as correlates of COVID‐19 morbidity and mortality

Kumru

Yiğit

Hayran

2021

Health Planning & Management

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Background During a pandemic, the occurrence of infections and case fatality rates are expected to vary from one country to another due to several variables such as poverty, existing comorbidities, population density, access to health care, availability and quality of health system resources, and environmental factors. Objectives Our aim is to investigate the relationship between various demographic and socioeconomic factors and reported COVID‐19 morbidity and mortality indicators in different countries. Also, to determine the position of the countries relative to each other in terms of three indicators including COVID‐19 cases, deaths and tests. Methods Canonical correlation analysis is used to investigate the intercorrelations between independent variables and the COVID‐19 cases and deaths for 92 countries. Countries' performances are measured by MULTIMOORA. Results Human Development Index, smoking habits, percentage of elderly population and test frequency are the most significant variables associated with COVID‐19 morbidity and mortality according to our study findings. Singapore, New Zealand and Australia are the best performed countries. Conclusions Several significant and unexpected associations exist between socioeconomic factors and the COVID‐19 cases and deaths. Singapore, New Zealand and Australia are surrounded by water, have been more successful in the pandemic process compared to other countries.

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“…Besides, Leichtweis, de Faria Silva [ 84 ] studied the formation of a connection between SARS-basic CoV-2's reproduction number (R0) and various climate variables, as well as the Global Health Security Index (GHS). From December 31, 2019, to April 13, 2020, they gathered information from reported cases of COVID-19 and their respective GHS notes and climate data for 52 countries.…”

Section: Covid-19 Detection Mechanismsmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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How the global health security index and environment factor influence the spread of COVID-19: A country level analysis

Cited by 18 publications

References 22 publications

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis

Demography, inequalities and Global Health Security Index as correlates of COVID‐19 morbidity and mortality

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

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