The Effect of the COVID-19 Vaccine on Daily Cases and Deaths Based on Global Vaccine Data.

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global pandemic, has caused over 216 million cases and 4.50 million deaths as of 30 August 2021. Vaccines can be regarded as one of the most powerful weapons to eliminate the pandemic, but the impact of vaccines on daily COVID-19 cases and deaths by country is unclear. This study aimed to investigate the correlation between vaccines and daily newly confirmed cases and deaths of COVID-19 in each country worldwide. METHODS: Daily data on firstly vaccinated people, fully vaccinated people, new cases and new deaths of COVID-19 were collected from 187 countries. First, we used a generalized additive model (GAM) to analyze the association between daily vaccinated people and daily new cases and deaths of COVID-19. Second, a random effects meta-analysis was conducted to calculate the global pooled results. RESULTS: In total, 187 countries and regions were included in the study. During the study period, 1,011,918,763 doses of vaccine were administered, 540,623,907 people received at least one dose of vaccine, and 230,501,824 people received two doses. For the relationship between vaccination and daily increasing cases of COVID-19, the results showed that daily increasing cases of COVID-19 would be reduced by 24.43% [95% CI: 18.89, 29.59] and 7.50% [95% CI: 6.18, 8.80] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Daily increasing deaths of COVID-19 would be reduced by 13.32% [95% CI: 3.81, 21.89] and 2.02% [95% CI: 0.18, 4.16] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. CONCLUSIONS: These findings showed that vaccination can effectively reduce the new cases and deaths of COVID-19, but vaccines are not distributed fairly worldwide. There is an urgent need to accelerate the speed of vaccination and promote its fair distribution across countries.

Association between temperature and COVID-19 transmission in 153 countries.

The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R0 and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine.

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level

Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi⁎ statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO2 was positively associated, and O3 was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19.

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19.

PURPOSE: Differentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT). METHOD: COVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images. RESULTS: We retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals. CONCLUSIONS: The developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.

Fast automated detection of COVID-19 from medical images using convolutional neural networks.

Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.