A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

Elevated Transaminases as Predictors of COVID-19 Pneumonia Severity.

BACKGROUND: This study aimed to calculate the frequency of elevated liver enzymes in hospitalized patients with coronavirus disease 2019 (COVID-19) infection and to test if liver enzyme biochemistry levels on admission could predict the computed tomography (CT) scan severity score of bilateral interstitial pneumonia. METHODS: This single-center study comprised of 323 patients including their demographic data, laboratory analyses, and radiological findings. All the information was taken from electronic health records, followed by statistical analysis. RESULTS: Out of 323 patients, 115 of them (35.60%) had aspartate aminotransferase (AST) and/or alanine aminotransferase (ALT) over 40 U/L on admission. AST was the best predictor of CT scan severity score of bilateral interstitial pneumonia (R2 = 0.313, Adjusted R2 = 0.299). CT scan severity score in the peak of the infection could be predicted with the value of AST, neutrophils, platelets, and monocytes count (R2 = 0.535, Adjusted R2 = 0.495). CONCLUSION: AST, neutrophils, platelets, and monocytes count on admission can account for almost half (49.5%) of the variability in CT scan severity score at peak of the disease, predicting the extensiveness of interstitial pneumonia related to COVID-19 infection. Liver enzymes should be closely monitored in order to stratify COVID-19 patients with a higher risk of developing severe forms of the disease and to plan the beforehand step-up treatment.

COVID-19 severity determinants inferred through ecological and epidemiological modeling.

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods.

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R 0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R 0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R 0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R 0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R 0 or affect it differently than suggested by naïve analysis.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA.

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM2.5 is a major predictor of R0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

The First Molecular Characterization of Serbian SARS-CoV-2 Isolates From a Unique Early Second Wave in Europe.

March 6, 2020 is considered as the official date of the beginning of the COVID-19 epidemic in Serbia. In late spring and early summer 2020, Europe recorded a decline in the rate of SARS-CoV-2 infection and subsiding of the first wave. This trend lasted until the fall, when the second wave of the epidemic began to appear. Unlike the rest of Europe, Serbia was hit by the second wave of the epidemic a few months earlier. Already in June 2020, newly confirmed cases had risen exponentially. As the COVID-19 pandemic is the first pandemic in which there has been instant sharing of genomic information on isolates around the world, the aim of this study was to analyze whole SARS-CoV-2 viral genomes from Serbia, to identify circulating variants/clade/lineages, and to explore site-specific mutational patterns in the unique early second wave of the European epidemic. This analysis of Serbian isolates represents the first publication from Balkan countries, which demonstrates the importance of specificities of local transmission especially when preventive measures differ among countries. One hundred forty-eight different genome variants among 41 Serbian isolates were detected in this study. One unique and seven extremely rare mutations were identified, with locally specific continuous dominance of the 20D clade. At the same time, amino acid substitutions of newly identified variants of concern were found in our isolates from October 2020. Future research should be focused on functional characterization of novel mutations in order to understand the exact role of these variations.

A systems biology approach to COVID-19 progression in population.

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.