Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

A novel analysis approach to determining the case fatality rate of COVID-19 in Italy

BACKGROUND

The Coronavirus Disease 2019 (COVID-19), which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate.

OBJECTIVE

The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) to make more accurate forecasts of epidemic trends the of COVID-19 in Italy.

METHODS

We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of daily deaths and total deaths to calculate the discharged case fatality rate (tDCFR), monthly discharged case fatality rate (mDCFR), and stage discharged case fatality rate (sDCFR). Furthermore, we analyzed the trend in mDCFR between January and December 2020 using Joinpoint Regression Analysis and used the ArcGIS version 10.7 software to visualize the spatial distribution of epidemic case fatality rate and assigned different colors to each province based on the CFR or tDCFR.

RESULTS

We calculate the number and obtain the new index tDCFR and mDCFR for calculating the fatality rate. The results show that the overall trend of tDCFR and mDCFR fluctuates greatly from January to May. After reaching the peak, it first rises rapidly, then falls rapidly, and finally stabilizes. The map shows that the provinces with high tDCFR were Emilia-Romagna, Puglia and Lombardia. The change trend of mDCFR over time was divided into two stages, the first stage (from January to May) and the second stage (from June to December). Among the six selected countries, the United States has the highest tDCFR (4.26%), while the tDCFR of the remaining countries is between 0.98% and 2.72%.

CONCLUSIONS

We provide a new perspective for assessing the mortality of COVID-19 in Italy,which can use these ever-changing data to calculate a more accurate case fatality rate and scientifically predict the development trend of the epidemic.

CLINICALTRIAL

Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis

The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.

Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections

Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. While ageing has been extensively demonstrated to be accompanied by some modifications at the gene expression level, a possible link with COVID-19 manifestation still need to be investigated at the molecular level. Objectives: This study aims to shed out light on a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people. Methods: We considered public datasets of ageing-related genes and their expression at the tissue level. We selected human proteins interacting with viral ones that are known to be related to the ageing process. Finally, we investigated changes in the expression level of coding genes at the tissue, gender and age level. Results: We observed a significant intersection between some SARS-CoV-2 interactors and ageing-related genes, suggesting that those genes are particularly affected by COVID-19 infection. Our analysis evidenced that virus infection particularly involves ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. We found that HMGA1 and NPM1 have different expressions in the lung of males, while HMGA1, APEX1, CHEK1, EEF2, and NPM1 present changes in expression in males due to ageing effects. Conclusion: Our study generated a mechanistic framework to clarify the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. We also provide testable hypotheses for future investigation and pharmacological solutions tailored to specific age ranges.

The impact of COVID-19 lockdown on cataract surgery: a surgeons' perspective

CLINICAL RELEVANCE

In the last months, the whole world is dealing with an unprecedented public health crisis due to COVID-19 outbreak. Consequently, many governments have implemented lockdowns on a national level, affecting, among others, ophthalmic surgical practice in a globe scale.

BACKGROUND

The aim of this study is to evaluate the impact of surgical theatre lockdown due to COVID-19 pandemic on the surgical performance of cataract surgeons.

METHODS

Intraoperative complications and surgical time of the first 160 cataract surgeries performed by eight consultants (20 cases each) after a two-month lockdown were recorded and analysed in a cross-sectional study. The results were plotted against the last 30 cases of each surgeon before the implementation of the lockdown (240 cases). Cataract surgeons were asked to rate their subjective perspective and difficulties faced after re-opening through a questionnaire.

RESULTS

The average duration of all surgeries after the lockdown was 19.1 ± 6.2 minutes showing a 14% increase compared to the one before the lockdown (16.8 ± 5.1 minutes, p = 0.0001). The complications rate was 2.09% (5/240 cases) before the abstention and 3.12% (5/160 cases) after the abstention not yielding any statistically significant difference (p = 0.74). When complicated surgeries were excluded from the analysis, surgical time was still higher after the lockdown (18.9 ± 5.9 minutes) than before (16.6 ± 5.0 minutes, p < 0.0001). 37.5% of consultants (3/8) stated that the two-month abstention from cataract surgeries has affected their surgical skills somewhat or a lot, while 62.5% (5/8) reported being more careful in their first cases after the lockdown. Most complications occurred in the hands of one surgeon who stated to be very anxious upon restart.

CONCLUSION

The operating theatres' lockdown due to COVID-19 pandemic did not seem to affect the intraoperative complications rate in cataract surgery. A slight increase of surgical duration was noted, while most surgeons reported being more careful upon restart.

Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19

Although the first coronavirus disease 2019 (COVID-19) wave has peaked with the second wave underway, the world is still struggling to manage potential systemic risks and unpredictability of the pandemic. A particular challenge is the "superspreading" of the virus, which starts abruptly, is difficult to predict, and can quickly escalate into medical and socio-economic emergencies that contribute to long-lasting crises challenging our current ways of life. In these uncertain times, organizations and societies worldwide are faced with the need to develop appropriate strategies and intervention portfolios that require fast understanding of the complex interdependencies in our world and rapid, flexible action to contain the spread of the virus as quickly as possible, thus preventing further disastrous consequences of the pandemic. We integrate perspectives from systems sciences, epidemiology, biology, social networks, and organizational research in the context of the superspreading phenomenon to understand the complex system of COVID-19 pandemic and develop suggestions for interventions aimed at rapid responses.

COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data

The management of the COVID-19 pandemic presents several unprecedented challenges in different fields, from medicine to biology, from public health to social science, that may benefit from computing methods able to integrate the increasing available COVID-19 and related data (e.g., pollution, demographics, climate, etc.). With the aim to face the COVID-19 data collection, harmonization and integration problems, we present the design and development of COVID-WAREHOUSE, a data warehouse that models, integrates and stores the COVID-19 data made available daily by the Italian Protezione Civile Department and several pollution and climate data made available by the Italian Regions. After an automatic ETL (Extraction, Transformation and Loading) step, COVID-19 cases, pollution measures and climate data, are integrated and organized using the Dimensional Fact Model, using two main dimensions: time and geographical location. COVID-WAREHOUSE supports OLAP (On-Line Analytical Processing) analysis, provides a heatmap visualizer, and allows easy extraction of selected data for further analysis. The proposed tool can be used in the context of Public Health to underline how the pandemic is spreading, with respect to time and geographical location, and to correlate the pandemic to pollution and climate data in a specific region. Moreover, public decision-makers could use the tool to discover combinations of pollution and climate conditions correlated to an increase of the pandemic, and thus, they could act in a consequent manner. Case studies based on data cubes built on data from Lombardia and Puglia regions are discussed. Our preliminary findings indicate that COVID-19 pandemic is significantly spread in regions characterized by high concentration of particulate in the air and the absence of rain and wind, as even stated in other works available in literature.