'Modern Healthcare Technology' in Infectious Diseases

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Corona SEIR Workbench

From www.codeproject.com - June 15, 2021 6:44 AM

Pandemic SEIR and SEIRV modelling software and infrastructure for the Corona SARS-COV-2 COVID-19 disease with data from Johns-Hopkins-University CSSE, Robert Koch-Institute and vaccination data from Our World In Data.

The SARS-COV-2 pandemic has been affecting our lives for months. The effectiveness of measures against the pandemic can be tested and predicted by using epidemiological models. The Corona SEIR Workbench uses a SEIR model and combines a graphical output of the results with a simple parameter input for the model. Modelled data can be compared country by country with the SARS-COV-2 infection data of the Johns Hopkins University. Additionally, the R₀ values of the Robert Koch Institute can be displayed for Germany. Vaccination data is used from Our World In Data.

view on Github : https://github.com/logiclink/corona_seir_workbench

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Analysis reveals SARS-CoV-2 infection causes deregulation of lung cell metabolism

From journals.plos.org - April 14, 2021 9:48 AM

A model has been developed by researchers at Indian Institute of Technology ,Kharagpur predicting alteration in metabolic reaction rates of lung cells post SARS-CoV-2 infection.

"We have used the gene expression of normal human bronchial cells infected with SARS-CoV-2 along with the macromolecular make-up of the virus to create this integrated genome-scale metabolic model. The growth rate predicted by the model showed a very high agreement with experimentally and clinically reported effects of SARS-CoV-2," said Dr Amit Ghosh, Assistant Professor, School of Energy Science and Engineering, IIT Kharagpur who coauthored the paper

The research would lead to a better understanding of metabolic reprogramming and aid the development of better therapeutics to deal with viral pandemics,

Summary:

Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition.

While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state.

We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.

read the paper at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008860

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Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database

From publichealth.jmir.org - May 4, 2021 3:39 AM

Communicable diseases including COVID-19 pose a major threat to public health worldwide.

To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential.

The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics.

This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness.

We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic.

Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020.

The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020.

The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

read the study at https://publichealth.jmir.org/2021/3/e27317

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