Open source aggregation pipeline for public COVID-19 data, including hospitalization/ICU/ventilator numbers for many countries.

Public shared code for doing scenario forecasting and creating reports for various governmental entities.

The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

This repository is a work-in-progress ML production pipeline for detecting Covid-19 from cough sounds and clinical information.

[Cell Patterns] COVID-19 EHR data analysis pipeline

The IHME COVID-19 SEIIR Model.

A Covid-19 data pipeline on AWS featuring PySpark/Glue, Docker, Great Expectations, Airflow, and Redshift, templated in CloudFormation and CDK, deployable via Github Actions.

Automated pipeline that uses Citibike, NYC TLC, & Yelp data to map out ideal locations for Citibike expansion as people return to work & entertainment without covid-safe public transit

A Customized Fake News Detection System Built Using Streamlit With SpaCy NLP Pipeline. The Web App Contains Frontend, Backend and Database Integration. Features: Detect Fake News For COVID and Afghanistan Crisis, Sign In/ Log In, View History, Get Sentiment Of The News Article

A fully-featured multi-source data pipeline for continuously extracting knowledge from COVID-19 data.

Data pipeline for uploading, preprocessing, and visualising COVID19 data

Covid-19 HGI WES/WGS burden test BQC-19 pipeline

Scan/Trim/Extra Pipeline for State Coronavirus Site

An NLP pipeline for COVID-19 surveillance used in the Department of Veterans Affairs Biosurveillance.

An Integrative Drug Repurposing Pipeline using KNIME and Programmatic Data Access: A case study on COVID-19 Data

Pipeline to automatically analyse SARS-CoV-2 Whole genome sequencing Illumina data obtained using EasySeq SARS-CoV-2/COVID-19 Whole Genome Sequencing kit.

Data pipeline for extracting, transforming, and visualising Covid-19 data

This software implements a high-throughput data processing pipeline to identify and charaterize SARS-CoV-2 variant sequences in specimens from COVID-19 positive hosts or environments.

Data engineering pipeline for the household COVID-19 Infection Survey (CIS)

Cleaning pipeline for the the National COVID-19 Chest Imaging Database (NCCID) clinical data.

Covid-19 analysis pipeline

No description available

COVID-19 Surveillance Data Modelling and Management Pipeline in Piedmont.

Fully automated daily Data-Plots based in data from https://github.com/CSSEGISandData/COVID-19 repository

A real-time dashboard for Virginia COVID-19 case reports and vaccinations with end-to-end data collection, storage, and processing pipeline

This analytic pipeline was developed as part of the Real World Evidence to Accelerate COVID-19 Therapeutics project to conduct a real-world observational study of the safety and effectiveness of Monoclonal Antibodies (mAbs) to fight SARS-CoV-2.

An RNAi pipeline is applied to the genome of SARS like corona virus (SARS-CoV-2) which causes COVID-19 yielding 17 candidate siRNA sequences for nebulization tests hitting 9 distinct loci over the genome of the virus.

A Data Engineering Project that implements an ETL data pipeline using Dagster, Spark, Plotly, Dash

Measurement unit harmonization and inference pipeline scripts, developed for the National COVID Cohort Collaborative (N3C)

We previously developed a program in python3 that flags possible lab-associated variants in SARS-CoV-2 data (see https://github.com/lgozasht/COVID-19-Lab-Specific-Bias-Filter). This repository hosts our improved pipeline which employs similar methods as COVID-19-Lab-Specific-Bias-Filter but includes additional modifications.