Embedded Files
Our goal is to provide easy access for current data and projects/studies relevant to NeTS. If you find additional data sources or projects that should be included in this resource, please use the contact form on the homepage.
*Dates attached to studies are the dates we have added links to this page, not the original publication date, which may be found through the link.
Cornell's arXiv.org is a significant resource for related efforts and used extensively here.
Development Projects and Apps
Development Projects and Apps
TraceTogether
(added 4/10)
TraceTogether is an app that can be downloaded voluntarily and facilitates the contact tracing process. With your consent, it exchanges Bluetooth signals with nearby phones running the same app. This allows you to be informed if you were in prolonged physical proximity with an infected person. While this is an urgent public health emergency, we are committed to safeguarding your privacy and ensuring you have control over your data.
Private Kit: Safe Paths
(added 4/10)
Safe Paths is a community-led movement to develop free, open-source, privacy-by-design tools for individuals, public health officials, and larger communities to flatten the curve of COVID-19, reduce fear, and prevent a surveillance-state response to the pandemic. Safe Paths is a ‘privacy-first’ app that allows you to log your GPS trails on your own phone. The information is stored locally and never shared with anyone (not even with us or MIT) until you explicitly decide to manually export the data. The location log generated by Safe Paths cannot be accessed from outside the user’s device. Location information can be imported and exported by the user and used in other projects and applications. Safe Paths logs your device’s location once every five minutes and stores 28 days of data in under 100KB of space – less space than a single picture. But what is truly exciting about Safe Paths is its privacy protection.
https://github.com/tripleblindmarket/covid-safe-paths
Hamagen- Used in Israel
(added 4/10)
HAMAGEN is an app that allows the identification of contacts between diagnosed patients and people who came in contact with them in the 14 days prior to the patient's diagnosis of the disease. The app is a technological tool designed to give each and every one of us the ability to know quickly and accurately if we have been in contact with a person infected with coronavirus. That way, we can stop the spread of the virus and protect those close to us. The app allows you to receive alerts (location and time) about your exposure to a diagnosed patient.
https://govextra.gov.il/ministry-of-health/hamagen-app/Privacy-policy-EN/
https://github.com/MohGovIL/hamagen-react-native
COVID Symptom Tracker
(added 4/10)
This app-based study is a way to find out where the COVID hot spots are, new symptoms to look out for, and might be used as a planning tool to target quarantines, send ventilators and provide real-time data to plan for future outbreaks.
Report your health daily even if you feel well
Help slow the outbreak near you
See how your area is affected
PACT: Private Automatic Contact Tracing
(added 4/10)
Their approach uses inter-phone Bluetooth communications (including energy measurements) as a proxy for inter-person distance measurement. Through applied cryptography this system can be used to collect and maintain weeks of contact events that can later be enriched by infection notifications (as specific individuals test positive) leading to exposure notifications to all cell phone owners who have had medically-significant contact (in terms of distance and time) with infected people in the past medically-significant time period (e.g., two weeks). All of this can be done without revealing any private information to anyone (not to the government, the health care providers, the cellular service providers, etc.)
No personal location information shared
All contact information anonymized
Health authorities cannot see locations
CoEpi: Community Epidemiology in Action
(added 4/5)
CoEpi is building a privacy-first system for anonymous bluetooth-based contact tracing / exposure matching based on voluntary symptom sharing and/or confirmed COVID-19 test results.
COVID Watch
(added 4/10)
COVID Watch aims to empower people to protect their communities from COVID-19 without sacrificing their personal privacy. Our app uses Bluetooth signals to detect when users are in proximity to each other and alerts them anonymously if they were in contact with someone later confirmed to have COVID-19, without anyone (including the government) being able to track who exposed whom. We were among the first groups to release an open-source protocol for privacy-preserving, decentralized Bluetooth contact tracing, and have been collaborating with MIT’s SafePaths and CoEpi to make this protocol a standard across apps and jurisdictions. We are aiming to release the full app by mid-April.
MD2K Center of Excellence's mContain
(added 4/10)
Researchers at the MD2K Center of Excellence, headquartered at the University of Memphis, in collaboration with a local infectious disease expert, announce the launch of a free mobile app called mContain to reduce the community transmission of COVID-19. mContain uses location and Bluetooth technologies in smartphones to detect proximity encounters (within 6 feet for several minutes) with other app users. Similar to a step count, the app counts and displays the number of daily proximity encounters with other app users. To reduce the chances of entering crowded places, the app displays the level of crowding at busy places on a map. mContain App also has an option of a confidential COVID-19 patient proximity alert, if a user and their COVID-19 test provider both agree to share the results of their test.
After installing the app, no action is needed from users. mContain does not collect or keep any identifying information such as their names, phone numbers, logins, or emails. The app minimizes battery consumption by using Bluetooth and location data sporadically. The free mContain app is currently available at the mContain website (www.mContain.org) and via the Google Play Store for Android devices; an iOS version for the Apple App Store will be released shortly.
Safe Paths App: MIT-private Industry Collaboration
(added 3/30)
Privacy-by-Design Contact Tracing: MIT secure location tracking app
TIBCO Software Visual Analysis Hub
(added 3/30)
Interactive tool using reliable international data feeds for on the fly analysis. Our goal is to understand the outbreaks in real-time at a community level, and assess the effects of the non-pharmaceutical, social interventions
CovidSafe
(added 4/20)
Doctors and researchers at the University of Washington with Microsoft volunteers have built a tool to alert you about highly relevant public health announcements, exposure to COVID-19, and to assist contact tracing without compromising your personal privacy.
https://covidsafe.cs.washington.edu
Save Face App Using Ultrasound Pulses: Saving Face Project by MIT Media Lab
(added 4/23)
We seek to deploy a sub-$5 system to greatly reduce the risk of users acquiring SARS-CoV-2 or other pathogens through surface transmission. Our device measures the distance between a user’s hands and face using ultrasound pulses between hand-worn sensors and receivers on the neck or head (e.g., earbuds, mics). When the user raises their hand to touch their face, their smartphone alerts them with a vibration or audible alarm. Our next steps include improving the robustness to interference from other users, removing the current calibration step, achieving functionality with 90% of randomly chosen inexpensive earbuds, and releasing Android and iOS apps.
https://github.com/camilorq/SavingFaceApp
Research Projects and Papers
Research Projects and Papers
Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) Project
(added 4/10)
PEPP-PT was created to assist national initiatives by supplying ready-to-use, well-tested, and properly assessed mechanisms and standards, as well as support for interoperability, outreach, and operation when needed.
The PEPP-PT mechanisms will have these core features:
Well-tested and established procedures for proximity measurement on popular mobile operating systems and devices.
Enforcement of data protection, anonymization, GDPR compliance, and security.
International interoperability to support tracing local infection chains even if a chain spans multiple PEPP-PT participating countries.
Scalable backend architecture and technology that can be deployed with local IT infrastructure.
Certification service to test and ensure local implementations use the PEPP-PT mechanisms in a secure and interoperable manner.
Our reference implementation is available under the Mozilla License Agreement.
Decentralized Privacy-Preserving Proximity Tracing
(added 4/10)
A proposal for a secure, decentralized privacy-preserving proximity tracing system. A multi-contributor project led by EPFL (Switzerland) researchers. This design provides proximity-tracing via smartphone-generated ephemeral bluetooth IDs.
This repository contains a proposal for a secure and privacy-preserving decentralized privacy-preserving proximity tracing system. Its goal is to simplify and accelerate the process of identifying people who have been in contact with an infected person, thus providing a technological foundation to help slow the spread of the SARS-CoV-2 virus. The system aims to minimize privacy and security risks for individuals and communities and guarantee the highest level of data protection.=
By publishing this document we seek feedback from a broad audience on the high-level design, its security and privacy properties, and the functionality it offers; so that further protection mechanisms can be added if weaknesses are identified. The white paper document is accompanied by an overview of the data protection aspects of the design, and a three page simplified introduction to the protocol.
https://github.com/DP-3T/documents
COVID-19 Community Mobility Reports
(added 4/10)
Each Community Mobility Report is broken down by location and displays the change in visits to places like grocery stores and parks.
We’re working to add more countries, regions and languages in the coming weeks. This is an early release and reports will be updated regularly.
https://www.google.com/covid19/mobility/
Anonymous Collocation Discovery: Harnessing Privacy to Tame the Coronavirus Paper-Only
(added 4/10)
We propose an alternative: an extremely simple scheme for providing fine-grained and timely alerts to users who have been in the close vicinity of an infected individual. Crucially, this is done while preserving the anonymity of all individuals, and without collecting or storing any personal information or location history. Our approach is based on using short-range communication mechanisms, like Bluetooth, that are available in all modern cell phones. It can be deployed with very little infrastructure, and incurs a relatively low false-positive rate compared to other collocation methods. We also describe a number of extensions and tradeoffs
https://arxiv.org/pdf/2003.13670.pdf
Privacy-Preserving Contact Tracing by Apple and Google
(added 4/10)
Across the world, governments, and health authorities are working together to find solutions to the COVID‑19 pandemic, to protect people and get society back up and running. Software developers are contributing by crafting technical tools to help combat the virus and save lives. In this spirit of collaboration, Google and Apple are announcing a joint effort to enable the use of Bluetooth technology to help governments and health agencies reduce the spread of the virus, with user privacy and security central to the design.
As part of this partnership Google and Apple are releasing draft technical documentation:
All of us at Apple and Google believe there has never been a more important moment to work together to solve one of the world’s most pressing problems. Through close cooperation and collaboration with developers, governments, and public health providers, we hope to harness the power of technology to help countries around the world slow the spread of COVID‑19 and accelerate the return of everyday life.
Privacy-Preserving Contact Tracing by Apple and Google
https://www.apple.com/covid19/contacttracing
CovidSense
(added 4/10)
For millions affected globally by COVID-19 - patients, caregivers, healthcare workers and everyone else stuck at home - mental wellbeing is now under threat. Rice University and Baylor College of Medicine researchers have joined forces in a citizen science project, CovidSense, to understand the COVID’s impact on people’s mental wellbeing. The longitudinal study aims to understand the pandemic’s impact on global wellbeing. All adults can participate in this citizen science study at CovidSense.org from anywhere - no app download needed and no private info will be ever shared. Only anonymized data will be used for all analysis
Immediate Call for Proposals: AI Techniques to Mitigate Pandemic
(added 4/5)
Formation of C3.ai Digital Transformation Institute, a collaboration between C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign (UIUC), the University of California, Berkeley, Princeton University, the University of Chicago, the Massachusetts Institute of Technology, Carnegie Mellon University, and the National Center for Supercomputing Applications at UIUC. C3.ai DTI
First Call for Research Proposals:
Which Industries Are Most Severely Affected by the COVID-19 Pandemic? A Data-mining Approach to Identify Industry-specific Risks in Real-time
(added 3/30)
Based on natural language processing techniques, we can identify specific corona-related risk topics and their relevance for different industries. Our approach allows to cluster the industries into distinct risk groups. The findings of this study are summarized and updated in an online dashboard that tracks the industry-specific risks related to the crisis, as it spreads through the economy.
https://arxiv.org/abs/2003.12432
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring Using Deep Learning CT Image Analysis
(added 3/30)
This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.
https://arxiv.org/abs/2003.05037v1
Outbreak and the Smart City: Universal Data Sharing Standards w/ (AI) to Benefit Urban Health Monitoring and Management
(added 3/30)
This perspective paper, written one month after detection and during the COVID-19 outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same
https://www.mdpi.com/2227-9032/8/1/46
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
(added 3/30)
(AI) systems based on deep learning have been proposed and results have been shown to be quite promising in terms of accuracy in detecting patients infected with COVID-19 using chest radiography images. However, to the best of the authors' knowledge, these developed AI systems have been closed source and unavailable to the research community for deeper understanding and extension, and unavailable for public access and use.
https://arxiv.org/abs/2003.09871
Preparedness and Vulnerability of African Countries against Importations of COVID-19: A Modeling Study
(added 3/30)
Findings Countries with the highest importation risk (ie, Egypt, Algeria, and South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (ie, Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, and Kenya) have variable capacity and high vulnerability. We identified three clusters of countries that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and the city of Beijing, respectively.
https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(20)30411-6.pdf
Mobile Phone Data and COVID-19: Missing an Opportunity?
(added 3/30)
How mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in assessing the effectiveness of control measures such as physical distancing. It identifies key gaps and reasons why this kind of data is only scarcely used, although their value in similar epidemics has proven in a number of use cases. It presents ways to overcome these gaps and key recommendations for urgent action, most notably the establishment of mixed expert groups on national and regional level, and the inclusion and support of governments and public authorities early on
https://arxiv.org/abs/2003.12347
A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 Using Smartphone Embedded Sensors: Design Study
(added 3/30)
New framework is proposed to detect coronavirus disease COVID-19 using onboard smartphone sensors. The proposal provides a low-cost solution, since most of the radiologists have already held smartphones for different daily-purposes. People can use the framework on their smartphones for the virus detection purpose. smartphones with computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, color-sensor, humidity-sensor, and wireless chipsets/sensors. The (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.
https://arxiv.org/abs/2003.07434
Early Dynamics of Transmission and Control of COVID-19: a Mathematical Modeling Study
(added 3/30)
Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.
https://www.thelancet.com/pdfs/journals/laninf/PIIS1473-3099(20)30144-4.pdf
Can the COVID-19 Epidemic be Managed on the Basis of Daily Data?
(added 3/30)
A simple mathematical model of the process and uses well-known results from control theory to prove that the approach taken by China and, to a slightly lesser extent, by Italy can work if the effect of delays is accounted for when taking the decision of the country lockdown, while the approach currently announced in the UK is likely to fail.
https://arxiv.org/abs/2003.06967
New Mathematical Model Can More Effectively Track Epidemics
(added 3/30)
A new model developed by National Science Foundation-funded researchers at Princeton and Carnegie Mellon improves tracking of epidemics by accounting for mutations in diseases. Now the researchers are advancing their model to allow leaders to evaluate the effects of countermeasures to epidemics -- before they deploy them.
https://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=300277&org=NSF&from=news
Using An Neural Network Aided Quarantine Control Model Estimation of COVID Spread in Wuhan, China
(added 3/30)
Epidemiological model driven approach augmented by machine learning, we show that the quarantine and isolation measures implemented in Wuhan brought down the effective reproduction number R(t) of the COVID-19 spread. We warn that immediate relaxation of the quarantine measures in Wuhan may lead to a relapse in the infection spread and a subsequent increase in the effective reproduction number to R(t) >1. Thus, it may be wise to relax quarantine measures after sufficient time has elapsed, during which maximum of the quarantined/isolated individuals are recovered.
https://arxiv.org/abs/2003.09403
Estimating Clinical Severity of COVID-19 from the Transmission Dynamics in Wuhan, China
(added 3/30)
At the population level, determining the shape and size of the ‘clinical iceberg’ above and below the observed threshold (in turn determined by symptomatology, care-seeking behavior and clinical access), is key to understanding the transmission dynamics and interpreting epidemic trajectories. Specifically, delineating the proportion of infections that are clinically unobserved under different circumstances is critical to refining model parameterization. Estimates of both the observed and unobserved infections are essential for informing the development and evaluation of public health strategies.
https://www.nature.com/articles/s41591-020-0822-7
SocioPatterns: Collection of Studies
(added 3/30)
Risk, contagion and related studies in animals and humans
http://www.sociopatterns.org/publications/
International Journal of Health Geographics
(added 3/30)
GIS - COVID-19 and SARS tracking
https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00202-8
COVID Act Now - US Model for Response Variables
(added 3/30)
A Model available as a google spreadsheet and interactive map: designed to answer the following questions:
What will the impact be in my region be and when can I expect it? How long until my hospital system is under severe pressure? What is my menu of interventions, and how will they address the spread of Coronavirus?
The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19)
(added 3/30)
Published 2/17 - China CDC. Data frequently used in dashboards.
http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51
Coronavirus Disease 2019 (COVID-19) Outbreak in China, Spatial Temporal Dataset
(added 3/30)
Daily statistics of the COVID-19 outbreak in China at the city/county level. For each city/country, we include the six most important numbers for epidemic research: daily new infections, accumulated infections, daily new recoveries, accumulated recoveries, daily new deaths, and accumulated deaths.
https://arxiv.org/abs/2003.11716
Pooling RT-PCR or NGS samples has the potential to cost-effectively generate estimates of COVID-19 prevalence in resource limited environments
(added 4/13)
COVID-19 has quickly spread worldwide causing a pandemic. Countries need rapid data on the prevalence of the virus in communities to enable rapid containment. However, the equipment, human and laboratory resources required for conducting individual RT-PCR is prohibitive. One technique to reduce the number of tests required is the pooling of samples for analysis by RT-PCR prior to testing. We conducted a mathematical analysis of pooling strategies for infection rate classification using group testing and for the identification of individuals by testing pooled clusters of samples.
https://www.medrxiv.org/content/10.1101/2020.04.03.20051995v1.full.pdf
COVID-19 Mobility Monitoring Project. The Reduction of Social Mixing in Italy Following the Lockdown
(added 4/13)
The mitigation measures enacted as part of the response to the unfolding COVID-19 pandemic are unprecedented in their breadth and societal burden. A major challenge in this situation is to quantitatively assess the impact of non-pharmaceutical interventions like mobility restrictions and social distancing, to better understand the ensuing reduction of mobility flows, individual mobility changes, and impact on contact patterns. Here, we report preliminary results on tackling the above challenges by using de-identified, large-scale data from a location intelligence company, Cuebiq, that has instrumented smartphone apps with high-accuracy location-data collection software. We focus our analysis on Italy, where the COVID-19 epidemic has already triggered an unprecedented and escalating series of restrictions on travel and individual mobility of citizens.
https://covid19mm.github.io/in-progress/2020/03/25/second-report.html
PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
(added 4/22)
The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information. More generally, we hope to participate in bringing together colleagues in industry, academia, and civil society to discuss and converge on ideas around a critical issue rising with attempts to mitigate the COVID-19 pandemic.
https://arxiv.org/abs/2004.03544
Magsense: Saving Face Project by MIT Media Lab
(added 4/23)
“Don’t touch your face” is a seemingly simple advice. Since coronaviruses are stable for days on many surfaces, a person can get COVID-19 by touching a contaminated handle or object and then touching their own mouth, nose, or possibly eyes. Most people touch their face frequently throughout the day, usually without thinking about it—it’s a very difficult habit to break and requires a surprising amount of conscious effort. The MIT Media Lab is advancing Saving Face: a suite of easily scaled technologies to help people fight the pandemic by warning them when they’re about to touch their faces.
https://github.com/irmandyw/magsense
SmartBand: Saving Face Project by MIT Media Lab
(added 4/23)
This smart band with "face tough" vibrating notifications has many features including:
nRF52 MCU + BLE by Nordic Semiconductor (compatible Arduino)
KX023 3d Accelerometer by Kionix
SSD1306 screen
Heart rate monitor
Vibrator
Capacitive touch button
Battery and charging circuit
Available GPIOs (UART, I2C, etc)
https://github.com/mitmedialab/SmartBand
Computing against Covid-19: Connecting People with Projects to Fight the Pandemic
(added 4/23)
Computing against Covid-19 facilitates connections between groups developing and deploying applications to fight the pandemic and expert developers, architects, and operators willing and able to provide help and support. Hospitals, local community organizations, researchers and others can submit projects of any size and scope to be featured on the site and individuals, both professionals and students are encouraged to join the team as volunteers!
https://computingagainstcovid19.org/
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