Empowering health decisions, delivered by intelligent algorithms to contain COVID-19

PI: Ayan Paul

Deutsches Elektronen-Synchrotron, Hamburg, Germany
Institut für Physik, Humboldt Universität zu Berlin, Germany

The CoVis platform will combine local and global datasets on the spreading characteristics of the pandemic with user data using intelligent machine learning algorithms to deliver the power of decisions to users. These algorithms will also allow us to build a broader picture of the disease spread that can assist in building policies based on local conditions, rather than basing them on uncertainties and best-guess estimates. Our goal is to deliver real-time risk assessment to the users. These unique measures developed by our multidisciplinary team will give the users a means of making informed decisions about their risk of getting infected and being hospitalized due to a COVID-19 infection.

Funding Agency: Deutsches Elektronen-Synchrotron

DESY Strategy Fund (DSF) call for COVID-19 Related Research Projects


  • Apr 5 2020

    The founding of CoVis with a MIT hackathon win

    CoVis is founded at the the MIT “Beat the Pandemic” hackathon proposing real-time risk assessment for COVID-19.

  • May 31 2020

    CoVis wins the MIT hackathon again!

    CoVis wins the MIT “Beat the Pandemic II” hackathon against advanced teams.

  • Jul 2 2020

    CoVis gets the DESY Strategy Fund Grant

    CoVis gets a 100,000€ grant from DESY to build the CoVis app.

  • Sep 23 2020

    CoVis and Sidebench to develop the app together.

    CoVis signs a contract with Sidebench to develop the app together with Sidebench building the frontend and CoVis building the backend.

  • Nov 8 2020

    The building of the CoVis app begins.

    CoVis delivers final designs to Sidebench for the app. The collaborative work for building the frontend and integrating it with the backend begins.

  • Feb 25 2021 goes live!

    The international travel advisory website goes live! Now you have access to the latest international travel restrictions.

  • Aug 1 2021

    CoVis goes live!

    Covis was launched in the Apple App Store and Google Play Store in the USA, Germany and Austria. It has been made available for free.

  • Aug 23 2021

    CoVis is now available in Austria!

    As part of a global expansion plan, CoVis is launched in Austria.

  • Sep 1 2021

    CoVis becomes popular!

    Over 4000 users now use CoVis to know their risk from COVID-19.


Efficacy of Automated Contact Tracing

Study the usage of automated contact tracing as an efficient means of mitifgating the spread of the pandemic.

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.

H. Kim, A. Paul, Automated Contact Tracing: a game of big numbers in the time of COVID-19

Transmission Dynamics of COVID-19

The spreading of the pandemic has followed patterns that involve the spread of the disease and the response for containing it. Can there be some universality in how the disease propagates in different geographies ans cultures?

The complexities involved in modeling the transmission dynamics of COVID-19 has been a major roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods also needs to be built into any effective model for making such predictions. We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The ensuing data collapse renders the transmission dynamics largely independent of geopolitical variations, the effectiveness of various mitigation strategies, population demographics, etc. We propose a simple two-parameter model---the Blue Sky model---and show that one class of transmission dynamics can be explained by a solution that lives at the edge of a blue sky bifurcation. In addition, the data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can also be realized in a model-independent manner as we show using a deep neural network. The methodology adopted in this work can potentially be applied to the transmission of other infectious diseases and new universality classes may be found. The predictability in transmission dynamics and the simplicity of our methodology can help in building policies for exit strategies and mitigation methods during a pandemic.

A. Paul, J. K. Bhattacharjee, A. Pal, S. Chakraborty, Emergence of universality in the transmission dynamics of COVID-19

Impact of socio-economic factors on the spread of COVID-19

We probe the dependence of the spread of COVID-19 on local socio-economic conditions gathered from census data.

COVID-19 is not a universal killer. We study the spread of COVID-19 at the county level for the United States up until the 15th of August, 2020. We show that the prevalence of the disease and the death rate are correlated with the local socio-economic conditions often going beyond local population density distributions, especially in rural areas. We correlate the COVID-19 prevalence and death rate with data from the US Census Bureau and point out how the spreading patterns of the disease show asymmetries in urban and rural areas separately and is preferentially affecting the counties where a large fraction of the population is non-white. Our findings can be used for more targeted policy building and deployment of resources for future occurrence of a pandemic due to SARS-CoV-2. Our methodology, based on interpretable machine learning and game theory, can be extended to study the spread of other diseases.

A. Paul, P. Englert, M. Varga, Socio-economic disparities and COVID-19 in the USA

Bringing predictability in the spread of COVID-19 and a sense of reassurance during the pandemic. Building a platform for the dissemination of acurate and pertinent information to individuals to help make informed decisions.


Automated Contact Tracing: a game of big numbers in the time of COVID-19

H. Kim and A. Paul

J. R. Soc. Interface.18:20200954, DOI:10.1098/rsif.2020.0954

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Socio-economic disparities and COVID-19 in the USA

A. Paul, P. Englert, and M. Varga

J. Phys. Complex. 2 035017 DOI:10.1088/2632-072X/ac0fc7

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Beyond COVID-19: Network science and sustainable exit strategies

J. Bell et. al

J. Phys. Complex. 2 021001 DOI:10.1088/2632-072X/abcbea

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Emergence of universality in the transmission dynamics of COVID-19

A. Paul. J. K. Bhattacharjee, A. Pal, and S. Chakraborty

arXiv:2101.12556 (accepted for publication by Scientific Reports)

Read the article


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