ViraTrace is a contact-tracing solution centered around a unique logical model that allows accounting for multiple degrees of interaction. This means it detects up to 3x more active cases than the standard approach employed by PEPP-PT, DP-3T, Google & Apple, etc.

Recently integrated into the official Indian contact-tracing app "Aarogya Setu"

We have two short term goals:

  1. Get the infection model implemented in more/all contact tracing apps;

  2. Build an alternate technical architecture based on Trusted Execution Environment (Secure Enclave) servers which allows additional data analysis resulting in the following value propositions:

    • Protect high-risk individuals (sick, elderly, etc) by designing a network topology that only allows safe interactions.
    • Account for the immune population (with antibody tests and/or users that had known active infections) when prioritizing commercial/essential activities.

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As the volume, velocity, and variety of incoming pandemic data continues to expand, the ability to leverage this data for actionable insights has become increasingly foundational to modern pandemic response. However, traditional data analysis tools and processes are slow, difficult to use, and resource-intensive, often requiring multiple steps by information technology, or IT, employees, data scientists, and other data workers to complete even the most basic analysis. As a result, these tools and processes are unable to keep pace with the rapid analytics demanded by today’s emerging viral threats.

Our platform democratizes access to data-driven insights by expanding the capabilities and analytical sophistication available to all pandemic response workers, ranging from healthcare analysts to expert programmers and trained data scientists. We bring the fragmented analytic process into one simple and powerful experience, combining tasks that were previously distributed among multiple tools and parties. Our platform allows a single user to access various data sources, clean and prepare data, and perform a variety of analyses. This is done through simple model-driven workflows and an intuitive interface that can eliminate the need to write code and reduce tedious, time-consuming tasks to a few mouse clicks. The resulting opportunity is significant, as our platform can enable governments and organizations worldwide to more quickly and effectively respond to emerging viral threats.

We believe that:

Pandemic Response Needs to Be Data Driven, Creating Challenges and Opportunities The amount of data and diversity of data type, format, and source location are rapidly increasing. More importantly, the variety of data a government or organization uses for pandemic response purposes is expanding.

This proliferation of data has created a significant opportunity for governments and organizations to make better pandemic response decisions, and improve the effectiveness, responsiveness and validity of data-driven decision making. However, looking directly at the current COVID-19 pandemic scenario, very few (if any) government agencies have the right people, tools, data, and intent to derive meaningful, actionable insights from their current data. We direct you to a 2013 survey of over 400 companies conducted by Bain, the purpose of which was to ascertain the ability of those companies to meet the metrics outlined previously utilizing similar analytics offerings to what ViraTrace© is developing for pandemic response. Of those companies surveyed, only 4% had the ability to derive actionable insights from their data. These data-driven companies were approximately two times more likely to be in the top quartile of financial performance within their industries, approximately three times more likely to execute decisions as intended, and approximately five times more likely to make decisions faster. Applying this data to our solution, we believe that there is significant benefit to users of our platform.

Technology Paradigm Shift Creates a Foundation for Reimagining Pandemic Response To manage the volume and variety of data that governments and organizations are now generating and consuming in hybrid environments, both on premise and in the cloud, data infrastructure is undergoing a transformative shift towards next generation “big data” technology.

Technology advances have also created significant improvements in the methods available to analyze massive quantities of data and the rise of programming languages, such as R and Python, and associated open source libraries has broadened access to data analysis.

Collectively, these advancements have created a foundation for significant changes in the approach to data-driven pandemic response, enabling the creation and wide distribution of sophisticated, fast, and easy-to-use analytical tools for healthcare analysts and their organizations.

Traditional Pandemic Response Methods Are Broken As the volume and diversity of data has expanded and evolved at an unprecedented pace, IT departments are struggling to provide the governments and organizations they service with the tools necessary for meaningful data analysis and pandemic response. Traditional methods are often resource intensive, requiring multiple steps and parties to draw analytical conclusions. Further, these traditional methods often separate the individual doing the analysis from the people preparing the data. This “assembly line” approach rapidly breaks down when analyses need to be conducted in near real-time against pandemic data sets that are large, complex, and constantly changing.

Traditional data tools do not offer the sophistication, scalability, and ease-of-use that healthcare analysts need to transform massive amounts of available pandemic data into intelligent, actionable insights. Traditional approaches are:

· Inefficient. Multiple parties and work streams are required to complete a single analytical process.

· Dependent. Activities, such as data preparation and blending, can require extensive involvement from IT departments. More advanced analysis, such as predictive or spatial analysis, is traditionally the domain of a small group of highly trained data scientists using proprietary software and scripting languages.

· Static. Inflexible, pre-packaged and rigid data sets are used, which typically cannot cope with the proliferation of data today.

· Limited. Analysts have traditionally relied on less sophisticated tools such as spreadsheets to perform data analysis.

We have identified our opportunity:

        Our self-service pandemic data analytics platform disrupts well-established portions of the data analytics software market. According to IDC, the worldwide market for data analytics software represented approximately $41 billion in 2015 and is expected to grow to approximately $61 billion throughout 2020. Within the broader data analytics software market, our platform currently addresses the healthcare intelligence and analytic tools, analytic data integrations and spatial information analysis markets, which collectively represented approximately $18 billion in 2015 and are expected to grow to approximately $27 billion by the end of 2020.

And we have developed an innovative solution:

        Our platform enables governments and organizations to dramatically improve pandemic response outcomes and the productivity of their data analysts. Our platform is:

· Efficient. We offer a self-service platform that allows data analysts to perform analysis that traditionally required multiple parties and work streams to complete. Once a model-driven workflow has been assembled, the analysis can be completed in minutes and shared with others who can easily replicate the analysis. With our platform, data analysis is automated, repeatable, and shareable.

· Independent. We enable data analysts to rapidly answer challenging pandemic response questions, without the need for support from expert programmers, trained data scientists, or other members of the IT department through easily understandable tools that have easy-to-configure parameters that do not require coding.

· Flexible. Our platform does not require a pre-packaged, static data set and instead allows the user to create a workflow to securely interact with GDPR and HIPPA compliant source data. Workflows can be easily changed and reconfigured to iterate an analysis and add a new data source or new logic. They can also be easily adapted to conform with changes in the underlying data to repeat the analysis.

· Sophisticated. Our platform provides data analysts an extensive set of analytical capabilities, including allowing users to: access data from a variety of locations; prepare data for analysis; blend multiple data sources regardless of the data structure or format; gain access to the most widely used procedures for predictive analytics, grouping and forecasting; and take advantage of geospatial data.

· Scalable. Our platform offers a secure collaboration environment for even the largest government or organization. Data analysts can create, publish, and share analytic applications, embed analytic processes into other internal applications, and save and access workflows within a secure centralized repository. By pushing analytical workloads to a reliable server architecture, governments and organizations can run compute-intensive processes more efficiently than local machines allow, while automating and scheduling these workflows.

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