COVID Contact Tracing (CCT)

COVID Contact Tracing (CCT) is a CA-based Public Benefit Corporation whose primary objective is to enhance COVID-19 response efforts through existing location data. For public health officials monitoring the spread of this disease, our software platform and database provide vital insights to inform ongoing prevention and containment strategies.

The current digital approaches to contact tracing will not be able to achieve sufficient penetration to be useful in response efforts. Google and other large technology companies already store the precise location data of many smartphone users. Using the appropriate tools and protections, we have made it possible for individuals to contribute this data directly to their healthcare providers and public health officials. 

Our crowd-sourced, anonymized dataset can dramatically increase the efficacy of manual contact tracing efforts, allowing us to reach contacts far more quickly, and therefore limit the transmission of SARS-CoV-2 to frontline workers, vulnerable populations, and the world.

The vast majority of existing digital tracing solutions rely on inter-phone communication through Bluetooth Low-Energy (BLE). Even with the API unified programming interface support from Google/Apple, usage (adoption rates) of these apps remains low. So far in the US, Utah has seen the most success with a tracing app. Their adoption rate is 1.6%. Contrast that with the 60% adoption rate that is needed for the useful collection of data, and it paints a stark picture for this technology. Other challenges are: high battery demands on personal devices; reliance on consistent cell phone signals, and ongoing opt-in for data. The BLE technology powering most digital tracing apps has comparatively little investment behind it.

Conversely, CCT uses existing data collected via some of the most heavily-invested technologies of the private sector (GPS, WiFi). Apple and Google have poured Billions of dollars into the advancement of location-based technology, and have gone to great lengths to make that data available to individuals. The true innovation of our solution is in its simplicity: users opt-in one time (via informed consent) by clicking twice through our portal, and immediately deliver anywhere from 1-3 months of anonymized location data to their health care provider.

CCT is a contact tracing software that relies on informed consent + google takeout to deliver a dataset that effectively tracks the historic and real-time movement of SARS-CoV-2. That data is delivered directly to healthcare organizations who have the option to run our software simulations on top of their technology stack. This anonymized data is protected with the same privacy measures as PHI. CCT never owns or stores any data. 

These are the principal distinguishing factors of CCT’s technology that help explain our process:

1. Immediate practicality through reliance on available, fine-grained location data, already collected via smartphone products

2. Easy-to-use web interface for patients to volunteer existing data

3. Direct location data sharing between COVID-19 patients and their medical provider without a third party intermediary

4. Interoperability across healthcare provider platforms

5. Automated, anonymized and secure reporting between healthcare providers and public health agencies

Our volunteer team of silicon valley engineers has built this solution over the last 8 weeks in a programming design sprint. With two formal Principal Investigators at Stanford, we are on track to complete a study in conjunction with the department of Epidemiology and Population Health proving the effectiveness of our solution. 

In the US and across the world, COVID-19 disproportionately affects those with the fewest available resources. As this pandemic further exposes our socioeconomic divide, it reminds us that solutions must immediately serve those same populations. 

To-date, we have conducted over 50 hours of expert interviews with Epidemiologists, Health Care professionals, Front line workers, Contact Tracers, and Policy Makers. Through our research, we know that the following factors contribute to this disproportionately high rates of COVID-19 in minority populations across the United States: (1) comorbidities, (2) lower-income employment (associated with worse health benefits), (3) a higher likelihood of working in essential jobs with less protection (food services, airports, etc.), and (4) the higher likelihood of living in multigenerational households. Our software will directly benefit frontline workers and vulnerable populations by providing geographically-defined exposure risks, and speeding up contact tracing efforts. 

We believe that community resilience to COVID-19 will come from community collaboration. The intake of data for health care providers is limited to individuals with a smartphone who use location-based applications. ButoOn the national level, especially in high-density urban areas, this does not exclude any significant population subsets.

To move from reactive to proactive, the world needs smarter tools for tracking nascent outbreaks. A very powerful tool already exists today, in the form of pre-collected historic geolocation data that is readily accessible by individuals. Prevention, accurate detection, and rapid response must be informed by better information. CCT is enabling individuals to take control of their own data in order to directly enhance: Contact tracing time efficiency (by 90%); Recall of high-exposure risk contacts (2X competitive baseline); Precision of testing (50% over baseline); Realized population health metrics (10% drop in morbidity). 

While sound in design, BLE apps have repeatedly failed to accelerate the work of contact tracers and create a digital map of where this virus has traveled.  

One of the best available alternatives to our technology is MIT’s Private Kit and Safe Paths efforts, which has ~10,000 installs. Unfortunately, the focus on this solution has been on the ongoing BLE communication between cellphone users, and a 3.1/5 rating in the app store has caused a dramatic slow-down in adoption. Utah’s Healthy Together, Carnegie Mellon’s NOVID app, and countless others have the same flaw in design: relying on active engagement by users.

By using passively-collected, pre-existing data to inform simulations, CCT’s digital COVID response solution is more cost-effective, reliable, efficient, and private than any existing alternative. We have an inherent advantage in a few other ways:

• Utilizing the best components of a centralized and decentralized methodology while retaining privacy protections

• Removal of human biases through clear data visualizations

• Inferred semantic data from Google’s existing algorithms 

• Direction for how public health agencies can act on the aggregation of data (simulation capabilities for where transmissibility is highest, etc)

The technology behind Covid Contact Tracing (CCT) stems from the realization that the data needed to inform successful public health responses to COVID-19 already exists. While the overwhelming focus of technologists is on new technologies to start collecting individuals’ location/proximity data, we believe that the focus should instead be on how to get the immense amount of data already being collected, into the hands of the healthcare professionals that desperately need it.

We are using public APIs to empower COVID-19 individuals to easily export the relevant location history that has already been collected by Google and contribute it directly to their local healthcare provider or public health agency. Individual privacy is tremendously important and CCT is designed from the ground up to offer a much lower level of practical risk to participants than other solutions, which is achieved by leveraging robust systems that are already in place, and by our two fundamental privacy tenants:

1. All data transfer is contingent on express informed consent from the subject of the data. 

2. The only two parties involved in data transfer are the individual that is the subject of the data, and their trusted, local, healthcare provider or public health agency.

Location data from positive individuals is used to power a sophisticated custom COVID-19 simulation that we have developed, capable of simulating mobility patterns of every individual of a major metropolitan area. This model computes the risk that un-tested individuals have been exposed and can inform epidemiological and policy responses to the pandemic.

Our concept has been validated through successful roll-outs in China and South Korea. While these countries have different privacy laws in place (i.e. government access to cellular location data), they are using the same conceptual tracing solutions. In order to better understand the efficacy of this contact tracing strategy, we have interviewed multiple academic leaders involved with the South Korean contact tracing efforts (Professor Sun-man Kwon, Sang-Hun Choe, and Haksoo Ko); chief data scientists from India working on their national contact tracing response, Aarogya Setu, and various machine learning and data privacy experts. The consistent response is that this tactic is proven, effective, and the most likely way to accelerate contact tracing efforts.

Our concept has also been validated by a group of software engineers and epidemiologists who teamed up in 2018 to develop a machine-learned model that identified sources of foodborne illnesses. They used similar datasets, including anonymous and aggregated location data (as well as web searches). Researchers were able to identify unsafe restaurants by comparing people’s location data (where they had been) with later search terms associated with food poisoning symptoms. With this data, the researchers reliably identified restaurants with insufficient food safety practices. This rapidly accelerated the efficiency of corrective actions and slowed the potential spread of foodborne illness. The findings from this tactic were later published in Nature.

COVID-19 has transformed life in virtually every country around the world. However, while tens of thousands of people are still contracting Coronavirus in the United States every day, and nationwide there has been little progress from the peak of the pandemic, there are some countries that have successfully controlled the disease.

The successful public health responses in these countries are defined by a successful application of test, trace, and isolate methodologies, and the corresponding failed American response can be attributed to a failure to effectively implement this approach. A key element to successful responses in countries including South Korea, China and New Zealand is the availability of individual location data to public health agencies.

Covid Contact Tracing has developed tools to enable American public health agencies to access the same kinds of data that have informed successful responses overseas, and to do it in a way that is consistent with American values of digital privacy. CCT solves this problem by providing simple-to-use tools for COVID-19 individuals to take control of the location data that has already been collected about them by large technology companies, and to contribute that data directly to their local healthcare providers and public health agencies.

Based on interviews with healthcare providers and patients that have tested positive for the disease, we believe that if COVID-19 positive patients want to volunteer their retrospective location histories and be part of the solution. The more people appreciate the effective public health responses of various international examples, the more eager they are to be part of the US response. 

Specifically, access to fine-grain semantic location histories of COVID-19 positive individuals will enable more scalable and effective contact tracing workflows than is currently possible, and will yield better informed decisions by policymakers and better insight into the basic science of COVID-19 transmission.

A more effective public health response is, as again has been demonstrated by international examples, the key to saving tens of thousands of American lives, and returning the economy to normal as soon as possible.

Currently serving: 0

Number of people served in 1 year: 500,000

Number of people served in 5 years: hundreds of millions

Because of the global implications of slowing the spread of COVID-19, these are extremely difficult numbers to estimate. In theory, if we are able to measurably slow the spread of this virus with our solution, we could see a global economic recovery that helps millions of people return to work and their normal lives. 

The best way to think about this is the different tiers of people that our solution will serve. It begins with COVID-19 cases and the contact tracers working to follow this outbreak. If the US has 1.6 million confirmed, unrecovered cases at the moment, our work would directly benefit these patients and their networks, as well as all of the 3,000 DPH across the US. DPH workers, governments, businesses and individuals are all categories of people our solution could benefit. In five years’ time, our reach is limited only by the spread of the virus.

It is the intention of CCT to inform the playbook for pandemic response efforts. We believe that digital tools are integral to ongoing efforts to prevent the spread of influenzas and contagious epidemiological diseases. By not integrating these tools into our response efforts, we would be doing humanity a significant disservice. 

The use of quarantine and other types of restrictive measures for controlling epidemic diseases has always been controversial. Such strategies require a careful balance between public interest and individual rights. They also invariably result in political, ethical, and socioeconomic disruption. Data privacy has been a growing issue of concern over the last decade. As such, large data companies have been reluctant to cooperate with government or third parties. Google and Apple, the current stewards of geolocation privacy data, could decide to restrict personal access to geolocation data, or make it’s collection more difficult. 

The largest barrier to accomplishing our goal is individual opt-in rates. If people are unwilling to provide informed consent to volunteer their data, our solution will not be as successful as we expect.

The cooperation of public health departments is another barrier to success. Achieving our ultimate goal by using data technology to save lives in the wake of global pandemics assumes that federal and state governments are interested in building an effective playbook for the control of epidemiological outbreaks going forward. Currently, a lack of leadership from federal and state-level governments has discouraged progress towards a sustainable, perdurable, digital contact tracing solution. 

Data privacy: CCT’s software was designed from the beginning with inherent privacy protection methods. By using systems already in place (data from Google and Apple), and by avoiding the storage and aggregation of that data in exposed locations, we dramatically reduce associated privacy risks. To reiterate, the only two parties involved in data transfer are the individual that is the subject of the data, and their trusted healthcare provider or public health agency. Beyond those protections, all data is stripped of any personally identifiable information. If we are able to effectively communicate these advantages, we will simultaneously be solving for the second identified hurdle, adoption. 

Adoption: People don’t download apps, so we didn’t build an app. We built a portal that can be inserted into existing protocols at testing sites, doctors offices, corporate “back-to-work” health surveys, and more. As a general rule, the simpler the UX, the higher the adoption rate. By partnering with Stanford, UCLA, Carbon Health, Color Genomics, and other established operations, we can reach thousands of COVID-19 patients who are interested in fighting this virus.

Government/DPH cooperation: In this moment of uncertainty, we believe that leadership will follow the best solution. Millions of dollars are being spent on tools and solutions that do not fundamentally improve the way we respond to pandemics. For very little money, our solution amplifies all of the tracing and prevention efforts currently underway. We understand that the onus is on us to prove this technology before its full potential is realized.

List Internal Team Members: 

Devon Proctor MBA - Founder and CEO

Otavio Good - Lead Engineer

Vahid Kazemi PhD, MBA - Lead Engineer

Jared Sun MD, MBA, PhD - Lead Medical Advisor

Jessica Hinman MS, PhD, - Advisor, IRB Study 

Lorene Nelson MS, PhD - Principal Investigator, IRB Study (Associate Professor, Stanford University & Faculty Director of Research, Stanford Center for Population Health Sciences)

Haley Proctor - Head of Finance

Kelly Shaffer, MBA - Director Marketing (GTM)

Forrest Carroll - Director, Business Development

Additionally, we have 15 part-time volunteer team members with varying backgrounds and expertise. 

Collectively, our engineering team has over 40 years of experience working for Google and other large tech companies. In fact, this effort was founded with the thought exercise of "how would we solve this if we were at Google, unencumbered by public relations?" Many current representatives at Google have stated, outright, that this is the exact manner in which they would approach this challenge if it didn't hurt their public image. 

External partners who have pledged support or are actively supporting work on this project:

Stanford Department of Epidemiology and Population Health

Jessica Hinman MS, PhD, - Advisor, IRB Study 

Lorene Nelson MS, PhD - Principal Investigator, IRB Study (Associate Professor, Stanford University & Faculty Director of Research, Stanford Center for Population Health Sciences)

Michael Halaas, Chief Information Officer and Associate Dean, Industry Relations and Digital Health at Stanford School of Medicine

We are currently working with the UCLA school of medicine as well to bring forth a similar trial. 

Our business is funded by DPH. Our operating costs will be covered by a portion of the capital that has been made available for COVID19 response efforts. Specifically, we intend to contract with different hospital systems who will request funding from their local DPH in order to pass through payment to CCT. 

A further look at our business model canvas and the various stakeholders of our solution can be found here: https://docs.google.com/presentation/d/1TdTYup1sYFMumponsBtxaH0gQ0lauIE5KRHKzpkAnv0/edit#slide=id.g84b04ebfca_1_84

As a public benefit corporation, we intend to operate under a for-profit business model that earns revenue from the licensing of our software technology. A lot of the most expensive engineering work has already been conducted, funded through in-kind grants from our engineering team and supporting staff.