Altering the spread of the COVID-19 Virus through Contact Avoidance

Altering the spread of the COVID-19 Virus through Contact Avoidance


Matrics2 has developed a contact avoidance system (CAS), not to be confused with current contact tracing apps currently in use or being developed (such as with Apple and Google), that we think could end the COVID-19 pandemic allowing the world governments to relax their shutdown sanctions and re-start their economies while revealing no personal identity information making it HIPAA compliant.  Much is being reported in the press currently about how to re-open without repeating the lessons from the 1918 Spanish flu where the pandemic rebounded after opening up from shutdown sanctions prematurely and only ended because of the 1/3 of the world’s population that became infected, 50 million died and the remainder developed “herd” immunity.   

What do we know about COVID-19 to date?  First, it is virulent and spreads like wildfire.  On average in the US unabated spread is that if you start off with 1 infected person on day 1, by day 14 there will be 15 infected persons.  But that is the average.  In China, a 63-year-old infected man having dinner in a restaurant ended up infecting 9 fellow diners before the evening was over. Secondly, it is sneaky.  In China 44% of the cases spread person to person before symptoms appear.  In Iceland 43% who tested positive didn’t have symptoms at the time of test.  You are most contagious 2.5 days before symptoms appear with peak contagion 17 hours before symptoms.  A majority of those with symptoms have no fever.  These insidious behaviors of COVID-19 limit what can be achieved with contact tracing, calling for the need of the additional capability that CAS provides.  The assumption should be that if you have been in contagious proximity to someone infected that you have been infected until proven otherwise with testing and your previous contacts should be immediately notified, as CAS does.


CAS stops the source of infection by preventing infectious contact with infected persons, including asymptomatic carriers. Contact Tracking (CT) approaches are a step behind, tracking infections after they occur, slowing the spread of COVID-19, while CAS stays a step ahead by stopping infectious contacts before they happen, which is the only way to stop the spread of COVID-19.   

An analogy is that COVID-19 is an express bullet train that is fueled by contagious contacts and CT is the local train.  From an Apple-Google effort web site, Bob talks with Alice and a few days later Bob is tested positive for COVID-19 and all of Bob’s CT contacts are notified, including Alice (who may be the asymptomatic carrier who infected Bob), who gets instructions on her phone of what to do next.  The local train just stopped at the station, and the COVID-19 express bullet train just roared past at 300 mph to feed on the continuing infections that occur from Alice and her infected contacts until they are eventually tested and quarantined.  CAS wires ahead by immediately flagging Alice with a red flag, and her contacts with orange and yellow flags, alerting people not to approach them avoiding further contagious contacts, depriving the COVID-19 express bullet train of its fuel, slowing it to a stop.


Now imagine walking into the grocery store and seeing stick figures with no faces throughout the store holding onto balloons of different colors, the colors red, orange, and yellow representing various degrees of exposure to someone recently diagnosed to be infected with COVID-19 providing you the visual warning to stay a safe distance away.  This is basically what CAS does: It provides knowledge to avoid contact with those recently exposed to the virus.  Those with blue balloons have had no significant recent exposure and are not likely to be infected. These alert colors will be furthered explained in the attached white paper.  To begin with, before the spread comes under control, there will be a lot of balloons in the air, but with time as the spread comes to a halt, there should be no balloons left in the air except for blue ones and green ones, the green ones indicating anti-body immunity making the holder of those balloons safe to approach and to re-enter the work place to re-start the economy.  


The Matrics2 CAS involves an Interface App (IApp) downloaded onto your smart phone with its own unique App Random Number (ARN) which serves as its identity to interface with a Random Number System Data Base (SDB) and other downloaded IApps on other smart phones protecting not only you but also your personal identity making it HIPAA compliant. What it does that contact tracing apps don’t do is provide an alert on your smart phone when in the vicinity of a smart phone belonging to someone either infected by the COVID-19 virus or someone who has been in close contact with someone infected, including asymptomatic carriers.  This alert warns you to avoid contagious contact with that person, keeping you safe and preventing the continued spread of the virus.  If an app already in use is not providing this essential function then it is just a contact tracing app.


Matrics2 has the know-how but not the resources to deploy CAS in a timely manner so are offering our technology for this effort as a public service to those who can accomplish it also as a public service.  The key is to make this offer known to those who do have the wherewithal to make it happen, which is why we are sending this package out which fully describes our approach.  We appreciate your wide distribution of it to all your contacts, urging them to do the same until eventually it will hopefully get into the hands of someone who can quickly deploy it either in a country’s government or business sector.


Matrics2 is pursuing a patent for the approach and it is our goal to provide a royalty-free license to whoever provides the resulting capability to end the pandemic as a public service.  

 

The Matrics2 Founders


Michael R. Arneson, CEO, mrarneson@matrics2.com
William R. Bandy, CSO, wrbandy@matrics2.com

 

The Math Behind the Matrics2 Claim to be Able to Stop COVID-19 with CAS
Recent articles on “contact tracing” explain the importance of identifying infected persons so that they can be tested and quarantined to remove them from infecting others, slowing the spread of COVID-19. But this approach is always one step behind the curve and so alone won’t be able to stop the spread of COVID-19. The key step is preventing contagious contact to begin with, which will stop the spread of COVID-19.  This statement becomes obvious from the basic equation that drives the growth of any pathogen epidemic: 

 

1) Ni/Nc =R0 where R0 is the number of people infected by a single contagious person overhe contagious period.  Ni is the number infected by Nc contagious people over the contagious period.   R0 represents the number of contagious contacts a person has while contagious and so is situational.  For a contagious person completely isolated and who has no contagious contacts, R0 is zero, but if the person lives in a crowded building, or is in a restaurant, at a beach party or church service, R0 could be ten or more during a period of time of close contagious contact.  For COVID-19 the average value for the US appears to be around 3 over a contagious period of 14 days.

 

The requirement for an epidemic to die out is:

 

2) R0<1 as during the contagion period Ni will be less than Nc.  For R0=0, Ni=0 and the spread will stop essentially suddenly.

Contact tracing apps notify people who have been in proximity to someone just diagnosed to be infected by COVID-19 so that they can be tested and if found infected can start quarantine. If found to be infected, notices are sent out to people they have been in proximity to for the last 14 days for testing. And so on down the contact “chain.” This process works because it gets contagious people out of circulation.  After exposure to the virus, it takes an average of 5 days before symptoms appear.  Adding a day for testing and notification, people exposed to the person could be exposing others from a period of anywhere from 6 days previous to just 1 day previous for an average of 3 days.  Therefore, a total average exposure time before quarantine begins is assumed to be 4 days for this analysis. If everyone (100%) uses the system, then:

 

3.) R0=3*4/14=.86 which is less than 1 and the COVID-19 spread will stop.

However, the above assumes that everyone when notified gets tested then quarantined if required, and if infected notices will be sent to those they have been in close contact with to have those people tested, and so on.  If exposed people either don’t or can’t get tested the notification chain stops and the pandemic continues to spread.  The fault line with contact tracing is the availability of immediate testing or the lack thereof.  Even with perfect testing availability it would take over 93% usage for R0<1 to stop the spread, an unlikely number.  A more likely number may be 50% for which:

 

4) R0(CT)=(3+.86)/2=1.93 which is greater than 1 and the pandemic continues to spread.


CAS works because it doesn’t wait for people to get tested.  Once diagnosed with the disease, notifications are sent to those in immediate contact, to those in contact with those in immediate contact, down the contact chain as explained further in the attached white paper.  Exposure alert flags are set according to the extent of exposure so that others can avoid contact with these people until they do get tested and the alert flags either cleared or set to black indicating disease infection.  In this way CAS gets out in front of the spread and prevents contacts to begin with. If everyone (100%) used CAS then R0 will be essentially driven to zero stopping the spread of COVID-19.  Again, a more likely number is 50% for which:

 

5) R0(CAS)=(0+1.93)/2=.965 which is barely less than 1 but the virus stops spreading.

Equation (5) derives from the fact for the 50% of the time CAS can’t prevent infectious contact, it can still track the exposed contacts with 50% utilization of that given by equation (4).


Now add the default alert feature so that when the IApp attempts to establish a Bluetooth link with a phone in proximity and it fails to do so because the phone doesn’t have the IApp downloaded (or is turned off), it provides an “unknown” alert status warning to remain at distance, avoiding contagious contact proximity.   Any effectiveness at all with this feature would drive R0 further below 1, as would, of course, any additional increase in people using CAS.


As important as contact tracing is for slowing the spread of COVID-19, it can’t stop it. The Matrics2 Contact Avoidance System, CAS, will have to be applied to stop it. Even 50% utilization would provide a known exposure status for 50% of the population providing them the “ticket” for freely moving about, allowing the economy to start opening up.  The remaining 50% not using it may not enjoy such free movement until they get their “ticket” by downloading and using the CAS app.  If a majority of people used it the economy could operate at 100% once again.

 

Comments on ACLU White Paper “The Limits of Location Tracking in an Epidemic”

 

Key point on their introduction to the key questions: We don’t use location tracking for CAS, which is the issue they are addressing.  Now for their Key Questions:

1.    What is the goal?  Primary goal is contact avoidance with infected persons.  Secondary goal is notification of contact with an infected person.


2.    What data? Totally anonymous with the IApp Random Number (RN) used in place of any personal identifying information with the database.  What is stored in the database is the IApp RNs of two phones in Bluetooth connection range along with distance between the phones determined by audio signature signal timing and the connection time.  No other information, such as location, is obtained or stored.

3.    Who gets the data? No one.  The time and distance data are used to calculate a connection strength factor with best guidance parameters which is used to set an alert flag that is sent to an IApp in proximity of an IApp of an infected person, or to those IApps that have been in proximity to an IApp of an infected person.


4.    How is the data used?  See #3 above.  It is used to avoid contact with an infected person or to be notified of contact with an infected person.  It is used for no other purpose.


5.    What is the life cycle of the data?  The disease infection period which for COVID-19 is 14 days, at which point RN connection data is erased.

 

 

Now on to their Proposed Use Cases:

 

1.    Mass Location Data.  We agree that this data in not accurate or reliable enough to determine distance between two smart phones which is why we use audio signature signal timing to determine the distance between them.


2.    Specific Location Data.  We think this is way too intrusive to even consider.


3.    Aggregate Location Data. Our approach doesn’t use, gather, or store such data.


4.    Enforcement.  Our CAS alert system doesn’t require law enforcement to prevent contact with infected persons.

 

Finally, for Other Possible Concepts and Models:  That would seem to be CAS.  According to their document, CAS would appear to be the solution they are looking for. Of course, all these points relate to fundamental privacy issues:

 

Privacy and security issues are limiting the adaption of contact tracing apps, so will be next discussed.

 


Database Security

 


As explained in the attached white paper the CAS database will contain the IApp ARNs, distance between the phones, and contact duration.  It will by necessity have the information needed by each IApp ARN how to connect with the smart phone on which the IApp is installed.  The database can be secured but the question remains can it still be “hacked” and who has the “key” to unlock the secured data.  Authoritarian governments could use phone tracking data along with this connection data to determine that a citizen was at a protest rally, whereby the citizen is arrested, thrown in jail and tortured.  Contact tracing apps such as those being developed by Google and Apple keep the data local to the phones, which helps with this issue but limits its functional usefulness.  The Matrics2 contact avoidance system requires a central database to provide for this key functionality.   


In order in remove extreme misuse of data from the discussion with other privacy concerns, Matrics2 will assume the CAS will be implemented on its own device, entirely separated from a smart phone, running autonomously without any information at all from the user who happens to be carrying it.  The device will be distributed randomly, such as from a basket or shelf, where a person picks it up, carries it home, turns it on, and it starts operation.  No personal information is revealed at all in this acquisition.  To prevent simple theft from a person along with identifying information, the device will be PIN or otherwise protected as with a smart phone.  Such use would be in the spirit of a public service for a mandated government emergency where a trusted not for profit organization or foundation would make the devices available.

 

The question is then what could be gained from hacking into the database which now has only random numbers, distance information between devices, and connection duration, along with IApp contact information to the device.  No device location is obtained or stored or the time/date of a connection.  No one would have any means to correlate such data to any particular person using the devices.  
The separate CAS device described above resolves all the possible data security issues, but not all the privacy issues as will be highlighted below.  Future specific implementation analysis will address the open question: Can CAS operate on a smart phone with the same security as operating on its own separate device?  But for now, the white paper avoids this issue by interpreting that the IApp is operating on its own separate device, even though the term “smartphone” is still employed.    

 

Privacy


Now that I know that my CAS device can’t reveal my location and doesn’t know where I have been or anything else about me, the focus shifts to the privacy issue of my CAS device flagging my COVID-19 infection exposure status to nearby other CAS devices to provide a contact avoidance alert so that others avoid contagious contact with me.  A key issue to be resolved in this regard is should I have control over whether my flag status is used or not to alert someone in proximity to avoid me. Two questions central to this issue are:

1. Does my choice infringe on the rights of others, such as the right to live and to not be discriminated against? 

2. Does not having a choice infringe on my rights, such as entering my place of employment or worship, or a restaurant, theater, or stadium, or some other place where my flag status could deny me entry?

If given the right to choose, I may choose to turn my flag status off if I am near someone I don’t like the looks of, infringing on their right not to be discriminated against.  I might want to enter a place or establishment that I would otherwise be denied access to because of my flag status as possibly being infected, and if as a result I end up infecting someone who later dies of the disease, I have certainly infringed upon that person’s right to live.


If not given the choice, my flag status of being possibly infected will deny me access to certain places.  But this denial is temporary and can be rectified with testing which will either clear the flag status as being not infected or send me to quarantine where I belong if infected.   


Given these alternative Matrics2 thinks the choice is clear, the flag status is not optional.  Users will have to engage CAS with the implied agreement that if I save your life you will save mine and any inconvenience in doing that is temporary.  The clear advantage to all is stopping COVID-19 and allow life to return to full normalcy.  However, users always have the option of turning the CAS app off.

 

Matrics2 Contact Avoidance System (CAS)


Stopping the COVID-19 epidemic, saving lives, and restoring financial health

 

The COVID-19 novel corona virus is wreaking devastation on the world causing death and economic misery to all the citizens of the planet.  Currently the only known way to contain the rapid spread is through “lock down” preventing people from coming into contact with one another. While effective it comes at high economic cost and once the number of new infections and deaths start declining the lock down has to remain in place to prevent a resurgence, which is not sustainable.  The ultimate vaccine solution is still at best a year away.  Self-quarantine would be an effective tool to use but symptoms don’t start, if at all, until 4 or 5 days after infection, allowing the carrier to infect others.   Indeed, asymptomatic carriers may be the majority of the source of new infections. The Matrics2 Contact Avoidance System (CAS) will not only stop contagious contact but will also identify these asymptomatic carriers allowing them to self-quarantine to stop spreading the virus.  With this double-barrel impact, CAS has the potential to stop the COVID-19 epidemic, saving countless lives, and re-starting the economy. 

What it is:


It involves an Interface App (IApp) downloaded onto your smart phone (or device) with its own unique App Random Number (ARN) which serves as its identity to interface with the Matrics2

Random Number System Data Base (SDB) and other downloaded IApps. It serves as your “avatar” in the world, protecting not only you but also your personal identity making it HIPAA compliant.  What it does for you, your family, your friends, and colleagues, co-workers and neighbors:

1.    It provides an audible, visual, or vibrating alert on your smart phone (or device) when in the vicinity of a smart phone (or device) belonging to someone either infected by the COVID-19 virus or someone who has been in close contact with someone infected, including asymptomatic carriers.  This alert warns you to avoid contagious contact with that person, keeping you safe and preventing the continued spread of the virus.  This feature is key to stopping the spread of COVID-19 and no one else is currently providing it including current contact tracing apps. If your IApp doesn’t get a response from the other phone it can set a default flag warning to stay at distance.


2.    If you come down with the virus, the SDB will alert through all their IApps the people  you have been in contagious proximity with in the past 14-day contagion period so that they can watch for symptoms, get tested, and self-quarantine, stopping the spread of the virus.  One of those people could be the asymptomatic carrier who infected you and who could then get tested and be discovered to be positive for the virus, stopping that person’s continued contagion. No personal identity is exchanged, just the ARNs. All contract tracing apps do this basic function, but they all stop here.  CAS performs the next key step by flagging the ARNs of these people in the SDB, so that their IApps will provide the contact avoidance alerts to nearby other IApps.  Further, the contacts of those persons will be also notified and flagged. This contact avoidance alert chain is what stops the spread of COVID-19.

 

3.    It notifies you through your IApp of recent contact with someone now symptomatic or tested positive, you can be tested or self-quarantined even if you are not yet symptomatic. This solves an insidious problem with COVID-19 where you can be infected and infectious days before becoming symptomatic which is a major contributor to the spread of the virus. All contact tracing apps perform this function but stop here.  CAS does the next essential step in “flagging” your IApp so that it provides the contact avoidance alert with nearby other IApps.  Further, the CAS notifies your contacts, setting their alert flags, so that they can be tested and quarantined if necessary.  Again, no personal identity is exchanged, just the ARNs.

 

4.    If you are anti-body tested to be immune, you become “green flagged” in the SDB which informs people in your proximity through their IApps that you are safe to approach.  This allows you to go back to work, helping the US hang out the “open for business” sign and get the economy moving again.  This feature is unique to CAS and is not provided by other approaches.

 

A simple example will clarify how the CAS process works:


3 days ago: Person A, an asymptomatic carrier infects person B.


2 days ago: Person A infects person C and person B infects person D.


1 day ago: A infects E, B infects F, C infects G, and D infects H. Person B is tested positive and alerts are sent through the CAS system connection chains with the warning to self-quarantine and get tested and their alert flags are set to red, warning those in proximity to stay a safe distance away, stopping further infection:

 

1.    B chain: B alerts A, D and F


2.    A chain: A alerts B, C and E and all A connections from previous days.


3.    C chain: C alerts G


4.    D chain: D alerts H

 

Today:  All chain connections above are tested positive and are quarantined, stopping the spread including the asymptomatic carrier who unknowingly started the chain of infections.

Note that the example is for the number of infected persons doubling every day, a very aggressive spread, which CAS tracks!  This most essential feature of CAS is that it keeps pace with the viral spread, which allows it to stop it.  The CAS mantra is “Stay ahead of the spread!”


CAS can be either government mandated or a “crowd” app, such as WAZE, or something in between, with self-reporting motivated by staying healthy, keeping your family and friends healthy and getting back to work and to that favorite restaurant of yours.  Widely adapted, it can stop the epidemic and re-start the economy.  Despite the safeguards, some may still find it overly intrusive to their personal lives and the use of it may be in the spirit of public service to help stop the pandemic.  A participation of 50% is all it would take to stop the spread of COVID19.  Any participation short of this would still help slow the spread until the magic bullets of a vaccine, anti-viral drugs, and ubiquitous testing arrive on the scene.  It is urgent for the public to embrace any app product that can help slow the spread, and Matric2 urges the immediate use of contact tracing apps currently available, such as the one from NOVID20.
Before explaining how it does that, first the basics.

 

The COVID-19 Pandemic:

 


The epidemic ravaging the world is driven by one simple principle:


1. Ni=R0Nc or Ni/Nc =R0

 

Where Nc is the number of ill and contagious people and R0 is the number of people who will be infected by one of these during the infectious period of the illness.  Ni then is the total number of people who will be infected during this period.  If Nc=1 then Ni=R0.  For example, if R0=2, then Ni=2 for Nc=1 from equation (1) increasing the number of contagious people to now Nc=2 and Ni will be=2*2=4 from equation (1).  Now there will be Nc=4 contagious people which gives Ni=2*4=8 people infected.  You see what is happening here.  The number of infected people keeps doubling every contagious period.  This is the classical exponential growth curve everyone is dreading with COVID-19.  Unchecked, it is unbounded as happened with the 1918 Spanish Flu which even with social distancing basically ended because those infected either died (50M) or developed immunity.  For COVID-19 the infection period appears to be 14 days.


COVID-19 is so novel that adequate data has yet to be established to define R0 by population groups, age, state of health, etc. and testing has yet to catch up to establish a reliable data base for the number of people actually infected.  The behavior of the disease varies widely across the world, the rate of infection death doubling from anywhere from every 1 day to 14 days depending on the country.  The US death rate has been doubling every 3 days.  This uncertainty has driven wildly different predictions of the impact of the disease and the value of R0.  One thing universally agreed upon is the necessity to slow its progression.  To consider that, we need to add a term to equation (1):

 

2. Ni=CR0Nc, or Ni/Nc =CR0 where C is a contact factor.


If contagious persons don’t come into contact with others to infect during the infectious period, C=0, and by definition Ni=0 then the disease can’t spread.  If C is reduced to and below the value 1/R0 the disease can’t spread as Ni is less than Nc and will, therefore, die out over time.  Therefore, the key to controlling, slowing, and stopping disease progression is getting C<1/R0.


While equations (1) and (2) are useful to explain the basic concept of infectious growth, the disease doesn’t just increase by the factor CRo over the infection period, but rather continuously, every second, every minute, every hour, every day.  Because disease progression is reported every day, the model uses daily growth:


3. Ni/Nc =CR0/14 every day for a 14-day infection period.

 

The model calculates equation (3) on a daily basis using standard Excel spreadsheets and accounts for the fact that 14 days after infection those persons no longer contribute to contagion growth.  The model adjusts the value of R0 to provide for the doubling of the death rate every 3 days, assuming a 1% fatality rate from the disease.  The adjusted value is R0=3.  The fatality rate considered is on the low side of that reported, but reported numbers are based on known cases, which are undercounted by an unknown amount because of the lack of testing data.   The values of R0 and fatality rate are model input parameters and can be changed for creating other scenarios.  Model intent is not prediction but rather to show cause and effect of reducing the value of C, and the model is a reasonable representation for this task.


Right now, without a vaccine, there are three ways to slow or stop COVID-19 growth by reducing the contact factor, C.  These will be considered separately breaking the C-factor into three separate components:

 

4. C=CqCsCa so that Ni/Nc =CqCsCaR0/14


The first, Cq, is with personal behavior, to self-quarantine when symptoms become evident, which unfortunately could be 3-5 days after infection, and many people are asymptomatic.  The quarantine period has been agreed upon to be at least 14 days, which appears to be the contagion period.  While this approach is effective in reducing the infection rate, because no widely available test exists today to determine infection, it currently can’t stop the spread.  If a reliable and easy to use home test becomes available that can determine infection within a day or two after it occurred, then the value of Cq could be lowered to the point where the disease spread is terminated.  An outcome of CAS is self-quarantine, which will also lower Cq to the value, with CAS contact avoidance, that terminates disease spread. 

Figures 1-3 show model results for self-quarantine starting day 8 of the 14-day infection period compared to unabated growth of the disease.  The numbers on the vertical axis for unabated growth show why this disease is so frightening.  Fully 98% of the US population becomes infected with over 3 million deaths within four months!  The partial self-quarantine results are 61% of those numbers which is significant, indicating self-quarantine is an effective tool to be deployed particularly as enabled by CAS and is also basically the outcome for contact tracing efforts such as being developed and Google and Apple and available now from NOVID20.  It is a desired result, but not enough by itself to stop the epidemic.


The second way, Cs, to reduce the contact factor is by government mandate for social distancing which has a huge financial price tag by basically keeping people isolated at home and shutting down the economy.  This scenario is shown in figures 4-7 with the blue curve. It is a difficult and painful process requiring lock down and isolation mandates with a 75% shutdown leaving open only 25% net average across the US. However, it limits deaths to less than 500,000 with 14% of the US population infected.  Figure 6 shows what happens if an attempt is made to loosen the shutdown and the isolation timeline. The infection and death numbers start ramping up again.  Once shut down and isolation begins within the country it will have to remain that way until something happens to keep the value of C low, such as a vaccine, a home test kit to enable immediate self-quarantine, and/or CAS which both prevents contact with infected people and takes infected people out of circulation with immediate self-quarantine.  

Already there is unrest in the populace for re-opening the economy, but doing so without a means for keeping the COVID-19 spread under control will repeat the disaster of the 1918 pandemic which rebounded fiercely after unfortunate loosening of shutdown restrictions.  Matrics2 recommends educating the populace that to allow any re-opening will require using contact tracing apps currently or soon to be available such as from NOVID20 and the Apple-Google effort to slow the spread until CAS is available to stop the spread.  


The third way, Ca, is by informed contact avoidance with infected people that current contact tracing apps don’t or can’t do.  With CAS, a person approaching someone either infected or who has been in contact with someone infected would get an audible, visual or vibrating alert on their smart phone warning them to maintain a safe distance from that person.  In any particular contact avoidance occurrence Ca=0, but on the average a 50% CAS effectiveness that also enables self-quarantine will quench the epidemic.  


The results of applying the M2 CAS are shown in figures 4-7 along with the shut-down curves. The main impact now of getting CAS operational is stopping the COVID-19 epidemic allowing the safe 100% re-opening of the US from the shutdown as shown in the figures. If the M2 CAS could be operational in about 60 days, it would save almost 24,000 lives and allow the shutdown to be released to regain full openness three months later. 

These timeline scenarios are highlighted in figure 7.  The grey line shows CAS ramping up to 50% effectiveness and remaining there, also enabling a net 50% effectiveness with self-quarantine.  The red and blue overlay line represents ramping down to a net average US 75% shutdown leaving 25% open.  This shutdown stops the growth of the epidemic as shown in figure 6.  CAS keeps it stopped, allowing the US to open back up to 100% operation as shown by the red line.  The dark blue line shows the attempted ramp up to 80% open without CAS in place, which allows the epidemic to roar back with exponential growth as shown in figure 6 with the dark blue line.  Even if shutdown extends past the peak of infection and death rate, it will have to remain in place until something like CAS is deployed to keep the pandemic controlled. At that point the shutdown can be allowed to be relaxed and then totally removed.


These model scenarios are created with the “% openness” and “% CAS effectiveness” parameters and can be used to create other scenario possibilities, such as the one shown in figures 8-10 with CAS operational at the beginning of a novel virus outbreak, which immediately quenches it to prevent it from becoming an epidemic.  Only 63,000 people become infected with 630 deaths. This scenario argues for keeping CAS operational as a tool to prevent the spread of any contagion that might occur in the future. 

 

What the M2 CAS is and how it works:


CAS would be a national resource and would ideally be used as a US government effort, perhaps hosted by the Center for Disease Control or the World Health Organization (WHO).  It could be a standard app pre-loaded or downloaded during any software upgrade on any new smart phone and activated by any country’s 911 Emergency Call Center when a national emergency is declared to deal with an impending novel pathogen epidemic. However, a crowd sourced approach, such as WAZE might also work, with a highly motivated populace using it for infection avoidance for them, their family, friends, co-workers, and neighbors and to remove shutdown restrictions to re-start the economy. Regardless of who implements it, a Random Number (RN) CAS database (SDB) will need to be established and hosted and a special purpose SDB Interface App (IApp) developed and distributed.  For the discussion below, it is assumed that CDC is operating the CAS.

 

With the arrival of a new pathogen, such as happened with COVID-19, with lethality warranting a national or world health organization  response, it will be mandated by each individual country’s CDC for everyone to download onto their smart phones the IApp from a hosted country’s CDC website in association with the SDB.  The IApp will be downloaded with a unique 32-bit RN (ARN) that is used for IApp identity with the CAS.  A 32-bit RN provides 232=4.3B possible unique numbers.  More bits could be used if desired or needed.  Upon download, the IApp will enroll the user as the person authorized to use the IApp by acquiring unique identity, such as with facial, voice and/or fingerprint recognition, and a PIN, such as is common with iPhones and Android phones.  At this point, the IApp serves as your “avatar” in the COVID-19 world, protecting not only you but also your personal identity making it HIPAA compliant.  The only identity provided to the SDB is the ARN and only your ARN can access its SDB files.  Each ARN is associated in the SDB with information required to communicate with the phone on which it is downloaded.


Alternatively, as discussed earlier, CAS could be its own standalone device, freely distributed by the CDC or a not-for-profit at the beginning of a pandemic, for optional use, the incentive for using it being the freedom of movement without constraint.

 

The IApp enables phones (or devices) in proximity through Bluetooth to exchange their ARNS.  This function is much like the iPhone Bluetooth airdrop feature for the exchange of photos but would be automated to discover and link with each other’s smart phone (or device) to exchange their ARNs.  For example, let phone/device A be in the vicinity of phone/device B, and they link up with Bluetooth and IAppA sends its ARNA to IAppB and IAppB sends its ARNB to IAppA.


Next IAppA immediately sends to the SDB its ARNA followed by the just retrieved ARNB.  At the same time IAppB sends to the SDB its ARNB followed by the just retrieved ARNA.

The SDB recognizes ARNA and opens up its file to store ARNB.  Simultaneously the SDB recognizes ARNB and goes to its file to check ARNB’s alert flag status.  If there is an alert it is sent immediately (within milliseconds) to the IAppA and the phone/device A provides an audible, visual or vibrating alert signal to user A warning user A not to approach user B, avoiding the possibility of infection.  The commonly used Class 2 Bluetooth provides a communication range of up to 30 feet, more than adequate to provide an approach distance warning.


Concurrently, The SDB recognizes ARNB and opens up its file to store ARNA.  Simultaneously the SDB recognizes ARNA and goes to its file to check ARNA’s alert flag status.  If there is an alert it is sent immediately to the IAppB and the phone/device B provides an audible, visual, or vibrating alert signal to user B warning user B to not approach user A, avoiding the possibility of infection. The alert flag system will be explained in detail below.


The Bluetooth connection could be continuous during the proximity period with a timer in both phone/device iApps running to record the connection time.  When the connection is terminated, both phone/device iApps will send this information to the SDB.  For example, both phones/devices record a connection time of tc.  Phone/device A iApp sends its ARNA to the SDB followed by tc.  The SDB recognizes ARNA and opens its file to record tc associated with the ARNB connection.  Phone/device B iAppB sends it ARNB to the SDB followed by tc.  The SDB recognizes ARNB and opens its file to record tc associated with the ARNA connection.


In a crowded environment where multiple phones/devices are present such as in stores, bars, restaurants, theater, places of worships, etc. the IApp would switch to “snapshot” mode where it would briefly pair with one Bluetooth signal at a time for ARN exchange and to determine distance between the connected phones/devices.  It would cycle through the Bluetooth signals within connection range.  In this case the SDB would do the connection timing by simply calculating the time between snapshots and summing up the total for a particularly ARN link. In the future the phones/device in crowded venues could form a situational self-assembled Bluetooth network managed by the SDB to immediately notify people if they are in a hot-spot environment and should leave.   


It is highly desirable and likely essential to be able to send accurate distance data between the two connected phones/devices to the SDB.  However, both GPS and cell tower phone location capabilities are poor and cannot be relied upon to provide such data.  Bluetooth signal strength is also not a reliable indicator of physical proximity.  A possible viable alternative would be to use audio signal timing.  It is likely that such a signal may be faint because of distance and/or path blockage (such as the cell phone being in a pocket, purse, or backpack for examples) and will have to compete in a noisy environment.  Therefore, an audio “signature” signal will be sent created by the iApp using its ARN, which is unique to every IApp.  The speed of sound in air is 1,125 feet/sec.  If the connected phones are only a foot apart it will take 1 millisecond for theaudio signature to travel the distance, a long time compared to the processing speed to recognize and respond to the signal. 

 
An example of how this could work is that iApp A creates an audio signature with its ARNA and uses the phone or device speaker audio output to send the audio signal and starts a timer.  At the same time, it uses the Bluetooth connection channel to send the digital file used to create the audio signature which phone/device B receives at the speed of light and iAppB creates the signature “filter” for which to receive only that unique signature audio signal for response.  This aspect is important in crowded environments where multiple signature audio signals are being sent from multiple phones or devices.  Once received iAppB sends a response signal back to phone/device A over the Bluetooth channel and iAppA stops the timer to record the time, td, it took the audio signal to travel from phone/device A to phone/device B.  Any known non-negligible electronic data processing time could be subtracted out to improve accuracy.  The distance, d, between the two phones or devices is then simply provided by d=td*vs where vs=1,125 feet/sec.  The value of td obtained by iAppA can be sent to iAppB over the Bluetooth connection, or it could obtain its own value by the process above with sending out its audio signature using ARNB to iAppA.  Either way, once obtained td is sent to the SDB ARN files by both phones or devices with the same process described above for sending tc.  This process can be repeated at a given time interval, such as every second, that the phones are in Bluetooth connection proximity.


Alert Flags:


The essential data to create alert flags are tc and td.  Algorithms are then applied to create alert flag levels.  There are many ways to approach this task, and we present one such way below, but it should be viewed only as by way of example.  First is to calculate the distance, d, between two Bluetooth connected phones:


1. d=td*vs where vs is the velocity of sound, 1,125 feet/sec.


An algorithm calculates the “strength” of the interaction to provide flag level alerts.  For example, define:


2. D=1/(1+(d/d0)n) where n and d0 are calibration factors given by the CDC.  

 

This equation has the behavior such that for 1) d=0, D=1; 2) d=d0, D=1/2 and 3) when d is large D tends to zero.   An example curve is shown in figure 11 for n=5 and d0=6 feet.

 

3. T=((tc/t0)m)/(1+(tc/t0)m)   where m and t0 are calibration factors given by the CDC.

 

This equation has the behavior such that when tc is small (a few seconds) T is small and when tc is large, T tends to the value T=1.  An example curve is shown in figure 12 for t0=4 seconds and m=3.

4. S=(D+T)/2 where S is defined as the interaction “strength.”  The maximum value for S is 1, when d is a short distance, less than about 4 feet, and t is long, more than about 8 seconds.


The above equations try to accommodate various time and distance scenarios from where:

1.    distance is short and time is long where both D and T tend to 1 so that S tends to 1,


2.    distance is long and time is short where D, T and S all tend to 0,


3.    distance and time are both short where D tends to 1 and T to 0 so that S tends to 1/2,


4.    distance and time are both long where D tends to 0 and T to 1 so that S tends to 1/2,


5.    distance=d0 and t=t0 where both D and T are the value 1/2 so that S=1/2,


6.    and everything else in between where S can be anything from 0 to 1 depending on the specific values of D and T.

As data slowly emerges from the characteristics of the COVID-19 pathogen and how it is transmitted, it will be desirable to set the exposure parameters D and T such that S is a reasonable representation of the probability of infection given those parameters of a particular connection.


Lacking specific data, flag alert levels and action message are suggested to be:

 

1.    Flag alert=BLUE (for S<.25), “No significant exposure to anyone known to be infected.”


2.    Flag alert=YELLOW (for .25 < S < .5), “You have been in proximity to someone just tested to be COVID-19 positive so are at some risk of being infected so it is advisable to get tested.”


3.    Flag alert=ORANGE (for .5 < S < .75), “You have been in significant proximity to someone just tested to be COVID-19 positive and may be infected so get tested as soon as possible.”


4.    Flag alert=RED (for .75 < S < 1), “You have had close, prolonged contact with someone just tested to be COVID-19 positive and are now likely to be infected so it is imperative to self-quarantine and get tested immediately.”

 

The value of the calculated S is stored associated with the interaction ARN in the user ARN file “folder.”  For user A above, the S value is stored in the ARNA file folder associated with the ARNB stored in that ARNA file folder. For user B above the S value is stored in the ARNB file folder associated with the ARNA stored in that ARNB file folder.  Every interaction ARN in a user file folder will have an S value associated with it.


At a point of care (PoC) facility, one check-in procedure could be the retrieval of the person’s ARN by the PoC Medical IApp (MIApp) that has special permission to set patient flag status.  If the person is found to have COVID-19, the MIApp sends to the SDB its ARN followed by the patient’s ARN, referred to as ARNp.  The SDB identifies the MIApp ARN as from a PoC provider then retrieves the patients ARNp file folder, sets the COVID-19 alert flag to BLACK, and sends out to all the ARNs in the folder an exposure alert message with a flag level and action message as defined above.  Note that no patient personal information is sent, just the patient’s IApp ARN. By definition this whole patient to doctor to database process is HIPAA compliant.  After 14 days, a notice will be sent to the patient to come in for anti-body testing to determine recovery and immunity by the PoC sending their MIApp ARN followed by the patient’s ARN to the SDB, and the patient’s IApp will notify the patient.  Alternatively, the patient’s ARN is not retrieved at a PoC check in but rather if diagnosed to have COVID-19 the patient’s IApp acquires the PoC MIApp ARN (by a permission process with the PoC) and the patient’s IApp sends its ARN followed by the PoC MIApp ARN which sets the flag.  However set with patient approval or participation, it sets the following alert process:

 

1.    The SDB retrieves one proximity interaction ARN, referred to ARNi at a time with its associated S value, referred to as Si, from the patient ARNp file folder,


2.    Uses the retrieved ARNi to locate that ARNi file folder,


3.    Sets the alert flag and message appropriate to the Si value as above,


4.    Retrieves the phone contact information in the file folder,


5.    Sends out the flag level and action message to the phone IApp to alert the user (one of which may have been the asymptomatic carrier that infected the patient),


6.    Retrieves from the user ARNi file folder one proximity interaction ARN, referred to as ARNj, at a time with its associated Sj value,


7.    Uses the retrieved ARNj to locate that ARNj file folder,


8.    Sets the alert flag and message appropriate to the Si*Sj value as below,


9.    Retrieves the phone contact information in the file folder,


10.    Sends out the flag level and action message to the phone to alert the user,


11.    Retrieves from the user ARNj file folder to one proximity interaction ARNk at a time with its associated Si*Sj*Sk value, and so on.

This process continues down the contact chain until a date 14 days previous which stops the alert chain.  The chain also stops when ProdSn<.25 where ProdSn is the product Si*Sj*Sk*…Sn… A person’s IApp ARN flag status can be re-set to “no flag set” on being tested negative or after 14 days of no symptoms which will re-set subsequent flags in that chain.

 

The alert flag and message appropriate to # 8 above is:

 

1.    Flag alert=BLUE (for ProdSn<.25), “No significant exposure to anyone known to be infected.”

2.    Flag alert=YELLOW (for .25 < ProdSn < .5), “You have been in contact chain proximity to someone just tested to be COVID-19 positive so it is advisable to get tested.”


3.    Flag alert=ORANGE (for .5 < PprodSn < .75), “You have been in significant contact chain proximity to someone just tested to be COVID-19 positive so get tested as soon as possible.”


4.    Flag alert=RED (for .75 < ProdSn < 1), “You have had close, prolonged contact chain proximity to someone just tested to be COVID-19 positive and may now be infected so it is imperative to self-quarantine and get tested immediately.”

 

If a person’s IApp ARN gets flagged by multiple different sources, then the flag status will reflect the average sum value AvgSn=(Si+Sj+Sk+…Sn+…)/n.  If any Sn flag is reset then that Sn will be removed from the average sum value.  Getting flagged from multiple sources is extremely indicative that the person is the asymptomatic carrier causing the infections and needs to be immediately tested.


Figure 13 illustrates this notification progression. The power of this approach is how widespread the alert flag messaging is.  If each ARN folder had 3 flagged proximity contact ARN listings during a 14-day period, then there would be a total of 3k messages sent where k is the depth of the chain.  If k=an average of 3 then 27 alert messages would be sent throughout the chain.  Note an unbroken infection chain of 3 new infections per 14-day period per contagious person yields 15 new infected persons by the end of day 14.  The 27 alert messages would prevent these new infections from occurring, which is what is required to stop the spread.


There will be a provision for self-diagnosis with a home test kit.  If the test is positive, the patient can confirm with a PoC to alert the SDB to set the black flag for the patient’s ARN and start the contact alert process.  If the patient goes out into the public domain during the 14-day quarantine period, a person in proximity would get the black flag on their phone warning them to avoid the patient.  


At day 14 after positive diagnosis and the patient has been tested to have anti-bodies to COVID-19, the SDB removes the alert flags.  The flags are also removed automatically after 14 days from contact interaction date in the ARN file folders.

There will be a provision to set a green flag notice that a person has just been tested to have antibodies and is immune.  Then such persons are free to move around in the public domain which would help re-start the economy.   


A New York Time article published April 6 2020 and updated April 7 2020 titled “How Will We Know When It’s Time to Reopen the Nation?” states that:


“A robust system of contact tracing and isolation is the only thing that can prevent an outbreak and a resulting lockdown from recurring.  Every time an individual tests positive, the public health infrastructure needs to be able to determine whom that person has been in close contact with, find those people, and have them go into isolation or quarantine until it is established they aren’t infected, too.”


But this alone isn’t enough to stop the spread of COVID-19.  To stop requires preventing infectious contacts to begin with.  This is what CAS will do, and with random numbers it protects individual privacy while it does it.  It does more than just contact tracing by preventing contagious exposure to begin with, including asymptomatic carriers, which other approaches such as the recently announced joint Apple and Google effort don’t or can’t do.  This feature is key to allowing the shutdown to be released and get the country moving again and it will prevent a future pathogen from getting to the pandemic stage.  It needs to be done.


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Figure 11

 

 

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Figure 13

 

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