My friends Seth Goldstein and Kaliya Young have started a new, timely podcast. First episode is up!

What will the world look like when we finally unlock our doors and emerge into the sunshine, after COVID-19 has passed? Regardless how long it will take, and how many fellow travelers we will have lost, eventually the pandemic will end and the survivors need to get back to their lives.

Back to their previous lives? I kinda doubt it. The longer the pandemic and shelter-in-place last, the more we will change our habits. And the more we’ll question our previous habits. As many have pointed out, this is actually a great opportunity, in particular because all the other catastrophes won’t have gone away, starting with the climate.

I have no crystal ball. But it is time to start thinking about this, and so I’m starting this new section on my blog to collect opinions on possible post-COVID-19 futures that I am coming across, starting with this one from Politico:

• Coronavirus Will Change the World Permanently. Here’s How. Predictions from 34 “big thinkers” (whatever that is).

None of them are particularly surprising. But worthwhile reading anyway.

This post provides more details on the “Dynamic Quarantine” exit path from the COVID-19 pandemic that I listed in a previous post.

The problem

We need to reduce transmission of the virus to a level where the number of infected people at any time shrinks, rather than grows.

Absent vaccines or other medications, this requires reduction of in-person contact between people (“social distancing”).

However, this makes normal functioning of the economy largely impossible. For example, the state of California just ordered all “non-essential businesses” to be closed. While this may work in the short term, the longer the lock-down continues, the more things “break”: from mass unemployment and resulting poverty/defaults/bankruptcies, to the availability of replacement parts and eventually essentials such as food.

Such “social distancing” may need to continue until a vaccine is available, which may take many months (12-18 months is a common estimate). It is unclear how to keep the economy functioning enough for such an extended period of time.

We need better ideas.

The basic idea

Instead of a blanket shutdown of all “non-essential” businesses, confining “everybody” to their residence, we could shut down only those businesses in which infection is likely, and confine only those people to isolation whose likelihood of infecting somebody is higher than a certain threshold. In this approach, those likelihoods are dynamically determined by means of data collection mostly through mobile phones, and an algorithm that produces a corresponding score for each person from the collected data.

The likelihood of a subject infecting somebody is determined as a function of what is known about the health of the subject so far, plus a history of the subject’s interactions with other people and those people’s likelihood of infecting somebody.

By tracking this information in real time, the blanket closure of businesses and blanket shelter-in-place of the population can be avoided, and instead be replaced with a sharp, pinpointed focus on isolating those that are most likely contributing to the spread of the disease. The remainder of the economy and population can continue to function.

Certain parameters in the algorithm can be tuned to provide different tradeoffs between reducing spread and inhibiting (or not) the economy.

Infectiousness score

The infectiousness score in this approach is an estimate for the likelihood that one person infects another when exposed for a certain time period (e.g. 5 min).

For our purposes here, the infectiousness score is a number between 0 and 1, where 0 means: not infectious (e.g. because a highly reliable test has just cleared the subject) and 1 means: known to be maximally infectious (e.g. because viral loads have been found to be high, and the subject behaves promiscuously).

Details

A few definitions first:

• P: a person (aka subject)
• S(P,t): the infectiousness score of person P at time t. Ranges between 0 (not infectious) and 1 (maximally infectious).

The core algorithm is as follows. It deals with direct infection between two people only, but an extension is discussed below.

• At each time unit (e.g. every hour), S(P,t) is calculated as a function of:
  • S(P,t-1): the infectiousness score of the person at the time prior;
  • S(Pi,τ): the infectiousness score of all people Pi that the subject interacted with in the time period τ = (t-tw) ... (t-1) (where tw is a parameter that determines the length of the time window that’s being considered; selection of this parameter depends on characteristics of the disease, such as incubation times, as well as the characteristics of enacted community interventions such as availability, frequency and accuracy of testing);
  • a rating of the subject’s current health derived from the subject’s self-assessment;
  • a rating of the subject’s current health based on information from the future (see below).

Rewriting history:

• Test results come in with a delay (e.g. one day between tTest and current time t). Once available, the estimate for the infectiousness of the subject between tTest and t will be “overwritten” with an updated, more accurate estimate for that already-passed time period that takes the results of the test into account.
• Similarly, subjects may be infectious prior to experiencing any symptoms. Once symptoms are apparent, all prior estimates of infectiousness of the subject will be recalculated over some time window whose length is determined by some assumptions about the disease (incubation time, time of infectiousness prior to symptoms etc).
• When subject P’s history is rewritten, the histories (and current score) need to be recalculated and rewritten of all subjects that have previously taken the history of subject P into account for their own scores. They need to now use the rewritten history. This may happen recursively. History may be overwritten repeatedly for a given subject, which again triggers rewrites for other subjects. (More efficient algorithms producing the same result can be found.)

Additional potential inputs to the algorithm:

• A rating of a subject’s interventions that may modify their infectiousness, such as:
  • wearing a mask;
  • intentionally exhaling at others;
  • etc.

Extension to other forms of transmission

So far, we have assumed that transmission can only occur between two people in the same location. However, there are other forms of transmissions, such as:

• transmission via a contaminated surface within a certain time interval that the virus remains active on that surface;
• transmission via air droplets in an enclosed space with a certain time interval.

To account for these forms of transmission, the algorithm is extended to also include estimates of the infectiousness of objects in certain locations. Similar to people, these objects have an infectiousness score that is a function of which people (and their scores) have interacted with it in times prior, its previous infectiousness score and the passage of time.

The score of objects in the vicinity is considered as part of the algorithm to update S(P,t) in a corresponding manner to that of people.

User experience

• Users run an app on their mobile phones.

• From time to time, the app asks the user about how they feel. Specifically it asks about symptoms related to COVID-19, such as fever, fatigue, cough etc.

• The app’s main screen shows an easy-to-understand visual representation of the likely infectiousness score, such as a color code (e.g. green: unlikely to infect).

• When the app reports a score above a certain threshold, the subject goes into shelter-in-place or quarantine. (Legal questions about whether this is voluntary or legally required are out of scope for this discussion; certainly regulations such as “must be sheltered-in-place unless score is green” would be possible.)

• Before two (or more) people meet in person, they can agree on a maximum score that participants are allowed to have to be allowed to participate in the meeting. (Such a maximum score may also be legally mandated.) The participants in the meeting check each others’ scores before the meeting.

• Before a business admits a customer (or employee) onto the premises, they require the customer or employee to share their score. They will be denied access if the score is above a certain threshold. They may also deny access to those visitors who do not have, or are unwilling to display their score.

• When the user gets tested, they enable the testing provider to add the test results to their record so it can be used to calculate the score going forward.

• Depending on the implementation choices made, the mobile phone may need to be connected to the internet, to a local WiFi network and/or have Bluetooth on as sender or receiver or both.

Assumptions / challenges

• Test results can be brought into the system in a way that defeats tampering: we cannot allow a subject to fake negative test results, for example, or eliminate from consideration positive test results.

• Individuals may be tempted to fake their scores in order to enter a certain venue, for example, such as by displaying a static screen shot on their phone instead of their live score. Technical means (e.g. timestamping the display, or simultaneously broadcasting the score via wireless networking) can be employed to make this more difficult. This approach would also use technical means (e.g. public keys, app stores) to prevent “rogue apps” with false scores to participate.

• In a naive implementation, the entire record of each subject (e.g. the entire world population) would be centrally collected. This would create a privacy nightmare and enable substantial future harm from dangers that are not biological in nature. So we assume that the implementation would need to be performed in a fashion that does not have a central point of data collection.

• Location accuracy for this app is paramount. The absolute coordinates are less important; but relative coordinates between two subjects need to be determined as well as possible, as a distance of 2ft vs 8ft has substantially different impact on likelihood of transmission. This could be addressed with technical means (e.g. Bluetooth, NFC), user input (e.g. verify / enter into the app the people currently in close proximity) or a combination.

• The space in which an encounter occurs is highly relevant. For example, a 10 min contact at 6ft inside a small, enclosed space without ventilation has dramatically different transmission characteristics than contact of the the same duration and distance in open nature with a slight wind. This also could be addressed with technical means (e.g. mapping information), use input (e.g. enter into the app whether the surroundings are enclosed space, ventilated, open window, city street, open nature etc) or a combination.

Approach to Privacy

It appears possible to keep most information needed for the functioning of the system on individual users’ mobile phones without requiring a centralized data repository:

• The algorithm can run locally on local data.
• Detection of other people in the neighborhood can be performed via local wireless networking (e.g. WiFi, zeroconf, Bluetooth).
• The communication between mobile phones of people in an encounter to exchange scores can be performed using secure end-to-end encryption between the phones using any networking technology including through a centralized backend. This would not compromise privacy significantly.
• To trigger history rewrites in other phones, those connections to other phones can be remembered and re-activated (including identity / encryption keys). This may use some existing centralized communication network (e.g. instant messenger) or a decentralized alternative with a distributed hash table for lookup, for example.
• None of the functionality, or communications require more than pseudonymous identity. No centralized account, or identity verification is required, with the potential exception of entering verified testing results. However, in this case, the identity correlation remains local on the user’s device and is never shared beyond.

Public health reporting and management

• The app can report scores to the public health authorities, who have the ability to track actual – and best-guess estimates – of the spread of the disease in real time.
• For privacy reasons, scores do not need to be associated with other identifying attributes, although it may be advantageous to share demographic info such as age, and approximate (maybe rasterized) geographic location of the subject.
• Key parameters of the algorithm – e.g. thresholds for “acceptable” scores for certain activities – could be centrally updated by the public health authorities, in order to “shape” the progression of the disease in real time.

Algorithmic improvements

• The intentional distribution of data and computation, instead of centrally collecting it all, for privacy reasons, needs to be weighed against the need to continually debug, and improve the algorithm.
• To be able to understand the functioning of the algorithm in the field, and to make improvements, it appears sufficient to report the time histories of scores centrally, including rewritten histories. It does not appear necessary to identify the specific other people whose scores were used as input to the algorithm, nor the locations where encounters took place.
• Should more detailed information be required, collecting such more detailed information from a relatively small sample of volunteers should be sufficient.

How we got ourselves into this pandemic was quite straightforward: too little, too late, too much incompetence, and a shocking lack of preparation.

How we will get ourselves out of it is not so obvious. Here are the avenues I see:

We do nothing.

If so, the pandemic grows exponentially, infects most humans on the planet in short order, the healthcare system is so overloaded it might as well not exist, but the pandemic burns itself out quickly as well.

• Duration: short (say 6 months)
• Healthcare system: overwhelmed by some factors
• Deaths: millions upon millions
• Economy: recovers

A medication is found relatively quickly.

I think of it like Robutussin extra-extra strength. This (hypothetical) medication gets symptoms of the infection down to non-lethal levels, say like the common cold. Of course, I have heard nothing to indicate that such a medication could exist, but if one were found then:

• Duration: medium
• Healthcare system: functioning
• Deaths: few
• Economy: largely not impacted

Lockdowns, until an effective vaccine is found/developed.

If so, everybody says it’s at least 12 months out, if it can be found. Then it needs to be mass-produced and delivered. So:

• Duration: 12-18 months
• Healthcare system: stressed
• Deaths: a few percent of population
• Economy: in shambles by the time the vaccine exists

Lockdowns, but no effective vaccine is found/developed.

That’s of course possible. In which case, we will either stay on indefinite lockdown or, because we do need to eat and need a functioning economy, the lockdowns will stop and the situation reverts to “We do nothing” after some time.

• Duration: 18-24 months
• Healthcare system: stressed, then overwhelmed by some factors
• Deaths: millions upon millions, but not immediately
• Economy: in shambles

And finally, perhaps there is a way to throw information technology at the problem:

Dynamic quarantine. See separate post with details.

The idea is to track the likely infection status, and infectiousness, of everybody on the planet, plus their movements with respect to other people and things that help transmission, and continuously update their likely status based on the infectiousness of the people and things they encountered. If this were done with enough information, and enough spatial and temporal resolution, we could with high confidence quarantine only those people who are likely infectious, and let the rest live their lives – in particular their work and consumption behavior – relatively unchanged.

• Duration: long term
• Healthcare system: functioning
• Deaths: a function of a parameter in the algorithm
• Economy: impacted, but to a degree determined by a parameter in the algorithm.

To make this kind of thing work, there would literally be thousands of problems to solve, and there is no guarantee whatsoever those problems could be solved, but it is an intriguing thought.

Summary: I wish we had the option of “and then a miracle occurs”.

When the Berlin Wall fell, I didn’t quite make the connection. In hindsight, it was the first major event happening in my lifetime that would get a big chapter in the history books, but I didn’t quite realize it at the time. History was the stuffy thing they quizzed you in school about, not something that happened in the world where you and I barbecued in the sunshine.

There have been several history-book-level events since: the disintegration of the Soviet Union; the emergence of the internet; the 9/11 attacks; the financial crisis in 2008; but not all that many.

Now, the global Coronavirus pandemic is another one. And I fear the chapter on it in future history books will be longer, containing more death and human misery, but also more disruptive impact than any of the others that happened in my lifetime.

The governor of California mused today that schools in the state – closed since yesterday – would probably not reopen for this school year. That sounds likely to me. More so, I don’t think they will open on time for the next school year either, and we’ll be hunkered down and “sheltering in place” for many months to come. I can see only two ways to get out of this mode:

• We have a vaccine – which everybody tells us is at least a year away; or:
• We have herd immunity – which would take years if the limiting factor is critical care beds, and it is.
• (Of course there is also “damn the torpedoes and who cares if millions die” but I hope that won’t be what happens in most places.)

So: what will the world look like if most stores, and restaurants, and hotels, and movie theaters, and conferences, and what have you, have been closed for a year or more? If you haven’t been able to visit your friends across town, or your family across the country for a year or more? If kids grow up without play dates, or without ever hanging out at the mall or the soccer game? If you haven’t been able to meet new people, or fall in love, for a year or more? Or: what if all of this gets somehow replicated on-line and life mostly moves into cyberspace as countless sci-fi novels have it? Whichever it is: as small as the virus is, its impact is as big.

And then there is economics. If China has record-low pollution right now (because there has been far less demand for coal-based electrical power), so low that it supposedly has saved the lives of 77,000 people already, and the canals in Venice have cleared for the first time in living memory so you can see the fish in them, this tells you more about what the GDP numbers will look like than any bespectacled talkshow guest ever will. As my friend Sari says, that’s great for the planet. Not so great if you have a 401k retirement plan or want to keep a job. The economic impact, and the ripple effect from there, will likely take up far more pages in the history books than even the pandemic itself. It’s such a big disruption.

We are in unchartered territory. Something like this has never happened in human history. The historians are going to have rows of unfilled PhD positions, so much is there to write about. For the rest of us: hang on tight, and throw out all preconceived notions of what your life should be like, because whatever you thought it was going to be 3 months ago is not going to happen. There is a chance it will be much better – the 77,000 certainly will think so! – but that’s not guaranteed even for those who don’t die of the virus. I shudder thinking about when real shortages start to happen, and they will.

We are looking at hard work and much hardship. But perhaps a better chance to save the planet than even 3 months ago was conceivable. The future is more uncertain than it has ever been in my lifetime. Fear permeates everything, and much pain is certain. But maybe, maybe, much good will come out of it, too. I tell myself: let’s try to focus on that.

Onwards.

As of midnight tonight, the residents of all counties in the 8-million people San Francisco Bay Area, including all of Silicon Valley, have been ordered to stay at home. What a little virus can do.

No meetups. No restaurants. No venture capital pitches. No trade shows. No business meetings. No shopping at the mall. No going to work.

Who can, will work on-line. So far, the interwebs are still up – although I experienced the first choppiness in the video feed during this very announcement today.

This shelter-in-place order is for 3 weeks. Which is laughably impossibly short, because just the incubation period for the virus might be that long! We’ll be holed up with cabin fever for much, much longer.

And when we finally re-emerge, the world will be drastically different, I think. How – I don’t know; visibility is very bad. But very different for sure.

Hang in there. See you in cyberspace, which is a corona-free zone :-)