We’re not going back to normal

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We’re not going back to normal

Mar 17, 2020  Social distancing is here to stay for much more than a few weeks. It will upend our way of life, in some ways forever.  

To stop coronavirus we will need to radically change almost everything we do: how we work, exercise, socialize, shop, manage our health, educate our kids, take care of family members.

We all want things to go back to normal quickly. But what most of us have probably not yet realized—yet will soon—is that things won’t go back to normal after a few weeks, or even a few months. Some things never will.

It’s now widely agreed (even by Britain, finally) that every country needs to “flatten the curve”: impose social distancing to slow the spread of the virus so that the number of people sick at once doesn’t cause the health-care system to collapse, as it is threatening to do in Italy right now. That means the pandemic needs to last, at a low level, until either enough people have had Covid-19 to leave most immune (assuming immunity lasts for years, which we don’t know) or there’s a vaccine.

How long would that take, and how draconian do social restrictions need to be? Yesterday President Donald Trump, announcing new guidelines such as a 10-person limit on gatherings, said that “with several weeks of focused action, we can turn the corner and turn it quickly.” In China, six weeks of lockdown are beginning to ease now that new cases have fallen to a trickle.

But it won’t end there. As long as someone in the world has the virus, breakouts can and will keep recurring without stringent controls to contain them. In a report yesterday (pdf), researchers at Imperial College London proposed a way of doing this: impose more extreme social distancing measures every time admissions to intensive care units (ICUs) start to spike, and relax them each time admissions fall. Here’s how that looks in a graph.

Periodic bouts of social distancing keep the pandemic in check. IMPERIAL COLLEGE COVID-19 RESPONSE TEAM.

The orange line is ICU admissions. Each time they rise above a threshold—say, 100 per week—the country would close all schools and most universities and adopt social distancing. When they drop below 50, those measures would be lifted, but people with symptoms or whose family members have symptoms would still be confined at home.

What counts as “social distancing”? The researchers define it as “All households reduce contact outside household, school or workplace by 75%.” That doesn’t mean you get to go out with your friends once a week instead of four times. It means everyone does everything they can to minimize social contact, and overall, the number of contacts falls by 75%.

Under this model, the researchers conclude, social distancing and school closures would need to be in force some two-thirds of the time—roughly two months on and one month off—until a vaccine is available, which will take at least 18 months (if it works at all). They note that the results are “qualitatively similar for the US.”

Eighteen months!? Surely there must be other solutions. Why not just build more ICUs and treat more people at once, for example?

Well, in the researchers’ model, that didn’t solve the problem. Without social distancing of the whole population, they found, even the best mitigation strategy—which means isolation or quarantine of the sick, the old, and those who have been exposed, plus school closures—would still lead to a surge of critically ill people eight times bigger than the US or UK system can cope with. (That’s the lowest, blue curve in the graph below; the flat red line is the current number of ICU beds.) Even if you set factories to churn out beds and ventilators and all the other facilities and supplies, you’d still need far more nurses and doctors to take care of everyone.

In all scenarios without widespread social distancing, the number of Covid cases overwhelms the healthcare system. IMPERIAL COLLEGE COVID-19 RESPONSE TEAM

How about imposing restrictions for just one batch of five months or so? No good—once measures are lifted, the pandemic breaks out all over again, only this time it’s in winter, the worst time for overstretched health-care systems.

If full social distancing and other measures are imposed for five months, then lifted, the pandemic comes back. IMPERIAL COLLEGE COVID-19 RESPONSE TEAM.

And what if we decided to be brutal: set the threshold number of ICU admissions for triggering social distancing much higher, accepting that many more patients would die? Turns out it makes little difference. Even in the least restrictive of the Imperial College scenarios, we’re shut in more than half the time.

This isn’t a temporary disruption. It’s the start of a completely different way of life.

Living in a state of pandemic

In the short term, this will be hugely damaging to businesses that rely on people coming together in large numbers: restaurants, cafes, bars, nightclubs, gyms, hotels, theaters, cinemas, art galleries, shopping malls, craft fairs, museums, musicians and other performers, sporting venues (and sports teams), conference venues (and conference producers), cruise lines, airlines, public transportation, private schools, day-care centers. That’s to say nothing of the stresses on parents thrust into home-schooling their kids, people trying to care for elderly relatives without exposing them to the virus, people trapped in abusive relationships, and anyone without a financial cushion to deal with swings in income.

There’ll be some adaptation, of course: gyms could start selling home equipment and online training sessions, for example. We’ll see an explosion of new services in what’s already been dubbed the “shut-in economy.” One can also wax hopeful about the way some habits might change—less carbon-burning travel, more local supply chains, more walking and biking.

But the disruption to many, many businesses and livelihoods will be impossible to manage. And the shut-in lifestyle just isn’t sustainable for such long periods.

So how can we live in this new world? Part of the answer—hopefully—will be better health-care systems, with pandemic response units that can move quickly to identify and contain outbreaks before they start to spread, and the ability to quickly ramp up production of medical equipment, testing kits, and drugs. Those will be too late to stop Covid-19, but they’ll help with future pandemics.

In the near term, we’ll probably find awkward compromises that allow us to retain some semblance of a social life. Maybe movie theaters will take out half their seats, meetings will be held in larger rooms with spaced-out chairs, and gyms will require you to book workouts ahead of time so they don’t get crowded.

Ultimately, however, I predict that we’ll restore the ability to socialize safely by developing more sophisticated ways to identify who is a disease risk and who isn’t, and discriminating—legally—against those who are.

We can see harbingers of this in the measures some countries are taking today. Israel is going to use the cell-phone location data with which its intelligence services track terrorists to trace people who’ve been in touch with known carriers of the virus. Singapore does exhaustive contact tracing and publishes detailed data on each known case, all but identifying people by name.

1 in 7 Americans would avoid care for suspected COVID-19 fearing cost of treatment

I'm stunned by the depth of information being released in .
On this website you can see every known infection case, where the person lives and works,
which hospital they got admitted to, and the network topology of carriers, all laid out on a time-series
pic.twitter.com/wckG8KpPDE

We don’t know exactly what this new future looks like, of course. But one can imagine a world in which, to get on a flight, perhaps you’ll have to be signed up to a service that tracks your movements via your phone. The airline wouldn’t be able to see where you’d gone, but it would get an alert if you’d been close to known infected people or disease hot spots. There’d be similar requirements at the entrance to large venues, government buildings, or public transport hubs. There would be temperature scanners everywhere, and your workplace might demand you wear a monitor that tracks your temperature or other vital signs. Where nightclubs ask for proof of age, in future they might ask for proof of immunity—an identity card or some kind of digital verification via your phone, showing you’ve already recovered from or been vaccinated against the latest virus strains.

I had to travel earlier this month and this is how my movements were being tracked for the purpose of containment.
Follow
@RadiiChina for more videos on !

We’ll adapt to and accept such measures, much as we’ve adapted to increasingly stringent airport security screenings in the wake of terrorist attacks. The intrusive surveillance will be considered a small price to pay for the basic freedom to be with other people.

As usual, however, the true cost will be borne by the poorest and weakest. People with less access to health care, or who live in more disease-prone areas, will now also be more frequently shut out of places and opportunities open to everyone else. Gig workers—from drivers to plumbers to freelance yoga instructors—will see their jobs become even more precarious. Immigrants, refugees, the undocumented, and ex-convicts will face yet another obstacle to gaining a foothold in society.

Moreover, unless there are strict rules on how someone’s risk for disease is assessed, governments or companies could choose any criteria—you’re high-risk if you earn less than $50,000 a year, are in a family of more than six people, and live in certain parts of the country, for example. That creates scope for algorithmic bias and hidden discrimination, as happened last year with an algorithm used by US health insurers that turned out to inadvertently favor white people.

The world has changed many times, and it is changing again. All of us will have to adapt to a new way of living, working, and forging relationships. But as with all change, there will be some who lose more than most, and they will be the ones who have lost far too much already. The best we can hope for is that the depth of this crisis will finally force countries—the US, in particular—to fix the yawning social inequities that make large swaths of their populations so intensely vulnerable.

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ORIGINAL STUDY(not peer reviewed) by Imperial College

====================================================

ORIGINAL IMPERIAL COLLEGE SIMULATION PARAMETERS and ASSUMPTIONS (comments)

  • one third of transmission occurs in the household, one third in schools and workplaces and the remaining third in the community. These contact patterns reproduce those reported in social mixing surveys .
  • Infectiousness is assumed to occur from 12 hours prior to the onset of symptoms for those that are symptomatic and from 4.6 days after infection
  • We assume that symptomatic individuals are 50% more infectious than asymptomatic individuals. WRONG? "unobserved symptoms. per person infections were 55% as contagious as observed" --- not 50% but 82%
  • Infection was assumed to have a doubling time of 5 days
  • two-thirds of cases are sufficiently symptomatic to self-isolate (if required by policy) within 1 day of symptom onset, and a mean delay from onset of symptoms to hospitalisation of 5 days
  • 4.4% of infections hospitalised
  • assume that 30% of those that are hospitalised will require critical care (invasive mechanical ventilation or ECMO)
  • 50% of those in critical care will die and an age-dependent proportion of those that do not require critical care die
  • a total duration of stay in hospital of 8 days if critical care is not required and 16 days (with 10 days in ICU) if critical care is required. With 30% of hospitalised cases requiring critical care, we obtain an overall mean duration of hospitalisation of 10.4 days
Age-group
(years)
% symptomatic cases
requiring hospitalisation
% hospitalised cases
requiring critical care

Infection Fatality Ratio
China CDC

Death Rate

0

to

9

0.1%

5.0%

0.002%

 

10

to

19

0.3%

5.0%

0.006%

0.2

20

to

29

1.2%

5.0%

0.03%

0.2

30

to

39

3.2%

5.0%

0.08%

0.2

40

to

49

4.9%

6.3%

0.15%

0.4

50

to

59

10.2%

12.2%

0.60%

1.3

60

to

69

16.6%

27.4%

2.2%

3.6

70

to

79

24.3%

43.2%

5.1%

8.0

80+

 

 

27.3%

70.9%

9.3%

14.8
  • Pre-existing conditions  Hypertension Diabetes Cardiovascular disease Chronic respiratory disease Cancer (any) NOT INCLUDED?

  • When examining mitigation strategies, we assume policies are in force for 3 months, other than social distancing of those over the age of 70 which is assumed to remain in place for one month longer. Suppression strategies are assumed to be in place for 5 months or longer.

  • Stopping mass gatherings is predicted to have relatively little impact (results not shown) because the contact-time at such events is relatively small compared to the time spent at home, in schools or workplaces and in other community locations such as bars and restaurants.
  • Overall, we find that the relative effectiveness of different policies is insensitive to the choice of local trigger (absolute numbers of cases compared to per-capita incidence), R0 (in the range 2.0-2.6), and varying IFR in the 0.25%-1.0% range.
  • social distancing would need to be in force for at least 2/3 of the time
  • As case numbers fall, it becomes more feasible to adopt intensive testing, contact tracing and quarantine measures akin to the strategies being employed in South Korea today. Technology – such as mobile phone apps that track an individual’s interactions with other people in society – might allow such a policy to be more effective and scalable if the associated privacy concerns can be overcome.
  • The WHO China Joint Mission Report suggested that 80% of transmission occurred in the household , although this was in a context where interpersonal contacts were drastically reduced by the interventions put in place. Social distancing of high-risk groups is predicted to be particularly effective at reducing severe outcomes given the strong evidence of an increased risk with age, though we predict it would have less effect in reducing population transmission.
  • there is a 2- to 3-week lag between interventions being introduced and the impact being seen in hospitalised case numbers

But the outbreak would still result in 250,000 deaths in Britain, and 1.1 to 1.2 million in the US, with the 'surge capacity' of intensive care units overwhelmed "at least 8 times greater than the availability".
Even if you set factories to churn out beds and ventilators and all the other facilities and supplies, you’d still need far more nurses and doctors to take care of everyone.
There is no guarantee that initial vaccines will have high efficacy.

Core Variable Assumptions

There are a few core variables that drive the model. These are listed below. SOURCE

Metric

Default Assumption

Explanation

Source Data

Estimated Initial R0

2.4

R0 determines how fast the disease spreads each period. The model uses actual data as reported by JHU. When none is present, this default is used.

Range provided by Imperial College paper.

Hospitalization Rate

7.3%

This is the rate at which infected people are hospitalized. Our best estimates vary quite a bit by age.

Range provided by Imperial College paper, weighted by actual USA demographics as reported by statistica here.

Case Fatality Rate

1.1%

2.3% from China CDC

This is the rate at which infected people die, assuming they can access treatment. Our best estimates vary quite a bit by age.

Range provided by Imperial College paper, weighted by actual USA demographics as reported by statistica here.

Fatality Rate Increase If Hospitals Overloaded

1.0%

This is the additional rate at which infected people die, assuming they cannot access treatment. It is the number of infected cases requiring at least ICU care.

Range provided by Imperial College paper, weighted by actual USA demographics as reported by statistica here.

Population

Varies by state

The population of each state.

Wikipedia here

Hospital Beds

Varies by state

The number of hospital beds in each state.

KFF here, somewhat outdated.

Hospital Bed Utilization

60%

The number of beds unavailable for CoVid cases due to being occupied.

Guess based on discussions with experts.

Emergency Bed Capacity Build

207.9% in 2 months

The number of additional beds made available by emergency preparation. Roughly equivalent to clearing out fully half of all other hospital bed occupants.

Guess based on discussions with experts.

Initial Cases

Initial Cases

Reported cases times 20

Cases estimated by multiplying confirmed cases by 20. 

A hospitalization rate of ~5% implies ~20x the number of cases as hospitalizations.

Once this becomes a poor signal, death rates will be used to estimate caseload. This would be estimated as: (Reported Deaths / Case Fatality Rate) * 2x2x2x2. The 2^4 multiplier adjusts for ~16 days delay between infection and death.

Core Model Dynamics & Disease Timeline Assumptions

Metric

Default Assumption

Explanation

Source Data

Modelling Interval

4 days

This is how frequently the model updates. It is roughly equivalent to one disease doubling period.

n/a. Chosen for simplicity.

Recovery Period

14 days

This is how long it takes the average patient to recover. This does not change regardless of the severity of the case.

Corroborated by various sources, but one source is here.

Incubation Period

4 days

5.1

The average delay between infection and onset of symptoms.

Corroborated by various sources, but one source is here.

Contagious Period

4 days

more?

The number of days the average case is contagious before isolating or recovering.

Corroborated by various sources, but one source is here.

 

 List of definitions about infectious diseases epidemiology.

  • Basic Reproduction Number (R0): The average number of secondary cases produced by a single infected case in an otherwise susceptible population.

  • Case fatality ratio (CFR): The proportion of detected cases of a given disease that die as a result of it.

  • Cluster: An aggregation of cases grouped in place and time that are suspected to be greater than the number expected, even though the expected number may not be known.

  • Effective Reproduction Number: The average number of secondary cases arising from an infected case, with a given level of immunity in the population.

  • Endemic: Refers to the constant presence, and/or usual prevalence of an infectious disease in a population within a geographic area. The amount of a particular disease that is usually present in a community is referred to as the baseline or endemic level of the disease.

  • Epidemic: The occurrence of disease cases in excess of normal expectancy, usually referring to a larger geographical area than "outbreak".

  • Exposed: A contact between a susceptible and infected person that could potentially lead to infection.

  • Fatality Rate of Infections (FRI): The proportion of overall infections that die as a result of it. *

  • Incidence: The number of new infections during a given interval of time (for example, weekly incidence).

  • Incubation period: Period between exposure and onset of clinical symptoms.

  • Infectious period: The length of time for which an infected individual is infectious to others.

  • Latent period: Period between exposure and ability to transmit to others.

  • Outbreak: The occurrence of disease cases in excess of normal expectancy, usually referring to a smaller geographical area than "epidemic".

  • Pandemic: An epidemic that has spread over several countries or continents, usually affecting a large number of individuals.

  • Pathogen: A micro-organism which can cause, or causes disease or damage to a host.

  • Prevalence: The number of infected people in a population at a given point in time.

    * Hard to know what the "Overall Infections" is

Severity of 2019-novel coronavirus (nCoV) OLD

 Imperial College London Foundations of Public Health Practice: Health Protection course. In particular, Week 2: Lesson Two: Incident management and outbreak control. https://www.coursera.org/learn/health-protection

Assessing the severity of the novel influenza A/H1N1 pandemic

phylogenetic analysis OLD ESTIMATES back in Feb 15th

We estimate a time of common ancestry of these sequences to be December 8th with a margin of error of about two weeks in either direction, and we also get very approximate estimates of the epidemic size through time. If we assume that the epidemic was growing exponentially throughout January, then we get an estimate of around 40,000 infections at the end of January. In order for those estimates to be consistent with the epidemiological record, which showed around 20,000 infections at the same time, we need to assume high levels of dispersion of transmission rates, which has a very strong effect on genetic diversity. So we think that this shows evidence for a high level of transmission and a minority of patients.
 

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