Comment by nbnoll | Hacker News Reader

Comment by nbnoll | Hacker News Reader

Hi everyone, My name is Nicholas Noll, a computational biologist and co-developer of the above tool. As of now there are two other people actively working on this project: Richard Neher, Ivan Aksamentov, and I. Richard and I have a background in theoretical physics but we have been working on quantitative biology and epidemiology. Richard is also one of the founders of nextstrain.org which leverages phylogenetic analyses to track the global spread of many pathogens -- including SARS-CoV-2. The other person, Ivan, is a super talented software engineer who just recently joined our group.

We started working on this a few weeks ago, in collaboration with Jan Albert and Roberk Dyrdak, as a helpful model for public health officials and hospitals to help predict the usage of local community hospitals, and explore the utility and immediate need for quarantine measures.

We are very much still actively developing the tool, now with an eye to help global communities at large. If anyone is interested to help, we are trying to organize a road map and put it together on our Github page. I'll be around to answer questions and listen to feedback.

Thank you for putting this together!

A suggestion: adding decimal separators to numbers in the "Cases through time" chart for better readability.

The linear plot is a lot more relatable (and scarier) than a log-scale plot, but it is impossible to filter out "susceptible" and "recovered" making the linear scale view impossible to read. Would be nice to be able to filter out.

Would be nice if the parameters could be encoded in a shareable link.

Yes I agree. We are working on this - albeit slowly. I think realistically we have an import parameters feature (obtained from the export) sooner.

I'd be happy to help you work on this feature

This is a wonderful tool, I am very much looking forward to seeing further development & countries

Thank you very much!

One observation I had while playing with the numbers was: The most accurately shaped curves were achieved by adjusting the "seasonal forcing" to values between 0.5 and 0.6 [1]

I extended the time range to one year and lo and behold there was the gigantic hump in all curves.

I hope that testing kits will be amply available within the next few months so that we can emulate the success of Vò [2]

[1] I believe best-fitting curves are a miss-representation as many cases are not recorded, this is why I optimized for a shape that counted the "Infectious"consistently above the "confirmed cases" by the factor of 2-4. I am curious to learn how well my assumptions hold up.

[2] https://www.ft.com/content/0dba7ea8-6713-11ea-800d-da70cff6e...

Your modelling - how well does it match the actual data? Does it reproduce the actual data from the countries (China, Iran, Italy) furthest in the epidemic's lifetime correctly?

What does the terms moderate mitigation, weak mitigation, strong mitigation mean? What measures are required to go from none to weak, to moderate mitigation?

This is the next push for our modeling. We are trying to integrate live case count data to provide more accurate scenarios for people. Once we have that data we can try to fit a few parameters to improve predictions. I should note we are actively looking for people to help us curate data sources if you are interested!

I think the most intuitive explanation to your second question is a reduction of number of contacts you have per day - i.e. .6 corresponds to 60% reduction in social contacts in a given day. This is not exact but should provide you rough handrails for the mitigation numbers.

1) the parameters that don't fit with the already observed data should be visibly marked in a sense how much they are away from the observation. That's the most important use of the tool: to show which parameters simply don't match: for each parameter one should assume that others remain the same, then show how much that specific parameter has to be for the observations to match. If the parameter can't be any value with the rest unchanged to produce the observed output, it should be marked as n/a or whatever, otherwise the difference from the nearest possible should be shown.

2) you should allow longer time frame for the simulation (it should be possible to run it for e.g. 12 months).

2) is already there, it's an input field where you select day-month-year for both start and end.

Thank you very much for this, it's really great work.

Do you have a sense, or any benchmarks, for where the current responses across the world lie along the weak - strong mitigation scale? Or is that something we'll need to estimate using the model as the situation evolves more?

Not enough that I would feel confident publically stating. I think our best hope for accurate calibration would be to fit the data as it comes out (hence why we are focusing our efforts here) or to do a post-hoc analysis of a few examples, e.g. Wuhan vs South Korea. I'll note that mitigation effects will be best measured as a reduction in exponential growth.

That makes sense, thank you!

> I should note we are actively looking for people to help us curate data sources if you are interested!

Would be very interested to help. I sent you an email yesterday with the subject title:

    Interested in helping with data for `COVID-19 Scenarios`

We’ve all seen the time series of diagnosed cases in Italy versus the U.S. in which the latter appears to track closely but with a time delay. Is it relevant that the U.S. population is some 5 to 6 times Italy’s? Is it meaningful to compare populations by cases per capita?

In theory, the exponential growth rate of the virus doesn't depend on the population size. It more strongly depends on the population structure - i.e. the average number of social contacts made in a day.

While there's exponential growth yes, but further up the logistic curve it makes a difference (you can't kill more people than the population of any country).

Yes I agree. However, I don't think we're close to the inflection point where this is relevant yet.

It also depends on the growth rate of the number of tests administered. If you exponentially grow the number of tests given (which one could argue is the case in many areas), the absolute number of “confirmed cases” will also look exponential.

If you start with a hypothesis that this virus is in wide global circulation already, all this testing is doing is confirming “yup, people have the virus!”.

Only way to prove or disprove the hypothesis that the virus is widespread is large scale testing of a truly random subset of the entire population. Last I checked that isn’t happening anywhere in the United States and possibly elsewhere.

Why is proving that hypothesis important? Because if true it means the severity of this virus is dramatically less than our limited data suggests. If true, most of the rather draconian measures we are taking are pointless.

If false, god help us all I guess...

Theoretically, unbridled transmission will follow a logisitic curve, which is exponential at the start. Test kit production, testing capacity would be linear with spikes when capacity is ordered to be increased or production facilities come online.

The growth rate of infected deaths and the measurable mortality displacement makes that hypothesis extremely unlikely.

As does (albeit in a weaker form) the testing of the full population of Vò.

As does the testing in at least a few European countries, where the growth rate of tests outpaces that of confirmed infections. And/or where the number of tests administered vastly exceeds the number of confirmed infections.

Sure, once we have anti-body tests widely available, the data will become better.

You do get finite size effects with non-mean field models.

I just want to say thank you for putting this together as a free resource. Clearly a lot of effort went into this (and you all are making it better over time)

Interested in the backend. What is this coded in? Python/dash?

It's all coded in typescript and javascript. I've been toying around with a more in-depth model in web assembly, but as I alluded to elsewhere in this thread, the coding is slow going.

Hey Nicholas, love the tool - great job! I'm working on a new modelling tool (https://causal.app) and built a COVID-19 model myself: https://my.causal.app/models/1432. This lets you easily run Monte-Carlo simulations. Might be useful for you. But maybe your model is too advanced for Causal.

There is a link to the GitHub repo in the About section: https://github.com/neherlab/covid19_scenarios

Hi, The assumptions for mitigation (strong, weak, ...) aren't described anywhere (I think?).

There is a bit of info in the About page, where it says the most agressive measures taken in Wuhan gives a mitigation factor of 0.1 (with a source for that data).

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