A suggestion: adding decimal separators to numbers in the "Cases through time" chart for better readability.
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...
What does the terms moderate mitigation, weak mitigation, strong mitigation mean? What measures are required to go from none to weak, to moderate mitigation?
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.
2) you should allow longer time frame for the simulation (it should be possible to run it for e.g. 12 months).
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?
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`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...
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.
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.