Open mikkokotila opened 4 years ago
Below 50
In Canada, there were 10 ICU beds capable of providing mechanical ventilation per 100,000 population, and 15 invasive mechanical ventilators, placing Canada above the U.K. but well below the U.S., Germany, and Belgium. SOURCE
According to figures shared with The Moscow Times by the Moscow-based Headway Group, which monitors government tenders, Russia has a total of around 42,000-43,000 ventilators in its state hospitals around the country — an average of about 29 ventilators per 100,000 residents and significantly higher than Italy’s 8 ventilators per 100,000 residents. SOURCE
We recommend the following team (see figure 1 from the ref):
Kallstrom notes that on average a single respiratory therapist can manage ten patients at once. But that’s in normal times. “If we have to ventilate 750,000 people, we will have a situation we can’t handle,” Kallstrom observes. SOURCE
"I work 12-hour shifts. Right now, we are running about four times the number of ventilators than we normally have going. We have such a large volume of patients, but it’s really hard to find enough people to fill all the shifts. The caregiver-to-patient ratio has gone down, and you can’t spend as much time with each patient, you can’t adjust the vent settings as aggressively because you’re not going into the room as often. And we’re also trying to avoid going into the room as much as possible to reduce infection risk of staff and to conserve personal protective equipment.”SOURCE
Depending on the source, maybe up to 10, but this is a controversial topic.
The duration of non-invasive ventilation was 4–22 d [median 9 d] and mechanical ventilation for 3–20 d [median 17 d].
Singhal, T., 2020. A Review of Coronavirus Disease-2019 (COVID-19). The Indian Journal of Pediatrics, pp.1-6.
Sequential simulation of n rounds where each round represents a single day.
mild
> recovery
mild
> severe
> recovery
mild
> severe
> hospitalisation
> recovery
mild
> severe
> hospitalisation
> icu
> recovery
mild
> severe
> hospitalisation
> icu
> ventilator
> recovery
mild
> severe
> hospitalisation
> icu
> ventilator
> death
mild
> severe
> critical
> death
Maybe we might first create two different simulations - one which is described above, and then this one - and combine them later?
Stage progressions can take place in two ways; either downstream or to death or recovery.
stage | reference value | range | distribution |
---|---|---|---|
mild ranges |
350 | ||
severe ranges |
|||
hospitalisation ranges |
|||
icu ranges |
|||
ventilation ranges |
|||
recovery ranges |
|||
death ranges |
NOTE: we probably want to first do this without any parameters per progression. But in the next, we would want to test parameter value ranges (e.g. factors that help to avoid the progression from hospitalisation
to ICU
)
ventilation_capacity
icu_bed_capacity
patients
patient_status
ventilation_capacity
icu_bed_capacity
population
and decrease_rate
, and intervention_start_date
and intervention_measures_rate
as inputs to provide 200 day infection, hospitalization, recovery, and fatality dataIt's actually not meaningful to use ICU stay rate from literature, because it is from tested, and not infected. To fix this, we must have input "test_rate" before "hospitalization_rate"
EDIT: here is a clearer version of the problem:
We are using SEIR model as base to get the number of infected, because it is widely used, and then add hospitalized, icu, and ventilation steps to the model based on available literature. The issue is the step between infected and hospitalized, which I think should be called "tested_positive" but at the moment we don't have a good way to approximate what the difference between infected and "tested_positive" is. Any ideas or inputs?
QUESTION: how do you handle this problem of the jump from infected to hospitalized in your model?
ANSWER: We model the time series of infected to generate symptomatic using Weibell distribution assuming 18.9% are asymptomatic (JP study nr). Then from infected we model using gaus distribution 10-15% getting to hospital with a time delay (smeared with gaussian again). Same for ICU and deaths.
So far we've spent few days with SEIR model to understand how to extend it, and what limitations it may have. A person familiar with the field said "public policy will not be made unless its based on SEIR" as well as "usually, staying very close to standard SEIR is most practical" and "adding things to SEIR tends to quickly become unpractical". It looks like, the funnel we are interested in is as follows:
To achieve this, we must either a) extend SEIR model to incorporate these or b) create a separate function that handles from Sy onwards. It seems latter is more appropriate, because SyCHIV part of the progression funnel is capacity/queue problem.
symptomatic_rate
inputconfirmed_rate
inputhospitalized_rate
and hospitalized_duration
ICU_rate
and ICU_duration
ventilation_rate
and ventilation_duration
duration
will add up to the number following days according to duration
In the SEIR model, R
refers to "removed" which means that they are no longer in the system. In our case we must ignore it, and instead follow an approach where death can only result from two different reasons:
ICU_mortality
)I think I could make a simulation using just the Disease progression table from @EnergyGuy3 's entry above. From those we don't get the mild> severe steps and icu/respirator needs.
As first outputs (without setting restrictions) we get the number of beds (and bodybags) needed. If we can estimate progression to icu then we can start to estimate respirator needs and staff needed to operate them (or set limits to them or beds and so on)
But the input from a population model is definitely needed - thoughts how we could get that as an input - maybe there is a model code that could used to generate that..
@samoojas two things to keep in mind here:
A person familiar with the field said "public policy will not be made unless its based on SEIR" as well as "usually, staying very close to standard SEIR is most practical" and "adding things to SEIR tends to quickly become unpractical".
So the idea is that if we use the gold standard way (SEIR) up until infected
and work from there as a queue management problem up to ICU admission. We now have two versions of SEIR, with different equations.
Whatever comes next, would output two numbers (icu_stay
and ventilation
) and the capacity of those two somehow affects mortality. Once the ICU capacity is exceeded, it follows that those needing ICU treatment, either for COVID-19 or something else, will die.
As first outputs (without setting restrictions) we get the number of beds (and bodybags) needed. If we can estimate progression to icu then we can start to estimate respirator needs and staff needed to operate them (or set limits to them or beds and so on)
Makes sense. I think @EnergyGuy3 has a good hold on the literature where those numbers are already available.
Sounds very good. I will take a look at the SEIR codes and start fiddling towards a python code on the queuing part.
After having spent the weekend dedicated with this matter, I have been able to confirm the below logic as robust for our problem:
data / parameter range sourcing