Note: This version of the model includes vaccinations, based on the following data and assumptions:
As of 3/17/2021 the number and percentage of the Los Angeles County population that have received both 1st and 2nd doses is:
We make the following assumptions to fit our model to this data:
Assumptions: General population - We model only full protection from the vaccine, i.e. 1st and 2nd doses of Moderna and Pfizer or 1 dose of J&J. - We assume 10,000 vax / day during January and February, and 20,000 vax / day from March 1 - Sys.Date()
. This equals approximately 1,057,794 1st and 2nd doses by 3/17/21
Assumptions: 65+ population - We estimate that by 3/8/2021, 50% of individuals 65+ that have received at least a 1st dose have received their 2nd dose, i.e. 407,636 of the 815,271 have received both their 1st and 2nd doses by 3/8/2021. - We assume 3,500 vax / day during January and February, and 7,000 vax / day from March 1 - Sys.Date()
. This equals approximately 407,636 1st and 2nd doses by 3/8/21. We estimate that the same rate continues through to Sys.Date()
.
Please see the last two sub-figures in the Model Fits figure for a visual of these timelines.
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Summarizes the epidemic model fit with COVID-19 data for LAC from March 1 through 2021-03-18 for all disease states across multiple views: New cases, representing new daily incidence; the current number in a compartment at a specific date, relevant for understanding current prevalence rates and comparing with healthcare capacity limitations; and cumulative counts until a specific date. Observed data are plotted as black dots. The figure demonstrates that good model fits are achieved in all compartments across time.
Projections under the assumption of the infectious rate as of 2021-03-20
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This plot shows the time-varying Reproductive Number R(t) but NOT its effective value. This means it does NOT account for herd immunity. What is presented here is an indication of how much transmission is happening, without accounting for herd immunity.
Probability of hospitalization given infection, \(\alpha(t)\)
Probability of ICU admission given hospitalization, \(\kappa(t)\)
Probability of death given ICU admission, \(\delta(t)\)
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mean (95% CI) | |
---|---|
R0 | 3.573 (3.375,3.712) |
R(t) 2020-03-27 | 0.869 (0.82,0.933) |
R(t) 2020-05-15 | 1.251 (1.101,1.372) |
R(t) 2020-11-26 | 1.98 (1.832,2.144) |
r(t) 2020-04-15 | 0.245 (0.153,0.338) |
r(t) 2020-08-15 | 0.385 (0.158,0.778) |
Probability of hospitalization given infection, \(\alpha(t)\)
Probability of ICU admission given hospitalization, \(\kappa(t)\)
Probability of death given ICU admission, \(\delta(t)\)
mean (95% CI) Alpha_t | mean (95% CI) Kappa_t | mean (95% CI) Delta_t | |
---|---|---|---|
2020-05-01 | 0.14 (0.131,0.146) | 0.607 (0.596,0.619) | 0.563 (0.551,0.58) |
2020-08-01 | 0.048 (0.033,0.067) | 0.548 (0.541,0.56) | 0.508 (0.5,0.52) |
CFR mean (95% CI) | IFR mean (95% CI) | |
---|---|---|
2020-03-01 | 0.0081 (0.0012,0.0182) | 0.002 (0.0003,0.0048) |
2020-03-15 | 0.008 (0.005,0.0115) | 0.002 (0.0009,0.0033) |
2020-04-01 | 0.0146 (0.0122,0.0176) | 0.0036 (0.002,0.0052) |
2020-04-15 | 0.0251 (0.022,0.0286) | 0.0062 (0.0034,0.009) |
2020-05-01 | 0.0326 (0.0288,0.0368) | 0.008 (0.0045,0.0116) |
2020-05-15 | 0.0353 (0.0313,0.0398) | 0.0087 (0.0049,0.0126) |
2020-06-01 | 0.0342 (0.0301,0.0396) |