Orange County, CA COVID-19 Situation Report Jul 11-Aug 15
Last updated: 2020-08-31
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Knit directory: uci_covid_modeling/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | <<<<<<< HEAD dbf4f20 ======= 5fd3a50 | igoldsteinh | 2020-08-31 | fixed issue 1, updated report with new pop size calculation |
| html | 5fd3a50 | igoldsteinh | 2020-08-31 | fixed issue 1, updated report with new pop size calculation |
| Rmd | 800b9bc >>>>>>> e5e49c0741dc177f3753dbf9a92440a9da9428ef | vnminin | 2020-08-31 | playing with text |
| html | dbf4f20 | vnminin | 2020-08-31 | playing with text |
| Rmd | 0f6ec4a | vnminin | 2020-08-30 | completing merge |
| Rmd | 3857e0a | vnminin | 2020-08-30 | started to rearrange figures in situation report |
| html | 3857e0a | vnminin | 2020-08-30 | started to rearrange figures in situation report |
| Rmd | c762f18 | igoldsteinh | 2020-08-28 | added software to about, made vladimir the contact |
| Rmd | 2eb7b73 | vnminin | 2020-08-27 | added some text with effexctive reproductive number |
| html | 2eb7b73 | vnminin | 2020-08-27 | added some text with effexctive reproductive number |
| Rmd | 7c5be6a | Damon Bayer | 2020-08-27 | Add R_eff to executive summary |
| Rmd | 33f234e | vnminin | 2020-08-27 | some formatting |
| html | 33f234e | vnminin | 2020-08-27 | some formatting |
| html | 6698589 | igoldsteinh | 2020-08-27 | change jun 20 - jul25 so that it looks like current report |
| Rmd | 4ca76fb | igoldsteinh | 2020-08-27 | Updating website, also adding latest report Jul 11 - Aug 15 |
| html | 4ca76fb | igoldsteinh | 2020-08-27 | Updating website, also adding latest report Jul 11 - Aug 15 |
| Rmd | a07b913 | igoldsteinh | 2020-08-26 | fixed model graphic, fixed legend sizes, fixed readme, added about |
| html | a07b913 | igoldsteinh | 2020-08-26 | fixed model graphic, fixed legend sizes, fixed readme, added about |
| html | 8d52b42 | igoldsteinh | 2020-08-26 | more updates to website |
| Rmd | 79a6fd6 | igoldsteinh | 2020-08-26 | updating about and license, experimenting with figure size |
| Rmd | ddbef62 | igoldsteinh | 2020-08-26 | changing website layout |
| html | ddbef62 | igoldsteinh | 2020-08-26 | changing website layout |
| Rmd | 64adfeb | igoldsteinh | 2020-08-20 | cleaning up helper functions and index |
| Rmd | 8abe830 | igoldsteinh | 2020-08-06 | fixed figure length, updated, tried a readme |
| html | 8abe830 | igoldsteinh | 2020-08-06 | fixed figure length, updated, tried a readme |
| Rmd | 99d68f7 | igoldsteinh | 2020-07-29 | bare bones report |
| html | 99d68f7 | igoldsteinh | 2020-07-29 | bare bones report |
| Rmd | 792b89a | vnminin | 2020-07-27 | Start workflowr project. |
Orange County, CA COVID-19 Situation Report, Jul 11 - Aug 15
The goal of this report is to inform interested parties about dynamics of SARS-CoV-2 spread in Orange County, CA and to predict epidemic trajectories. Methodological details are provided below and in the accompanying manuscript.
Summary (statements are made assuming 95% credibility levels)
- The number of reported cases (15,000 in this period) underestimates the actual number of infections by a factor that ranges between 1.6 and 11. This means that we estimate that the total number of infections occurred between July 11, 2020 and August 15, 2020 is between 23,000 and 160,000.
- Prevalence (number of infectious individuals at any time point) is declining, but is projected to stay tens of thousands in September.
- Deaths are also underreported, but not significantly so. Somewhere between 0.2% and 1% of all infections (not cases!) result in death.
- Basic reproductive number (\(R_0\)), defined as the average number of secondary infections one infectious individual produces in a completely susceptible population, is inside the interval (0.43, 0.78).
- Effective reproductive number (\(R_e\)), defined as its basic counterpart above, but allowing for some fraction of the population to be removed (recovered or deseased), as of August 15, 2020 is inside the interval (0.41, 0.72). We want to keep \(R_e < 1\) in order to control virus transmission.
Abbreviated technical details
Our approach is based on fitting a mechanistic model of SARS-CoV-2 spread to multiple sources of surveillance data.
Model inputs
Our method takes three time series as input: daily new tests, case counts, and deaths. However, we find daily resolution to be too noisy due to delay in testing reports, weekend effect, etc. So we aggregated/binned the three types of counts in 3 day intervals. These aggregated time series are shown below.
Model structure
We assume that all individuals in Orange County, CA can be split into 6 compartments: S = susceptible individuals, E = infected, but not yet infectious individuals, \(\text{I}_\text{e}\) = individuals at early stages of infection, \(\text{I}_\text{p}\) = individuals at progressed stages of infection (assumed 20% less infectious than individuals at the early infection stage), R = recovered individuals, D = individuals who died due to COVID-19. Possible progressions of an individual through the above compartments are depicted in the diagram below.
| Version | Author | Date |
|---|---|---|
| 4ca76fb | igoldsteinh | 2020-08-27 |
Mathematically, we assume that dynamics of the proportions of individuals in each compartment follow a set of ordinary differential equations corresponding to the above diagram. These equations are controlled by the following parameters:
- Basic reproductive number (\(R_0\))
- mean duration of the latent period
- mean duration of the early infection period
- mean duration of the progressed infection period
- probability of transitioning from progressed infection to death, rather than to recovery (i.e., IFR)
We fit this model to data by assuming that case counts are noisy realizations of the actual number of individuals progressing from \(\text{I}_\text{e}\) compartment to \(\text{I}_\text{p}\) compartment. Similarly we assume that observed deaths are noisy realizations of the actual number of individuals progressing from \(\text{I}_\text{p}\) compartment to \(\text{D}\) compartment. A priori, we assume that death counts are significantly less noisy than case counts. We use a Bayesian estimation framework, which means that all estimated quantities receive credible intervals (e.g., 80% or 95% credible intervals). Width of these credible intervals encode the amount of uncertainty that we have in the estimated quantities.
Appendix
Sensitivity to Prior for \(R_0\)
We examine how sensitive our conclusions about \(R_0\) to our prior assumptions by repeating estimation of all model parameters under different priors for this parameter. The priors are listed in the titles of the figures. Although the prior distribution of \(R_0\) does have some effect on its posterior (as it should), the our results and conclusions are not too sensitive to a particular specification of this prior.
=======Appendix
Sensitivity to Prior for \(R_0\)
We examine how sensitive our conclusions about \(R_0\) are to our prior assumptions by repeating estimation of all model parameters under different priors for this parameter. The priors are listed in the titles of the figures. Although the prior distribution of \(R_0\) does have some effect on its posterior (as it should), the our results and conclusions are not too sensitive to a particular specification of this prior.
>>>>>>> e5e49c0741dc177f3753dbf9a92440a9da9428ef
Sensitivity to prior for fraction initially infected
We examine how sensitive our conclusions about \(R_0\) to our prior assumptions by repeating estimation of all model parameters under different priors for the parameter controlling how many people are infected initially. This prior changes depending on the time period, so we adjust by changing the prior mean to be twice as large or one half as large as the default prior. As we would expect, changing this prior changes the number of people we estimate will become infected or are currently infectious. However, it seems to have little impact on the posterior of \(R_0\).
Sensitivity to prior for fraction initially infected
We examine how sensitive our conclusions about \(R_0\) are to our prior assumptions by repeating estimation of all model parameters under different priors for the parameter controlling how many people are infected initially. This prior changes depending on the time period, so we adjust by changing the prior mean to be twice as large or one half as large as the default prior. As we would expect, changing this prior changes the number of people we estimate will become infected or are currently infectious. However, it seems to have little impact on the posterior of \(R_0\).
Last updated on 2020-08-31.
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