Last updated: 2020-08-31

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Knit directory: uci_covid_modeling/

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<<<<<<< HEAD The results in this page were generated with repository version dbf4f20. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files. ======= The results in this page were generated with repository version 5fd3a50. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files. >>>>>>> e5e49c0741dc177f3753dbf9a92440a9da9428ef

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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
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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.

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Summary (statements are made assuming 95% credibility levels)

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.

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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.

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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.

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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.

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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.

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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 \(R_0\).

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33f234e vnminin 2020-08-27
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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 \(R_0\).

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Last updated on 2020-08-31.


R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.0.1 scales_1.1.1    tidybayes_2.1.1 forcats_0.5.0  
 [5] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[13] here_0.1        lubridate_1.7.9 workflowr_1.6.2

loaded via a namespace (and not attached):
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[67] glue_1.4.1           evaluate_0.14        V8_3.2.0            
[70] RcppParallel_5.0.2   modelr_0.1.8         vctrs_0.3.1         
[73] httpuv_1.5.4         cellranger_1.1.0     gtable_0.3.0        
[76] assertthat_0.2.1     xfun_0.15            broom_0.7.0         
[79] coda_0.19-3          later_1.1.0.1        ellipsis_0.3.1      
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