Orange County, CA COVID Situation Report Aug 16, 2020

Last updated: 2020-12-28

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File	Version	Author	Date	Message
html	c9b71f2	Damon Bayer	2020-12-22	2020-12-21 Update
Rmd	6e0caf7	Damon Bayer	2020-12-21	City Models
html	6e0caf7	Damon Bayer	2020-12-21	City Models
Rmd	bcf3831	Damon Bayer	2020-12-18	Fix Reff date mismatch
html	bcf3831	Damon Bayer	2020-12-18	Fix Reff date mismatch
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html	ad35c96	Damon Bayer	2020-12-16	Slight plot modification
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html	7c7928f	Damon Bayer	2020-12-16	Deaths Plot
Rmd	9596b3d	Damon Bayer	2020-12-16	piece-wise constant IFR and R0
html	9596b3d	Damon Bayer	2020-12-16	piece-wise constant IFR and R0
Rmd	b4111ab	Damon Bayer	2020-12-16	Minor plot tweaks
html	b4111ab	Damon Bayer	2020-12-16	Minor plot tweaks
Rmd	41de9bf	Damon Bayer	2020-12-16	2020-12-14 Update
html	41de9bf	Damon Bayer	2020-12-16	2020-12-14 Update
Rmd	dcffe20	Damon Bayer	2020-12-16	Initial Commit
html	dcffe20	Damon Bayer	2020-12-16	Initial Commit

Orange County, CA COVID-19 Situation Report, September 28, 2020

Report period: Aug 16 - Sep 20 (we don’t use the most recent data due to reporting delays)

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. We are also contributing to COVID Trends by UC Irvine project that provides data visualizations of California County trends across time and space.

Version	Author	Date
6e0caf7	Damon Bayer	2020-12-21
ad35c96	Damon Bayer	2020-12-16
7c7928f	Damon Bayer	2020-12-16
dcffe20	Damon Bayer	2020-12-16

Version	Author	Date
6e0caf7	Damon Bayer	2020-12-21
9596b3d	Damon Bayer	2020-12-16
b4111ab	Damon Bayer	2020-12-16
41de9bf	Damon Bayer	2020-12-16

Version	Author	Date
6e0caf7	Damon Bayer	2020-12-21
9596b3d	Damon Bayer	2020-12-16
b4111ab	Damon Bayer	2020-12-16
41de9bf	Damon Bayer	2020-12-16

Version	Author	Date
6e0caf7	Damon Bayer	2020-12-21
d5a3650	Damon Bayer	2020-12-17
41de9bf	Damon Bayer	2020-12-16

Summary (statements are made assuming 95% credibility levels)

The number of reported cases (51,199, shown as black bars in the top-middle plot above) underestimates the actual number of infections by a factor that ranges between 4.8 and 12.5. This means that we estimate that the total number of infections which occurred by September 20, 2020 is between 244,016 and 638,953. We estimate that the total number of infections will be between 270,851 and 721,041 on October 20, 2020.
Prevalence (number of infectious individuals at any time point) is decreasing and projected to be between 10,135 and 73,660 on October 20, 2020.
Somewhere between 0.16% and 0.77% 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 estimated to be between 0.7 and 1.1.
Effective reproductive number (\(R_e\)), defined as its basic counterpart above, but allowing for some fraction of the population to be removed (recovered or deceased), is estimated to be between 0.6 and 1 on September 29, 2020. We want to keep \(R_e < 1\) in order to control virus transmission.

Note: We previously created a report using a similar model with a different implementation. Archives of the old report can be found here.

Abbreviated technical details (optional)

Our approach is based on fitting a mechanistic model of SARS-CoV-2 spread to multiple sources of surveillance data. A more fleshed out method description is in the manuscript.

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
dcffe20	Damon Bayer	2020-12-16

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.