Orange County, CA COVID Situation Report Jun 7, 2020

Last updated: 2020-12-16

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Orange County, CA COVID-19 Situation Report, July 20, 2020

Report period: Jun 07 - Jul 12 (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
dcffe20	Damon Bayer	2020-12-16

Version	Author	Date
41de9bf	Damon Bayer	2020-12-16

Version	Author	Date
41de9bf	Damon Bayer	2020-12-16

Version	Author	Date
41de9bf	Damon Bayer	2020-12-16

Summary (statements are made assuming 95% credibility levels)

The number of reported cases (44,109, shown as black bars in the top-middle plot above) underestimates the actual number of infections by a factor that ranges between 4.6 and 16.3. This means that we estimate that the total number of infections which occurred by July 12, 2020 is between 202,457 and 716,842. We estimate that the total number of infections will be between 202,457 and 716,842 on August 11, 2020.
Prevalence (number of infectious individuals at any time point) is increasing and projected to be between 46,415 and 282,502 on August 11, 2020.
Somewhere between 0.32% and 1.17% 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 1.2 and 1.9.
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 1.2 and 1.7 on July 12, 2020. We want to keep \(R_e < 1\) in order to control virus transmission.

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.