Prepared by the Colorado COVID-19 Modeling Group
Colorado School of Public Health: Andrea Buchwald, Elizabeth Carlton, Debashis Ghosh, Irina Kasarskis, Jonathan Samet, Laura Timm, Emily Wu; University of Colorado School of Medicine: Kathryn Colborn; University of Colorado-Boulder Department of Applied Mathematics: Sabina Altus, David Bortz; University of Colorado-Denver: jimi adams; Colorado State University: Jude Bayham

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Key messages in this report are:


This report provides the results of epidemic models for regions of Colorado, using methods similar to that used for the state-level model. Estimates are presented for the 11 Local Public Health Agencies (LPHAs) regions in the state and for 8 selected counties with populations that are sufficiently large to allow for county-level estimates. The model results are subject to greater uncertainty than those for the entire state because there are fewer hospitalizations and cases in each region than in the state as a whole. Estimates are most uncertain for the regions with the smallest population size. We use the model as well as COVID-19 hospital, case and vaccination data to generate four measures for each region. These measures can be used to gauge the current state of SARS-CoV-2 in each region.

Table 1. The estimated effective reproductive number, prevalence of infections, percent of the population infected and vaccinated to date by region. These metrics are estimated using hospitalization data from the Colorado COVID Patient Hospitalization Surveillance (COPHS) through 02/08/2021. In regions with smaller populations, reported cases are also used to generate these estimates. Vaccination data are provided by CDPHE and reflect the proportion of the population that has received at least one dose of either Pfizer or Moderna vaccines.

Are infections increasing or decreasing?
How many people are infectious?
How many people have been infected to date?
Re Infections are… Prevalence per 100,000 People infectious Cumulative Infections to Date Proportion of population infected to date Proportion of population vaccinated
LPHA Regions
Central 0.7 Decreasing 283 1 in 353 134,000 16.6 9.7
Central Mountains 1.1 Increasing 842 1 in 119 22,100 12.1 13.8
East Central 0.6 Decreasing 660 1 in 152 22,800 52.9 8.0
Metro 0.8 Decreasing 582 1 in 172 797,000 24.2 11.0
Northeast 1.0 Flat 1,286 1 in 78 223,000 29.1 11.2
Northwest 0.6 Decreasing 422 1 in 237 35,000 17.2 11.0
San Luis Valley 0.8 Decreasing 548 1 in 182 8,530 18.4 15.2
South Central 0.8 Decreasing 1,125 1 in 89 89,300 36.7 14.3
Southeast 0.7 Decreasing 1,295 1 in 77 19,800 42.1 13.1
Southwest 1.0 Flat 684 1 in 146 11,100 10.9 16.5
West Central Partnership 1.4 Increasing 3,004 1 in 33 15,200 14.2 12.5
Eight select counties
Adams 0.7 Decreasing 589 1 in 170 201,000 38.0 8.5
Arapahoe 0.8 Decreasing 790 1 in 127 192,000 28.8 10.1
Boulder 0.5 Decreasing 171 1 in 586 45,700 13.8 12.2
Broomfield 1.2 Increasing 974 1 in 103 11,100 15.2 13.8
Denver 0.8 Decreasing 865 1 in 116 266,000 36.0 11.0
Douglas 0.9 Decreasing 709 1 in 141 49,000 13.8 11.2
El Paso 0.4 Decreasing 187 1 in 534 137,000 18.5 9.1
Jefferson plus 0.5 Decreasing 127 1 in 785 70,300 11.7 13.2
Due to the small population sizes of Gilpin and Clear Creek counties, these counties are combined with Jefferson County. Jefferson County comprises 97% of the population in the Jefferson plus county cluster.
Due to lags between infection and hospitalization, the estimated effective reproductive number (Re) reflects the spread of infections approximately two weeks prior to the data of the last observed hospitalization.

Figure 1. Map showing the 11 LPHA regions for which estimates were generated. Regions in yellow (effective reproductive number > 1), orange (prevalence > 1%), or red (both effective reproductive number > 1 and prevalence > 1%) indicate areas of concern.

Effective Reproductive Number

The figure below shows the estimated effective reproductive number for each region since March.

The effective reproduction number (Re) is a measure of how rapidly infections are spreading or declining in a region at a given point in time. When the effective reproductive number is below 1, infections are decreasing. When the effective reproductive number is above 1, infections are increasing.

The effective reproductive number is estimated using our age-structured SEIR model fit to hospitalization data. In the four LPHA regions with smaller populations, reported SARS-CoV-2 case data are also used (San Luis Valley, Southeast, Southwest, and West Central Partnership). Because we base our parameter estimates primarily on COVID-19 hospitalization data, and hospitalizations today generally reflect infections occurring approximately 13 days prior, our most recent estimates of the effective reproductive number likely reflect the spread of infections occurring on approximately 01/26/2021.

Figure 2. The estimated effective reproductive number (Re) over time in the 11 LPHA regions in Colorado, and 8 selected counties and county clusters. Estimates shown using COVID-19 hospitalization data through 02/08/2021.

Infection prevalence

Infection prevalence provides an estimate of the proportion of the population that is currently infected with SARS-CoV-2 and capable of spreading infections. At higher levels of infection prevalence, individuals are more likely to encounter infectious individuals among their contacts. Because many people experience no symptoms or mild symptoms of COVID-19, many infections are not identified by surveillance systems. The estimates we present here are intended to provide an approximation of all infections, including those not detected by the Colorado Electronic Disease Reporting System (CEDRS).

The figure below shows the estimated infection prevalence per 100,000 individuals for each region. These are estimated from SEIR models fit separately to each area’s reported data.

Figure 3. Estimated prevalence per 100,000 population for each of the 11 LPHA regions (top), plus the 8 selected counties and county clusters (bottom). All prevalence values over 1,000 per 100,000 are shown in dark red. Prevalence values estimated up to 02/08/2021.

The percent of the population infected to date

As more people develop immunity, due to vaccination or prior infection, the spread of infections slows because infectious individuals are less likely to encounter individuals that are susceptible to infection.

The figure below shows model-generated estimates of the percent of the population that has been infected to date for each region. This is a partial snapshot of the population currently immune to infection in each region. The roll out of vaccinations throughout Colorado is increasing the size of the immune population which will help to slow the spread of infections. Estimates of the proportion of the population vaccinated to date are provided in Table 1. There is growing evidence that immunity to SARS-CoV-2 following infection wanes over time. This means it is possible that people infected with SARS-CoV-2 early in the pandemic are now susceptible to becoming infected again if they are not vaccinated. We are working to update our estimates to describe the estimated population immune in each region.

Figure 4. Estimated proportion of the population recovered to date for each of the 11 LPHA regions in Colorado (top) and each of the 8 selected counties and county clusters (bottom). Exposed proportion values estimated up to 02/08/2021. Black dashed line indicates mean of Colorado (top) and selected counties (bottom).

COVID-19 hospitalizations

The figures below show the daily number of individuals hospitalized with COVID-19 from each region. Hospitalization data are from the COVID Patient Hospitalization Surveillance (COPHS) maintained by the Colorado Department of Public Health and the Environment (CDPHE). Each COVID-19 patient is assigned to a region based on their home zip code. COVID-19 hospitalizations are shown per 100,000 population to allow comparability across regions.

COVID-19 hospitalizations are a sensitive measure of SARS-CoV-2 transmission and are an important indicator of the severity of infections in a region. While many SARS-CoV-2 infections are not captured by state surveillance systems, we expect that almost all COVID-19 hospitalizations are identified.