What is Disease Modeling?

Disease models make forecasts about what could happen. They show us how a disease might spread in a community. These forecasts help us to plan for different situations.

Just like weather forecasts, disease models are not perfect. And like weather forecasts, disease models are most accurate for forecasting the near future.

Because COVID-19 is a new disease, we learn more about it all the time. We adjust our forecast models as we learn.

You can see Santa Cruz County’s modeling code for yourself here.


 
 
 

Santa Cruz County COVID-19 Hospitalization Projections

For current information on hospital usage including ICU beds, please visit the California COVID-19 Hospital data page.

How to read the model: For the actual number of hospitalizations in the past, we use the blue dots. To look at the future, we use the dark blue line and the light blue area. The dark blue line is the most likely number of hospitalizations in the future. Since models are not perfect, the light blue wider area shows the range of likely hospitalizations.

Why We Forecast Hospitalizations

We all rely on our hospitals to take care of us when we are very ill. If a hospital gets too full, it doesn’t have enough space or staff to care for everyone.

So, it is important to keep track of how many people are staying in a hospital at one time. It is also important to use our forecasts to predict when hospitals might get too full.

When our hospitals start to get too full, we need to take actions to slow the spread of COVID-19.

Tracking and forecasting how many people are in our hospitals help us SAVE Lives in our community.

How to read the plot: The plot shows green (for good) when Rt is below 1 and COVID-19 spread is decreasing. When Rt is above 1 and COVID-19 spread is increasing, the plot is yellow (take caution). The darker line shows the most likely Rt in Santa Cruz county. Since models are not perfect, the shaded areas around the darker line show the range of likely Rt values. Please note that case data from the last 7 days are frequently updated and are excluded from the estimation of Rt. New plots are published every Wednesday.

Rt: COVID-19 Spread in Our Community

The Effective Reproductive Number, shown here as “Rt” helps us understand how fast COVID-19 is spreading in our community. For COVID-19, RRt tells us the average number of people who will contract this disease from each infected person.

For example, if Rt equals 1, each existing infection causes one new infection. An Rt equal to 1 means the disease will stay present and stable in our community.

If Rt is less than 1, each existing infection causes less than one new infection. Therefore, if Rt stays below 1, spread of the disease declines and it eventually leaves the community.

When Rt is more than 1, each existing COVID-19 infection causes more than one new infection. The disease will be transmitted between more and more people and the spread of the disease is growing. If Rt stays greater than 1, it can lead to many challenges, including hospitals not being able to care for everyone who gets sick.

Rt depends on people’s behavior, like wearing a mask or keeping social distance. This is why Rt can change over time. For example, in the plot around March 20th the COVID-19 value for Rt in our county was probably about 2. Then, when many people stayed home through April and May, Rt dropped below 1.

The plot above displays the 14-day average of the daily, confirmed COVID-19 cases in Santa Cruz County. The plot also shows the percent change from 2 weeks prior, with colors indicating an increasing or decreasing trend. Note that this plot is updated on Wednesdays only.

If you are experiencing difficulties with viewing data within the Dashboards, please clear your browser’s cache and refresh the web page to correct the issue.

This dashboard is updated every Monday and Thursday.

Case numbers are an accumulation of cases reported to the Local Health Jurisdiction, and are recorded by episode date. Total known cases of COVID-19 will fluctuate due to adjustments such as jurisdiction transfers and routine data cleaning, which may involve the resolution of existing cases (i.e., probable cases either confirmed or invalidated). Cases will continue to be monitored and are likely to be adjusted further.

Disclaimer: Due to known data delays in COVID-19 reporting systems, data is preliminary and modeling is subject to change.

 

Interested in more COVID-19 models?

Many models exist online to help answer questions about COVID-19. Since every model is based on a set of assumptions, it is helpful to review other models to compare projections, trends, and methods. Each model will likely show different results. For more information, we recommend visiting CDC’s website: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/mathematical-modeling.html or the CDPH CalCat website: https://calcat.covid19.ca.gov/cacovidmodels/.