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.

R_t: COVID-19 Spread in Our Community

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

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

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

When R_t 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 R_t stays greater than 1, it can lead to many challenges, including hospitals not being able to care for everyone who gets sick.

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

How to read the plot: The plot shows COVID-19 rates since the 10th COVID-19 case, for each California County. The COVID-19 rates are calculated as the number of cases per 100,000 residents in each county. This helps account for different population sizes when comparing COVID-19 spread across California.

County Comparison

While disease modeling is helpful for planning, additional analyses help us understand the spread of COVID-19 in our community relative to others. Santa Cruz County has a lower cumulative case count and rate compared to many other California counties.