Location determines the demographic data used by the model, including population, existing data about the spread of Covid-19 in the region, and historical social distancing levels.
The social distancing scenario models what the people and governments in the region might do in the future — how socially distanced will they be, and for how long?
Instead of selecting a single scenario, our model provides several potential options for social distancing. Many scenarios illustrate what happens when distancing measures are stopped entirely: a second wave of cases. Several scenarios suppress the virus enough for a robust “test and trace” strategy to become feasible. We model these options to show how our policies and collective actions could impact the overall spread of the virus.
The following graph displays social distancing levels relative to regular social activity. The current distancing level, , is calculated based on the average the past seven days of available mobility data for California, which was last updated on .
Past social distancing levels are based on available mobility data for California, and prospective social distancing levels are based on the selected scenario: .
We use the data available to us — location demographics, reported fatalities, and positive test cases — to estimate when Covid-19 began to spread in a location. The model estimates that Covid-19 began to spread in California on .
Epidemiologists measure how quickly a disease spreads through R₀, its basic reproduction number, defined as the number of people a disease will spread to from a single infected person. R₀ differs across geographic locations based on population demographics and density. The model couples this information with confirmed fatality and positive testing data to estimate how contagious Covid-19 is in each location. The model estimates that Covid-19 had an R₀ of in California when the virus first arrived and there were no distancing measures in place.
When social distancing measures are introduced, it becomes more difficult for a disease to spread through a population. We represent this usingRt, the effective reproduction number. Rt represents how many people a single case of the disease will spread to at a given point in time, taking social distancing measures into account.
If the virus spreads through a significant portion of the population, it has a decreasing chance of reaching a susceptible person. This also contributes to a reduced Rt.
Our model is based upon a standard epidemiological model called the SEIR model. The SEIR model is a compartmental model, which estimates the spread of a virus by dividing the population into different groups:
This graph shows a detailed view of how we project that Covid-19 will affect the population of California over time. While only a small portion of the population actively has Covid-19 at any given time, it can quickly spread. The graph in the top right shows how small changes compound to impact the population as a whole.
The model estimates that by , of the California population will have contracted Covid-19.
We use two primary data sources to calibrate the curves for each state: confirmed positive tests and confirmed fatalities. The model takes these data points alongside the distancing data and computes a set of curves that satisfy the epidemiological constraints of the SEIR model.
Both datasets are imperfect. The model assumes that the number of reported positive tests is less than the number of cases in a region and predicts the fraction of cases detected by tests in California, accounting for how testing capacity varies over time. While the number of fatalities can also be underreported, the model doesn’t include additional adjustments beyond predicting the fraction of cases detected.
If we look at this data on a logarithmic scale, we can see how the actual data aligns with the model’s predictions:positive tests andreported fatalities, and how they compare to the predicted number oftotal Covid-19 cases in California after accounting for estimated testing rates.
Reading the graph: Each line represents the model’s best estimation. The shaded area around a line indicates uncertainty: the darker the area, the more likely the outcome.
To arrive at a prediction of viral spread for each state that best matches the available data for California, the model adjusts three values: the date Covid-19 arrived, the R₀ (basic reproduction number), and fraction of cases being detected in California. Reported fatalities and confirmed positive cases are weighed at a 3:1 ratio during the fitting process.
The graph above shows the impact of the virus on a cumulative basis: this gives us a sense of overall impact, but doesn’t give us a good look at the daily change in cases. While daily reports tend to fluctuate, over time they indicate if there is an increase or decrease in viral spread.
The following graph comparesnew infections per day,positive tests per day, andreported fatalities per day, along with their respective confirmed data points:
The following charts show our projections of hospitalizations due to Covid-19. Unlike the fatality and confirmed case data, we do not fit the model to hospitalization data. Instead, the model projects where hospital occupancy is expected to fall based on published times from symptom onset to hospitalization. Hospitalized cases are not consistently reported by all states. When reported, they have variable reporting delays, may not reflect all hospital systems in a state, and usually only include cases with confirmed positive tests (and not unconfirmed suspected cases).
We estimate the hospital capacity for Covid-19 patients by taking the number of available beds and discounting for that hospital system’s typical occupancy rate. Note that these hospitalization estimates do not include patients who are admitted to the intensive care unit, which is modeled separately below.
The graph shows how projections forpatients requiring hospitalization andpatients currently reported hospitalized compare to estimated hospital capacity. The distinction being that the number reported hospitalized is delayed from the actual number of infections severe enough to require hospitalization and there are a percentage of cases that will require hospitalization but never be tested (the model assumes all reported hospitalizations have tested positive). We don’t expect the number reported to ever exceed the hospital capacity.
The model estimates that hospitals in California capacity.
Next, we look at an analagous graph for cumulative hospitalizations, which is how some states report this statistic.
Reading the graph: This graph shows cumulative hospitalizations as a result of Covid-19, as some states report only cumulative numbers.
We also model the expected number of Covid-19 cases that will require intensive care. Similar to hospitalizations, we do not fit the model to the reported ICU admission data. Instead, we show what the model would expect for California.
California typically has total ICU beds. As the number of patients who currently require intensive care approaches ICU capacity, California can add ICU beds and personnel to care for incoming patients. As a result, the model allows the number of patients currently reported in intensive care to exceed the typical total number of ICU beds.
While we expect that exceeding ICU capacity would have a dramatic effect on the fatality rate of Covid-19, the model currently does not adjust the fatality rate in this situation.
Next, we look at an analagous graph for cumulative ICU admissions, which is how some states report this statistic.