Badger Bounce Back is Wisconsin’s plan to reopen and contains the recommendations of our public health experts. Based on the science of the virus and public health practices, a gradual, phased process continues to be the safest way to open Wisconsin.

COVID-19 remains very contagious and our data and metrics (below) tell us that we should continue to limit our interactions with others as much as possible to protect ourselves and our communities and reduce the spread of COVID-19. In order to safely reopen Wisconsin, we continue to work with our partners across the state to increase our testing and expand our contact tracing. We are also working with local leaders to help ensure access to safe isolation, shore up our hospital capacity, and monitor the prevalence of COVID-19. We will continue to maintain and update the statewide gating criteria and provide consistent localized data for use in local decision-making for reopening plans.

We are committed to continuing to do the work to protect and promote your health by giving you guidance based on the best science and public health practices available.

We are using gating criteria (metrics and data) to determine when we can safely reopen Wisconsin. .

green indicator = gating criteria met

red indicator = gating criteria not met​

Symptoms

Symptoms: Downward trajectory of influenza-like illnesses (ILI) reported within a 14-day period, AND

Downward trajectory of COVID-19-like syndromic cases reported within a 14-day period. Daily number of emergency department visits with influenza related concerns (last 14 days) About the data: Data are lagged by two days to allow for completeness in reporting. These data are from the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). Influenza-like illnesses meet a nationally recognized definition of symptoms. The black line represents the actual data. We assessed trends in the data, shown by the dashed orange line, using a simple statistical analysis (linear regression). There has been no downward trend in the past 14 days. Statistical analysis details: Trends are calculated using a common statistical analysis (linear regression). If the downward trend is considered statistically significant, we consider the metric to be met. Data can fluctuate over time, and using linear regression helps determine if the trend is believable or if it is due to chance and random fluctuations. While a graph can show daily peaks and valleys in the data, the linear regression looks at the trend over a set period of time. A downward trend is thought to be believable, or statistically significant, if the linear regression analysis results in a p-value of less than 0.05, a standard threshold for statistical testing. Having a p-value of less than 0.05 means that the probability that the downward trend is due to random fluctuations is less than 5%. When we look at the trajectory of our COVID-19 case and symptom data, we are looking for a statistically significant trend because that significance indicates the trend is not by chance. When we do see a downward trend for 14 days, using the p-value of less than 0.05 will determine that the trend is believable because there is a less than 5% chance that the downward trend is random. If we see a statistically significant downward trend over the course of 14 days, the gating criteria status will turn green. These data can change on a daily basis as new information about cases is gathered or updated, which means the gating criteria status could change from red to green and back to red. Daily number of emergency department visits with suspected COVID-19 related concerns (last 14 days) About the data: Data are lagged by two days to allow for completeness in reporting. These data are from the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). COVID-19 visits are emergency department visits that meet the COVID-19 definition, created in collaboration with national partners. The black line represents the actual data. We assessed trends in the data, shown by the dashed orange line, using a simple statistical analysis (linear regression). There has been a downward trend in the past 14 days; this trend is not statistically significant (p=0.05)*. *p<0.05 indicates statistical significance. Statistical analysis details: Trends are calculated using a common statistical analysis (linear regression). If the downward trend is considered statistically significant, we consider the metric to be met. Data can fluctuate over time, and using linear regression helps determine if the trend is believable or if it is due to chance and random fluctuations. While a graph can show daily peaks and valleys in the data, the linear regression looks at the trend over a set period of time. A downward trend is thought to be believable, or statistically significant, if the linear regression analysis results in a p-value of less than 0.05, a standard threshold for statistical testing. Having a p-value of less than 0.05 means that the probability that the downward trend is due to random fluctuations is less than 5%. When we look at the trajectory of our COVID-19 case and symptom data, we are looking for a statistically significant trend because that significance indicates the trend is not by chance. When we do see a downward trend for 14 days, using the p-value of less than 0.05 will determine that the trend is believable because there is a less than 5% chance that the downward trend is random. If we see a statistically significant downward trend over the course of 14 days, the gating criteria status will turn green. These data can change on a daily basis as new information about cases is gathered or updated, which means the gating criteria status could change from red to green and back to red.

Cases

Cases: Downward trajectory of positive tests as a percent of total tests within a 14-day period. Percent of people tested for COVID-19 who had positive results, by day (last 14 days) About the data: These data are from the Wisconsin Electronic Disease Surveillance System (WEDSS). Data in this figure are based on the date the test results were reported. The black line represents the actual data. We assessed trends in the data, shown by the dashed orange line, using a simple statistical analysis (linear regression). There has been no downward trend in the most recent 14 days. Statistical analysis details: Trends are calculated using a common statistical analysis (linear regression). If the downward trend is considered statistically significant, we consider the metric to be met. Data can fluctuate over time, and using linear regression helps determine if the trend is believable or if it is due to chance and random fluctuations. While a graph can show daily peaks and valleys in the data, the linear regression looks at the trend over a set period of time. A downward trend is thought to be believable, or statistically significant, if the linear regression analysis results in a p-value of less than 0.05, a standard threshold for statistical testing. Having a p-value of less than 0.05 means that the probability that the downward trend is due to random fluctuations is less than 5%. When we look at the trajectory of our COVID-19 case and symptom data, we are looking for a statistically significant trend because that significance indicates the trend is not by chance. When we do see a downward trend for 14 days, using the p-value of less than 0.05 will determine that the trend is believable because there is a less than 5% chance that the downward trend is random. If we see a statistically significant downward trend over the course of 14 days, the gating criteria status will turn green. These data can change on a daily basis as new information about cases is gathered or updated, which means the gating criteria status could change from red to green and back to red.

Hospital and Health Care