The Station provides information regarding when to expect disease activity and its implications for the community. This is important for preventive health activities such as:
Many people base important health decisions, such as whether to vaccinate, on this kind of anticipatory information- especially if we are seeing highly unusual conditions. Occasionally, we notify healthcare providers who work in critical areas of our healthcare system such as the intensive care unit of a hospital, to prepare for unexpectedly high patient demand. This important information helps communities respond appropriately to health crises.
We use public health data provided by local, state, and national public health agencies. Because the data is stripped of any information that identifies individual people, the data we receive is simply the number of patients diagnosed with a particular infectious disease (e.g. influenza) on a weekly basis. The data for our baselines must be reliable, accurate, and (ideally) produced the same way, year after year.
Although there are many ways to estimate future infectious disease activity, we are currently using an approach borrowed from weather forecasters referred to as "unskilled forecasting." An example of an unskilled weather forecast is a graph of the average monthly rainfall. We are able to provide reliable information for several infectious diseases by publishing a range of the most likely number of cases expected per week, up to one year in the future.
We are employing unskilled forecasting to gradually build public trust in this fairly reliable, although occasionally imprecise process. Over time, we expect to be able to introduce a more sophisticated approach called "skilled forecasting." This category of forecasting enables us to predict, on a given date, the precise number of cases of an infectious disease expected. However, at this time, skilled forecasting is more experimental and less reliable than unskilled forecasting.
Weather forecasting is very similar to defining infectious disease baseline and prognosis. Both rely on data that has been reported on a regular basis over a long period of time. Studying past patterns allows scientists to make predictions such as future weather or disease activity. In both cases, the information must then be communicated effectively to the public. Weather forecasters have been doing this since the 1800s and have learned many valuable lessons that are applicable for defining infectious disease activity as well!
For more technical information regarding our operational approach to raising public awareness and education, please see the World Weather Research Programme (WWRP) / World Climate Research Programme (WCRP) Joint Working Group on Forecast Verification Research's website
If the baseline fails to predict when a disease might hit our community, or to what magnitude, this typically indicates unusual disease activity. For example, consider a year when high numbers of people come down with flu so severe they have to be hospitalized -- or worse -- they die from it. The baseline "failed" because it hadn't seen that kind of flu pattern before, at least, not often enough to make it part of flu's "normal" pattern in our community. However, this unusual pattern then becomes part of future instances and observations, especially if that pattern of severe flu repeats itself. So, if severe flu became a new normal for a particular region, then in a few years, the baseline would recognize this, and people wouldn't be taken by surprise whenever another season of severe flu hits. Instead, the local healthcare system would have expected the possibility, and would likely be prepared for it.
All intelligent systems have inherent limitations.
The data is not perfect and neither is the prognosis. In summary, this site and its resources should be used as a tool to help us recognize changes in disease activity that still needs people to properly interpret the information.