Health Big data: networked fever curves - and what they...

Big data: networked fever curves – and what they can tell


If you want to know whether you have temperature or even a fever, measure your body temperature (ideally: basal body temperature) with a thermometer – so far, so banal. Some do this with a modern ear sensor. And in the United States, millions of thermometers are already in use that are connected to the smartphone via Bluetooth.

There, the current temperature is automatically “noted” after the measuring process and then displayed in a curve. Manufacturer Kinsa is particularly successful: it has sold several million such thermometers in the USA – and is currently selling more than 10,000 new devices. And this is where it gets exciting.

Millions of networked thermometers in use

Because Kinsa uses this (anonymized) data for an interesting service: If you like, you can have a look at the current US Weather Map. It shows the current average temperature of US citizens. The service can therefore identify earlier than any health care system where regionally elevated temperatures are accumulating. In this way, impending flu waves can be predicted – or possibly the spread of corona can also be recognized.

Of course: Not every corona patient gets a fever – but it is one of the most common symptoms. So if you evaluate the numbers in sufficient detail, you may be able to recognize patterns – and also get useful information about the development of corona in a country or region.

Use herd data wisely

In this context, I find it interesting what data is generated when a sufficient number of measuring devices in medicine are networked – and spit out data. Bluetooth thermometers are already widely used in the USA. Not yet with us in Germany. But sooner or later it will come to us too.

Google and Apple already offer health areas in their devices, where pretty much all health apps can submit their data so that they are available in all health apps. Of course, only if the user authorizes it. But this is interesting data that can be useful in an evaluation.

We know that Google can also use a striking cluster of certain search terms (by symptoms) to predict the spread of flu, earlier than any healthcare system. Combining such data with the population data, undoubtedly results in even more precise diagnoses and predictions.

Since it is herd data and not individual data, the data protection concerns should be low. In my opinion, however, the model is suitable as a blueprint for even smarter concepts: we should think about how existing data can be used intelligently, for example in order to plan the resources made available in the healthcare system wisely. So that beds are ready where they are needed.

COSMO TECH: Tech against the corona virus


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