> For the complete documentation index, see [llms.txt](https://prep.pathcheck.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://prep.pathcheck.org/privacy-preserved-ai-for-healthcare/risk-calculator.md).
# Risk Calculator
The next example for on-device calculation is a risk calculator. John contributes background status and activity with no-peek, and then alerts and risk scores are delivered to John also using no-peek methods. The calculator can have inputs including John’s age, comorbidities, test results, data from phone sensors, as well as external data such as mobility, the caseload in the region, and so on. Further, the calculator can also be told the activity John expects to do: Is it very vigorous, like going to a gym and for how long, and is the gym crowded? Is it indoors or outdoors? And is John going to spend time with other people who are vaccinated? By taking this input, the risk calculator uses machine learning to calculate John’s risk.
The server sends these risk calculations in no peek form to John's phone so that he can conduct operations locally without the server knowing the end results. This maintains John’s privacy.
**References**
* [**COVID-driven Risk Profile**](https://www.researchgate.net/publication/345823059_COVID-driven_Risk_Profile)
* [**COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms**](https://arxiv.org/abs/2101.10266)
* [**Can Self Reported Symptoms Predict Daily COVID-19 Cases?**](https://arxiv.org/abs/2105.08321)
* [**https://www.symptomchallenge.org/**](https://www.symptomchallenge.org/)
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