> 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/)
{% embed url="" %}
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://prep.pathcheck.org/privacy-preserved-ai-for-healthcare/risk-calculator.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.