Risk Level Calculation for Contact Tracing: an Example of Apple IOS framework

You know in Australia there is a ‘Covidsafe app‘  for everyone.


covidsafe-app_1 The COVIDSafe app speeds up contacting people exposed to coronavirus (COVID-19). This helps us support and protect you, your friends and family. Please read the content on this page before downloading. At the end of the Australian COVID-19 pandemic, users will be prompted to delete the COVIDSafe app from their phone. This will delete all app information on a person’s phone. The information contained in the information storage system will also be destroyed at the end of the pandemic. 

Here is the introduction video:

So, all those descriptions are trying to tell your information is safe and your privacy is protected with this app. By the way, the COVIDSafe app is the only contact tracing app approved by the Australian Government. I think this means it is the first official one.

This post is for viewers who want to understand a little bit deeper technical details about the technology used in this app. I will quote the document from Apple and keep it as simple as possible. I am not an IOS developer. I am just as curious as you, trying to understand how it measures the risk. And I am not sure if the COVIDSafe app used apple’s framework, LOL~

My only sources are from the webpages of the Australian Government Department of Health and Apple [iOS Framework Document] Exposure Notification April 2020. You can click these Keywords to learn more background knowledge around this app: COVIDSafe, Mesh Network; GDPR; DP3T; Beacon.

So, according to Apple’s document, the following diagram illustrates the general format of Exposure Risk Level Calculation:

Example Contact Tracing Apple IOS 03

Exposure Risk Level Parameters

  • Transmission Risk — An app-defined flexible value to tag a specific positive key. This value could be tagged based on symptoms, level of diagnosis verification, or other determination from the app or health authority.
  • Duration (measured by API) — Cumulative duration of the exposure. Days (measured by API) – Days since the exposure incident.
  • Attenuation (measured by API) – Minimum Bluetooth signal strength attenuation (Transmission Power subtract RSSI).
  • Level Value: The value, ranging from 1 to 8, that the app assigns to each Level in each of the Exposure Risk Level Parameters.
  • Level: The eight levels contained within each Exposure Risk Level Parameter.

Exposure Risk Level Parameter Weights (A, B, C, D)

  • The weights defined by the app (ranging from 0-100) that assign the relative importance to each of the Exposure Risk Level Parameters.

 

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How to Build an Artificial Intelligent System (II)

This post is following upgrade with respect to the early post How to Build an Artificial Intelligent System (I) The last one is focused on introducing the six phases of the building an intelligent system, and explaining the details of the Problem Assesment phase.

In the following content, I will address the rest phases and key steps during the building process.  Readers can download the keynotes here: Building an Intelligent System with Machine Learning.

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How to Build an Artificial Intelligent System (I)

Phase 1: Problem assessment – Determine the problem’s characteristics.

What is an intelligent system?

The process of building Intelligent knowledge-based system has been called knowledge engineering since the 80s. It usually contains six phases: 1. Problem assessment; 2. Data and knowledge acquisition; 3. Development of a prototype system; 4. Development of a complete system; 5. Evaluation and revision of the system; 6. Integration and maintenance of the system [1].

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Increasing Transparency into What It Takes to Achieve Performance Gains of Machine Learning Algorithms

The computations required for Deep Learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. AI could account for as much as one-tenth of the world’s electricity use by 2025 according to this article [1].

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Just Got a Reviewer Certificate from Data Mining and Knowledge Discovery (WIREs)

Thanks to the Editors and Board of WIREs for supporting me. As an independent reviewer, I will be fair to everyone and never give in to the “scientific mafia” and “citation cartels”.

Data Mining and Knowledge Discovery (WIREs) (Impact Factor: 2.541)

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Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning

Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.

Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany

Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.

Road from Above

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