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:
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.
Generative models, like Generative Adversarial Networks (GAN), are a rapidly advancing area of research for computer science and machine intelligence nowadays. It’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers have achieved and been working on.
The following figures demonstrate some results of the current works ( Images from https://blog.openai.com/generative-models/).
I think it is necessary to understand the basic pros and cons of it, and it may be very helpful to your own research. I have not fully reviewed the theory and papers, but after skimmed a few papers, I got the impression that the training process of GAN models is very tricky as well as any neural networks model. Thus, there must be a huge improving space for people to make.
Thanks to the internet! There are papers and codes everywhere and nobody will be left behind in these days unless he/she wants to. So working hard and to be a better man (or women or anything good for humanity), cheers!
Here are some papers and blogs that summarized the literature very well.