Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. Here, we present LRTrend, an interpretable machine learning framework to identify outbreaks in real time. LRTrend effectively aggregates diverse health and behavioral data streams within one region and learns disease-specific epidemic networks to aggregate information across regions. We reveal diverse epidemic clusters and connections across the United States that are not well explained by commonly used human mobility networks and may be informative for future public health coordination. We apply LRTrend to 2 years of COVID-19 data in 305 hospital referral regions and frequently detect regional Delta and Omicron waves within 2 weeks of the outbreak's start, when case counts are a small fraction of the wave's resulting peak.

I have got a new doorbell. It should be; it cost £89. It's a Ring video doorbell; you'll have seen them around. There are others available, made by other companies, with other four-letter names such as Nest and Arlo. When someone rings my doorbell, I'm alerted on my smartphone. I can see who is there, and speak to them. C major first inversion chord, arpeggiated, repeated, for the musically trained – you'll recognise it if you've heard it. Amazon, as it happens; Amazon acquired Ring in 2018, reportedly for more than $1bn.

In a 2020 letter to management, Max Eliaser, an Amazon software engineer, said Ring is "simply not compatible with a free society". We should take his claim seriously. Ring video doorbells, Amazon's signature home security product, pose a serious threat to a free and democratic society. Not only is Ring's surveillance network spreading rapidly, it is extending the reach of law enforcement into private property and expanding the surveillance of everyday life. What's more, once Ring users agree to release video content to law enforcement, there is no way to revoke access and few limitations on how that content can be used, stored, and with whom it can be shared.

The National Science Foundation has launched a program that seeks to use artificial intelligence-based technologies for early disease outbreak detection to help contain transmission and prevent pandemics like the ongoing one, FedScoop reported Wednesday. The Predictive Intelligence for Pandemic Prevention program is focused on research into algorithms that are needed for the development of AI systems for pandemic forecasting. Katharina Dittmar, program director of the Environmental Biology Division at NSF, said intelligent prediction needs new AI models that are capable of identifying the biological and sociological causes of pathogen emergence. "We all recognize from the past year, from our experience that our approach to this continued challenge must really evolve away from crisis response during discrete outbreaks toward a sustained and integrated cycle of intelligent prediction, preparation, response and recovery," said Dittmar. NSF has hosted four workshops in support of the PIPP initiative since February.

According to the World Health Organisation (WHO) [World Health Organization, 2013], the United Nations directing and coordinating health authority, public health surveillance is: The continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice. Public health surveillance practice has evolved over time. Although it was limited to pen and paper at the beginning of 20th century, it is now facilitated by huge advances in informatics. Information technology enhancements have changed the traditional approaches of capturing, storing, sharing and analysing of data and resulted efficient and reliable health surveillance techniques [Lombardo and Buckeridge, 2007]. The main objective and challenge of a health surveillance system is the earliest possible detection of a disease outbreak within a society for the purpose of protecting community health. In the past, before the widespread deployment of computers, health surveillance was based on reports received from medical care centres and laboratories.

"Machine learning is very good at identifying patterns in the data, such as risk factors that might identify zip codes or cohorts of people that are …

An Israeli startup invested in heavily by American companies, including Microsoft, produces facial recognition software used to conduct biometric surveillance on Palestinians, investigations by NBC and Haaretz revealed. In June, Microsoft -- which has touted its framework for ethical use of facial recognition -- joined a group investment of $78 million to AnyVision, an international tech company based in Israel. One of AnyVision's flagship products is Better Tomorrow, a program that allows the tracking of objects and people on live video feeds, even tracking between independent camera feeds. AnyVision's facial recognition software is at the heart of a military mass surveillance project in the West Bank, according to the NBC and Haaretz reporting. An Israeli Defense Forces statement in February acknowledged the addition of facial recognition verification technology to at least 27 checkpoints between Israel and the West Bank to "upgrade the crossings" and, in an effort to "deter terror attacks," rapidly installed a network of over 1,700 cameras across the occupied territories.

The scope of Amazon's partnership with police departments across the US continues to grow. In a new report, The Washington Post revealed that video-sharing programs between the Amazon-owned company, Ring -- purveyors of popular home security equipment -- number 400, which nearly doubles previous estimates. That figure, which the Post says was gleaned from Ring's company data, marks the first ever hard number on Ring's police partnerships which facilitate the exchange of user's home video footage with local law enforcement. A previous estimate from Vice using documents obtained through a Freedom of Information Act (FOIA) request put the number of partnerships at 200. Partnerships between Amazon and police departments are far more prolific than previously though according to a new report that details 400 collaborations across the U.S. Data obtained by the Post shows those partnerships mostly speckled across the across the Northeast, Midwest, and South East along the coast with many located in Florida, Texas, and Illinois.

Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the detection rate of outbreaks and the chances of raising a false alarm. Recent research has shown that the use of machine learning for the fusion of multiple statistical algorithms improves outbreak detection. Instead of relying only on the binary output (alarm or no alarm) of the statistical algorithms, we propose to make use of their p-values for training a fusion classifier. In addition, we also show that adding additional features and adapting the labeling of an epidemic period may further improve performance. For comparison and evaluation, a new measure is introduced which captures the performance of an outbreak detection method with respect to a low rate of false alarms more precisely than previous works. Our results on synthetic data show that it is challenging to improve the performance with a trainable fusion method based on machine learning. In particular, the use of a fusion classifier that is only based on binary outputs of the statistical surveillance methods can make the overall performance worse than directly using the underlying algorithms. However, the use of p-values and additional information for the learning is promising, enabling to identify more valuable patterns to detect outbreaks.

An advanced research arm of the U.S. government's intelligence community is looking to develop AI capable of tracking people across a vast surveillance network. As reported by Nextgov, the Intelligence Advanced Research Projects Activity (IARPA) has put out a call for more information on developing an algorithm that can be trained to identify targets by visually analyzing swaths of security camera footage. The goal, says the request, is to be able to identify and track subjects across areas as large as six miles in an effort to reconstruct crime scenes, protect military operations, and monitor critical infrastructure facilities. To develop the technology, IARPA will collect nearly 1,000 hours of video surveillance from at least 20 camera networks and then, using that sample, test various algorithms effectiveness. The agency's interest in AI-based surveillance technology mirrors a broader movement from governments and intelligence communities around the globe, many of whom have ramped up efforts to develop and scale systems.