We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.

Example of a longitudinal comparison between radiologist-segmented MS lesions and ANN-segmented MS lesions, showing a good overlap between the two volumes. At the MRI follow-up of August 2016, the patient showed a large new lesion with a faint central contrast enhancement (both indicated by the arrows in FLAIR and T1 after contrast, respectively), which the ANN correctly detected. The lesion then regressed under therapy and was unremarkable at the next follow-up. While underestimating the volumetric size of the lesions, the ANN consistently reproduced the volumetric trend of the follow-up for this patient.

Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expected disease burden differentially across various subgroups or communities in the population? In this work, we define the precision disease control problem -- the problem of optimally allocating vaccines in a social network in a step-by-step fashion -- and we use the ML Fairness Gym to simulate epidemic control and study it from both an efficiency and fairness perspective. We then present an exploratory analysis of several different environments and discuss the fairness implications of different treatment strategies.

By definition, a smart city is an urban area that relies heavily on data to monitor and manage resources efficiently. The concept of smart cities assimilates information and communication technology (ICT) and internet of things (IoT) to ensure the efficacy of city operations. Smart city as a concept is gaining popularity because it promises to harness the potency of the available infrastructure and create new-age living practices to help its residents lead a better, happier life. Health is an indispensable part of smart living, and unless we improve the level of health conditions the dream of smart cities would remain incomplete. A smart city should not only aim to provide smart healthcare solutions for its citizens but foster an ecosystem that curtails the growth of disease and nurtures a healthier way of life for its citizens.

Artificial Intelligence (AI) promises everything from self-driving cars to self-writing newspapers, but AI may be missing its greatest opportunity in healthcare, where AI-driven "conversational interfaces" hold untapped potential to influence the health and wellbeing of billions of people. Fueled by the massive popularity of messaging platforms such as WhatsApp, "conversational UI" is providing an emerging generation of chat-based digital services that may be the next thing in consumer technology. Instead of manipulating a graphical interface, users have a conversation with a chat-bot: software that is able to understand and respond to natural language inputs. The pace of technical advances combined with a shift in cultural norms is making AI conversations feel normal for increasing numbers of people. The idea of a "computer you can talk to" has captured the imagination of the computer science community, and the general public, for decades.