AI4PAN
Artificial Intelligence for Pandemics
Centered at The University of Queensland
Twitter: @ai4pan
YouTube: channel
Admin contact e-mail Roxanne Jemison
Seminar academic contact e-mail Hamid Khataee
Academic contact e-mail Yoni Nazarathy
Welcome to AI4PAN, The Artificial Intelligence for Pandemics group centered at The University of Queensland (UQ). The group's focus is the application of data science, machine learning, statistical learning, applied mathematics, computation, and other "artificial intelligence" techniques for managing pandemics both at the epidemic and clinical level.
Our fortnightly AI4PAN Seminar runs via Zoom at 10am on Wednesdays (AEST = Brisbane time zone) and is organized by Roxanne Jemison, Hamid Khateee, and Aminath Shausan. See the Seminar Schedule with links to recorded talks.
Active Research Projects
- Safe Blues. Live updates from The University of Auckland 2021 campus experiment
- ISARIC IInternational Severe Acute Respiratory and Emerging Infections Consortium) contributed by Sally Shrapnel and team.
- Project Acute Kidney Injury in COVID-19 led by Sally Shrapnel
- The host response as an alternative early diagnostic for viral infection
by Meagan Carney and Kirsty Short
Click to expand
Since the initial outbreak of Coronavirus Disease 2019 (COVID-19) in Wuhan, China, over 215 million subsequent cases have been recorded. The causative agent, Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2) is highly transmissible and characterized by a heterogenous respiratory disease that ranges from mild symptoms to acute respiratory distress syndrome and death. Over 4.5 million deaths have been recorded to date. Direct detection of viral RNA via quantitative polymerase chain reaction (qPCR) is a highly sensitive and specific diagnostic tool for SARS-CoV-2 and the current gold standard for testing. However, whilst highly sensitive, a certain threshold of viral RNA must be present for subsequent amplification and detection by qPCR. Accordingly, it is possible for a close contact of a SARS-CoV-2 positive individual to initially test negative for the virus but then later in the incubation period, when there is increased viral replication, to test positive. As a result, current public health guidelines in Australia require close contacts of a SARS-CoV-2 positive case, as well as passengers from overseas, to quarantine for the entirety of the viral incubation period (14 days). The time taken for an infected individual to be identified by qPCR also has implications for anti-viral therapeutics. Current monoclonal antibody therapeutics are most efficacious if given early in infection. Thus, the importance of identifying SARS-CoV-2 positive individuals as early as possible early in infection is appreciable. Here, we hypothesise that there is a gene signature in the nasopharynx that can be detected in SARS-CoV-2 positive individuals prior to the detection of viral RNA using qPCR. To test this hypothesis we will use a combination of clinical samples and unsupervised machine learning clustering methods to potentially develop a new, early diagnostic for SARS-CoV-2 infection.
Related UQ Projects and Groups
- CRISPER project Colleen Lau
Research Publications
- COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study by ISARIC Clinical Characterisation Group and Sally Shrapnel.
- Intensive care digital health response to emerging infectious disease outbreaks such as COVID-19 by Marianne Kirrane, Sally Shrapnel, Mahesh Ramanan, Pierre Clement, John F Fraser, Kevin B Laupland, Clair M Sullivan, and Kiran Shekar.
- Diabetes and overweight/obesity are independent, nonadditive risk factors for in-hospital severity of COVID-19: an international, multicenter retrospective meta-analysis by Danielle K Longmore, Jessica E Miller, Siroon Bekkering, Christoph Saner, Edin Mifsud, Yanshan Zhu, Richard Saffery, Alistair Nichol, Graham Colditz, Kirsty R Short, and David P Burgner.
- The role of T-cell immunity in COVID-19 severity amongst people living with type II diabetes by Zhen Wei Marcus Tong, Emma Grant, Stephanie Gras, Melanie Wu, Corey Smith, Helen L. Barrett, Linda A. Gallo and Kirsty R. Short.
- A meta-analysis on the role of children in SARS-CoV-2 in household transmission clusters by Yanshan Zhu, Conor J Bloxham, Katina D Hulme, Jane E Sinclair, Zhen Wei Marcus Tong, Lauren E Steele, Ellesandra C Noye, Jiahai Lu, Yao Xia, Keng Yih Chew, Janessa Pickering, Charles Gilks, Asha C Bowen, and Kirsty R Short.
- Transition from growth to decay of an epidemic due to lockdown by Hamid Khataee, Jack Kibble, Istvan Scheuring, Andras Czirok, and Zoltan Neufeld.
- Modelling the collective mechanical regulation of the structure and morphology of epithelial cell layers by Hamid Khataee, Madeleine Fraser and Zoltan Neufeld.
- Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data by Hamid Khataee, Istvan Scheuring, Andras Czirok and Zoltan Neufeld.
- Targeted adaptive isolation strategy for COVID-19 pandemics by Zoltan Neufeld, Hamid Khataee and Andras Czirok.
- Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics by Raj Dandekar, Shane G. Henderson,Hermanus M. Jansen, Joshua McDonald, Sarat Moka, Yoni Nazarathy, Christopher Rackauckas, Peter G. Taylor, and Aapeli Vuorinen.
- Safe Blues: A Method for Estimation and Control in the Fight Against COVID-19 by Raj Dandekar, Shane G Henderson, Marijin Jansen, Sarat Moka, Yoni Nazarathy, Christopher Rackauckas, Peter G Taylor and Aapeli Vuorinen.
- The host response as an alternative early diagnostic for viral infection by Meagan Carney and Kirsty Short
Group Contributors
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Dr Meagan Carney's
research interests include probability and statistics of extremes in chaotic systems,
rare event analysis, dynamical systems, unsupervised learning algorithms, and
applications of importance sampling methods.
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A/Prof. Anders Eriksson's
research expertise areas include optimization theory and
numerical methods applied to the fields of Computer Vision and Machine Learning.
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A/Prof. Marcus Gallagher's
expertise is in Artificial Intelligence, Optimization and Machine Learning
algorithms, including the theory, development and practical applications of these
techniques. He collaborated with Queensland Health in the area of anomaly detection
in drug prescription data.
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A/Prof. Cecilia González Tokman's
expertise is in Dynamical Systems, Ergodic Theory and related areas.
Her recent work focuses on random dynamical systems, transfer operators, Lyapunov exponents
and coherent structures.
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Dr. Hamid Khataee's
expertise is in Mathematical and computational modelling, Applied mathematics, Theoretical Biology
including computational modelling of morphological dynamics of cell populations, mechanics of
molecular motors, and quantification and modelling of epidemic data.
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Prof. Geoff McLachlan's
expertise is in statistics in the related fields of classification, cluster and discriminant
analyses, image analysis, machine learning, neural networks, and pattern recognition, and in
the field of statistical inference.
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A/Prof. Yoni Nazarathy's (coordinator)
expertise is in Machine Learning, Applied Probability,
Statistics, Operations Research, Simulation, Scientific Computing, Control Theory,
Queueing Theory, Scheduling, and Mathematical Education. His involvement with AI4PAN
is through the Safe Blues project.
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Dr. Zoltan Neufeld's
expertise is in Mathematical and computational modelling, Applied mathematics, Mathematical Biology
including analysis of epidemic models, computational models of multicellular tissue biology,
collective cell motility, mechano-biology, pattern formation and tissue development.
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Dr. Ash Rahimi's research interests fall
within the fields of Natural Language Processing, Social Network Analysis and Machine
Learning. I am specifically interested in exploiting both structured and unstructured
data to help machines understand conversational language in Emergency Situations
and Health Informatics.
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Dr. Aminath Shausan's
expertise focuses on statistical and probability modelling and analysis of epidemics.
Expertise in Bayesian statistical analysis of dengue modelling, Artificial intelligence
to project to understand pandemics.
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Dr. Kirsty Short
is an influenza virologist by training with extensive experience in emerging viral
pathogens and pandemic preparedness. With expertise working on SARS-CoV-2 and
in particular the role of children in disease spread, the impact of this disease
on people with diabetes and the development of new antiviral therapies.
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Dr. Sally Shrapnel
is an interdisciplinary scientist working at the interface of causality and machine
learning. She has both a clinical and technical background, with bachelor's degrees
in Medical Science, Medicine and Surgery, a Fellowship of the Royal Australian
College of General Practitioners, a Master's degree in Bioengineering (Imperial College, London)
and a PhD in physics and philosophy—on the topic of quantum causal machine learning.
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Dr. Ian Wood's
research expertise covers classification, bioinformatics, stochastic optimisation,
machine learning and mixture models.
Media
Tweets by ai4pan- September 2021: ABC News, Experts examine fears and misconceptions fuelling Queensland's slow COVID-19 vaccine uptake
- September 2021: ABC News, Influenza cases hit an all-time low in Australia in 2021 — that could be a problem when it returns
- September 2021: The Australian, Q&A: Dr Kirsty Short, virologist, 34
- September 2021: Bloomberg, Improving Pandemic Preparedness
- September 2021: ABC News, Queensland Premier Annastacia Palaszczuk defends demand for updated research on COVID-19 impacts of reopening on children
- September 2021: The Courier Mail, How Queensland researchers are preparing for the next pandemic with Kirsty Short
- August 2021: ACEMS Podcast, Episode 59: Experiment in Lockdown
- August 2021: ABC News, When can my child get vaccinated against COVID? Are the vaccines safe for kids?
- August 2021: The University of Auckland, University experiment mimics Covid-19
- August 2021: UQ Faculty of Science, NZ's lockdown effectiveness tracked with digital 'virus'
- August 2021: Sydney Morning Herald, No dodging Delta as COVID outbreak spreads in Queensland
- August 2021: Brisbane Times, No dodging Delta as COVID outbreak spreads in Queensland
- July 2021: UQ Contact Magazine, Ticket to Freedom
- July 2021: Stochastic Lifestyle, Learning Epidemic Models That Extrapolate, AI4Pandemics
- June 2021: FQXi, The quantum clock-maker investigating COVID-19, causality, and the trouble with AI
- June 2021: The University of Queensland, Machine Learning for acute kidney injury in COVID 19
- June 2021: The Conversation, What’s the Delta COVID variant found in Melbourne? Is it more infectious and does it spread more in kids? A virologist explains
- March 2021: The Australia Business Review, Artificial intelligence to help pinpoint COVID diseases. Sally Shrapnel, Data scientists will use artificial intelligence to identify which COVID patients will likely experience longer-term conditions such as kidney damage.
- March 2021: The Conversation, We’ve designed a safe ‘virtual’ epidemic. Spreading it is going to help us learn about COVID