# AI4PAN Seminar Series Welcome to the AI for Pandemic Seminars organized by the [AI4PAN group](https://ai4pandemics.org/). Our fortnightly AI4PAN Seminars run via [Zoom](https://uqz.zoom.us/j/86567032193) at 10am on Wednesday's (AEST = Brisbane time zone). See also our [YouTube Channel](https://www.youtube.com/channel/UChfQzhyKowwzYFAXh6ziRlg). **UPCOMING SEMINAR TALKS** **Wednesday, December 1:** [Amalie Dyda](https://public-health.uq.edu.au/profile/5957/amalie-dyda), The University of Queensland >**Title:** Associations between vaccine coverage and vaccine information exposure on Twitter >**Abstract:** Vaccination is a key public health measure to control the spread of vaccine preventable diseases, highlighted by the current COVID-19 pandemic. <details> <summary>Click to expand</summary> However, uptake of adult vaccination is influenced by a number of factors including awareness, perceived risk and safety, which may be affected by exposure to misinformation. With current high levels of social media use, many individuals may now be influenced by health information in this medium which can include exposure to vaccine misinformation. This seminar will describe a case study, using Human papillomavirus (HPV) vaccination as an example, of an investigation into associations between information people are exposed to on social media and levels of vaccination coverage and discuss future directions for research. </details> **Wednesday, December 15:** [Pranesh Padmanabhan](https://researchers.uq.edu.au/researcher/11648), The University of Queensland >**Title:** Predicting the effectiveness of COVID-19 vaccines and treatments **Wednesday, February 2, 2022:** [Fahad Ahmed](https://scholar.google.com/citations?hl=en&user=39-51HMAAAAJ&view_op=list_works&sortby=pubdate), Yale University **PAST SEMINAR TALKS** **Wednesday, November 17:** [Lewis Mitchell](https://lewismath.github.io/), The University of Adelaide [AI4Pandemics Talk #10: YouTube](https://www.youtube.com/watch?v=7gfYOjX6DYc&t=20s) >**Title:** Risk mapping for COVID-19 using social media data >**Abstract:** Much has been made of the possible application of novel datasets such as social media and search data for combatting infectious diseases, particularly during the COVID-19 pandemic. <details> <summary>Click to expand</summary> In this talk we evaluate one such dataset – mobility data provided by Facebook’s Data for Good program – for quantifying risk associated with human mobility in Australian cities during 2020. We describe the dataset, how it is created, its limitations, and present a methodology for using this dataset with a simple mathematical model to predict spatial risk. Results show that the dataset and method were effective in estimating outbreak risk for 2 key outbreaks in 2020. </details> **Wednesday, November 3:** [Romesh Abeysuriya](https://www.burnet.edu.au/people/578_romesh_abeysuriya), Burnet Institute [AI4Pandemics Talk #9: YouTube](https://www.youtube.com/watch?v=KbAJWx2rEhE) >**Title:** Long-term COVID-19 strategies with intermittent control measures >**Abstract:** The COVID-19 vaccines used in Australia have all demonstrated high efficacy against severe disease and death. However, multiple models have shown that the combination of imperfect protection against infection and more infectious variants means that Australia, and other countries globally, are unlikely to achieve herd immunity. <details> <summary>Click to expand</summary> A key question is therefore what control strategies are proportionate and sustainable, in a world with high vaccine coverage, no herd immunity, and ongoing importations of cases into the community from relaxed quarantine and increased travel. In this seminar, I will present a modelling study carried out by the Burnet Institute where we used the Covasim model to explore options for using intermittent measures to maintain long-term epidemic control. </details> **Wednesday, October 20:** [Rebecca Chisholm](https://scholars.latrobe.edu.au/rchisholm/publications), La Trobe University [AI4Pandemics Talk #8: YouTube](https://www.youtube.com/watch?v=0mOuBGtfX3M&t=3s)) >**Title:** Contribution of mathematical modelling to COVID-19 response strategies in regional and remote Australian Aboriginal and Torres Strait Islander communities >**Abstract:** The health and science communities recognised early on in the SARS-CoV-2 pandemic that Aboriginal and Torres Strait Islander Australians were likely to be at high risk of COVID-19 infection and severe outcomes, due to high rates of comorbidities associated with severe outcomes, and multiple factors predisposing to increased SARS-CoV-2 transmission. <details> <summary>Click to expand</summary> In March 2020, the Australian Government convened the Aboriginal and Torres Strait Islander Advisory Group on COVID-19 (IAG), co-chaired by the Department of Health and the National Aboriginal Community Controlled Health Organisation. The role of the IAG was to develop and deliver a National Management Plan to protect Aboriginal and Torres Strait Islander communities. Our research groups—located at the Doherty Institute, the Kirby Institute and La Trobe University—were commissioned to carry out modelling, under the guidance of the IAG, to help inform aspects of this plan related to regional and remote communities. In this presentation I will describe how we used modelling to address a number of questions of interest to the IAG regarding the importance of a timely response to the first identified case of COVID-19, who should be quarantined and/or tested in communities, and whether there is a role for community-wide lockdown in initial containment. </details> **Wednesday, October 13:** [Colleen Lau](https://researchers.uq.edu.au/researcher/2260),The University of Queensland [AI4Pandemics Talk #7: YouTube](https://www.youtube.com/watch?v=uctiEO8hNDc&t=12s) >**Title:** CRISPER: COVID-19 Real-time Information System for Preparedness and Epidemic Response >**Abstract:** A major challenge during the COVID-19 pandemic has been the need to share data and information public while protecting data privacy. <details> <summary>Click to expand</summary> Effective communication of real-time data is critical for informing risk assessment and decision making, and to support a unified national response. Real-time dashboards have become important platforms for information sharing during the COVID-19 pandemic. This presentation will describe the development of CRISPER, a COVID-19 Real-time Information System for Preparedness and Epidemic Response, including the challenges experience. CRISPER includes a suite of interactive visualisation and mapping tools and automatic alerts for COVID-19 cases, deaths, testing, and contact tracing exposure sites across Australia. The system also allows the use of differential privacy algorithms to protecting data privacy. ![](https://i.imgur.com/khNXf3W.jpg) </details> **Thursday, October 7:** [Clair Sullivan](https://researchers.uq.edu.au/researcher/13187), The University of Queensland. >**Title:** The Algorithm will see you now **Wednesday, September 8:** [Peter Frazier](https://people.orie.cornell.edu/pfrazier/), Cornell University [AI4Pandemics Talk #5: YouTube](https://youtu.be/QHVruw121RA) [Presentation Slides](https://docs.google.com/presentation/d/1q4G4sURbQL7xrivO9SvAal9kNKL5OYsL8HCkiNCnIPs/edit?usp=sharing) >**Title:** Fighting COVID-19 at Cornell University >**Abstract** Universities around the world faced a challenging decision during the summer of 2020: whether to reopen for in-person instruction despite the pandemic and how to protect campus populations if they did. <details> <summary>Click to expand</summary> Operations research and data science were a fundamental part of these decisions at Cornell University in the USA. First, models developed by Cornell's COVID-19 Mathematical Modeling Team were used to design the testing interventions that are a cornerstone of Cornell’s COVID-19 control strategy: targeted asymptomatic screening that tests all undergraduates twice per week and an adaptive testing program that goes beyond traditional contact tracing to test the full social circle of positive cases. Second, these same models were the basis for Cornell's decision to reopen for a fall semester with in-person instruction. They showed that reopening with aggressive mandatory testing was surprisingly less risky than virtual instruction. Data suggested that thousands of students would return to the area whether in-person instruction was offered or not, and a weaker ability to enforce mandatory testing for these students risked being unable to control clusters in that population. Reopening with asymptomatic screening was successful, with only 0.5% of students, staff and faculty infected over the semester. This talk will share insights from this experience and explain practical tools that supported this work. </details> **Wednesday, August 25:** [Joel Miller](https://scholars.latrobe.edu.au/jcmiller), La Trobe University [AI4Pandemics Talk #4: YouTube](https://www.youtube.com/channel/UChfQzhyKowwzYFAXh6ziRlg) >**Title:** COVID and the misunderstood denominator… >**Abstract:** Like past epidemics, the efforts to stop the transmission of SARS-CoV-2 have been hindered by the parallel transmission of misinformation (inaccurate information) as well as disinformation (intentionally deceptive inaccurate information). <details> <summary>Click to expand</summary> Unlike historical epidemics, the social media landscape has accelerated the spread of misinformation. I will discuss the role misinformation has played in the pandemic. </details> **Wednesday, August 18:** [Jeremy Howard](https://en.wikipedia.org/wiki/Jeremy_Howard_(entrepreneur)), fast.ai & University of San Francisco. [AI4Pandemics Talk #3: YouTube](https://www.youtube.com/watch?v=9jQE27bZOcU&t=195s) >**Title:** How a little-known data scientist convinced the West to wear face masks >**Abstract:** The title of this talk is actually the title of an article in [The Telegraph](https://archive.is/Jkxdj) about my journey in co-founding the [Masks4All](https://masks4all.co/) movement. I share how I found myself becoming the face of Masks4All globally, and what I learned about making an impact as a data scientist. **Wednesday, July 28:** [Kirsty Short](https://scmb.uq.edu.au/profile/4618/kirsty-short), UQ. [AI4Pandemics Talk #2: YouTube](https://www.youtube.com/watch?v=KYufp__7NQg&t=246s) >**Title:** The role of children in the spread of SARS-CoV-2 >**Abstract:** The role of children in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains highly controversial. <details> <summary>Click to expand</summary> To address this issue, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n = 213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a pediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR], 0.17; 95% confidence interval [CI], 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate in pediatric household contacts was assessed. The secondary attack rate in pediatric household contacts was lower than in adult household contacts (RR, 0.62; 95% CI, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic, including potential vaccine prioritization strategies. </details> **Wednesday, July 14:** [Christopher Rackauckas](https://chrisrackauckas.com/), MIT. [AI4Pandemics Talk #1: YouTube](https://www.youtube.com/watch?v=7yPU_04Unb8&t=359s) > **Title:** Learning Epidemic Models That Extrapolate. >**Abstract:** Modern techniques of machine learning are uncanny in their ability to automatically learn predictive models directly from data. <details> <summary>Click to expand</summary> However, they do not tend to work beyond their original training dataset. Mechanistic models utilize characteristics of the problem to ensure accurate qualitative extrapolation but can lack in predictive power. How can we build techniques which integrate the best of both approaches? In this talk we will discuss the body of work around universal differential equations, a technique which mixes traditional differential equation modeling with machine learning for accurate extrapolation from small data. We will showcase how incorporating different variations of the technique, such as Bayesian symbolic regression and optimizing the choice of architectures, can lead to the recovery of predictive epidemic models in a robust way. The numerical difficulties of learning potentially stiff and chaotic models will highlight how most of the adjoint techniques used throughout machine learning are inappropriate for learning scientific models, and techniques which mitigate these numerical ills will be demonstrated. We end by showing how these improved stability techniques have been automated and optimized by the software of the SciML organization, allowing practitioners to quickly scale these techniques to real-world applications. </details>