COVID-19

Modelling community response to outbreaks

In the first six months of the COVID-19 pandemic a range of different responses were observed across the world. Not only are interventions diverse, the changes of behaviour in populations around the world has also varied. To account for this, our epidemiological modelling describes the tendency for a population to change their interaction patterns as the reports of cases and deaths increase. By including these mechanisms, the sensitivity and timing of the communities response could be estimated and compared against the size of the outbreaks across the globe.

Publications

T Balasubramaniam, DJ Warne, R Nayak, K Mengersen. (2022) Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization. International Journal of Data Science and Analytics. DOI
D.J Warne, A Ebert, C Drovandi, W Hu, A Mira, K Mengersen. (2020) Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic. BMC Public Health 20:1868 DOI medRxiv.org

Inferring vaccine hesitancy from COVID-19 dashboard data

In this project, we assess the potential for publicly available COVID-19 data, such as those available from dashboards like Johns Hopkins University or Our World In Data, to identify signals of vaccine hesitancy.

Publications

DJ Warne, A Varghese, AP Browning, MM Krell, C Drovandi, W Hu, A Mira, K Mengersen, AL Jenner. (2022) Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours. medRix.org

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