ODeSI decision support tools

The Side effect calculator for Japanese encephalitis vaccine estimates the risk of adverse events following immunisation in concomitant administration of Japanese encephalitis and other vaccines. It is an interactive, scenario-based online application that encompasses age group, sex, JE vaccine and vaccine to be co-administered to estimate the risk of adverse events following immunisations.

The poolTestR R package, developed by Angus McLure, lets you easily analyse your data on pooled  samples, such as a tube of mosquitoes, to estimate the prevalence of a disease in your overall sample. With just a few lines of R code you can estimate infection prevalence in vectors, even from large and complex molecular xeno monitoring surveys across many sampling sites and vector species, split by however many levels you like. This means you can quickly compare the results between sites, villages, regions, time, or species. You can read more about how and why the software was developed on this COR-NTD blog post.

Related publications

McLure, A., O'Neill, B., Mayfield, H., Lau, C. & McPherson, B. PoolTestR: An R package for estimating prevalence and regression modelling for molecular xenomonitoring and other applications with pooled samples. Environmental Modelling & Software 145, 105158, doi:https://doi.org/10.1016/j.envsoft.2021.105158 (2021).

The CoRiCAL COVID-19 vaccine risk-benefit calculator, hosted by the Immunisation Coalition, combines the latest evidence on the benefits of being vaccinated against COVID-19 compared to the risks from vaccines. The webpage is powered by a Bayesian Network model than combines results from published studies with Government published reports and knowledge from medical experts. CoRiCAL is the result of a dedicated team of academics and medical practitioners who collate and present the evidence into a user-friendly format to help people make an informed choice on vaccination.  It’s updated regularly to account for new evidence, vaccines and COVID-19 variants and is feely available from the IC website.

Related publications

Lau, C. L. et al. Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework. Vaccine, doi: https://doi.org/10.1016/j.vaccine.2021.10.079 (2021).

Sinclair, J. E. et al. Quantifying the risks versus benefits of the Pfizer COVID-19 vaccine in Australia: a Bayesian network analysis. medRxiv, 2022.2002.2007.22270637, doi:10.1101/2022.02.07.22270637 (2022).

Mayfield, H. J. et al. Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine. Vaccine 40, 3072-3084, doi: https://doi.org/10.1016/j.vaccine.2022.04.004 (2022).

CRISPER is an interactive dashboard that provides accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. The system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Related publications

Field E, Dyda A, Hewett M, Weng H, Shi J, Curtis S, Law C, McHugh L, Sheel M, Moore J, Furuya-Kanamori L, Pillai P, Konings P, Purcell M, Stocks N, Williams G, Lau CL. Development of the COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER), Australia. https://doi.org/10.3389/fpubh.2021.753493 (2021).

Field E, Dyda A, Lau CL. COVID‐19 Real‐time Information System for Preparedness and Epidemic Response (CRISPER). doi: 10.5694/mja2.51019 (2021).

Dyda A, Purcell M, Curtis S, Field E, Pillai P, Ricardo K, Weng H, Moore JC, Hewett M, Williams G, Lau CL. Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality. doi: 10.1016/j.patter.2021.100366 (2021).