Alexander E Zarebski
@aezarebski
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Joined December 2017
While not exhaustive, our results suggest (for COVID-19) outcome equity can co-exist with utilitarian minimization of disease burden. We hope to expand our approach to other public health values (e.g. autonomy, privacy) and other interventions. I'd love to hear your thoughts! 💬
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In the aftermath of the COVID-19 pandemic, infectious disease modelling is taking a hard look at health equity. We did this work to help understand the potential impacts of considering equity.
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Exploring different weightings on equity and aggregate burden in a model of COVID-19 in Melbourne. We found a trade-off in equity of vaccine-induced burden, and total burden. Importantly, there was no trade-off between total burden and equity of disease burden 💉
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We considered: total days spent in hospital due to infection, and rare vaccine side effects; how fairly the infection burden shared; and how fairly vaccination burden is shared. The triangle shows possible weightings, much of the literature focuses on aggregate burden.🔬
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We look at optimizing public health interventions while prioritizing both equity and clinical burden. Basically “what level of vaccination should you aim for in the different age groups if they have different risk profiles?”🤒😷
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A huge thank you to @nefeltellioglu, @JEStockdale, @SpencerScience, Wasiur Rahman Khuda Bukhsh, @joel_c_miller and Cameron Zachreson for including me on this @MATRIX_Inst project we just preprinted results from https://t.co/9ioaiNWMQa 🤓
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Excited to share our latest research into P. vivax elimination via MDA just published in @Epidemics 📄Thank you to my collaborators @j_mccaw, @aezarebski, @rihickson and @JenniferFlegg
https://t.co/80diQDCCg7 1/5
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Super excited to finally share our preprint “Jump-Switch-Flow: hybrid stochastic-deterministic solutions of compartmental models”! 🥳 Huge thanks to @aezarebski, Sophie Hautphenne, @rob_models, @JenniferFlegg and @mark_flegg 📊, go #ScienceTeam! A 🧵 1/n. https://t.co/aMNtmSV3ZE
arxiv.org
Multi-scale systems often exhibit a combination of stochastic and deterministic dynamics. In compartmental models, low occupancy compartments tend to exhibit stochastic dynamics while high...
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(5/n) We have released a BEAST2 package, called Timtam, that implements this approximation. It produces well-calibrated estimates of the prevalence of infection and the effective reproduction number and runs much faster than existing methods.
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(4/n) To demonstrate how Timtam can be applied to real data sets we carried out case studies on SARS-CoV-2 onboard the Diamond Princess cruise ship and poliomyelitis in Tajikistan.
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(3/n) Time-series Integration Method Through Approximation of Moments (Timtam) is an approximation that enables us to use both time series and genomic data in estimation of the prevalence and reproduction number.
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(2/n) Estimating prevalence and transmissibility of a pathogen is critical for infectious disease epidemiology. Birth-death models are popular approach, but they struggle with estimating the prevalence of infection.
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(1/n) A huge thank you to @AntoineZwaans, @laduplessis, @B_Gutierrez_G and @EvolveDotZoo for all their hard work on Timtam for BEAST2 🥳 https://t.co/t04EnWNgLU
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Enthusiastic about using #AI, #control and #complexity science to combat #epidemics? I'm hiring a postdoc (2 yrs @imperialcollege) to delve into these fields to improve spatiotemporal outbreak prediction and control. Deadline 24 April. Please share! https://t.co/zPJQTZOCxB+
imperial.ac.uk
Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities ment...
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Please RT: I am looking for potential PhD students to work on infectious disease models. My university has a deadline of Mar 30. Australian students will have an advantage in the application, but I would welcome applications from strong foreign students.
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📰Our paper (led by @CarlosPrete1, @aezarebski, and myself) looking at sociodemographic inequalities in the Brazilian vaccination campaign is out @BMJ_Open! We combined data on COVID-19 cases, vaccinations, socioeconomic factors, and recent population estimates. 3⃣key highlights:
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Thank you to the team behind Jupytext. This makes working with notebooks so much easier 👍
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Thank you @ematsen for the chance to present on @phyloseminar yesterday. Very excited to hear from Nicola De Maio @nicola_de_maio on Wednesday, October 5.
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