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followers: 213.0 following: 318.0 repos: 111.0 gists: 57.0

Name: Sam Abbott

Type: User

Company: @epiforecasts @cmmid

Bio: Real-time infectious disease modelling. Developing tools and methods for outbreak response, routine surveillance, and pandemic preparedness.

Twitter: seabbs

Location: Bristol

Blog: samabbott.co.uk

Hi there 👋

I'm an infectious disease researcher interested in real-time analysis, forecasting, semi-mechanistic modelling, and open-source tool development.

NOW

Mostly working @Epiforecasts (part of the London School of Hygiene and Tropical Medicine);
Also supporting @CDC Centre for Forecast and Outbreak Analytics;
⭐ Contributing to the epinowcast community developing tools and methods for current surveillance of infectious disease and preparing for future outbreaks;
🧻 Currently working on:
- If we want an estimate of the reproduction number do we need the renewal process? and associated Julia modelling toolkit.
- Developing a workbench for real-time and outbreak infectious disease modelling
- Course materials on nowcasting, forecasting, and evaluation
- 😴 🔌 🐑

BIO

🏢 2024-: Assistant professor in real-time modelling of infectious disease w/ Sebastian Funk at the London School of Hygiene and Tropical Medicine
🏢 2023-: Contractor supporting the CDC Centre for Forecast and Outbreak Analytics
🏢 2020-2023: Postdoc in real-time modelling of infectious disease w/ Sebastian Funk at the London School of Hygiene and Tropical Medicine
🏢 2017-2019: Data Scientist at Funding Circle
📜 2016-2019: PhD at the University of Bristol on modelling the use of the BCG vaccine in the UK
⚙️ I use daily: R, julia, stan
🏗️ I like to perform analysis using novel models on interesting data and generalise those approaches into software 📦s
🌍 I'm mostly active within the epinowcast community
🌱 Learning all about Julia and Turing.jl - currently wondering why in the world you would use callables.
📘 Reading: Probability and random processes
💬 Ping me about statistical modelling of infectious diseases, real-time analysis of infectious diseases, estimating transmission dynamics in real-time, and team science opportunities
📫 Reach me: [email protected] prob

Sam Abbott's Projects

80-percent-good-enough

What is 80% good enough for real-time analyses during the COVID-19 pandemic

analysisskeleton

A repository containing an analysis skeleton based around an R package framework

ancrtadjust

An R package to adjust routine HIV testing data from antenatal care to reduce bias in estimating HIV prevalence trends

assessbcgpolicychange

Assessing the evidence for the 2005 BCG policy change in England

backup

Code to automate local backups of remote code

badtobetter

Better computational practice - Bad to better

Bayesian Cost Effectiveness Analysis. Given the results of a Bayesian model (possibly based on MCMC) in the form of simulations from the posterior distributions of suitable variables of costs and clinical benefits for two or more interventions, produces a health economic evaluation. Compares one of the interventions (the "reference") to the others ("comparators"). Produces many summary and plots to analyse the results