People

Dr Tim Lucas

Lecturer

School/Department: Department of Health Sciences

Email: tcdl2@leicester.ac.uk

Web:

To say hello on twitter see either of my handles.

www.twitter.com/StatsForBios

www.twitter.com/timcdlucas

 

My github webpage has more content as I have more control over it.

https://timcdlucas.github.io/

 

I host my code on github.

www.github.com/timcdlucas

 

My publications are on google scholar.

Google Scholar

Profile

  • Geostatistics
  • Machine Learning
  • Epidemiology

I am a lecturer in biostatistics. My interests lie in problems involving observational data and often with a focus on prediction. This has involved work on geospatial models, machine learning models and disaggregation regression models as well as how to combine models from different modelling paradigms such as combining Bayesian statistics and machine learning models. I studied an MBioSci in Zoology at the University of Sheffield followed by a PhD in disease ecology (bats and zoonotic diseases) at University College London. I then joined the Malaria Atlas Project for a postdoc developing disaggregation regression models for predicting malaria incidence. After a short post-doc studying neglected tropical diseases and one year as a research fellow at Imperial College studying air pollution I joined the Department of Health Sciences as a lecturer. For more information on my work see my website https://timcdlucas.github.io/. Feel free to say hello on twitter https://twitter.com/StatsForBios.

Research

In many cases we have low resolution disease data (daily symptom reports weekly hospital episodes etc.) but high resolution covariate data (minute-resolution air pollution data for example). My main project at the moment involves developing exposure disaggregation regression models to appropriately handle these data. During the COVID epidemic I have been involved in modelling the effectiveness of contact tracing and evaluating how important adherence and delays are. During my time at the Malaria Atlas Project I developed methods for estimating high-resolution (5x5 km pixel) malaria incidence from low-resolution (county level for example) routine surveillance data from hospitals. We created the first global spatio-temporal maps of Plasmodium falciparum and P. vivax. We also developed methods for combining low-resolution routine surveillance data with prevalence survey data using joint-likelihood models and machine learning models. We also performed simulation studies to evaluate when these methods fail made software available for other researchers to use and applied these methods to SARS-CoV-2 data.

Teaching

I teach on the Medical Statistics MSc. I teach the R section of the module Statistical Computing. I also teach on the module Computationally Intensive Methods.
Back to top
arrow-downarrow-down-3arrow-down-2arrow-down-4arrow-leftarrow-left-3arrow-left-2arrow-leftarrow-left-4arrow-rightarrow-right-3arrow-right-2arrow-right-4arrow-uparrow-up-3arrow-up-2arrow-up-4book-2bookbuildingscalendar-2calendarcirclecrosscross-2facebookfat-l-1fat-l-2filtershead-2headinstagraminstagraminstagramlinkedinlinkedinmenuMENUMenu Arrowminusminusrotator-pausec pausepinrotator-playplayc playplussearchsnapchatsnapchatthin-l-1thin-l-2ticktweettwittertwittertwitterwechatweiboweiboyoutubeyoutube