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Dr Sam Abbott

Assistant Professor

United Kingdom

I’m an infectious disease researcher interested in real-time analysis, forecasting, semi-mechanistic modelling, and open-source tool development. I am currently based in the Epiforecasts group at the London School of Hygiene. I completed my PhD in the optimal usage of the BCG vaccine at the University of Bristol with a short sojourn into Data Science at a company specialising in peer to peer lending.

Affiliations

Department of Infectious Disease Epidemiology and Dynamics
Faculty of Epidemiology and Population Health

Teaching

I teach on the modern methods for infectious disease modelling short course (in particular leading the best practices session).

Research

My main research interest lies in developing, evaluating, and applying methods for improving our understanding of infectious disease dynamics in real-time. I am committed to doing science in the open, and collaboratively, with the aim of producing useful and actionable output. Most of my recent work has been targeted towards the COVID-19 response but my underlying focus is pathogen agnostic sparse data settings.

My current main areas of work are developing and evaluating methods for nowcasting right truncated data, developing and evaluating methods to forecast and understand variant dynamics, reconstructing unobserved infections from a range of data sources (such as count data and prevalence measures), and developing methods for the estimation of the effective reproduction number, the growth rate, and generation interval distribution as well as use cases for these estimates and understanding their interactions.
Research Area
Modelling
Statistical methods
Epidemiology
Mathematical Modelling
Mathematics
Disease and Health Conditions
Emerging infectious diseases
Infectious diseases

Selected Publications

Best practices for estimating and reporting epidemiological delay distributions of infectious diseases.
Charniga, K; Park, SW; Akhmetzhanov, AR; Cori, A; Dushoff, J; FUNK, S; Gostic, KM; Linton, NM; Lison, A; Overton, CE; Pulliam, JR C; Ward, T; Cauchemez, S; ABBOTT, S;
2024
PLoS computational biology
EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters
ABBOTT, S; Hellewell, J; SHERRATT, K; Gostic, K; Hickson, J; Badr, HS; DeWitt, M; AZAM, JM; EpiForecasts,; FUNK, S;
2024
Zenodo
Characterising information gains and losses when collecting multiple epidemic model outputs.
SHERRATT, K; Srivastava, A; Ainslie, K; Singh, DE; Cublier, A; Marinescu, MC; Carretero, J; Garcia, AC; Franco, N; Willem, L; Abrams, S; Faes, C; Beutels, P; Hens, N; Müller, S; Charlton, B; Ewert, R; Paltra, S; Rakow, C; Rehmann, J; Conrad, T; Schütte, C; Nagel, K; ABBOTT, S; Grah, R; ... FUNK, S.
2024
Epidemics
Human judgement forecasting of COVID-19 in the UK.
BOSSE, NI; ABBOTT, S; Bracher, J; VAN LEEUWEN, E; Cori, A; FUNK, S;
2024
Wellcome open research
Combined analyses of within-host SARS-CoV-2 viral kinetics and information on past exposures to the virus in a human cohort identifies intrinsic differences of Omicron and Delta variants.
RUSSELL, TW; Townsley, H; ABBOTT, S; Hellewell, J; Carr, EJ; Chapman, LA C; Pung, R; QUILTY, BJ; HODGSON, D; Fowler, AS; Adams, L; Bailey, C; Mears, HV; Harvey, R; Clayton, B; O'Reilly, N; Ngai, Y; Nicod, J; Gamblin, S; Williams, B; Gandhi, S; Swanton, C; Beale, R; Bauer, DL V; Wall, EC; ... KUCHARSKI, AJ.
2024
PLoS biology
Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic.
SHERRATT, K; Carnegie, AC; KUCHARSKI, A; Cori, A; PEARSON, CA B; Jarvis, CI; Overton, C; Weston, D; Hill, EM; Knock, E; Fearon, E; NIGHTINGALE, E; Hellewell, J; EDMUNDS, WJ; Villabona Arenas, J; PREM, K; Pi, L; BAGUELIN, M; Kendall, M; Ferguson, N; DAVIES, N; EGGO, RM; Van Elsland, S; RUSSELL, T; FUNK, S; ... ABBOTT, S.
2024
Wellcome open research
Improving modelling for epidemic responses: reflections
SHERRATT, K; ABBOTT, S; Carnegie, A; LIU, Y;
2023
Open Science Framework
The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.
GRIEVE, R; Yang, Y; ABBOTT, S; Babu, GR; Bhattacharyya, M; Dean, N; EVANS, S; Jewell, N; LANGAN, SM; Lee, W; Molenberghs, G; SMEETH, L; WILLIAMSON, E; Mukherjee, B;
2023
PLOS global public health
thimotei/legacy_ct_modelling: Final version
ABBOTT, S; RUSSELL, TW; Hellewell, J; KUCHARSKI, AJ;
2023
Zenodo
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