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Dr Sam Clifford

B AppSc PhD

Research Fellow
in Machine (Statistical) Learning

Room
120

LSHTM
Keppel Street
London
WC1E 7HT
United Kingdom

Sam obtained a Bachelors Degree in Applied Science (with Honours 2A) from Queensland University of Technology (QUT, Brisbane, Australia), majoring in Applied and Computational Mathematics. After a short break teaching mathematics to video game developers at Krome Studios, Sam undertook his Doctoral studies at the International Laboratory for Air Quality and Health.

Under Professors Lidia Morawska and Kerrie Mengersen and Dr Samantha Low-Choy, Sam's thesis investigated spatio-temporal modelling of ultrafine particles in Brisbane, Australia. After five and a half years of postdoctoral work at QUT, Sam joins us at the London School of Hygiene and Tropical Medicine for 2.5 years as a postdoctoral fellow.

Affiliations

Faculty of Epidemiology and Population Health
Department of Infectious Disease Epidemiology

Centres

Mathematical Modelling of Infectious Diseases

Teaching

Sam has five years experience in undergraduate lecturing at QUT, teaching mathematics and statistics in the R computational environment to first year Bachelor of Science students. His teaching makes use of blended learning approaches to prepare students for lecture material, and collaborative workshops with project-based learning.

In 2015, Sam and his team were awarded a Vice Chancellor's Award for Innovation on the basis of their work transforming SEB113.

Sam's current teaching at LSHTM includes the following short courses:

Research

Sam's research at LSHTM includes the following:

  • Spatio-temporal variation in Streptococcus pneumoniae serotypes
  • Maternal colonisation of Group B Streptococcus
  • Modelling the health and economic impact of dengue vaccination

In addition to his doctoral thesis on spatio-temporal modelling of ultrafine particle number concentration, Sam's research interests include

  • exposure assessment
  • personal monitoring equipment for air pollution
  • non-parametric regression
  • jaguar conservation
  • Great Barrier Reef conservation
  • machine learning for spatial data
  • Bayesian hierarchical modelling
  • data visualisation
  • mathematics and statistics education
Research Area
Statistical methods
Vaccines
Bayesian Analysis
Environmental Health
Mobile technologies
Schools
Spatial analysis
Modelling
Education
Discipline
Mathematical modelling
Statistics
Disease and Health Conditions
Asthma
Dengue
Respiratory disease
Dengue

Selected Publications

Interventions targeting air travellers early in the pandemic may delay local outbreaks of SARS-CoV-2
Clifford S; Pearson C; Klepac P; Van Zandvoort K; Quilty B; Quilty B; Eggo R; Flasche S; CMMID COVID-19 working group
2020
Early dynamics of transmission and control of COVID-19: a mathematical modelling study.
Kucharski AJ; Russell TW; Diamond C; Liu Y; Edmunds J; Funk S; Eggo RM; Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group
2020
The Lancet. Infectious diseases
A Bayesian spatiotemporal model of panel design data: Airborne particle number concentration in Brisbane, Australia
Clifford S; Low‐Choy S; Mazaheri M; Salimi F; Morawska L; Mengersen K
2019
Environmetrics
Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species.
Leigh C; Heron G; Wilson E; Gregory T; Clifford S; Holloway J; McBain M; Gonzalez F; McGree J; Brown R
2019
PloS one
Characteristics of school children's personal exposure to ultrafine particles in Heshan, Pearl River Delta, China - A pilot study.
Mazaheri M; Lin W; Clifford S; Yue D; Zhai Y; Xu M; Rizza V; Morawska L
2019
Environment international
New insights into the spatial distribution of particle number concentrations by applying non-parametric land use regression modelling.
Rahman MM; Karunasinghe J; Clifford S; Knibbs LD; Morawska L
2019
The Science of the total environment
Evaluating health facility access using Bayesian spatial models and location analysis methods.
Tierney NJ; Mira A; Reinhold HJ; Arbia G; Clifford S; Auricchio A; Moccetti T; Peluso S; Mengersen KL
2019
PloS one
See more Publications