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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

Centre for the Mathematical Modelling of Infectious Diseases (CMMID)

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

Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.
Davies NG; Kucharski AJ; Eggo RM; Gimma A; Edmunds WJ; Centre for the Mathematical Modelling of Infectious Diseases COV
2020
The Lancet. Public health
Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK.
Jarvis CI; Van Zandvoort K; Gimma A; Prem K; CMMID COVID-19 working group; Klepac P; Rubin GJ; Edmunds WJ
2020
BMC medicine
Inferring the number of COVID-19 cases from recently reported deaths
Jombart T; van Zandvoort K; Russell T; Jarvis C; Gimma A; Abbott S; Clifford S; Funk S; Gibbs H; Liu Y
2020
Inferring the number of COVID-19 cases from recently reported deaths.
Jombart T; van Zandvoort K; Russell TW; Jarvis CI; Gimma A; Abbott S; Clifford S; Funk S; Gibbs H; Liu Y
2020
medRxiv
The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study.
Prem K; Liu Y; Russell TW; Kucharski AJ; Eggo RM; Davies N; Centre for the Mathematical Modelling of Infectious Diseases COV; Jit M; Klepac P
2020
The Lancet. Public health
The effect of inter-city travel restrictions on geographical spread of COVID-19: Evidence from Wuhan, China
Quilty B; Diamond C; Liu Y; Gibbs H; Russell T; Jarvis C; Prem K; Pearson C; Clifford S; Flasche S
2020
COVID-19 length of hospital stay: a systematic review and data synthesis
Rees E; Nightingale E; Jafari Y; Waterlow N; Clifford S; Pearson C; Jombert T; Procter S; Knight G; CMMID Working Group
2020
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