Professor George Streftaris
Heriot-Watt University
Research Grants
·
CAE SOA grant, PI, 2019-2023
Predictive
Modelling for Medical Morbidity Risk Related to Insurance
·
SCOR Foundation of Science , CoI, 2022-2024
Estimating
the impact of the COVID-19 pandemic on breast cancer deaths - an application on
breast cancer life insurance
·
The Data Lab with Aberdeen Asset
Management, PI, 2017-2018
Machine
learning to support big data for advanced multi-asset strategies
·
IFoA grant, Co-I, 2016-2021
Modelling,
Measurement and Management of Longevity and Morbidity Risk
·
Actuarial Profession (IFoA) grant, PI, 2012-14
Incorporating
model and parameter uncertainty in rate graduation and pricing for Critical
Illness Insurance.
·
Scottish Government, Centre of
Expertise for Waters, Co-I, 2013-14
Land
management for increased ood resilience (with Built
Environment, HWU).
·
Catalyst Grant:
MRC/BBSRC/NERC/ESRC, Co-I, 2010-11
Episystem: Designing
biological, social and economic environments to enhance resistance to zoonotic
outbreaks
(UK consortium).
·
Bridging the Gaps (EPSRC), PI,
2010
Bayesian
Modelling of culvert blockage data (SBE, MACS at
HWU)
Research
supervision
I am currently interested in
supervising PhD projects on the following topics:
·
Model diagnostics for infectious epidemics
Stochastic
modelling of communicable disease outbreaks is challenging due to
inter-dependence in the involved
transmission
dynamics and imperfect observation of infection-related events. Estimation in
such models is now
well
established, but research on model assessment and comparison is still under
progress. This project will build on
recently
developed tools for epidemic model diagnostics to investigate the use of
Bayesian methodology related to
latent
residuals, in cases where the epidemic outbreak is: (a) under-reported, (b) at
early stages of its course,
and/or
(c) under the impact of intervention measures.
Supervisor:
Prof. G
Streftaris
·
Bayesian predictive modelling for morbidity risk
The principal aim of this research is to develop,
evaluate and assess models for morbidity risk and related insurance
rates, under statistical and machine learning
frameworks that allow for uncertainty quantification. The proposed work
will address the timely need to develop robust
predictive models for rapidly changing morbidity risks and the relevant
impact on health-related insurance. This research area
requires forward-thinking attention, as morbidity trends are
changing dynamically due to various complex factors
including changes in life expectancy, improvements in health
and care and developments in medical science. Earlier
work has shown that morbidity and health-insurance-related
rates are idiosyncratic to a number
of factors, including demographic, socio-economic and policy-linked
characteristics.
The proposed project will build on this work to
identify suitable morbidity risk factors for a wide range of illnesses, also
relating to insured populations. We will also assess
the robustness of the developed predictive models and select an
ensemble of models that perform well under a set of
criteria designed to optimise both the interpretability and predictive
quality for risks associated with certain medical
morbidity causes.
Supervisor:
Prof. G
Streftaris
Current and recent PRRAs
·
Dr. Yen Lok, 2017-2018
Statistical
machine learning for financial multi-asset strategies
·
Dr. Arik Ayse,
2018-2020
Current
and previous PhD students
1)
Farhad Waseel, 1st
year (HWU Malaysia)
2)
Wai Meng Kwok, 2nd year (HWU Malaysia)
3)
Niamh Graham, 2nd year (MIGSAA, with Edinburgh
U)
4)
Alex Jose, 2nd year
5)
Ying Zhang, 3rd year (MIGSAA, with Edinburgh U),
completed 2021
6)
Alexander Yiu,
completed 2022
7)
Eirini Dimakakou,
completed 2020
8)
Chunxiao
Xie, completed 2021
9)
Ian Duncan, completed 2020
10) David
Thong, completed 2019
11) Adrakey Kwame,
completed 2017
12) Jeff
Pollock, completed 2015
13) Hani
Syahida Zulkafli,completed
2017
14) Max
Lau, completed 2015
15) George
Mavros, completed 2014
16) Kokouvi
Gamado, completed 2012
17) Yumn
Yusoff, completed 2013
18) Erengul
Ozkok, completed 2011
19) Charnchai
Leuwattanachotinan, completed 2011
Publications
Refereed papers:
·
Kwok, W. M., Dass, S. C., and Streftaris, G. (2022).
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological
Systems.
arXiv:2210.08865
·
Yiu, A.M.T.L.,
Kleinow, T. and Streftaris, G. (2022) Cause-of-death contributions to declining
mortality improvements and life
expectancies using cause-specific scenarios.
arXiv:2210.12442
·
Jose, A., Macdonald, A.S., Tzougas,
G and Streftaris, G. (2022) A combined neural network approach for the
prediction of admission rates
related to respiratory diseases. Risks,
accepted for publication.
·
Arik, A., Dodd, E., Cairns, A., Shao, A.,
Streftaris, G. (2022) Uneven outcomes: findings on cancer mortality.
The Actuary, https://www.theactuary.com/features/2022/05/30/uneven-outcomes-findings-cancer-mortality
·
Arik, A., Dodd, E., Cairns, A., STREFTARIS, G.
(2021) Socioeconomic disparities in cancer incidence and mortality in England
and
the impact of age-at-diagnosis on cancer mortality.
PLoS ONE. 16, 7, e0253854
10.1371/journal.pone.0253854
·
Dimakakou, E.,
Johnston, H.J., STREFTARIS, G., Cherrie, J.W. (2020) Is environmental and
occupational particulate air pollution
exposure
related to diabetes and dementia? A cross-sectional
analysis in UK Biobank.
International
Journal of Environmental Research and Public Health. 17, 24,
9581.10.3390/ijerph17249581
·
Thong,
D., Streftaris, G. and Gibson, G.J. (2020) Latent likelihood ratio tests for assessing spatial kernels in epidemic
models.
J. Math.
Biol. 81, 853-873. DOI: 10.1007/s00285-020-01529-3
·
Zulkafli, H.S.,
Streftaris, G., Gibson. G.J. (2020). Stochastic latent residual approach for
consistency model assessment.
Mathematics
and Statistics, 8(5), 583 - 589. DOI: 10.13189/ms.2020.080513.
·
Dimakakou, E.;
Johnston, H.J.; Streftaris, G.; Cherrie, J.W. (2020) Evaluation of the
Suitability of an Existing Job–Exposure
Matrix for the Assessment of Exposure of UK Biobank
Participants to Dust, Fumes, and Diesel Exhaust Particulates.
Int. J.
Environ. Res. Public Health, 17, 4919. DOI: 10.3390/ijerph17144919
·
Arık A., Dodd E., Streftaris G. (2020) Cancer
morbidity trends and regional differences in England – A Bayesian
analysis.
PLoS ONE 15(5): e0232844. https://doi.org/10.1371/journal.pone.0232844
·
Dimakakou, E., Johnston, H. J., Streftaris, G., & Cherrie, J.
W. (2018). Exposure to Environmental and Occupational Particulate Air Pollution
as a
Potential Contributor to Neurodegeneration and Diabetes: A Systematic Review of
Epidemiological Research.
International Journal of Environmental Research and Public Health,
15(8), [1704]. DOI:
10.3390/ijerph15081704
·
Gibson,
G.J., Streftaris, G. and Thong, D. (2018) Comparison and assessment of epidemic
models.
Statistical Science, Vol. 33, No. 1, 19–33, https://doi.org/10.1214/17-STS615
·
Stone,
V., …, Streftaris, G. et al.
(2017) The Essential Elements of a Risk Governance Framework for Current and
Future Nanotechnologies,
Risk Analysis, DOI: 10.1111/risa.12954
·
Lau,
S. Y., Gibson, G. J., Kwame, A. H., McClelland, A., Riley, S., Zelner, J.,
Streftaris, G., Funk, S., Metcalf, J., Dalziel, B.
& Grenfell, B. (2017) A mechanistic spatio-temporal
framework for modelling individual-to-individual transmission in a
heterogeneous
landscape - with an application to the 2014-2016 West
Africa Ebola outbreak. PLoS Computational Biology
13(10): e1005798.
https://doi.org/10.1371/journal.pcbi.1005798
·
Adrakey, H.K., Streftaris, G., Gunniffe, N., Gottwald, T., Gilligan, C.A., and Gibson, G.J. (2017) Controlling spatio-temporal
epidemics using latent processes in a Bayesian framework.
JRS Interface, DOI:
10.1098/rsif.2017.0386
·
Mavros,
G., Cairns, A.J.G, Kleinow, T. and Streftaris, G. (2017) Stochastic Mortality
Modelling: Key Drivers and Dependent Residuals.
North American Actuarial Journal, 21, 343-368,
https://doi.org/10.1080/10920277.2017.1286992
·
Gamado,
K.M., Streftaris, G. and Zachary, S. (2016) Estimation of under-reporting in
epidemics using approximations.
Journal of Mathematical Biology, 74: 1683- 1707,
https://doi.org/10.1007/s00285-016-1064-7
·
Dodd,
E. and Streftaris, G. (2016) Prediction of settlement delay in critical illness
insurance claims using GB2 distribution.
Journal of the Royal Statistical Society C, , 66 (2), 273-294.
·
Zulkafli, H.S , STREFTARIS, G. , Gibson, G.J., and Zammitt,
N.N. (2016) Bayesian modelling of the consistency of symptoms reported
during
hypoglycaemia for individual patients. Malaysian
Journal of Mathematical Sciences, 10(s), 27-39.
·
Lau,
M.S.Y., Marion, G., Streftaris, G. and Gibson, G.J. (2015) A Systematic
Bayesian Integration of Epidemiological and Genetic Data.
PLoS Computational Biology, 11(11): e1004633.
doi:10.1371/journal.pcbi.1004633
·
Streftaris,
G., Ozkok-Dodd, E., Waters, H.R. and Stott, A.D. (2015).
Model and parameter uncertainty in critical illness insurance –
Abstract of the Edinburgh Discussion. British Actuarial Journal, 20, 387-403,
DOI: 10.1017/S1357321715000112
·
Ozkok-Dodd, E., Streftaris, G., Waters, H.R. and Stott, A.D. (2015) The
effect of model uncertainty on the pricing of critical illness insurance.
Annals of Actuarial Science,
DOI:
10.1017/S1748499514000244
·
Lau,
M.S.Y., Marion, G., Streftaris, G. and Gibson, G.J. (2014) New model
diagnostics for spatio-temporal systems in
epidemiology
and ecology. Journal of the Royal Society Interface, 11: 20131093, DOI 10.1098/rsif.2013.1093.
·
Yusoff
Y. S., STREFTARIS, G. and Waters, H. R 2014) Modelling Sudden Deaths from
Myocardial Infarction and Stroke. International
Journal of Medical, Health,
Biomedical, Bioengineering and Pharmaceutical Engineering, 8 (2), 84-87.
·
Gamado,
K.M, Streftaris, G. and Zachary, S. (2013) Modelling under-reporting in
epidemics. Journal of Mathematical
Biology,
DOI 10.1007/s00285-013-0717-z.
·
Streftaris,
G., Wallerstein, N.P., Gibson, G.J. and Arthur, S. (2013) Prediction of flood
risk associated with blocked culverts using Bayesian
modelling. Journal of Hydraulic Engineering,
DOI:
10.1061/(ASCE)HY.1943-7900.0000723.
·
Ozkok, E., Streftaris, G., Waters, H.R., and Wilkie, A.D. (2013) Modelling
critical illness claim diagnosis rates II: results.
Scandinavian Actuarial Journal, DOI:10.1080/03461238.2012.728538.
·
Ozkok, E., Streftaris, G., Waters, H.R., and Wilkie, A.D. (2012) Modelling
critical illness claim diagnosis rates I: Methodology.
Scandinavian Actuarial Journal, DOI:10.1080/03461238.2012.728537.
·
Streftaris G. and Gibson, G.J. (2012)
Non-exponential tolerance to infection in epidemic systems – modelling,
inference and assessment,
Biostatistics, 13,
580–593, doi:10.1093/biostatistics/kxs011.
·
Ozkok, E., Streftaris, G., Waters, H.R., and Wilkie, A.D. (2012) Bayesian
modelling of the time delay between diagnosis and settlement
for Critical Illness Insurance using a Burr
generalised-linear-type model. Insurance:
Mathematics & Economics, 50, 266-279,
doi:10.1016/j.insmatheco.2011.12.001
·
Zammitt, N.N., Streftaris, G., Gibson, G.J., Deary, I.J. and Frier, B.M. (2011)
Modelling the consistency of symptoms reported during
hypoglycaemia: high variability in people with type 1 and
type 2 diabetes. Diabetes Technology
& Therapeutics, 13(5), 571-578.
·
Gibson,
G.J., Streftaris, G. and Zachary, S. (2011) Generalised data augmentation and
posterior inferences, Journal of
Statistical Planning
and Inference, 141(1), 156-171, doi:10.1016/j.jspi.2010.05.025
·
Streftaris,
G. and Worton, B.J. (2008) Efficient and accurate approximate Bayesian
inference with an application to insurance data.
Computational Statistics & Data Analysis 52, 2604-2622, doi:10.1016/j.csda.2007.09.006
·
Streftaris,
G. and Worton, B.J. (2007) Hierarchical and empirical Bayes estimators in the
analysis of insurance claims. Proceedings of the
22nd international Workshop on
Statistical Modelling,
Barcelona, pp556-559.
·
Streftaris,
G. and Gibson, G.J. (2004) Bayesian analysis of transmission dynamics of
experimental epidemics. Proceedings of the 19th
international Workshop on Statistical Modelling, Florence, pp184-188.
·
Streftaris,
G. and Gibson, G.J. (2004) Bayesian analysis of experimental epidemics of
foot-and-mouth disease. Proc. R. Soc. Lond. B 271,
1111-1117, doi:10.1098/rspb.2004.2715
·
Streftaris,
G. and Gibson, G.J. (2004) Bayesian inference for stochastic epidemics in
closed populations. Statistical Modelling 4, 63-75,
·
Streftaris,
G. and Gibson, G.J. (2002) Statistical inference for stochastic epidemic
models. Proceedings of the 17th international
Workshop on Statistical Modelling, Chania, pp609-616. (Full paper in pdf form)
Under review/revision:
·
Streftaris, G., Xie, C., Dodd, E. Bayesian modelling
of critical illness insurance claim rates (2020, in preparation)
·
Thong, D., Jewell, C., Streftaris, G., Gibson, G.J.
Model Assessment of Epidemic Models of the 2001 Foot-And-Mouth Disease Epidemic
using Infection Link Residuals
(2020, in preparation).
Research
seminars
I have given a number of invited seminars at
universities and conferences in the UK and internationally.
A detailed list can be
found here.
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