Title of PhD project
Using Spatio-temporal models to understand how the environment and human immunity shape the seasonality of dengue virus transmission in the past, present, and future
Supervisory team
LSHTM
Yang Liu (Faculty of Epidemiology and Population Health, yang.liu@lshtm.ac.uk)
Oliver Brady (oliver.brady@lshtm.ac.uk)
Nagasaki University
Lina Madaniyazi (lina.madaniyazi@nagasaki-u.ac.jp)
Advisory Committee
Stefan Flasche (stefan.flasche@lshtm.ac.uk)
Aurelio Tobias (Nagasaki University, aurelio.tobias@nagasaki-u.ac.jp)
Brief description of project
Dengue fever (DF) is a mosquito-transmitted infectious disease with a significant burden of disease [1]. Sensitive to climate and environmental conditions as well as mosquito activities and human behaviours, DF burden of disease may substantially increase in the context of climate change [2, 3]. Understanding the spatial and temporal dynamics of Dengue virus (DENV) circulation is thus crucial to the prevention and control of DF in the next decades [4].
DF epidemics follow seasonal cycles in some parts of the world while occurring sporadically in others. Where seasonal cycles are present, we observe greater inter-annual, between-cycle variabilities (e.g. epidemic sizes and timing) compared to other seasonal diseases (e.g. seasonal influenza). It is hypothesised that these seasonal trends are shaped by an interaction between climate factors that affect the mosquito’s ability to transmit DENV and human immunity factors that reduce susceptibility to infection.
The proposed PhD project aims at understanding the seasonality (or the lack of) of DF. The project will be one of the first to make use of a new global database of monthly dengue case counts between 1990-2020 developed by Dr Brady’s team at LSHTM. This dataset covers wide longitudinal and latitudinal ranges encompassing multiple climate zones, which will allow us the resolution needed to examine seasonality in different contexts. The successful candidate may pursue research directions including but not limited to
(i) Use statistical procedures (e.g. [5]) to divide countries (and subnational regions where available) into areas that do or do not exhibit regular DF seasonality based on the case data interannual variability and peak duration, timing and size;
(ii) Use statistical approaches to test if consistent seasonality is associated with various hypothesised characteristics (e.g. longitude, latitude, climate type, population density, urbanicity, water storage behaviours);
(iii) Develop, fit and test a hybrid mathematical and statistical model that aims to incorporate both climate and immunological features to explain and predict both regular and irregular seasonality;
(iv) Use future climate and demographic projections to predict how dengue seasonality and its impact may change globally in 2030, 2050 and 2080.
These outputs will provide new insights into DENV’s transmission dynamics and generate valuable evidence informing decisions in both DF prevention and control and in climate change adaptation.
[1] Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).
[2] Brady, Oliver J., et al. "Refining the global spatial limits of dengue virus transmission by evidence-based consensus." (2012): e1760.
[3] Ryan, Sadie J., et al. "Global expansion and redistribution of Aedes-borne virus transmission risk with climate change." PLoS neglected tropical diseases 13.3 (2019): e0007213.
[4] Colón-González, Felipe J., et al. "Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study." The Lancet Planetary Health 5.7 (2021): e404-e414.
[5] Madaniyazi, Lina, et al. "Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial." International Journal of Epidemiology (2022): dyac115.
The role of LSHTM and NU in this collaborative project
The student will be based primarily in London, with at least 6 months at Nagasaki University, for training, data analysis, and supervisory input. The full supervisory team will meet with the student monthly, and lead supervisors from the two institutions bi-weekly (remotely).
YL, OB, and SF provide experience working with Dengue Fever outbreak analytics and are familiar with the mathematical methods required to carry out this project.
OB leads the efforts on developing the global database on dengue fever incidence and seroprevalence and will ensure access.
YL, LM, AT, and OB will guide on Spatio-temporal statistical methods and environmental/ climate/ climate change models required to carry out this project.
Particular prior educational requirements for a student undertaking this project
This is a transdisciplinary project encompassing infectious disease epidemiology, environmental/ climate science, statistics (spatial and/ or time-series), and mathematics (transmission model) - knowledge of at least one to two of these disciplines is essential. Experience working with programming languages (e.g. R or Python) and strong communication skills are highly desirable.
Skills we expect a student to develop/acquire whilst pursuing this project
The student will acquire knowledge in dengue epidemiology, environmental/ climate models, advanced statistical and mathematical modelling techniques, and climate health. The student will also gain experience working with data products generated on different scales (e.g. province vs. national) and by different sources (e.g. environmental vs. health, health care system vs. public health agencies).