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Using Spatio-temporal models to understand how the environment and human immunity shape the seasonality of dengue virus transmission

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.

PhD position
London School of Hygiene and Tropical Medicine/ Nagasaki University
London/ Nagasaki
UK/ Japan
Closing date
January 15th, 2023
Posted on
November 30th, 2022 01:40
Last updated
November 30th, 2022 01:40