Socio-ecological dynamics of zoonotic and vector-borne diseases in changing landscapes: implications for surveillance and control
- Funded by Wellcome Trust
- Total publications:5 publications
Grant number: 221963
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Key facts
Disease
Zika virus disease, DengueStart & end year
20212026Known Financial Commitments (USD)
$1,636,323.96Funder
Wellcome TrustPrincipal Investigator
Professor Daniel HaydonResearch Location
United KingdomLead Research Institution
University of GlasgowResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease transmission dynamics
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
Landscape changes disrupt infectious disease dynamics, requiring new approaches to characterise risks and prevent outbreaks. Focusing on emerging (Chikungunya, Zika) and epidemic (malaria, dengue) zoonotic and vector-borne diseases in Malaysia and the Philippines, I aim to design and evaluate enhanced surveillance systems linking health and environmental data to detect and prevent pathogen spillover and transmission. By developing novel models relating social and ecological processes across spatial and temporal scales, I will bridge critical gaps linking environmental change with human behaviour and health systems. Fine-scale studies of human mobility, behaviour and infection risks will be integrated within a large-scale experiment on tropical forest modification to understand how landscape change both interacts with and alters environmental factors (e.g. seasonality, biodiversity) and socioeconomic and biological factors (e.g. demography, mobility, immunity) to determine disease dynamics. Statistical and mathematical models will be used to explore factors across ecological settings, integrating routine surveillance data, population-based serological surveys and multitemporal Earth Observation data to reconstruct historical disease transmission over major environmental shifts. Predictive models will be designed to identify how future land use can reduce disease risks and how control programmes can use environmental data from new sources of real-time Earth Observation data to improve disease surveillance.
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