Understanding the impact of global change on animal-borne diseases
- Funded by Wellcome Trust
- Total publications:2 publications
Grant number: 220179
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Key facts
Disease
Lassa Haemorrhagic FeverStart & end year
20202025Known Financial Commitments (USD)
$897,506.28Funder
Wellcome TrustPrincipal Investigator
Dr. David ReddingResearch Location
United KingdomLead Research Institution
University College LondonResearch Priority Alignment
N/A
Research Category
Animal and environmental research and research on diseases vectors
Research Subcategory
Animal source and routes of transmission
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
We know little about how future climate change, habitat destruction, human population increases and greater globalisation processes will impact human zoonotic diseases. Here, I investigate the use of dynamic, seasonal host population models to better predict the impact of real-time environmental change on disease-carrying host species, within a general systems-dynamics, disease framework. Specifically, I will combine a mathematical compartmental disease model with a host population ecology model, within a spatial and temporal Bayesian framework. Using this approach, I will first model Lassa Fever using climate and land-use observations, collaborating with the Nigerian government. I will then augment my model to account for animal movement patterns and vector species abundances, to examine arboviral disease spread in North America. Then, I will integrate these threads into a general, dynamic modelling framework for zoonotic diseases, which will contain both the newly developed components and my previously developed model of human movement and behaviour. Working with the World Health Organisation, I will create short- and long-term disease forecasts for a set of high priority zoonoses. Once validated against human case data, these mechanistic models can be used to test interventions and create future disease management plans that are robust to upcoming global change.
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