HETEROGENEOUS AND LATENT EFFORT MODELING FOR NON-SYSTEMATIC WILDLIFE AND HUMAN HEALTH DATA

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Studentship strategic priority area:RESEARCH AREA 1: Biological Informatics; RESEARCH AREA 2: Mathematical biology;RESEARCH AREA 3: Statistics and applied probability;RESEARCH AREA 4: New mathematics in biology and medicineKeywords: Biodiversity, Disease monitoring, Latent models, Missing effort data, Statistical modellingAbstract: One of the biggest challenges facing the life sciences in the 21st Century is the effective use of the vast quantity of data available. Studying the health of human populations and the health of ecosystems, requires data that are both spatially/temporally expansive and locally detailed/precise. This presents us with a conundrum because very often, budgetary constraints mean that scientifically precise data are highly limited in their spatial and temporal scope. One appealing route through this impasse is to analyse scientific survey data simultaneously with platform-of-opportunity data (e.g. citizen-scientist reports on the distribution of wildlife, general practitioner records on disease prevalence). This is challenging because these different data types have different limitations. However, they are also complementary in their strengths. This project will use current thinking and up-to-date technical experience in advanced statistical modelling to analyse scientific and opportunistic data simultaneously, achieving answers that make the best of both worlds. In particular, we will:1. Develop formal probabilistic models for the processes of missed, or biased reporting of disease incidence or species abundance through multiple observation methods.2. Quantify how these sources of uncertainty are affected by various spatiotemporal and socioeconomic covariates.3. Tease apart the variables that explain partial reporting, from those that drive the underlying biological process.The student will acquire highly transferrable training in modern statistical modelling and will be able to contribute to the analysis of as-yet untapped data sets in diverse and highly timely areas of application (pollinator ecology and digital health records).