Estimating latent characteristics of infectious disease burden, such as incidence, prevalence and severity, requires the integration of multiple, disparate, data sources, in a single, often Bayesian, joint model. Evidence synthesis can result in greater precision than inferences from single datasets, if all included data sources provide consistent evidence on the parameters of interest. Often, however, unaccounted biases in included observational data can lead to conflicting evidence. Cross-validatory posterior-predictive checks to detect and quantify such conflict have been proposed (Presanis et al, 2013; 2017), but can be computationally challenging for complex models. This project will explore approximate methods such as INLA (Ferkingstad et al, 2017) and Gaussian mixture approximations (Chakraborty et al, 2022), as well as Reverse Bayes ideas (Held et al, 2022), aiming to build an easily accessible framework for computationally efficient and systematic conflict quantification. Such a framework will be applied to examples including estimating the prevalence of undiagnosed HIV (Presanis et al, 2021); and estimating the severity of SARS-CoV-2 infection (Kirwan et al, 2022; Nyberg et al, 2022).

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