Spatial methods to study pathogen spread
A fundamental aspect of studying disease spread is exploring drivers that determine when and where infections occur. However, due to substantial heterogeneities in population distributions, underlying risk factors for diseases and biases in reporting systems, it can be difficult to translate simple descriptions of the location and timing of cases into descriptions of underlying risk. To fill this knowledge gap, we work on developing statistical and mechanistic modelling approaches that can account for (1) partially observed data; (2) biases in reporting to characterise disease spread and identify risk factor for infection. We work with different data types, including point pattern datasets and epidemiological time series.