In this paper we describe the spatial data challenges faced in terms of providing accurate and timely analysis for a clinic during a cholera epidemic that spread through Port au Prince, Haiti in late 2022. This “triage” spatial epidemiology involved developing a bespoke geocoder that allowed for weekly maps of spread to be created in near real time. Resulting case data were also analyzed using a novel grid heatmapping approach which considers the epidemiological curve for each neighborhood. Adding further complexity during this period to both the data generation, and explaining cholera amplification and spread patterns, was a rising gang presence in the Port au Prince neighborhoods. Results identify a coastal pattern of amplification, which is expected given the informal settlement style living environments found in many of these neighborhoods. A second pattern then emerges of spread along a western and southern axis, which is far better captured in the grid heat mapping approach because of the lower numbers of patients seeking care at the clinic. The combination of traditional cartography and grid heat mapping help reveal the overall pattern of the epidemic, while also identifying key neighborhoods that require additional epidemiological investigation. Knowing why these neighborhoods played such an important role, possibly due to specific gang activity, is important in terms of understanding future disease spread in and around Port au Prince. Indeed, results presented can help contextualize official cholera reporting in 2025 where data availability is still hampered by ongoing gang rule.

Childhood cancer constitutes a major cause of death in children. In a recent study of the Georgia Cancer Registry, joint exposures to environmental and social/behavioral stressors were associated with spatial clustering of lymphomas and reticuloendothelial neoplasms among the 159 counties in Georgia, USA. The present study aims to further investigate these associations on a more granular level. Bayesian Poisson and zero-inflated Poisson regression models with spatial and non-spatial variance structures were utilized to investigate whether county-specific cancer patterns may be explained by single or combinations of social stressors and ambient air pollution while adjusting for confounding and accounting for overfitting using differences in expected log predictive densities. While we did not find associations between lymphoma rates and social variables, air pollution, or their interactions, our proposed analysis workflow can serve as a blueprint for future studies investigating dependencies in regression models that feature combinations of unobserved and observed dependency structures.

Every pandemic is embedded in specific spatial and temporal context. However, spatial patterns have almost always only been considered in the context of one individual pandemic. Until now, there has been limited consideration of spatial similarities or differences between pandemics. In this study, Bayesian spatial models for disease mapping were used to estimate excess mortality for the pandemics of 1890, 1918 and 2020. A robust linear regression was used to assess the association between ecological determinants and excess mortality. Spatial variations of excess mortality across Switzerland were observed in each pandemic, but the spatial patterns differ between the pandemics. Different determinants contribute to excess mortality, and these factors vary between COVID-19 and the previous pandemics. Spatial excess mortality from COVID-19 is most likely due to cultural and SEP differences, whereas in historical pandemics, mobility, pre-existing tuberculosis or remote mountain living likely contributed to spatial excess mortality.