Biomedical subjects

Marc L Serre

Publications and source records attributed to Marc L Serre.

2 recordsLinked to original sources

PubMed2006

Modeling a syphilis outbreak through space and time using the Bayesian maximum entropy approach.

PURPOSE: The aim of the study is to describe changes in the spatial distribution of syphilis before, during, and after an outbreak in Baltimore, MD, by using Bayesian maximum entropy (BME), a modern geostatistical technique for space-time analysis and mapping. METHODS: BME was used to conduct simple and composite space-time analyses of the density of syphilis infection based on primary, secondary and early latent syphilis cases reported to the Baltimore City Health Department between January 1, 1994, and December 31, 2002. RESULTS: Spatiotemporal covariance plots indicated that the distribution of the density of syphilis cases showed both spatial and temporal dependence. Temporally dependent disease maps suggested that syphilis increased within two geographic core areas of infection and spread outward. A new core area of infection was established to the northwest. As the outbreak waned, density diminished and receded in all core areas. Morbidity remained elevated in the two original central and new northwestern core areas after the outbreak. CONCLUSIONS: Density of syphilis infection was a simple informative measure easily compared across years. The BME approach was useful for quantitatively and qualitatively describing the spatial development and spread of syphilis. Our results are specific to Baltimore; however, the BME approach is generalizable to other settings and diseases.

Baltimore↗

PubMed2003

Efficient mapping of California mortality fields at different spatial scales.

A meaningful characterization of epidemiologic fields (mortality, incidence rate, etc.) often involves the assessment of their spatiotemporal variation at multiple scales. An adequate analysis should depend on the scale at which the epidemiologic field is considered rather than being limited by the scale at which the data are available. In many studies, for example, data are available at a larger scale (say, counties), whereas the epidemiologist is interested in a smaller-scale analysis (say, residential neighborhoods). We propose a mathematically rigorous and epidemiologically meaningful multiscale approach that uses the well-known BME theory to study important scale effects and generate informative scale-dependent maps. The approach is applied to a real-world case study involving daily mortality counts in the state of California. The approach accounts for scale effects and produces mortality predictions at the zip-code scale by downscaling data from the county scale. The multiscale approach is tested by means of a verification data set with detailed mortality information at the zip-code level for 1 day. A measure of mapping accuracy is used to demonstrate that the multiscale approach offers more accurate mortality predictions at the local scale than existing approaches, which do not account for scale effects.

California↗