Stochastic Modelling of the Spatial Spread of Influenza in Germany

In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different timescale, cancer registries typically report yearly incidence or mortality counts in administrative regions.A major methodological challenge lies in building realistic models for spacetime interactions on discrete irregular spatial graphs. In this paper we will discuss an observation-driven approach, where past observed counts in neighboring areas enter directly as explanatory variables, in contrast to the parameterdriven approach through latent Gaussian Markov random fields (Rue and Held, 2005) with spatio-temporal structure. The main focus will lie on the demonstration of the spread of influenza in Germany, obtained through the design and simulation of a spatial extension of the classical SIR model (Hufnagel et al., 2004).

Clinico-epidemiological spectrum of melioidosis: a 2-year prospective study in the western coastal region of India

Objective: To determine the geographical epidemiology, clinical presentations and risk factors associated with melioidosis.Methods: A total of 28 confirmed cultures of Burkholderia pseudomallei were isolated and prospectively analysed with respect to clinico-demographic factors.Results: Age groups ranged from neonate to 84 years of age. Five children were less than 12 years of age. There was a male predominance of 71.4%. There was a crude mortality of 25%; all deaths were due to septic shock, 42.8% of which occurred within 48 hours of admission. A strong linear association (p = 0.01) was found between intensity of rainfall and presentation of cases. Proportion of cases and mortality were greater among those with occupational or recreational exposure to soil (75%). Pneumonia (32.1%) was the most frequent primary clinical presentation and diabetes mellitus (64.3%) constituted a major risk factor for both development and death due to melioidosis. Higher occurrence of mortality was noted among patients with chronic kidney disease (CKD) than others (Fischer’s exact test p = 0.04). One case of recurrence was noted.Conclusion: Melioidosis is quite prevalent in the western coastal region of India and is strongly associated with intensity of rainfall. There is increased risk among diabetics and to those who are exposed to soil and surface water. Melioidotic lymphadenopathy may mimic tuberculosis, hence should be considered as a differential diagnosis. Patients with bacteraemia have a poorer prognosis; and, septic shock relates to imminent death.