The geographical spread of influenza

Abstract

How infectious diseases spread in space within one cycle of an epidemic is an important question that has received considerable theoretical attention. There are, however, few empirical studies to support theoretical approaches, because data are scarce. Weekly reports obtained since 1984 from a network of general practitioners spanning the entire French territory allows the analysis of the spatio-temporal dynamics of in£uenza over a ¢ne spatial scale. This analysis indicates that di¡usion over long distances, possibly due to global transportation systems, is so quick that homogeneous global mixing occurs before the epidemic builds up within infected patches. A simple model in which the total number of cases is given by the empirical time-series and cases are randomly assigned to patches according to the population weight of the patches exhibits the same spatio-temporal properties as real epidemic cycles: homogeneous mixing models constitute appropriate descriptions, except in the vicinity of the epidemic's peak, where geographic heterogeneities play a role.

Epidemiology and Ecology of H3N8 Canine Influenza Viruses in US Shelter Dogs

“…As temporal, spatial, and seasonal patterns have been observed with human influenza A virus infections,22, 23 explanatory variables of interest included number of days in the shelter before sampling, geographic region of the shelter, study year, and sampling month. Additional covariates included to evaluate the hypotheses of interest were sample type (intake/discharge), receipt of CIV vaccination upon admission, and allowance of comingling or cohousing.…”

Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza

“…The spatial spread of influenza has been much studied, particularly with respect to pandemic invasion waves (5)(6)(7)(8)(9)(10)(11). Simulation models incorporating air and surface transportation have generated important insights into the spread of influenza (6,7,10,11); however, the key underlying relationship between human movement and disease spread has not been verified across wide spatial scales nor contrasted among multiple interpandemic seasons of varying severity and viral subtype dominance.…”

Applications of bioinformatics and computational biology to influenza surveillance and vaccine strain selection

“…Adding antigenic dirft and cross-immunity into classical models greatly complicates the mathematics and although advances have been made, much work remains [27][28][29]. Advances in incorporating spatio-temporal information into epidemiological models has been successful in models of other pathogens including measles, whooping cough, and foot and mouth disease [30][31][32][33][34], and some progress has been made for influenza though much work remains [35,[46][47][48].…”