Commuter mobility and the spread of infectious diseases: application to influenza in France

Abstract

Commuting data is increasingly used to describe population mobility in epidemic models. However, there is little evidence that the spatial spread of observed epidemics agrees with commuting. Here, using data from 25 epidemics for influenza-like illness in France (ILI) as seen by the Sentinelles network, we show that commuting volume is highly correlated with the spread of ILI. Next, we provide a systematic analysis of the spread of epidemics using commuting data in a mathematical model. We extract typical paths in the initial spread, related to the organization of the commuting network. These findings suggest that an alternative geographic distribution of GP accross France to the current one could be proposed. Finally, we show that change in commuting according to age (school or work commuting) impacts epidemic spread, and should be taken into account in realistic models.

Figure 1. Spatial spread of influenza like illness in France.

Incidence for 100000 inhabitants as monitored by the Sentinelles network during season 1985–1986. Maps are 2 weeks apart.

Figure 2. Measuring similarity in spread above randomness .

Lines correspond with overlap measures for a given pair of district at different times after introduction of a single infected. For a particular pair (green line), we also present the overlap measure obtained using reshuffled networks for the same pair (red line). Criterion was defined as the time when the green line crossed the red line.

Figure 3. Commuter mobility in France.

(a,b)Total number of individuals leaving each district via work commuting (a) and school commuting (b). (c) Proportion of commuters and travelled distance in the school network (red) and the work network (green). (d,e) Clusters identified in the work (d) and schoool (e) commuting networks.

Figure 4. Autocorrelation in incidence for observed and simulated epidemics.

(a) Mean value of Moran's Index computed on the 26 epidemics from the Sentinelles network, and (b) on 100 simulated epidemics. In each case, the blue line uses work commuting based weights or school (red line). Gray areas corresponds to the 95% expected values when no autocorrelation is present.

Figure 5. Typical pathways according to initial infective location.

For each district, values were averaged over all neighbors less than 100 km away. Basins of attraction were identified by clustering.

Figure 6. School and work commuting networks and the spatial spread of epidemics.

(a,b,c) ILI epidemic curves using all commuting networks (a), only work commuting (b) and only school commuting (c). Epidemics were started form 1000 randomly chosen districts. (d) Overlap between epidemics using work (blue curve) or school commuting (red curve).

Figure 7. Sensitivity analysis.

Overlap between epidemics simulated with first model and epidemics propagating only by school (red) or work (blue) commuting (a), with epidemics for which asymptomatic adults do not have a reuced genration time (b), with epidemics simulated with different parameters of transmission (c). (d) Overlap between epidemics in which 80% of adults are susceptible with epidemics with different rates of susceptibility.