Travel Patterns in China

Abstract

The spread of infectious disease epidemics is mediated by human travel. Yet human mobility patterns vary substantially between countries and regions. Quantifying the frequency of travel and length of journeys in well-defined population is therefore critical for predicting the likely speed and pattern of spread of emerging infectious diseases, such as a new influenza pandemic. Here we present the results of a large population survey undertaken in 2007 in two areas of China: Shenzhen city in Guangdong province, and Huangshan city in Anhui province. In each area, 10,000 randomly selected individuals were interviewed, and data on regular and occasional journeys collected. Travel behaviour was examined as a function of age, sex, economic status and home location. Women and children were generally found to travel shorter distances than men. Travel patterns in the economically developed Shenzhen region are shown to resemble those in developed and economically advanced middle income countries with a significant fraction of the population commuting over distances in excess of 50 km. Conversely, in the less developed rural region of Anhui, travel was much more local, with very few journeys over 30 km. Travel patterns in both populations were well-fitted by a gravity model with a lognormal kernel function. The results provide the first quantitative information on human travel patterns in modern China, and suggest that a pandemic emerging in a less developed area of rural China might spread geographically sufficiently slowly for containment to be feasible, while spatial spread in the more economically developed areas might be expected to be much more rapid, making containment more difficult.

Using Mobile Phone Data to Predict the Spatial Spread of Cholera

“…Most importantly, the predictions based on the mobile operator data did not rely on retrospective optimization of parameter models and could thus be available from the start of an outbreak. This is important as gravity model parameters are highly context specific 33 34 . These results indicate that outbreak preparedness and response to epidemic agents, such as cholera, can be enhanced.…”

Social networks with strong spatial embedding generate non-standard epidemic dynamics driven by higher-order clustering

“…An individual's propensity to choose a given workplace was determined by the distance between their home and workplace and parameters of a gravity-like kernel. The kernel was inversely proportional to distance raised to the power α, with movement scenarios generated solely by changing the value of α: a control value α = 0 that removed the embedding and produced a nonspatial model; a wide kernel with α = 3 typical of developed populations [32,34]; and a highly local kernel with α = 6 representing less developed populations (SI Appendix, Fig S1 part C compared with rural Huangshan in Ref [13]). The resulting distributions of distances from home to work were driven strongly by our choice of α, with 95% of journeys: less than 24.12km for α = 0; less than 12.91km for α = 3; and less than 6.68km for α = 6.…”

A Highly Flexible Trajectory Model Based on the Primitives of Brownian Fields—Part II: Analysis of the Statistical Properties

“…This means that the probability of jumping far from ( p x(l−1), p y(l−1)) to ( p x(l), p y(l)) tends to zero for all p = 0, 1, ..., which is a prerequisite for a realistic path model. An important property of the PDF p p d (d; l) in (19) is its dependency on l, which allows us to conclude that the incremental travelling length process p d(l) in (18) is in general non-stationary in the strict sense. Analogously to the discussion in Section IV-B, this dependency can be relaxed by setting p = 0.…”