Determinants of Short-term Movement in a Developing Region and Implications for Disease Transmission

Abstract

Background: Human mobility is important for infectious disease spread. However, little is known about how travel varies by demographic groups and how this heterogeneity influences infectious disease risk.

Methods: We analyzed 10 years of survey data from 15 communities in a remote but rapidly changing region in rural Ecuador where road development in the past 15-20 years has dramatically changed travel. We identify determinants of travel and incorporate them into an infection transmission model.

Results: Individuals living in communities more remote at baseline had lower travel rates compared with less remote villages (adjusted odds ratio [OR] = 0.51; 95% confidence interval [CI] = 0.38, 0.67). Our model predicts that less remote villages are, therefore, at increased disease risk. Though road building and travel increased for all communities, this risk differential remained over 10 years of observation. Our transmission model also suggests that travelers and nontravelers have different roles in disease transmission. Adults travel more than children (adjusted OR = 1.73; 95% CI = 1.30, 2.31) and therefore disseminate infection from population centers to rural communities. Children are more likely than adults to be infected locally (attributable fraction = 0.24 and 0.09, respectively) and were indirectly affected by adult travel patterns.

Conclusions: These results reinforce the importance of large population centers for regional transmission and show that children and adults may play different roles in disease spread. Changing transportation infrastructure and subsequent economic and social transitions are occurring worldwide, potentially causing increased regional risk of disease.

Figure 1

Rotavirus transmission model diagram. Dashed arrows represent transmission events and solid arrows represent movement of people. Villagers and city residents are classified as susceptible (S), infected (I) and Recovered (R), with separate compartments for individuals under age 5 and ages 5 and older (not shown). Susceptible people can become infected by direct transmission within their own community. Villagers can infect and be infected by the city during travel. We ignore travel by city dwellers and between smaller villages. Village susceptible infectious recovered (SIR) model is stratified by baseline remoteness (close, medium and far) and both village and city SIR models are stratified by year (2004, 2007, 2010, and 2013) resulting in 12 models. Each model is stratified by age (less than or greater than 5 yrs.). This stratification results in 8 transmission parameters (6 village-level due to 2 age-groups and three community groups, and 2 city level due to two age groups), and 24 travel rate parameters to and from the city (2 age groups, 4 years, and 3 community types, where). Population size also varies by age, community type, and year. The recovery rate parameter is the same for all SIR models. See eAppendix for model equations and details.

Figure 2

Demographic changes over time by study year and remoteness strata from the survey data. Close, medium, and far villages are shown as solid line, small dashed line, and longer dashed line respectively. A) Percent out-of-region travel (weighted by population size), B) Percent reporting no occupation, C) Maximum household education (years), D) Percent reporting salaried occupation.

Figure 3

Attributable fraction for local transmission by heterogeneity in travel (adults vs. children) from the transmission model for A) low within-village transmission (Baseline R₀=0.79) and B) high within-village transmission (Baseline R₀=1.43). R₀ increases slightly with increasing heterogeneity. Increasing heterogeneity was done using a proportionality constant where travel_adult = ctravel_child and c takes values of 1 (none), 2 (medium), or 3 (high); c=1.73 in the study data. Adults are shown in black and children are shown in gray.