Human movement patterns of farmers and forest workers from the Thailand-Myanmar border

Abstract

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality among study participants. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Keywords: Thailand-Myanmar border; disease ecology; farms; forests; human ecology; human movement; infectious disease epidemiology.

Figure 1.. Maximum daily Euclidian distances traveled by participants in kilometers.

Distance was calculated from the location a person was at the end of the prior night (most often, this location is their home location). Wilcoxon rank-sum test results are shown on the top of the lines connecting the age groups chosen for the tests. “ns” represents a p-value of > 0.05. **** represents a p-value of <= 0.0001.

Figure 2.. Utilization of the farm, forest, and home (calculated as a proportion of number of days being at the respective places) over the participation period for different age groups.

The bigger dots represent the mean values, while the smaller dots represent the outliers.

Figure 3.

A. Example of GPS points (red points) recorded for a person. B. Corresponding utilization probability calculated from the GPS points. Its 3D version can be found here.

Figure 4.. Total number of nights spent in the farms and the forests by each person over the participation period.

Figure 5.. Number of consecutive nights spent in the farms and forests.

In each panel, each of the points in each column represents a person of a specific age group and gender, defined in the legend. A single person may contribute a point in each of the columns (e.g., In the panel named Farms, a single person may contribute one point for each of the ranges of consecutive nights). Dotted lines connect the points contributed by the same person across different columns.