Exploring spatiotemporal variation in host population mobility and vector-borne disease exposure

Vector-borne diseases are widespread, diverse and disproportionately affect certain populations. It is well-known that the mobility of host populations is critical to vector-borne disease spread and persistence, and understanding spatiotemporal aspects of this mobility can help predict exposure risk at both fine and large scales. This thesis aims to examine variations in host mobility in the context of vector-borne diseases at opposing ends of the spatiotemporal scale in a ‘three-paper format’. The first paper examines the mobility of a small sample population of humans and livestock in a rural area of western Kenya at a very fine spatiotemporal resolution using surveys and GPS loggers. Several important demographic characteristics are linked to movement patterns, and some seasonal differences in time spent on different types of landcover are observed. Individual variations in movement patterns are likely to be causing differential exposure to some types of vector-borne disease. The second paper further explores the human factors linked to mobility, focusing on the activity-driven movements of the local population in relation to various types of resource access, as well as demographic differences in activity-driven mobility. Both gender and age are found to be linked to activity-driven movements in this small rural population, and women reported spending longer than men accessing health facilities, highlighting how some population subgroups may have differential access to treatments and preventions for vector-borne disease. The final paper is set at the other end of the spatiotemporal scale and quantifies the movement patterns of the population of Mozambique over several months, combining these with country-wide epidemiological data to examine how large-scale differences in mobility may affect exposure to malaria. Human-mediated parasite movements are shown to be highly heterogeneous across Mozambique, and individual movements between rural and urban areas are likely to be driving malaria transmission in some parts of the country. This thesis makes important contributions to our understanding of individual differences in mobility patterns and highlights how both small-scale and large-scale perspectives are valuable for understanding the factors that may increase individual risk of exposure to vector-borne diseases. The work concludes that while mobility underpins much of the dynamics of vector-borne diseases, it is also crucial for understanding differences in the mobility of host populations, as these play an important part in perpetuating transmission and therefore contribute to disease persistence

Using Google Location History data to quantify fine-scale human mobility

Abstract Background Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100 m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions

Using Google location history data to quantify fine-scale human mobility

Background: Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods: Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results: We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions: GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.</p