Changes in local population density and patterns of human movement are critical elements of disease dynamics that can be extremely difficult to measure empirically or model theoretically, particularly at high-resolution spatial and temporal scales. Recent advances in theoretical models have highlighted deviations from the classic predictions (largely based on well-studied industrialized nations from the pre-vaccination era) due to localized variation in density and contact rates, as driven by local movement patterns (e.g. invasion thresholds, critical community size, and vaccination coverage for elimination).
While basic information on static population size and density can be extracted from GIS data, ethnographic field studies, and demographic records, high spatiotemporal resolution measurements of population movements or variation in density have rarely been obtained in low-income nations. Disruptive events, such as political or societal instability, conflict, food shortages, and natural disasters, can quickly lead to human migration and impact human health. Rapid detection of displaced individuals and prompt humanitarian responses to newly established, vulnerable settlements are doubly beneficial during crises, mitigating secondary disruptive events such as epidemics, malnutrition, and violence. We present an application of a recently introduced method using satellite imagery to measure anthropogenic light to detect near-real time changes in settlements. This method provides a platform for retrospective analyses and characterizations of population responses to perturbations, such as the 2010 earthquake in Haiti, with potential use for predictive situations.
Results/Conclusions
To assess the utility of this method, we compare dynamic estimates of population density and distribution as obtained from this recent method to estimates using previously established methods for population detection. We specifically investigate population movement immediately after the earthquake in Haiti, which was followed by a devastating outbreak of cholera; these incidents were well documented. Both our method and the conventional methods (surveys and other forms of high-resolution satellite imagery) detect coarse regional movements and overall trends of population growth urban areas but our method additionally detects an immediate loss of resources, followed by rapid large-scale local migration from the site and pinpoints relocation in nearby areas. The finer scale characterization of changes in population density provided by our method can be advantageous to quantify near-real time local heterogeneities in density dependent transmission, health system demand, and prioritization for public health interventions.