Statistical regression analysis of functional and shape data.

We develop a multivariate regression model when responses or predictors are on nonlinear manifolds, rather than on Euclidean spaces. The nonlinear constraint makes the problem challenging and needs to be studied carefully. By performing principal component analysis (PCA) on tangent space of manifold, we use principal directions instead in the model. Then, the ordinary regression tools can be utilized. We apply the framework to both shape data (ozone hole contours) and functional data (spectrums of absorbance of meat in Tecator dataset). Specifically, we adopt the square-root velocity function representation and parametrization-invariant metric. Experimental results have shown that we can not only perform powerful regression analysis on the non-Euclidean data but also achieve high prediction accuracy by the constructed model.

The Influence of New Surveillance Data on Predictive Species Distribution Modeling of Aedes aegypti and Aedes albopictus in the United States.

The recent emergence or reemergence of various vector-borne diseases makes the knowledge of disease vectors' presence and distribution of paramount concern for protecting national human and animal health. While several studies have modeled Aedes aegypti or Aedes albopictus distributions in the past five years, studies at a large scale can miss the complexities that contribute to a species' distribution. Many localities in the United States have lacked or had sporadic surveillance conducted for these two species. To address these gaps in the current knowledge of Ae. aegypti and Ae. albopictus distributions in the United States, surveillance was focused on areas in Texas at the margins of their known ranges and in localities that had little or no surveillance conducted in the past. This information was used with a global database of occurrence records to create a predictive model of these two species' distributions in the United States. Additionally, the surveillance data from Texas was used to determine the influence of new data from the margins of a species' known range on predicted species' suitability maps. This information is critical in determining where to focus resources for the future and continued surveillance for these two species of medical concern.

Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics.

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM2.5) and mortality statistics are increasingly used to examine the health effects of PM2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM2.5-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM2.5concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM2.5-mortality for the years of 2000, 2005, 2010 and 2015 using three PM2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM2.5-mortality from the 10 km CTM-derived PM2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM2.5 datasets. The estimated PM2.5-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM2.5 concentration is the primary factor that leads to the PM2.5-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM2.5-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach.

Accurate measurements of ground-level PM2.5 (particulate matter with aerodynamic diameters equal to or less than 2.5 µm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM2.5 datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage. The CTM-based approaches, however, often yield results with a coarse spatial resolution (typically at 0.1° of spatial resolution) and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM2.5 concentrations. In this study, with a focus on the long-term PM2.5 distribution in the contiguous United States, we adopt a random forests-based geostatistical (regression kriging) approach to improve one of the most commonly used satellite-derived, gridded PM2.5 datasets with a refined spatial resolution (0.01°) and enhanced accuracy. By combining the random forests machine learning method and the kriging family of methods, the geostatistical approach effectively integrates ground-based PM2.5 measurements and related geographic variables while accounting for the non-linear interactions and the complex spatial dependence. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with existing PM2.5 datasets. This manuscript also highlights the effectiveness of the geographical variables in long-term PM2.5 mapping, including brightness of nighttime lights, normalized difference vegetation index and elevation, and discusses the contribution of each of these variables to the spatial distribution of PM2.5 concentrations.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance.

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons.

Short communication: emerging technologies for biometeorology.

The first decade of the twenty-first century saw remarkable technological advancements for use in biometeorology. These emerging technologies have allowed for the collection of new data and have further emphasized the need for specific and/or changing systems for efficient data management, data processing, and advanced representations of new data through digital information management systems. This short communication provides an overview of new hardware and software technologies that support biometeorologists in representing and understanding the influence of atmospheric processes on living organisms.