Spatial Analysis

Coccidioidomycosis, commonly referred to as Valley Fever, is caused by the soil-borne saprophytic fungus C. immitis and posadasii. These species have historically been found in the desert southwest and Mexico; however, in 2010 there were three coccidioidomycosis cases identified in central Washington. Colonization of soils by C. immitis has been confirmed at exposure sites associated with these cases. Multiple studies have identified a relationship between environmental conditions and C. immitis growth areas, but these relationships have not been evaluated in Washington. The Washington State Department of Health has been conducting environmental surveillance in an effort to understand the geographic distribution of C. immitis in central Washington and the associated risk to humans and animals. Here we describe our environmental surveillance efforts and present preliminary findings related to environmental conditions of C. immitis growth areas in central Washington.

Objective

Our objective is to describe the environmental conditions associated with confirmed Coccidioides immitis growth and accumulation sites in south central Washington in an effort to understand the ecology and identify additional potential sites across this emerging endemic zone. 

To develop a spatially accurate biosurveillance synthetic data generator for the testing, evaluation, and comparison of new outbreak detection techniques.

In the United States, surveillance of vaccine uptake for childhood infections is limited in scope and spatial resolution. The National Immunization Survey (NIS) - the gold standard tool for monitoring vaccine uptake among children aged 19-35 months - is typically constrained to producing coarse state-level estimates. In recent years, vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despite availability of vaccination services) has resurged in the United States, challenging the maintenance of herd immunity. In December 2014, foreign importation of the measles virus to Disney theme parks in Orange County, California resulted in an outbreak of 111 measles cases, 45% of which were among unvaccinated individuals. Digital health data offer new opportunities to study the social determinants of vaccine hesitancy in the United States and identify finer spatial resolution clusters of under-immunization using data with greater clinical accuracy and rationale for hesitancy.

Objective

The purpose of this study was to investigate the use of large-scale medical claims data for local surveillance of under-immunization for childhood infections in the United States, to develop a statistical framework for integrating disparate data sources on surveillance of vaccination behavior, and to identify the determinants of vaccine hesitancy behavior. 

Technology that combines traditional manipulations with databases and complete visualization of geographic (spatial) analysis employing maps has been developed in order to explore the possibilities for Geographical Information Systems (GIS) to be used in sanitary and epidemiological surveillance system based on the analysis of morbidity and identification of influence of hazardous chemical environmental factors on human health. 

Hand-foot-mouth disease (HFMD) is a common childhood illness and the drivers of HFMD incidence are still not clear [1]. In mainland China, continuing and increasing HFMD epidemics have been recorded since 2008, causing millions of infections and hundreds of deaths annually. In Beijing, 28,667 cases were reported in 2015 and the incidence was 133.28/100,000. The variations in Beijing HFMD epidemics over population, space, and time that have been revealed [2] emphasize the need for further research about risk factors of HFMD occurrence. This study aims to explore local effects on HFMD incidence led by potential factors. 

Objective

HFMD incidence varies between geographic regions at the township in Beijing. The objective of this study was to examine spatial heterogeneity for the association between HFMD incidence and demographic and socioeconomic factors.