Spatial Analysis

Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan (PFSS)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of individual points into a cluster with additive point-specific penalty terms. We propose the support vector subset scan (SVSS), a novel method that iteratively assigns penalties according to distance from the separating hyperplane learned by a kernel support vector machine (SVM). SVSS efficiently detects disease clusters that are geometrically compact and irregular.

Objective

We present the support vector subset scan (SVSS), a new method for detecting localized and irregularly shaped patterns in spatial data. SVSS integrates the penalized fast subset scan3 with a kernel support vector machine classifier to accurately detect disease clusters that are compact and irregular in shape.

The Bureau of Communicable Disease (BCD) at the NYC Department of Health and Mental Hygiene performs daily automated analyses using SaTScan to detect spatio-temporal clusters for 37 reportable diseases. Initially, we analyzed one address per patient, prioritizing home address if available. On September 25, 2015, a BCD investigator noticed two legionellosis cases with similar work addresses. A third case was identified in a nearby residential facility, and an investigation was initiated to identify a common exposure source. Four days later, after additional cases living nearby were reported, the SaTScan analysis detected a corresponding cluster.  In response to this signaling delay, we implemented a multiple address (MA) analysis to improve upon single address (SA) analyses by using all location data available on possible exposure sites.

Objective

To improve timeliness and sensitivity of legionellosis cluster detection in New York City (NYC) by using all addresses available for each patient in one analysis.

SOS Médecins France (SOS Med) is the first private and permanent network of general practitioners providing emergency care in France. Besides Hospital emergency departments (HED), SOS Med is therefore a major source of data for detecting and measuring nearreal-time health phenomena. The emergency services provided by the SOS Med have been subject to important changes in the recent years. Their services are enriched by a medical consultation center together with extended working hours. Besides, the south of the region is markedly affected by a declining number of medical practitioners This study was conducted to analyze the regional population coverage of emergency healthcare data provided by HED and SOS Med to the French syndromic surveillance system (SurSaUD®) taking into account distance, health care offer, demographic factors and ecological deprivation factors.

Objective

To analyse population coverage of syndromic surveillance(SS) based on emergency care data by studying i)the attractiveness of respectively SOS Médecins (Emergency care general practitioners) and Hospital emergency departments in the Centre-Val de Loire region and ii) the contribution of ecological deprivation factors in emergency access to healthcare.

Local transmission of Zika virus has been confirmed in 67 countries worldwide and in 46 countries or territories in the Americas. On February 1, 2016 the World Health Organization declared a Public Health Emergency of International Concern due to the increase in microcephaly cases and other neurological disorders reported in Brazil. Several countries issued travel warnings for pregnant women travelling to Zika-affected countries with Brazil, Colombia, Ecuador, and El Salvador advising against pregnancy. The risk of local transmission in unaffected regions is unknown but potentially significant where competent Zika vectors are present and also given the additional complexities of sexual transmission and population mobility. Despite the rapid spread of Zika virus across the Americas and global concerns regarding its effects on fetuses, little is known about the pattern of spread. Knowledge of the direction and the speed of movement of disease is invaluable for public health response planning, including the timing and placement of interventions.

Objective

To estimate the velocity of Zika virus disease spread in Brazil using data on confirmed Zika virus disease cases at the municipal-level.

Unhealthy diet is becoming the most important preventable cause of chronic disease burden. Dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment. Assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality. Mass administration of dietary surveys is impractical and prohibitory expensive, and surveys typically fail to address variation of food selection at high geographic resolution. Marketing companies such as the Nielsen cooperation continuously collect and centralize scanned grocery transaction records from a geographically representative sample of retail food outlets to guide product promotions. These data can be harnessed to develop a model for the demand of specific foods using store and neighborhood attributes, providing a rich and detailed picture of the “foodscape” in an urban environment. In this study, we generated a spatial profile of food selection from estimated sales in food outlets in the Census Metropolitan Area (CMA) of Montreal, Canada, using regular carbonated soft drinks (i.e. non-diet soda) as an initial example.

Objective

To demonstrate a method for estimating neighborhood food selection with secondary use of digital marketing data; grocery transaction records and retail business registry.

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. One common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. Traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of Infectious Diseases in the reality. 

Objective

The purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ILI) by a three-level Poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. 

Transparency of information on infectious disease epidemics is crucial for not only public health workers but also the residents in the communities. Traditionally, disease control departments created official websites for displaying disease maps or epi-curves with the confirmed case counts. The websites were usually very formal and static, without interaction, animation, or even the aid of spatial statistics. Therefore, we tried to take advantage of open data and use a lightweight programming language, JavaScript, to create an interactive website, named “Taiwan Infectious Disease Map (http://ide.geohealth.tw/)“. With the website, we expect to provide real-time incidence information and related epidemiological features using interactive maps and charts. 

Objective

To visualize the incidence of notifiable infectious diseases spatially and interactively, we aimed to provide a friendly interface to access local epidemic information based on open data for health professionals and the public. 

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore it is appropriate to consider the analysis of their spatial distributions together. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans. Tularemia is an infectious disease endemic in North America and parts of Europe. In Finland tularemia is typically mosquito-transmitted with rodents serving as a host; however, a country-wide understanding of the relationship between rodents and the disease in humans is still lacking. We propose a methodology to help understand the association between human tularemia incidence and rodent population levels. 

Objective

We seek to integrate multiple streams of geo-coded information with the aim to improve public health surveillance accuracy and efficiency. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution can help early prediction of human incidence. To this end, we develop joint modeling approaches to evaluate the contribution of vector or reservoir information on early prediction of human cases. A case study of spatiotemporal modeling of tularemia human incidence and rodent population data from Finnish health care districts during the period 1995-2013 is provided. Results suggest that spatial and temporal information of rodent abundance is useful in predicting human cases. 

Most surveillance methods in the literature focus on temporal aberration detections with data aggregated to certain geographical boundaries. SaTScan has been widely used for spatiotemporal aberration detection due to its user friendly software interface. However, the software is limited to spatial scan statistics and suffers from location imprecision and heterogeneity of population. R Surveillance has a collection of spatiotemporal methods that focus more on research instead of surveillance.

 Objective

To build an open source spatiotemporal system that integrates analysis and visualization for disease surveillance. 

Zika virus was declared an international public health emergency by the World Health Organization on February 1, 2016. With Georgia hosting the world’s busiest international airport and a sub- tropical climate that can support the primary Zika virus vector, Aedes aegypti, and secondary vector, Aedes albopictus, the CDC designated Georgia as a high risk state for vector transmission. Faced with a lack of mosquito surveillance data to evaluate risk of autochthonous transmission and a few counties statewide that provide comprehensive mosquito control, the DPH rapidly scaled up a response. DPH updated existing mosquito surveillance and response plans targeted for West Nile Virus (WNV) and expanded capacity to areas that lacked previous surveillance targeting the Zika virus vector. 

Objective

To describe the Georgia Department of Public Health’s (DPH) mosquito surveillance capacity before and after Zika virus was declared a public health emergency, review and compare mosquito surveillance results from 2015 to 2016, and evaluate the risk of autochthonous vector transmission of Zika virus based on 2016 surveillance data of Aedes aegypti and Aedes albopictus mosquitoes.