Spatial Analysis

Bovine cysticercosis is a zoonotic foodborne disease caused by Taenia saginata involving cattle as the intermediate host and humans as the final host. Humans are infected by eating raw or undercooked meat of infected cattle. Cattle are infected after grazing on pasture infected by human feces. Disease detection in cattle is performed during post-mortem meat inspection at the slaughterhouse through the identification of cysts in muscle tissue. Cysts develop from a viable stage to a degenerated stage in one to nine months, both stages being visible and distinguishable in cattle muscle. Due to the slow development of cysts and the complexity of cattle movements (up to ten different herds from birth to slaughter in France), there is a strong bias to consider the last farm location before slaughter as the location of infection.

Objective

Spatial analysis of infectious diseases enables identification of areas at high risk for infection, a useful tool for implementation of risk-based surveillance. For chronic diseases, the period between infection and detection can be long and when animal movements are important, identifying the place of infection is difficult. The objective of this study is to propose an innovative approach for spatial analysis that takes into account uncertainty regarding the location where animals were infected. An animal-herd-level weighted analysis was used and applied to bovine cysticercosis in France.

 

Absenteeism has been considered as a potential indicator for the early detection of infectious disease outbreaks in population, especially in primary schools. However, in practice this data are often characterized by an excess of zeros and spatial heterogeneity. In a project on integrated syndromic surveillance system (ISSC) in rural China, Random effect zero-inflated Poisson (RE-ZIP) model was applied to simultaneously quantify the spatial heterogeneity for “occurrence” and “intensity” on school absenteeism data.

Objective

To describe and explore the spatial heterogeneity via Random effects zero-inflated Poisson model (RE-ZIP) for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.

GFT is a surveillance tool that gathers data on local internet searches to estimate the emergence of influenza-like illness in a given geographic location in real time.3 Previously, GFT has been proven to strongly correlate with influenza incidence at the national and regional level.2,3 GFT has shown promise as an easily accessed tool to enhance influenza surveillance and forecasting; however, further geographic validation of city-level data is needed. 1,2,6

Objective

To test if Google Flu Trends (GFT) is predictive of the volume of influenza and pneumonia emergency department (ED) visits across multiple United States cities.

 

Many syndromic surveillance systems use spatio-temporal analysis to detect local outbreaks such as gastrointestinal illnesses and lower respiratory infections. In Reunion Island, the syndromic surveillance system is based mainly on ED visits. Spatial analysis was first used in 2013 to validate retrospectively a cluster of viral meningitis. At the end of 2014, the Regional Office of French Institute for Public Health Surveillance implemented a prospective computer-automated space-time analysis in order to launch daily analyses of ED visits.

Objective

To present the implementation and the first results of a prospective spatio-temporal analysis from emergency department (ED) data in Reunion Island.

LAC hosted the 2015 Special Olympics (SO) which welcomed approximately 6,500 athletes from 165 countries, as well as 30,000 volunteers and 500,000 spectators from July 25 to August 2, 2015. International athletes were not required to show proof of vaccinations and were housed in dormitories for nine days, creating potential for infectious disease outbreaks. In response to these unique public health challenges, we describe how LAC’s syndromic surveillance system (SSS), which captures over 65% of all Emergency Department (ED) visits, was used to detect potential emerging health events congruent with SO games and pre-game events.

Objective

To describe how syndromic surveillance was used to monitor health outcomes in near real-time during the 2015 Special Olympics in Los Angeles County (LAC), California.

Geographic Information System (GIS) applications are increasingly being used for public health purposes. GIS technology provides visual tools – through the creation of computerized maps, graphs, and tables of geographic data – that can assist with problem solving and inform decision-making. PHIMS aims to enable visualization and spatial analysis of environmental data with underlying population based indicators. PHIMS displays many layers of environmental information across Ontario, and users can view maps demonstrating environmental or demographic data as they apply to specific geographic areas. This is useful for observing where environmental events are occurring, detecting potential emergency situations, and identifying areas with vulnerable populations. By displaying available, real-time, environmental data from multiple partners through PHIMS, public health events can be identified earlier to better prevent, prepare for, and respond to emergencies.

Objective

To describe how the Public Health Information Management System (PHIMS) tool is used by KFL&A Public Health to enhance real-time situational awareness and assist with evidence informed decision-making to help protect the health of the population.

Nontyphoidal Salmonella, consisting of >2,500 distinct serotypes, is the leading bacterial agent of foodborne illness in the U.S., causing an estimated 1 million infections per year. In NYC, interviews of all case-patients (N≈1,100 annually) are attempted to support outbreak investigation and control. Salmonella clusters in NYC are typically identified either by notification from PulseNet, CDC, or other health departments or by a weekly analysis using the historical limits method. More systematic and timely cluster detection could inform resource prioritization and improve the effectiveness of public health interventions. We initiated daily analyses in May 2015 to detect spatio-temporal clusters by serotype among cases since February 23. In July 2015, an analysis was added to detect purely temporal clusters among cases since May 1.

Objective

To prospectively identify serotype-specific clusters of salmonellosis in New York City (NYC).

Rabies is a zoonotic disease caused by an RNA virus from the family Rhabdoviridae, genus Lyssavirus. Worldwide distributed, control of rabies has been considered to be particularly amenable to a “One Health” strategy (1). In Chile, rabies was considered endemic in domestic dog population until the late 1960s, when a surveillance program was established, decreasing the number of human cases related to canine variants until the year 1972 (2). Rabies is recognized as a endemic infection in chiropterans of Chile and prompted the surveillance of the agent in this and other species (3).

Objective

This study aims to analyze the evolution of the epidemiological behavior of rabies in Chile during the period 2003 to 2013, through the epidemiological characterization of a number of variables and description of spatial and temporal patterns of animal cases.

Emerging disease clusters must be detected in a timely manner so that necessary remedial action can be taken to prevent the spread of an outbreak. The Exponentially Weighted Moving Average method (EWMA) is a particularly popular method, and has been utilized for disease surveillance in the United States.

A spatio-temporal EWMA statistic is proposed for on-line disease surveillance over multiple geographic regions. To capture spatial association, disease counts of neighboring regions are pooled together, similar to a method originally proposed by Raubertas for a different control chart. Also to increase statistical power in testing multiple EWMA statistics simultaneously, false discovery rate (FDR) is used instead of the traditional family-wise error rate (FWER).

Objective

To propose a computationally simple and a fast algorithm to detect disease outbreaks in multiple regions