Using a spatial filter and a geographic information system to improve rabies surveillance data

Wildlife and Bait Density Monitoring to Describe the Effectiveness of a Rabies Vaccination Program in Foxes

Fox rabies has been eliminated from vast areas of West and Central Europe, but cases still occur in the Balkans. Oral vaccination is an effective method for reducing the incidence of the disease in wildlife, but it requires monitoring if bait density is adequate for the density of the wildlife reservoir. We developed a methodology to evaluate the effectiveness of aerial vaccination campaigns conducted in Montenegro during autumn 2011 and spring 2012. The effectiveness of the vaccination campaign was assessed by (i) estimating the density of baits, (ii) estimating the distribution of the red fox, (iii) identifying critical areas of insufficient bait density by combining both variables. Although the two vaccination campaigns resulted in 45% and 47% of the country’s total area not reaching recommended density of 20 baits/km², the consecutive delivery of both campaigns reduced these “gaps” to 6%. By combining bait and reservoir density data, we were able to show that bait density was lower than fox density in only 5% of Montenegro’s territory. The methodology described can be used for real-time evaluation of aerial vaccine delivery campaigns, to identify areas with insufficient bait densities.

Spatio-temporal epidemiology of hand, foot and mouth disease in Liaocheng City, North China

Hand, foot and mouth disease (HFMD) has posed a notable threat to public health and become a public health priority in China. This study was based on the reported cases of HFMD between 2007 and 2011. A total of 34,176 HFMD cases were geo-coded at town level (n=134). Firstly, a descriptive analysis was conducted to evaluate the epidemic characteristics of HFMD. Then, the Kulldorff scan statistic based on a discrete Poisson model was used to detect spatial-temporal clusters. Spatial distribution of HFMD in Liaocheng City, China from 2007 to 2011 was mapped at town level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFMD was non-random and clustered with a significant Moran’s I value every year. The local Moran’s I Z-score detected three significant spatial clusters for high incidence of HFMD. The space-time analysis identified one most likely cluster and twenty-five secondary clusters for high incidence of HFMD. We demonstrate evidence of the existence of statistically significant HFMD clusters in Liaocheng City. Our results provide better guidance for formulating regional prevention and control strategies.

Genetic structure and rabies spread potential in raccoons: the role of landscape barriers and sex-biased dispersal

Identifying natural barriers to movements of hosts associated with infectious diseases is essential for developing effective control strategies. Raccoon rabies variant (RRV) is a zoonosis of concern for humans because its main vector, the raccoon (Procyon lotor), is found near residential areas. In Québec, Canada, all cases of RRV found in raccoons since 2006 were detected on the eastern side of the Richelieu River, suggesting that this river acts as a barrier to gene flow and thus the potential for RRV to spread. The objectives of this study were to characterize the genetic structure of raccoon populations and assess the effect of the Richelieu River on the population structure in southern Québec, Canada. We also evaluated whether RRV spread potential differed between sex and at a larger spatial scale. Our analyses revealed a weak signal of genetic differentiation among individuals located on each side of the Richelieu River. At a larger spatial scale, genetic structuring was weak. Our results suggest that rivers might not always efficiently restrain raccoon movements and spread of RRV. We suggest that the difference in genetic structure found between sexes can be partly explained by male movements during the breeding season in winter, when ice bridges allow passage over most rivers in Québec.

A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique-the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.

Identifying high-risk regions for schistosomiasis in Guichi, China: a spatial analysis

Schistosomiasis epidemic is reemerging in some areas of China. The extensive snail habitat is a major challenge for a sustainable schistosomiasis control. Direct surveillance on snails for the disease control is no longer a desirable disease control approach due to current low density of infected snails and reduced funding. In this study the benefit of indirect monitoring of acute schistosomiasis cases, using spatial methods including disease mapping and spatial clustering analysis was explored in Guichi, China. Significant global clustering existed for acute cases and two statistically significant spatial clusters were detected, and subsequently validated by field surveys. Our study indicates that the application of geographic information system (GIS) and spatial methods are useful in the epidemiologic surveillance and risk assessment for acute schistosomiasis, providing an alternative approach with minimal funds required.

Innovative approaches to application of information technology in disease surveillance and prevention in Western Kenya

We describe an electronic injury surveillance system that provides data for improving patient care and monitoring injury incidence and distribution patterns. Patients with injuries visiting a rural Kenyan primary care center were enrolled consecutively over 14 months. Injury information was added onto an existing medical record database that captures data for each patient visit. A new injury data encounter form and entry screen were created that included geographical coordinates of the injury site. These coordinates were obtained using a handheld global positioning system (GPS) device, and data were downloaded to the database and linked to each patient. We created digital maps of injury spatial distribution using geography information systems (GIS) software and correlated injury type and location with patients' clinical data. A computerized medical record system, complemented by GIS technology and an injury-specific component, presents a valuable tool for injury surveillance, epidemiology, prevention and control for communities served by a specific health facility.

A meta-BACI approach for evaluating management intervention on chronic wasting disease in mule deer

Advances in acquiring and analyzing the spatial attributes of data have greatly enhanced the potential utility of wildlife disease surveillance data for addressing problems of ecological or economic importance. We present an approach for using wildlife disease surveillance data to identify areas for (or of) intervention, to spatially delineate paired treatment and control areas, and then to analyze these nonrandomly selected sites in a meta-analysis framework via before-after-control impact (BACI) estimates of effect size. We apply these methods to evaluate the effectiveness of attempts to reduce chronic wasting disease (CWD) prevalence through intensive localized culling of mule deer (Odocoileus hemionus) in north-central Colorado, USA. Areas where surveillance data revealed high prevalence or case clusters were targeted by state wildlife management agency personnel for focal scale (on average <17 km2) culling, primarily via agency sharpshooters. Each area of sustained culling that we could also identify as unique by cluster analysis was considered a potential treatment area. Treatment areas, along with spatially paired control areas that we constructed post hoc in a case-control design (collectively called "management evaluation sites"), were then delineated using home range estimators. Using meta-BACI analysis of CWD prevalence data for all management evaluation sites, the mean effect size (change of prevalence on treatment areas minus change in prevalence on their paired control areas) was 0.03 (SE = 0.03); mean effect size on treatment areas was not greater than on paired control areas. Excluding cull samples from prevalence estimates or allowing for an equal or greater two-year lag in system responses to management did not change this outcome. We concluded that management benefits were not evident, although whether this represented true ineffectiveness or was a result of lack of data or insufficient duration of treatment could not be discerned. Based on our observations, we offer recommendations for designing a management experiment with 80% power to detect a 0.10 drop in prevalence over a 6-12-year period.

Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic

BACKGROUND

The World Health Organization has declared tuberculosis a global emergency in 1993. It has been estimated that one third of the world population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis. The emergence of TB/HIV co-infection poses an additional challenge for the control of tuberculosis throughout the world. The World Health Organization is supporting many developing countries to eradicate tuberculosis. It is an agony that one fifth of the tuberculosis patients worldwide are in India. The eradication of tuberculosis is the greatest public health challenge for this developing country. The aim of the present population based study on Mycobacterium tuberculosis is to test a large set of tuberculosis cases for the presence of statistically significant geographical clusters. A spatial scan statistic is used to identify purely spatial and space-time clusters of tuberculosis.

RESULTS

Significant (p < 0.05 for primary clusters and p < 0.1 for secondary clusters) high rate spatial and space-time clusters were identified in three areas of the district.

CONCLUSION

There is sufficient evidence about the existence of statistically significant tuberculosis clusters in Almora district of Uttaranchal, India. The spatial scan statistics methodology used in this study has a potential use in surveillance of tuberculosis for detecting the true clusters of the disease.

Spatial analysis of hemorrhagic fever with renal syndrome in China

Background

Hemorrhagic fever with renal syndrome (HFRS) is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS)-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation.

Methods

Annualized average incidence at a county level was calculated using HFRS cases reported during 1994–1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country.

Results

Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001). Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p < 0.01) with maximum spatial cluster sizes of ≤ 20% and ≤ 10% of the total population, respectively.

Conclusion

The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.

Neighborhood size and local geographic variation of health and social determinants

BACKGROUND: Spatial filtering using a geographic information system (GIS) is often used to smooth health and ecological data. Smoothing disease data can help us understand local (neighborhood) geographic variation and ecological risk of diseases. Analyses that use small neighborhood sizes yield individualistic patterns and large sizes reveal the global structure of data where local variation is obscured. Therefore, choosing an optimal neighborhood size is important for understanding ecological associations with diseases. This paper uses Hartley's test of homogeneity of variance (Fmax) as a methodological solution for selecting optimal neighborhood sizes. The data from a study area in Vietnam are used to test the suitability of this method. RESULTS: The Hartley's Fmax test was applied to spatial variables for two enteric diseases and two socioeconomic determinants. Various neighbourhood sizes were tested by using a two step process to implement the Fmaxtest. First the variance of each neighborhood was compared to the highest neighborhood variance (upper, Fmax1) and then they were compared with the lowest neighborhood variance (lower, Fmax2). A significant value of Fmax1 indicates that the neighborhood does not reveal the global structure of data, and in contrast, a significant value in Fmax2 implies that the neighborhood data are not individualistic. The neighborhoods that are between the lower and the upper limits are the optimal neighbourhood sizes. CONCLUSION: The results of tests provide different neighbourhood sizes for different variables suggesting that optimal neighbourhood size is data dependent. In ecology, it is well known that observation scales may influence ecological inference. Therefore, selecting optimal neigborhood size is essential for understanding disease ecologies. The optimal neighbourhood selection method that is tested in this paper can be useful in health and ecological studies.

Pseudorabies virus infections in wild boar: data visualisation as an aid to understanding disease dynamics

Spatio-temporal survey data often form the main decision support in disease management. Consequently, disease managers rely on adequate description of the data supplied from epidemiological analysis. Practically speaking, such a description comprises of a visualisation, a deduced hypothesis and a statistical evaluation of the hypothesis. Prior to the application of traditional methods of data visualisation and hence data description, an aggregation of the data is required. This paper illustrates the resulting uncertainty in hypotheses that can be generated by traditional analytical techniques. Additionally, a method of avoiding this typical drawback in traditional data exploration is demonstrated: This is the use of data movies. Data from a 10-year study on the spread of pseudorabies virus (PrV) infections in wild boar populations were revisited. The visualisation of the data using a data movie instead of using traditional approaches for the first time enabled immediate observation of spreading infection in time and space. This dynamic perception of the spread drastically changes the understanding of the epidemiology of the virus. The paper demonstrates how data movies can account for the complete information contained within the data set. It is concluded that data movies can provide a more appropriate starting point. We recommend the data movie approach as an important addition in the epidemiological toolbox, because data movies can easily be integrated into the usual demonstration and publication process.

Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut

Geographic information systems combined with methods of spatial analysis provide powerful new tools for understanding the epidemiology of diseases and for improving disease prevention and control. In this study, the spatial distribution of a newly recognized tick-borne disease, human granulocytic ehrlichiosis (HGE), was investigated for nonrandom patterns and clusters in an area known to be endemic for tick-borne diseases. Analysis of confirmed cases of HGE identified in 1997-2000 in a 12-town area around Lyme, Connecticut, showed that HGE infections are not distributed randomly. Smoothed HGE incidence was higher around the mouth of the Connecticut River and lower to the north and west. Cluster analysis identified one area of increased HGE risk (relative risk=1.8, p=0.001). This study demonstrates the utility of geographic information systems and spatial analysis to clarify the epidemiology of HGE.

Spatial patterns of diarrhoeal illnesses with regard to water supply structures--a GIS analysis

This paper presents an application of a geographical information system (GIS) in the field of research of drinking water epidemiology. A retrospective study regarding gastrointestinal infections was carried out in the Rhine-Berg District (North Rhine-Westphalia, Germany), which is characterised by different drinking water supply structures. The main objective was to examine the hypothesis that spatial variations of diarrhoeal illnesses may be linked with different drinking water sources (groundwater or surface water). We introduced a GIS for storing and analysing the wide range of data sets comprising features of the water supply structure and the epidemiological databases which constitute the basic elements of a surveillance-system for waterborne infectious diseases. GIS-techniques supported the generation of hypotheses regarding disease distribution and causation. The data bases which are routinely available turned out to be of sufficient quantity and quality for running a waterborne disease surveillance-system. Geo-statistical analysis revealed spatial variations in the incidence of diarrhoeal illnesses. Parameters depicting the water supply structures, especially the amount of drinking water produced from surface or groundwater, were correlated with the age-standardised incidence rates of gastrointestinal infections. The correlation models showed a trend of positive linkage between disease incidence and amount of groundwater. We found GIS-techniques extremely useful to carry out area-based correlation studies and to analyse the exposure of populations in drinking water epidemiology.