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GIS maps of residential address locations where cases of shigellosis occurred among Womack Army Medical Center health care beneficiaries in Cumberland County, North Carolina,
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A personal computer—based commercial geographic information system (GIS) was applied to an outbreak of Shigella sonnei infection at Fort Bragg, North Carolina. We used a database consisting of demographic, temporal, and home-address information
for all recognized cases of S. sonnei that occurred among health care beneficiaries from 23 May 1997 thro...
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Objectives. To investigate a shigellosis outbreak in Genesee County, Michigan (including the City of Flint), and Saginaw County, Michigan, in 2016 and address community concerns about the role of the Flint water system.
Methods. We met frequently with community members to understand concerns and develop the investigation. We surveyed households aff...
Citations
... Several researches have been conducted to find out the determining factors of waterborne diseases (McKee et al. 2000;Musa et al. 2013;Siddiqui et al. 2012;Olowe et al. 2016). Rodrigues et al. (2015) examined the impact of socioeconomic factors on the spread of drinking water diseases. ...
This study presents the application of fuzzy analytic hierarchy process (FAHP) aided with geospatial analysis for identifying vulnerable zones to waterborne diseases in Azamgarh city, India. It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterborne diseases, five from socio-economic factors, one from criteria from drinking water (WQI) and two criteria from environmental factors to support the second stage analysis using FAHP. Moreover, weighted overlay analysis was utilised to produce final vulnerability map. The study of triangular fuzzy numbers and extent analysis shows that major responsible factors for controlling the distribution of waterborne diseases in the city are water quality index, irregular water supply and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. Similarly, it has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. Validation of the results with ground data of occurrence of waterborne diseases has shown that high incidence of waterborne diseases were in conformity with the most vulnerable zones of waterborne diseases. Hence, the current model to identify vulnerable zones to waterborne diseases is validated. The study result also suggests that the study approach adopted and its application process can be employed in other studies to identify vulnerable zones for various diseases.
... Literatures survey proves the applicability and effectiveness of GIS techniques in public health and diseases zonation assessment. GIS was used for identifying the geographical location and distribution of various diseases (Moonan et al. 2004;Bessong et al. 2009;Dhama et al. 2013;George et al. 2013), mapping of risk zonation and stratify risk factors (Daniel et al. 2004;Sarkar et al. 2007;Danson et al. 2008;Mourits et al. 2010;Tu et al. 2014;Qayum et al. 2015;Nazri et al. 2016;Ahmad et al. 2017;Wondim et al. 2017;Ali and Ahmad 2018;Ali and Ahmad 2019a, b), forecasting epidemics (Castronovo et al. 2009;Bergquist and Rinaldi 2010;Eisen and Eisen 2011;DeGrootem et al. 2012;Aimone et al. 2013), Monitoring diseases and developing surveillance strategies (Sanson et al. 1991;Briggs and Elliott 1995;Norstrom 2001;Sipe and Dale 2003;Reinhardt et al. 2008), utilization of health centres and diseases monitoring (Rushton 2003;Mourits et al. 2010;Li et al. 2012), service locations and route optimization to supplies equipments and service and trace the nearest health facility (McKee et al. 2000;Rushton 2003). Control and awareness programs can also be focused toward these areas with more confidence and effectiveness. ...
... The groundwater quality parameters like chloride (Cl -), fluoride (F -), nitrate (NO 3 -), sulphate (SO 4 2-), alkalinity, calcium (Ca 2? ), magnesium (Mg 2? ), bicarbonate (HCO 3 -) pH, total dissolved solid (TDS), total hardness (TH) etc. can be analysed and GIS based geostatistical (Kriging) technique can be used for susceptibility mapping of water quality index for knowing the suitability of drinking purpose (Tiwari et al. 2014;Yan et al. 2015;Gharbia et al. 2016;Yadav et al. 2018). Different studies have been carried out for mapping of water-borne diseases in association with water quality parameters (McKee et al. 2000;Ashbolt 2004;Daniel et al. 2004;Gupta and Shriram 2004;Moonan et al. 2004;Sarkar et al. 2007;Danson et al. 2008;Castronovo et al. 2009;Li et al. 2012;Aimone et al. 2013;Dhama et al. 2013;George et al. 2013;Devendra et al. 2014;Ghrefat et al. 2014;Tu et al. 2014;Al-Omran et al. 2015). ...
The varieties of water-borne disease are caused by pathogens transmitted and spread by different routes. The spread of such infection increases while the water quality affected by external sources. Changes in the quality of water can influence the dynamics of microbial pathogens which can also influence the prevalence and transmission dynamics of water-borne diseases. The present study highlighted on selected chemical parameters of ground water to signify the water quality index and mapping of water-borne diseases susceptibility areas in Kolkata Municipal Corporation, India. The main objective of present study was to demonstrate the significance of geographical information system based Geostatistical technique for identifying water-borne diseases susceptibility by taking different ground water parameters, water quality index and reported water-borne diseases into consideration. Using Kriging interpolation, the spatial map of water parameters, water quality index and reported disease cases were prepared and reclassified by putting risk rank to generate susceptibility map of water-borne diseases. The accuracy of the result was assessed through error estimation and spatial autocorrelation. The result from error estimation, it was found that the root mean square error between diseases susceptibility and selected chemical parameters, water quality index and reported water-borne diseases are 0.0847, 0.1182 and 0.0640 respectively which is always < 1. The correlation of diseases susceptibility with chemical parameters has resulted highly positive (r² = 0.97) and with water quality index has also resulted positive (r² = 0.82). The study result also suggests the applicability of geographical information system in other types of diseases susceptibility analysis by setting proper objectives and selecting suitable study criteria with spatial justification.
... Over the last few years, artificial neural networks, as nonlinear modelling techniques, had been proposed for use in predictive microbiology [55][56][57][58][59][60][61]. In our study, two neural network models, General Regression Neural Network (GRNN) model and Polynomial Net model, were used to predict the effects of temperature on Salmonella outbreaks in Mississippi. ...
Objectives: Salmonella is a major public health concern particularly in areas of low socio- economic status (SES) and high temperature. In this chapter, we examined several socio- economic and environmental factors that may increase the spread of Salmonella in the southern states of the USA.
Methods: In our recent studies, relevant US-southern states data of foodborne illnesses, from 2002 to 2011, were collected and used in various analytical models. The associations among low socioeconomic status, climatic variables and Salmonella infections were deter- mined using several software packages.
Results: Our studies showed a significant increase in Salmonella outbreaks in Mississippi during the observed periods with regional and district variations. Regression and neural network models revealed a moderate correlation between Salmonella infection rates and low socioeconomic factors. A seasonal trend was observed for Salmonella infections. In one of our study, an increase of 1°F (0.556°C) was shown to result in four new cases of Salmonella infection in Mississippi.
Conclusions: Geographic location besides socioeconomic status may contribute to the high rates of Salmonella outbreaks. There are consistent evidence that gastrointestinal infections with bacterial pathogens are positively correlated with ambient temperature. Warming trends in the USA may further increase rates of Salmonella infections.
Keywords: Salmonella infection, socioeconomic status, climate change, global warming
... With the development of this data infrastructure it will be possible to utilize GIS to address a wide range of food safety issues. For instance, researchers in the United States used commercial GIS software to spatialize epidemiological data (e.g., demographic, survey data) (McKee et al., 2000). By visualizing the spatial relationships within these datasets the researchers were able to identify a source and epicenter of the outbreak. ...
... Water/sanitation 20 [20,21,27,30,34,36,41,42,45,46 neighbour method to identify significant spatiotemporal clustering of shigellosis [36]. They used dot maps to locate the area with intense transmission and targeted it with educational interventions to bring the outbreak under control. ...
... Water/sanitation 20 [20,21,27,30,34,36,41,42,45,46 neighbour method to identify significant spatiotemporal clustering of shigellosis [36]. They used dot maps to locate the area with intense transmission and targeted it with educational interventions to bring the outbreak under control. ...
... Spatially targeted interventions to control the outbreak or prevent future cases were described in many studies. Measures that aimed to control outbreaks included cleaning implicated cooling towers [40], issuing water boiling orders to areas served by contaminated supplies [41,42], vaccination catch-up campaigns [28], removal of breeding sites for mosquito larvae [43] and targeted information campaigns [36]. For example, Acheson and colleagues placed postcode-targeted information on social networks during an outbreak of heterosexually acquired syphilis in Teesside, UK [44]. ...
Investigations of infectious disease outbreaks are conventionally framed in terms of person, time and place. Although geographic information systems have increased the range of tools available, spatial analyses are used relatively infrequently. We conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of spatial methods, describe the techniques applied and explore their utility. We identified 80 reports using spatial methods published between 1979 and 2013, ca 0.4% of the total number of published outbreaks. Environmental or waterborne infections were the most commonly investigated, and most reports were from the United Kingdom. A range of techniques were used, including simple dot maps, cluster analyses and modelling approaches. Spatial tools were usefully applied throughout investigations, from initial confirmation of the outbreak to describing and analysing cases and communicating findings. They provided valuable insights that led to public health actions, but there is scope for much wider implementation and development of new methods.
... Generally speaking, researches have shown that spatial patterns can provide stimuli for formulating hypotheses of disease outbreaks [18,19] . As human-to-human transmission of influenza is through close contacts, factors affecting the behavior of population including variation in demographic and environmental characteristics may affect the disease patterns [6] . ...
Objective: To explore socio-demographic data of the population as proxies for risk factors in disease transmission modeling at different geographic scales.
Methods: Patient records of confirmed H1N1 influenza were analyzed at three geographic aggregation levels together with population census statistics.
Results: The study confirmed that four population factors were related in different degrees to disease incidence, but the results varied according to spatial resolution. The degree of association actually decreased when data of a higher spatial resolution were used.
Conclusions: We concluded that variables at suitable spatial resolution may be useful in improving the predictive powers of models for disease outbreaks.
... Over the last few years, artificial neural networks, as nonlinear modeling techniques, had been proposed for use in predictive microbiology (Ibarra and Yang, 1999;McKee et al., 2000;Jacoboni et al., 2001;Garcíaa-Gimeno et al., 2003;Hervas et al., 2007;Valero et al., 2007;Khanzadi et al., 2010). In the current study, two neural network models-General Regression NN Model and Polynomial Net Modelwere used to predict the effects of temperature on Salmonella outbreaks in MS. ...
Abstract Background: Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used.
Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN.
A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R(2)=0.554; R(2)=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed.
There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections.
... Without sound analyses, and in particular those supported by proper statistical models, to produce new and value-added information, WGPHSSs will remain -data rich‖ but -information poor‖ [77]. In fact, only by combining the use of mapping and data analysis techniques can spatial disease patterns be identified [78]; however, our findings suggest that most of the identified WGPHSSs focus solely on data sharing and visualization. To uncover more useful, value-added information, WGPHSSs should employ more spatial and non-spatial analysis methods, especially when they are used by decision makers. ...
Web Geographic Information System (Web GIS) has been extensively and successfully exploited in various arenas. However, to date, the application of this technology in public health surveillance has yet to be systematically explored in the Web 2.0 era. We reviewed existing Web GIS-based Public Health Surveillance Systems (WGPHSSs) and assessed them based on 20 indicators adapted from previous studies. The indicators comprehensively cover various aspects of WGPHSS development, including metadata, data, cartography, data analysis, and technical aspects. Our literature search identified 58 relevant journal articles and 27 eligible WGPHSSs. Analyses of results revealed that WGPHSSs were frequently used for infectious-disease surveillance, and that geographical and performance inequalities existed in their development. The latest Web and Web GIS technologies have been used in developing WGPHSSs; however, significant deficiencies in data analysis, system compatibility, maintenance, and accessibility exist. A balance between public health surveillance and privacy concerns has yet to be struck. Use of news and social media as well as Web-user searching records as data sources, participatory public health surveillance, collaborations among health sectors at different spatial levels and among various disciplines, adaption or reuse of existing WGPHSSs, and adoption of geomashup and open-source development models were identified as the directions for advancing WGPHSSs.
... Similarly, various diseases are successfully being predicted by GIS technology, including highly pathogenic H5N1 avian influenza in poultry (pandemic or seasonal influenza), poliomyelitis, tuberculosis, Legionnaire's disease, Shigellosis, Guinea worm disease, blinding trachoma, African trypanosomiosis (sleeping sickness), onchocerciasis (river blindness), swine flu, FMD, West Nile fever, BSE (mad cow disease), sexually transmitted diseases (STD) viz. Chlamydia, gonorrhea, syphilis and HIV/AIDS infection, MRSA infection in pigs, cattle and human, malaria, Lyme disease, lymphatic filariasis (elephantiasis), cystic echinococcosis and many other vector-borne diseases (McKee et al., 2000;Sipe and Dale, 2003;Moonan et al. 2004;Gesink et al. 2006;Cringoli, 2007;Konrad et al., 2012). ...
... Another limitation of the GIS technique in infectious disease epidemiology and outbreak investigation is that mapping of diseases will describe the 'where' but not the 'why there' of the outbreak [39]. Nevertheless map patterns can provide potential clues as to what is causing the disease which can then be followed up by further investigation [40]. Modern GIS software that incorporates multiple map layers with advanced geostatistical analysis is expensive. ...
In 2011, there was a large measles outbreak in Dublin. Nationally 285 cases were notified to the end of December 2011, and 250 (88%) were located in the Dublin region. After the first case was notified in week 6, numbers gradually increased, with 25 notified in June and a peak of 53 cases in August. Following public health intervention including a measles-mumps-rubella (MMR) vaccination campaign, no cases were reported in the Dublin region in December 2011. Most cases (82%) were children aged between 6 months and 14 years, and 46 cases (18%) were under 12 months-old. This is the first outbreak in Dublin to utilise a geographic information system for plotting measles cases on a digital map in real time. This approach, in combination with the analysis of case notifications, assisted the department of public health in demonstrating the extent of the outbreak. The digital mapping documented the evolution of two distinct clusters of 87 (35%) cases. These measles cases were infected with genotype D4-Manchester recently associated with large outbreaks across Europe. The two clusters occurred in socio-economically disadvantaged areas and were attributable to inadequate measles vaccination coverage due in part to the interruption of a school-based MMR2 vaccination programme.
