104 research outputs found

    Agent-based modelling of cholera diffusion

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    This paper introduces a spatially explicit agent-based simulation model for micro-scale cholera diffusion. The model simulates both an environmental reservoir of naturally occurring V. cholerae bacteria and hyperinfectious V. cholerae. Objective of the research is to test if runoff from open refuse dumpsites plays a role in cholera diffusion. A number of experiments were conducted with the model for a case study in Kumasi, Ghana, based on an epidemic in 2005. Experiments confirm the importance of the hyperinfectious transmission route, however, they also reveal the importance of a representative spatial distribution of the income classes. Although the contribution of runoff from dumpsites can never be conclusively proven, the experiments show that modelling the epidemic via this mechanism is possible and improves the model results. Relevance of this research is that it shows the possibilities of agent-based modelling combined with pattern reproduction for cholera diffusion studies. The proposed model is simple in its setup but can be extended by adding additional elements such as human movement and change of behaviour of individuals based on disease awareness. Eventually, agent-based models will open opportunities to explore policy related research questions related to interventions to influence the diffusion process

    Cholera and Spatial Epidemiology

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    Using GIS to map the spatial and temporal occurrence of cholera epidemic in Camaroon

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    Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial TechnologiesGlobally, Cholera has been a major infectious disease due to its intercontinental, environmental and cultural factors. This study focused on evaluating the climatic and fresh water proximity factors influencing Cholera epidemic in Cameroon. To this effect, Cholera and climatic datasets in 2004, 2010, 2011 and 2012 to June 2013 were collected and mapped. Both high and low rainfall and temperature extremes were designated as promoters of V. Cholerae development and the highest cases were identified in the Littoral, Extreme North and Centre regions. Spatial autocorrelation using Local (Anselin) Moran I on Cholera cases revealed a cluster of Low-Low positive autocorrelation in Adamawa region in 2004, a High-High cluster of positive autocorrelation in the Littoral region and a Low-High negative autocorrelation in the South region in 2012, a Low-High negative autocorrelation in the South West region and a High-Low negative autocorrelation in the North West in 2013. Furthermore, using population numbers to count Cholera cases (prevalence) from 2010 to June 2013, Local Moran I results show a Low-Low cluster of positive autocorrelation in the South region, a Low-High negative autocorrelation in the North region and a High-Low negative autocorrelation in the Adamawa region in 2010, a High-Low negative spatial autocorrelation in the North region in 2011, a High-Low negative spatial autocorrelation in the South region in 2012 and a High-Low negative spatial autocorrelation in the North region in 2013. Spatial Poisson Regression analysis allowed concluding that Average Temperature, Distance to Streams, Population Distribution and Latitude are statistically significant predictors of increased Cholera cases, whereas Average Rainfall and Longitude are significant predictors of lower Cholera cases

    Characterizing Multiple Spatial Waves of the 1991-1997 Cholera Epidemic in Peru

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    Background Due to a lack of sanitary infrastructure and a highly susceptible population, Peru experienced a historic outbreak of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease for several years. Though case-fatality was low, the epidemic put massive strain on healthcare and governmental resources. Here we explore the transmission dynamics and spatiotemporal variation of cholera in Peru using mathematical models and statistical analyses that account for environmental conditions favoring the persistence of bacteria in the environment. Methods The authors use dynamic transmission models that incorporate seasonal variation in temperature, concentration of vibrios in the environment, as well as separate human and environmental transmission pathways. The model is fit to weekly department level data obtained from the cholera surveillance system in Peru. The authors also assess the spatial patterns of cholera transmission and correlations between case incidence, time of epidemic onset, and department level variables. Reproductive numbers are compared across departments. Results Our findings indicate that the epidemic first hit the coastal departments of Peru and later spread through the highlands and jungle regions. There was high seasonal variation in case incidence, with three clear waves of transmission corresponding to the warm seasons in Peru. Department level variables such as population size and elevation also played a role in transmission patterns. Finally, basic reproductive numbers most often ranged from one to eleven depending on department and time of year. Lima had the largest reproductive number, likely due to its population density and proximity to the coast. Conclusions Incorporating environmental variables into an epidemic model predicts the multiple waves of transmission characteristic of \textit{V. cholerae}, and effectively differentiates transmission patterns by geographic region even in the absence of unique parameter estimates. Mathematical models can provide valuable information about transmission patterns and should continue to be used to inform public health decision making

    Wastewater use in irrigated agriculture : confronting the livelihood and environmental realities

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    Co-published with CAB International and the International Water Management Institut

    Wastewater irrigation and health: assessing and mitigating risk in low-income countries

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    Wastewater irrigation / Public health / Health hazards / Risk assessment / Epidemiology / Sewage sludge / Excreta / Diseases / Vegetables / Leaf vegetables / Economic impact / Wastewater treatment / Irrigation methods / Developing countries

    Combating substance abuse with the potential of geographic information system combining multivariate analysis

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    Substance abuse problems have been a growing concern for people from all over the world. The objective of this study is to demonstrate the usefulness of a combination between a geographic information system and a multivariate analysis in substance abuse research. However, due to the limited studies on a combination of both these methods in the substance abuse field, we review some other studies in various fields indicating the potential of this method in future substance abuse studies. As the expertise of GIS lies in spatial analysis and the multivariate analysis lies in analyzing huge data thus being capable of interpreting the results very well, we hope this method will attract researchers to applying it in their studies and can consequently and indirectly help in combating substance abuse problems.Keywords: substance abuse; geographic information system; multivariate analysis;  spatial analysis; comba
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