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SES for Monitoring Diseases Outbreak: case of Onchocerciasis in Nigeria
1. OGUNDELE Olukunle Ayodeji 370063 Environmental factor based SES for monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria. Institute for Geoinformatics (Ifgi) University of Muenster, Germany. July 8, 2011 M.Sc. Geoinformatics (2010 – 2012) Masters‘ Thesis of Prof. Pebesma Edzer Dr. Remke Alber Supervisors
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5. Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11
6. Epidemiological Concept and Geospatial Technology Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11
7. Onchocerciasis Event Service: - Study framework Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11 Rule Modeling Sensor Data Characteristics: E.g. Water Temperature, Water Velocity and PH Environmental Factors: E.g. Hydrology Physical and Chemical Conditions Service Model Model Language: Event Pattern Markup Language (EML) Event Logic: (SES) Filtering Method for Notification Web Notification Model WNS Service: Notification on email and SMS System Input: E.g. Output Events; Messages; Location Information: E.g. Endemic Region, Rivers, Settlements Input: Conditions (Parameters) Data Characteristics EML Schema User Requirement: E.g. Location requirement; Stage interest Rules Documentations and Formalization; Process Flow; Geoprocesing; Complex Event Modeling
8. Model Formalization and Evaluation Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11
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11. …… I can do all things through Christ, my strength. Thanks everyone Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11
12. Environmental factor based SES for Monitoring Diseases Outbreak: Case of Onchocerciasis in Nigeria . Ogundele Olukunle A. | Date: 04.08.11 Yes!, More thanks these people Prof. Pebesma Edzer. Prof. Mafuyai A. Dr. Remke Albert. Dr. Ajayi Ola Simon Jirka Thomas Everding Andreas Johnen
Hinweis der Redaktion
I will first give the background and objectives of the study in the Introduction part, then I will give the case overview of the study. Next, I will discuss the involvement of geospatial technology in epidemiological concepts; the current state and propossed strategy. I will highlight the motivation for deciding on rule based model for this study. Then I will present the proposed service model and give the current work state in summary
There are factors that affects affect disease outbreak, they play important role and determine possible time when there will be an outbreak. One main factor is the environmental factor which directly affects the host, vectors and diseases organisms. Environmental factors relate with the overall lifecycle of the disease and of the vector and they are key determinant to every stage of the disease lifecycle. Such stages are crucial for outbreak of diseases. For instance the moment a fly is out to spread the disease, the infection is already on An important question will be “What are the possible means off modelling these important stages in the cycle, provide notifications for possible outbreaks. Therefore I look into how to use environmental factors in developing models which can be implemented in an event service environment to notify users of possible outbreaks in near real time. The objectives are:…..
For the purpose of this study, I take the case of River blindness diseases caused by Onchocerciasis parasite. The parasite causes blindness and severe skin diseases in tropical regions of the world. Nigeria is one of the countries having this disease challenge, having 100 thousand of its inhabitants blinded already by the parasite. One great problem is that the damage caused by this disease is irreversible. Once someone is blind by the parasite, there is no cure; you can only kill the parasites. This disease carried in the head of black fly and deposited in human host during feeding on blood. The outbreak and spread of this disease rely on the activities and survival of black fly. It is common the rural areas where black flies have favourable environment to habit and reproduce. So, it will be profitable to create a system that supports decisions against outbreak of this disease and action for intervention.
A challenging case will be for CHEW in a village has the responsibility to treat and also warn the community of the danger of diseases. He has the challenge of routinely warning them of the knowledgeable diseases because he did not know exactly when the disease is breaking out. He does not know when the parasites will mature, when the larvae will develope in the water and when the flies are breaking out to infest people with the disease. Therefore, he needs a system where he can register his location and the stage interested in (e.g. fly outbreak) and the system takes care of the rest by checking for his subscription if there is a match and send message as SMS to him to inform him of the action to be taken.
Looking at this requirement, the question is what existing techniques can provide such near real time notification. I tried to look at current method of integrated Geospatial technology and disease monitoring in epidemiological concept. The current methods are not enough… The common and most used method is the creation of risk maps and predictive models using geo-archive and health surveillance data. This is core geoprocessing which is not adequate for notification support for outbreak. The maps are presented through the web map service techniques to support decisions and inform the public to bring the analysis output close to users. But this is a pull method that requires constant access or request of updates and update exercises from experts Therefore I proposed an integration of the last layer that is powered by Sensor web Enablement in completing on time notification of intending outbreak. This should take less effort from users. Using the observations from sensors to create events that will in turn trigger notifications which are sent to the users, users can receive alert and warnings to help in their decision making.
I decided to narrow down the formalization of the model to the event service component of the system. I worked on the event model that can be used to create notification for early warning of possible outbreak of disease and how it fits into the whole system. This formed the core of my study framework which I divided into three part. I will only concentrate on two with time permission. I have been able to develop set of rules using the environmental parameters as defined by scholars to make a process flow for different stages of events. This I then formalized using modeling language and diagram. The model is to be translated into SES service using Event Pattern Markup Language (EML) and will be registered as services.
I have been able to formalize the event model and to briefly explain it, I will use the SES diagram as introduced to me by Thomas Everding. I developed the filters into components that I used through out the process model. There are two main filters, the Simple Filter and the Complex Filter. The simple filter only takes in one input and gives out multiple outputs. The complex filter takes in maximum of 2 inputs and carry out an operations on them and gives multiple output as well. This was built into a tree-like process model. Taking the example of breeding notification where water temperature, velocity and PH are main factors to successful breeding. There are minimum and maximum range that support survival and activities of the vector. This determine the long or short period of lifecycle, the strength of breeding and the level of risk These conditions were modeled over the period of underwater lifecycle to trigger an out going notification of an outbreak of larvae and fly.
I am motivated to examine and propose the SWE layer integration of geospatial technology with epidemiological concept because I want to use the strength of rule based model which is more efficient in modeling complex event process to monitor the complex lifecycle of the diseases and also the vectors. This approach also will enhance the use of advanced geospatial technology in public health domain better than the common risk mapping and mathematical predictive model . By developing an event service that uses environmental phenomenon to accurately model the lifecycle of vectors and disease outbreak, near real time early warning can be achieved. Also promoting optimum use of the environmental data. Lastly, dispersed expert knowledge from previous and current researches can use useful to build a warning system that can accurately predict and control diseases. At the same time, experts knowledge will be put into better use.
I already completed the rule formalization and modeling. I have it modeled using annotations and diagrams. What is left to do technically is creating the SES service using EML. There are several challenges in making rule based models for black fly lifecycle which is the same for other vector of diseases. One of the problem is the event handling by the complex filter that needs two parameters with an AND operation. If tone of the event input is no available, then that part of the model will not run until there is an input. This is a complication I still need to fully understand and find a way to adapt my model to the situation or propose a solution. I expect to have a framework for using rule based model for Diseases Outbreak using the environmental factors to predict outbreak in the end, stating some challenges and prospects of using SES as one of the rule based model for the model. Probably recommend the model for implementation and testing in Nigeria.
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