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Spatial Modeling Techniques for Disease Surveillance

The main purpose of this study is to provide a better understanding of the spatial distribution of the risk of a disease in an area of interest. This will bring new insights to the policy makers and provide information for the scenario planning of public health authorities to decrease the risk of actual deaths due to the disease (mortality). Or, where not fatal, to suppress the disease burden with diminishing the number of people who suffer from the disease (morbidity) in a certain period of time, for the population at risk. Visceral Leishmaniasis (VL) and Cutaneous Leishmaniasis (CL) in Iran as well as Chronic Vascular Disease (CVD) were used as case study diseases.

Spatial statistics were used in this study to investigate the spatial variation of the incidence of a CVD to detect areas where the disease is particularly prevalent, which may lead to the detection of previously unknown risk factors. Moreover, possible associations between disease incidences and socioeconomic and environmental variables such as social deprivation and air pollution were investigated with general and local spatial regression methods.

For VL, spatial data mining models were developed by integrating Machine Learning algorithms into a GIS-based modeling approach. Artificial neural networks, fuzzy models and Bayesian probability models were all utilized to identify the most susceptible areas for a fatal disease incidence. These models were trained with the disease incidence and topographical, environmental and demographical data and successfully found the most risky areas.

The spatial dynamics of a CL epidemic emergence and related vectors (e.g. mosquitos, sand flies) and the mammalian reservoirs were explored using spatial simulation techniques. Hence, agent based modeling approaches were applied to simulate various socio-ecological processes associated with spatial patterns of disease incidences. The models highlighted areas where pathogens of infectious disease were dispersed locally by examining the interactions between vectors, reservoirs and susceptible people (hosts) in a spatially explicit environment.


Contact information

Ali Mansourian

Researchers in this project

Mohammadreza Rajabi, PhD Student
Petter Pilesjö, Professor