systemarchi
© 2009 - 2018 Systemarchitects Partnership

Durban

Computer Based Assessment of Epidemic Outbreak Risks in the Urban

Scale

Background

The intergovernmental panel on climate change IPCC warned recently about the potential of disease spread through temperature rise. First long term studies that apply satellite remote sensing data of a time span of more than 30 years confirm the influence of climate change on this phenomenon. SARS, the influenza A virus subtype H5N1, Cholera and Ebola are only some of the most recent examples. Man made natural catastrophes and bio-terrorism count also as possible outbreak causes. Recent studies conclude that nowadays large cities face the highest threat of transnational infectious disease epidemics since almost a century. It has been observed that the growth of urban population leads to an increasing risk of disease spread by vectors. Advanced computer technologies like GIS and Big Data are being applied today for disease spread monitoring. In North America, linking climate variables such as precipitation and temperatures as well as land cover and remote sensing data with zoonotic disease occurrences have lead to outstanding forecasts for Plague, Lyme Disease and Sin Nombre Hantavirus outbreaks. However, and especially for developing countries, which contain most of the world's megacities, there is scarce data on the correlations of disease prevalence on humans and their environmental and socio-economic aspects.

Objectives

This system was developed using a mathematical model linked to spatial data and parameters taken from findings of published studies on infectious disease spread by vectors, particularly rodent borne diseases.  The main objective is to apply this model to an existing epidemiological study (Project RatZooMan, NRI, University of Greenwich, Kent, UK). The objectives of the system are: To identify and analyse existing risks in the urban area on the spread and impacts of infectious disease; To evaluate the vulnerability of urban areas in case of a major infectious disease event; To identify areas that represent a hazard in case of disease outbreak; To analyse the effects of urban renewal, when adequate measures are taken to minimize the risks. Sketch detail of a green edge between formal and informal neighbourhoods in Durban

Model Results

Figure 1 Among others, a number of socio-economic, topographic, environmental, land use and infrastructure factors were considered to identify the potential incidence risk. For an easier interpretation the range of values was translated into a range of yellow tones, graded in five degrees of natural brakes, using the Jenks natural breaks optimization.  Figure 1 shows the study site and the results displayed in the colour range. The darker the cells, the better the survival conditions for rodents (and therefore for the pathogens, if carried by the rodents). Figure 2 (Figure rodent traps) Model displaying site of traps and number of caught rodents. Figure 3 (Figure infected rodents) Figure 3 shows next to each trap site the percentage of infected rodents caught there. Only locations where more than four rodents were caught have been considered for this calculation. This graphic also in part confirms the calculations of the model about areas of higher risk. Two trap sites in areas calculated of low and lowest risk show a considerable percentage of infected rodents (29% and 17%, respectively). The existence of spatially limited hotspots suggests that the spread of Leptospirosis and Toxoplasmosis is limited to the micro habitat of few rodents. This assumption, known as the super-spreader theory or the "20/80 rule", was brought up by a study with wild deer mice in the Great Basin Desert, Utah, USA.
RESEARCH FOR PLANNING
© 2006 - 2015 Systemarchi

Durban

Computer Based Assessment of

Epidemic Outbreak Risks in the

Urban Scale

Background

The intergovernmental panel on climate change IPCC warned recently about the potential of disease spread through temperature rise. First long term studies that apply satellite remote sensing data of a time span of more than 30 years confirm the influence of climate change on this phenomenon. SARS, the influenza A virus subtype H5N1, Cholera and Ebola are only some of the most recent examples. Man made natural catastrophes and bio-terrorism count also as possible outbreak causes. Recent studies conclude that nowadays large cities face the highest threat of transnational infectious disease epidemics since almost a century. It has been observed that the growth of urban population leads to an increasing risk of disease spread by vectors. Advanced computer technologies like GIS and Big Data are being applied today for disease spread monitoring. In North America, linking climate variables such as precipitation and temperatures as well as land cover and remote sensing data with zoonotic disease occurrences have lead to outstanding forecasts for Plague, Lyme Disease and Sin Nombre Hantavirus outbreaks. However, and especially for developing countries, which contain most of the world's megacities, there is scarce data on the correlations of disease prevalence on humans and their environmental and socio- economic aspects.

Objectives

This system was developed using a mathematical model linked to spatial data and parameters taken from findings of published studies on infectious disease spread by vectors, particularly rodent borne diseases.  The main objective is to apply this model to an existing epidemiological study (Project RatZooMan, NRI, University of Greenwich, Kent, UK). The objectives of the system are: To identify and analyse existing risks in the urban area on the spread and impacts of infectious disease; To evaluate the vulnerability of urban areas in case of a major infectious disease event; To identify areas that represent a hazard in case of disease outbreak; To analyse the effects of urban renewal, when adequate measures are taken to minimize the risks. Sketch detail of a green edge between formal and informal neighbourhoods in Durban

Model Results

Figure 1 Among others, a number of socio- economic, topographic, environmental, land use and infrastructure factors were considered to identify the potential incidence risk. For an easier interpretation the range of values was translated into a range of yellow tones, graded in five degrees of natural brakes, using the Jenks natural breaks optimization.  Figure 1 shows the study site and the results displayed in the colour range. The darker the cells, the better the survival conditions for rodents (and therefore for the pathogens, if carried by the rodents). Figure 2 (Figure rodent traps) Model displaying site of traps and number of caught rodents. Figure 3 (Figure infected rodents) Figure 3 shows next to each trap site the percentage of infected rodents caught there. Only locations where more than four rodents were caught have been considered for this calculation. This graphic also in part confirms the calculations of the model about areas of higher risk. Two trap sites in areas calculated of low and lowest risk show a considerable percentage of infected rodents (29% and 17%, respectively). The existence of spatially limited hotspots suggests that the spread of Leptospirosis and Toxoplasmosis is limited to the micro habitat of few rodents. This assumption, known as the super- spreader theory or the "20/80 rule", was brought up by a study with wild deer mice in the Great Basin Desert, Utah, USA.
RESEARCH FOR PLANNING
Systemarchi