Objectives:
The purpose of this study is to identify the maximum reproductive range of Anopheline mosquitos, so as too give an indication of the areas where moneys spent on the eradication of Malaria can be most useful.
Background:
According to the World Health Organization, the number one cause of premature death in the world is malaria. Over 270 million people are currently infected, and the problem is most pronounced in Africa where more than 1 million people die from its effects every year.(Oaks and Pearson, 9, 1991)
There are ways of treating and even abating malaria, the expense is too great for most afflicted countries to handle, and resources are too few to completely handle the problem. To make matters worse, while some afflicted areas are neglected, other areas are treated unnecessarily.(Ibid, 37) This simple, inefficient distribution of resources condems thousands to a life of recurring suffering, if not death. If a map could be created which could identify all the areas which have little danger of malaria, then perhaps eradication efforts could be more focused, and thus more efficient. To this end, a GIS will be used to create a map comprised of various types of spatial data, which could then be altered and updated as more data became available.
Malaria:
Malaria is a blood related disease, and once you have it, you have it for life. Malaria is caused by four species of protozoan parasites of the genus Plasmodium: P. falciparum, P. vivax, P. ovale, and P. malariae. (Valentine, 1991)
This parasite lives inside the red blood cells of its host. Efforts have been made for many years to eradicate the protozoa, but it appears to be terribly resilent. For many years drugs such as Chloroquinine were used, but the parasite has rapidly adapted, and now even new derivitives such as Chloroquine Phosphate are largely uneffective.(Oaks et al , 13, 1990)
While malaria, as with most blood related diseases, can be passed in a number of ways. As a matter of fact it can be passed in the same manner as AIDS or Hepatitus B, but the principle vector of the disease is a certain species of Anopheline mosquitos.
Since it has proven very difficult to attack the disease, the primary way to prevent malaria is to interfere with, or attack the primary vector and weakest link: Anopheline mosquitos.
Mosquitos:
Anopheline mosquitos are highly sensitive to their environment, and it has been shown that ecological conditions inevitably affect mosquito breedingspots and densities, which directly contribute to the incidence of malaria.(World Bank, 1990)
Contrary to common belief, mosquitos do not eat blood. The female mosquito, as part of the reproductive cycle, must take a blood feast in order to prepare the eggs. It is in this blood feast that malaria is passed.
The Environmental Limitations:
Both parasite development and mosquito reproduction are closely tied to temperature. Parasite sporogony completley below 16 degrees centigrade and prefers at least 20 degrees centigrade. Similarly, mosquito development from the egg takes 7 days at 30 degrees centigrade, 25 days at 20 degrees centigrade, and ceases at 15 degrees centigrade. Hence the reason most of N. America, and Europe are not plagued with Malaria, it gets too cold, and the eggs cease to be viable(Oaks et al, 135, 1990)
In addition to a favorable temperature, mosquito reproduction is also dependent on water: pooled, stagnant water where the eggs are laid to mature. When there is insufficient rainfall, or in the absence of standing water bodies such as lakes, mosquito development is halted. Thus in many parts of the tropics where rainfall is seasonal, so is malaria.
Mosquitos only live about three to six weeks under favorable conditions, and thus rarely travel more than 10 kilometers from their larval development site.
Hypothesis
The hypothesis of this project is not to determine where anopheline mosquitos are, but to delineate the areas where they could be. The resultant maps should divide the landscape into two areas, those which meet all criteria for mosquito survival, and those areas that fail in at least one regard.
Methodology
Since this study is concerned with the non-seasonal range of mosquito reproduction, all that is needed are maps that identify where there is sufficient temperature and water year-round. For this study I determined that only six primary data sets would be needed:
-A Digital Elevation Model
-Monthly Precipitation Measures
-Average Monthly Minimum Temperatures
-Population Distribution
-Base Map(including lakes, rivers, resevoirs, canals, etc.)
-Land-Use(identifying irrigation use)
Data collection is the most difficult, time consuming, and expensive part of this project. Thus the project was wholly dependent on the data that I could aquire at minimal cost. The data set used is part of the public domain and was gathered and assembled by NOAA as part of the world climate change project.
The data was in a generic binary format which had to be imported in ERDAS Imagine. As long as I was already in Imagine I performed all the preparation of the images there(i.e. clipping, coloring,clearing-up atmospheric noise, etc.) The data was then expoted in a ARC/GRID raster format.
Since all of the data was in a raster format there were no problems involving sliver polygons, but there was the limitation of resolution. At a continental scale, I expected the resolution of the rasters to be fairly coarse, but one which could support a scale of about 1:5,000,000.
Water: There are three sources or types of standing water; those associated with rivers, lakes, resevoirs, etc., puddled water from precipitation, and water bodies due to irrigation, or human activity related.
Using the 10 km travel distance, all water bodies(both natural and human related) will be buffered by 10 km(the traveling extent of mosquitos). -Non-seasonal rivers, lakes, and resevoirs come from a base map. -Precipitation/puddled water areas derived from combining precipitation monthly precipitation totals with a slope model(derived from DEM using GRID)of areas with slope <30%. -And the final source would be irrigated lands, which was unavailable at the time of this analysis.
Using Arc/INFO GRID I created a slope map from the DEM. This slope model was then coded into three categories:0%, 0%< but <30%, and >30%. Then a second map was created from this slope map, including only those areas with a slope less than 30%. The thirty% slope criteria was used since slopes which are steeper than 30%(a generous figure) are unlikely to be associated with significant pooling of water.
The next step was to calculate precipitation amounts. The data from NOAA included monthly corrected precipitation amounts. From these twelve maps I created a new map which eliminated both arid regions and seasonal wetlands. This was accomplished by selecting out the areas whic had suffiecient rainfall in each month(>30mm), and then taking these maps and selecting out only those areas which met that criteria every month. Areas which met the criteria every month were coded as suitable, those which failed at least one month were coded seasonal, and areas which never met the criteria were coded unsuitable.
Temperature:Once again the NOAA data included monthly average min. and max. temperatures. Only the minimum temperature was of interest to me. Each map was recoded similarly to the precipitation maps; areas which averaged >16 degrees centigrade, >16 but <20, and <20. The temperature maps were derived by recoding all temperatures into two classes:>16, and >16 With these twelve new maps, all were overlayed to form a composite map consisting of three classes, 1-Avg. minimum temp. which is always above 16 degrees, 2-sometimes below 16 degrees, and 3-always below 16 degrees.
Results
Once each of these component maps were finished then the a final map was created by intersecting the maps that had sufficient temperature every month, and sufficient water every month.
Final Areas
The resulting areas appear to be almost solely delineated according to precipitation and water criteria, while temmperature had a minimum impact. This may change in future analyses(see Future Research)
Limitations, Future Research
The primary limitation of this data was the resolution, particularly the DEM. As the pixel size grows larger there is ever more generalizations about the area. It appeared that the DEM was so coarse, that the derived slope model became generalized almost to the point of uselessness. Additionally the missing irrigation data hurt the analysis, as those areas may have added significantly to the area with sufficient water(in addition to pointing out where malaria may not naturally exist but now does due to human action.
The next area of research to pursue, which I was unable to accomplish in time, is to now develop twelve more maps showing the seasonal extent of the environmental factors limiting mosquitos, and perhaps see if these maps correlate with perhaps a monthly vegetation index or an ecosystem map, which would then provide a simpler way to delineate probable malarial areas. And finally to overlay human population distribution maps to prioritize probable areas by the number of people likely to be effected.
Sources:
Goodchild, Michael, Bradley Parks, and Lois Steyart, eds.. 1993 'Environmental Modeling With GIS'. New York, Oxford University Press.
James, Valentine. 1991. 'Resource Management in Developing Countries:Africa's Ecological Problems.'New York, Bergin and Garvey
NOAA. 1988. All imagery and data
Oaks, Stanley, and Greg Pearson. 1991. 'Malaria:Obstacles and Opportunities' Washington D.C., National Academy Press.
World Bank. 1990. 'Human Development Report 1990'. New York, Oxford University Press.