Amazon Rainforest

Cerrado/Savanna

Tropical Decid Forst

Caatinga/Dry Forest

Pampa/Grassland

Pantanal/Water Marsh

Physical And Demographic Aspects of Dengue FeverPhysical And Demographic Aspects of Dengue FeverPhysical And Demographic Aspects of Dengue Fever Occurrences and Spread in BrazilOccurrences and Spread in BrazilOccurrences and Spread in Brazil

Kelly DuncanKelly DuncanKelly Duncan Introduction To Geographical Information SystemsIntroduction To Geographical Information SystemsIntroduction To Geographical Information Systems June 2009June 2009June 2009

Introduction

Methods

Results

Seasonal Spread

Population Spreads

Roads, Rivers, and Lakes

Biomes and Climates

Summer: January, February, March

Fall: April, May, June

Winter:July, August, September Spring:October, November, December

January February

March April

Since the re-introduction of the Aedes aegypti mosquito to Brazil and the first case in the 1980s, (Mondini et al., 2005), Dengue Fever has become a major public health issue in Brazil. Dengue is transmitted by the bite of the vector mosquito, and has a four day incu-bation period, before an infected person will develop a fever and rash, which he or she will recover from in approximately five days. Dengue Fever, itself, does not have a high mortality rate, but Dengue Hemorrhagic Fever (5% morality rate), with an extra symptom of vascular permeability, blood loss, and Dengue Shock Syndrome (up to 40% mortality rate), where there is the loss of too much blood, resulting in hyperten-sion, are where the majority of the Dengue deaths come from. Due to the fact that the Aedes aegypti mosquito prefers areas with temperatures over 68° F (20° C, Nakhapakorn, “An Information value…” 2005), areas in Tropical and Subtropical climate regions have the highest volume of the vector mosquito. Brazil’s location relative to the equator, makes it a “hot spot” for the vector mosquito and in ef-fect Dengue. The health concerns of Dengue, has caused health officials to track the disease as well as the development of models to help track the number of occurrences and spreads, in order to understand the disease’s patterns and facilitate the optimization of resources in the eradication of the disease. Looking at different factors of the mos-quito and human components of the disease, should be helpful in finding areas of risk for Dengue. Certain factors that are important to these components are, seasonality, popula-tion, transportation methods, and the different biomes and climate zones. Seasonality is important because in many areas of Brazil the different seasons bring hotter and colder temperatures to an region where mosquitoes live, if a temperature falls below the Aedes aegypti desired temperature they will not be found in that region. The popu-lation of a region is important feature because where population density is higher there are more potential targets in an infected mosquito’s life span, causing greater spread of Dengue. Transportation methods are important for both humans and mosquitoes, for humans, the movement of the disease from an infected region to an uninfected region, as well as the density of mosquitoes in an area. Finally the different biome and climate regions are important to the mosquitoes habitation and seasonal patterns.

Using information on the number of cases in 2007 of Dengue Fever by region, from Bra-zil’s Ministry of Health, along with information on population, biomes and climates from the Brazilian Institute of Geography & Statistics, to view how different types of physical and demographical (population) features cause the occurrences and spread of Dengue. Dengue occurrences and spreads are incredibly complex, to help simplify the correlation factors, a breakdown of the some of the physical and demographic factors was used. Af-ter using ArcGIS to visually display and edit the Shapefile data, Microsoft Excel was usedto create graphs to show the correlation between the different factors (seasonal, population, proximity to roads, lakes, and river, and biome & climate areas) and the num-ber of cases in different regions.

After examining the maps and graphs to see the correlation between the spread and number of occurrences of Dengue and different physical and demographic properties of Brazil, this correlation is shown below: Seasonal: Due to seasonal changes in the temperature and humidity, there is a high correlation between the seasons and the number of occurrences and spread of Dengue fever. In warmer and/or more humid seasons, over all of Brazil, there are a higher num-ber occurrences and greater spread of Dengue Fever then in colder and drier months. Population: The correlation of the number of cases versus population cannot be com-pletely determined by this study. In larger cities there are more cases of Dengue Fever, but looking at the rates of cases by population, there is no clear correlation between population and how many people are affected (see below). In larger cities, Dengue spreads more rapidly then in the smaller population regions (see above). But this is more likely due to population density then strictly the number of peo-ple. The reason the population density has much more to do with the amount of spread is due to the rates of mosquito travel and lifespan then the total amount of people does. Roads, Rivers, and Lakes: There is a correlation between the spread of Dengue and the roads, rivers, and lakes. This is due to two facts, one is that roads and rivers are used for human transportation, hence the transportation of the disease in human form. The second being that there are more mosquitoes around rivers and lakes (mosquitoes use water as breeding grounds), hence the transportation by mosquitoes and mosquito density of an area is greater. Biomes and Climates: In the biomes which are more “tree covered” then other biomes (hence wetter), there are greater number of cases and a higher spread of Dengue then ones which are drier. The climate has a greater effect on the occurrences and spread (as talked about above in the seasonal section). Climate regions with more moderate seasons have higher oc-currences and spread then climate regions with more extreme seasons, due to the tem-perature preferences of the vector mosquito. Overall:By putting together the different factors for the occurrences and spread of Den-gue Fever, a model can be made for the number of occurrences and spread in any gen-eral area in Brazil. This model could be used in order to target and treat high risk areas better, so there is less spread and total number of occurrences in Brazil. An un-weighted example of a model is shown below (the Actual data is Summer 2007):

Equatorial

Highland Tropical

Tropical

Subtropical

Semi-Arid

Tropical

Acknowledgments: Statistics of the Number of Dengue Cases and Map of Brazil: Anthony Stevens, Universidade Federal do Rio Grande do Sul/Ministry of Health of Brazil Sources: Biome & Climate Maps and Population Statistics: Brazilian Institute of Geography and Statistics, www.ibge.gov.br & mapas.ibge.gov.br; Rivers, Roads, and Lakes: GfK Geomarketing Mondini et al., “Spatial Analysis of dengue transmission in a medium-sized city in Brazil.” 2005 Nakhapakorn et al. “An information value based analysis of physical and climatic factors affecting dengue fever and dengue hemorrhagic fever incidence.” 2005 Projection (All): Polyconic (world) Units (All): Decimal Degrees

These map shows the correlation between prox-imity to rivers, roads, and lakes to the spread and number of cases. The months of September and October were used because September shows the least number of cases in a year, hence

it is easiest to see the spread of Dengue Fe-ver between these two months. Looking at these two cases, it is apparent, that the spread and occurrences are corre-lated to the rivers/lakes (dark to light green) and many areas on major roads (east: green/yellow to orange; west: light green to red).

Rivers

Major Roads

Lakes

% Cases/Population

0.0001% - 0.01%

0.0101% - 0.02%

0.0201% - 0.03%

0.0301% - 0.04%

0.0401% - 0.1297%

No Cases

September October

These maps show the spread of Dengue in Rio De Janeiro’s Metropolitan region, through the months

with the highest number of Den-gue cases (January through April, Summer). In January (the beginning of summer) there are a number of cases, especially in the areas with a larger population, and very few in the areas with smaller populations. As the season progresses, the larger population areas show

faster growth in the number of cases (the larger the number of cases the larger the dots), while the ar-eas with less popu-lation show slower

or no growth in the number of cases.

Number of Cases1 - 10

11 - 100

101 - 1000

1001 - 5235

Population12,597 - 200,000

200,001 - 400,000

400,001 - 600,000

600,001 - 800,000

800,001 - 6,093,472

One of the biggest fac-tors in the spread of Den-gue is the season. In many parts of Brazil, sea-sons directly affect the climate. In the summer it is hotter and more humid, then in winter, when it is cooler and drier. Because the vector mos-

quito is very susceptible to the heat and hu-midity, during winter months there would be far less mosqui-toes (in areas

where there are four sea-sons, such as the more south regions) then in the summer months. Finally, due to the decrease in the amount of mosqui-toes there are less cases in the winter then the summer.

Number of Cases

1 - 10

11 - 100

101 - 1000

1001 - 10000

10001 - 41752

No Cases

Seasonal Population Roads, Rivers and Lakes

Biome

Correlation High High Medium Medium

Climate

High

These maps show the correlation between the num-ber of cases and the different biomes and climate zones. The overlay is of the summer number of cases. The biome map shows there is a high correlation between the number of cases and the areas. The biomes Savanna, Dry Forest, and the Water Marsh area have a higher number of occurrences (where there is more red then yellow). While the biomes of Tropical Deciduous Forest and Grassland have a lower number of occurrences. The biomes with a higher amount of occurrences are most likely have habitats more fit to the mosquitoes (ex. more water for breeding). The climate map shows high correlation between all the biomes. In the Tropical and Highland Tropical areas, where seasons have less variation in tem-perature and humidity, there are a large number of cases. In the Subtropical area, where the seasons are more extreme, hence periods of time when the vector mosquitoes can not survive there (hence less mosquitoes overall), there are less cases then in the areas where there are more moderate seasons.

Number of Cases1 - 10

11 - 100

101 - 1,000

1,001 - 10,000

10,001 - 41,752

No Cases

BiomeDry Forest

Savanna

Water Marsh

Grassland

Rainforest

Tropical Deciduous ForestNumber of Cases

1 - 10

11 - 100

101 - 1,000

1,001 - 10,000

10,001 - 41,752

No Cases

Climate ZoneEquatorial

Subtropical

Highland Tropical

Semi-Arid

Tropical

0 1,000 2,000

Miles

0 500 1,000

Miles

0 1,000 2,000

Miles

0 20 40

Miles

0 500 1,000

Miles

Actual Number of Cases1 - 10

11 - 100

101 - 1,000

1,001 - 10,000

10,001 - 41,752

No Cases

Level of RiskVery High

High

Medium

Low

Very Low

Predicted Actual

Biome

Climate

Maps

The correlations between the factors and Dengue occurrence and spread, show high risk areas for Dengue. Some of these areas though have low number of cases, this could be for many reasons, such as immunity, isolation of groups of people, or better use of mos-quito nets. By looking at these areas, public health officials could bring techniques used these areas into areas of high risk with higher number of cases of Dengue Fever. A more comprehensive study could also be done using smaller time periods and more accurate, if not exact, data on the location of cases, to track the spread more accurately, as well as getting more quantitatively accurate results.

Conclusions

Cases Versus Population: Full Year y = 0.0023x

0

10,000

20,000

30,000

40,000

50,000

60,000

10,000 100,000 1,000,000 10,000,000 100,000,000

Population

Nu

mb

er o

f C

ases

% (Cases/Population) by Population

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

10,000 100,000 1,000,000 10,000,000

100,000,000

Population

% (

Cas

es/P

op

ula

tio

n)