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Revista Brasileira de Epidemiologia

Print version ISSN 1415-790X

Rev. bras. epidemiol. vol.16 no.3 São Paulo Sept. 2013 

Original Articles

Spatial distribution of dengue disease in municipality of Mossoró, Rio Grande do Norte, using the Geographic Information System

Francisco Narcísio Bessa JúniorI 

Renan Flávio de França NunesI 

Marcos Antonio de SouzaI 

Antônio Carlos de MedeirosI 

Maria Jocileide de Medeiros MarinhoI 

Wogelsanger Oliveira PereiraI 

IDepartment of Biomedical Sciences of Universidade do Estado do Rio Grande do Norte - Mossoró (RN), Brazil


The dengue viral infection is one of the most relevant vector-borne diseases in the world. The disease can manifest in a variety of forms, from asymptomatic to a condition of dengue hemorrhagic fever (DHF). The last reported cases in Brazil correspond to 80% of the cases reported in the Americas, which emphasizes the magnitude of the problem. This study was conducted using Geographic Information System (GIS) techniques, in order to evaluate the spatial distribution of the disease in the urban area of Mossoró, Rio Grande do Norte. In the period between 2001 and 2007, 867 new cases were listed. About 85.7% of the addresses were georeferenced, with a larger number of cases, 14.8%, in the neighborhoods of Santo Antônio and Santa Delmira (north region), and 11.7% in the neighborhoods of Conjunto Vingt-Rosado and Alto de São Manoel (east region). There were 18 confirmed cases of dengue hemorrhagic fever associated with regions with the highest incidence of classic cases of the disease. The use of Geographic Information System (GIS) proved a great benefit for better visualization of the endemic, especially in elucidating the actual distribution of dengue cases in the county and providing an effective tool for planning the monitoring of the disease at a local level.

Key words: Spatial Analysis; Epidemiology; Dengue; Dengue hemorrhagic fever


Dengue is an acute infectious disease whose etiologic agent is a flavivirus, with Aedes aegypti as its only vector in Brazil. Considered the most important arboviral disease in the world, dengue has been in the Americas for centuries, but its current situation is dynamic and aggravating1 , 2.

During the last two decades, the incidence of dengue increased significantly in this region. It is estimated that over three million new cases were reported in 30 Latin American countries. Dengue hemorrhagic fever (DHF) has occurred in over 20 countries, with 17,000 reported cases, including 225 deaths3.

In Brazil, the last epidemic peak occurred in 2002, due to the introduction of DEN-3, with 794,000 cases reported, which corresponds to an incidence rate of 398 cases per 100 thousand inhabitants. Northeastern Brazil was the second most affected region, with 25% of total cases4 , 5.

In the last decade, the number of new cases of dengue diagnosed in the state of Rio Grande do Norte showed a sharp growth. In 2008, it was registered as the State's greatest epidemic, with an incidence rate of 1,123 cases per 100 thousand inhabitants, larger than the rate observed in Brazil and in the Northeast, representing an increase of 158.7% compared to the previous year. Over the past 10 years, new cases were identified in all the municipalities of Rio Grande do Norte, especially the municipalities of Mossoró and Natal, with 51% of cases6.

Control strategies, to date, have proved ineffective due to the intense urbanization of the disease and to a limitation in the monitoring process of the disease itself. Given the above, the instrumentalization of control measures and support to the actions prescribed by vector and epidemics control programs prove to be essential7.

The spatial distribution of diseases can be mapped and analyzed using the Geographic Information System (GIS), capable of storing geographic information, correlating them with tabular data; it can be used for collection, storage, interrogation and display of spatial data, helping to determine the spatial location of diseases and the graphical analysis of epidemiological indicators. According to the World Health Organization (WHO), it is an effective tool in the management of the Brazilian Dengue Control Program (NPDC)8 , 9.

Taking the above into account, and considering the epidemic nature of dengue in the city of Mossoró, Rio Grande do Norte, the second largest and most populous in the State, this study used GIS in order to determine the spatial location of new dengue cases diagnosed between 2001 and 2007 in the urban area of Mossoró. It is believed that the data obtained this way will allow a better understanding of the dynamics of dengue transmission in the city of Mossoró, Rio Grande do Norte.

Materials and Methods

The study object was the urban area, located in the city of Mossoró, situated at 37°20ʹ west longitude and 05°11ʹ south latitude, at an altitude of 18 meters (approximately 60 ft). The municipality is approximately 285 kilometers (177 miles) from Natal, the State capital. It occupies a geographical area of 2099.328 km2 (approximately 810.428 square miles) and a population of 254,032 inhabitants. Its climate is dry, with rainfall concentrated in the beginning of the year. The mean annual temperature and humidity, respectively, are 27°C and 69%10.

This is a descriptive ecological study conducted in the municipality's urban area, using Mossoró city's cadastral map as cartographic bases, in addition to database from the Brazilian System of Diseases and Notification (SINAN) and from report files from the city's Health Management.

Subjects included in the study are living in households of Mossoró's urban area and represent all dengue cases diagnosed between 2001 and 2007.

Armed with the city's cadastral map and spreadsheets created for this study, the researchers georeferenced the addresses of registered patients using a GPS unit (Garmin 76). Coordinates collected in the field were transcribed into an Excel spreadsheet and later transformed into dBase files (dbf) for direct use on the ArcView GIS 3.2a software. After formatting the data in spreadsheets, the ArcMap application was used, transforming the information available in a variety of thematic maps showing the distribution of georeferenced cases in the city. To explore the variability and spatial relationships data, we used the interpolation module (IDW) spatial analyst to obtain disease density maps that represent the number of cases within 100 m2 (1076.39 ft2) with areas of different colors.

This study was approved by the Research and Ethics Committee of Hospital Universitário Onofre Lopes (CEP-HUOL 077-07).


Mossoró is a fast-growing city, particularly in the economic and social aspects, due to its geographical location. This phenomenon has generated a process of uncontrolled urbanization, producing regions with large clusters, mainly characterized by poor housing, inadequate collection of organic waste and deficiencies in water supply11. This scenario, coupled with a good adaptation to urban centers, offers a favorable environment for Aedes aegypti to breed, which makes vector control an arduous task.

In the State of Rio Grande do Norte, two epidemic peaks were observed, in 2002 and 2008, due to the introduction of serotypes DENV-3 and DENV-2, respectively, in most Brazilian States. The municipality of Mossoró followed the State's trend and presented an incidence rate of 47.01 cases per 10,000 inhabitants in 2008 (Graph 1).

Graph 1 - Incidence rate of confirmed cases of dengue in Mossoró and the State of Rio Grande do Norte from 2001 to 2008. 

Between 2001 and 2007, approximately 1,212 new cases of dengue were confirmed in city residents, with 867 (71.5%) located in the urban area, which was the object of this study. From the study population, the most affected were the females with 60.2%, and the predominant age group was 21 - 40 years (Table 1). These results are similar to those found by Santos12 and Vasconcelos13 which describe the prevalence of females due to greater permanence of women in or around their homes, where the transmission usually occurs.

Table 1 - Dengue cases by gender and age, Mossoró, Rio Grande do Norte, 2001 to 2007.  

Age group (years) Gender Total (n)
Male Female
0 – 20 114 (41.9%) 158 (58.1%) 272
21 – 40 141 (38.2%) 228 (61.8%) 369
41 – 60 79 (41.6%) 111 (58.4%) 190
> 60 11 (30.5%) 25 (69.5%) 36
Total 345 (39.8) 522 (60.2%) 867

Of the total cases selected for the study, 745 addresses (85.7%) were located and georeferenced, which reflects some difficulties, such as lack of information in the notification forms, the city's size (over 200,000 inhabitants)10, the large number of cases and the fact that many of those affected are living in invaded areas, in which they have no defined address.

Building a density map of cases by city neighborhoods in the period 2001 - 2002, an initial prevalence of the disease in the neighborhoods Presidente Costa e Silva (east region) and Barrocas (north region) was observed. However, after overlapping the cases during the period of 2001 - 2007, it was possible to observe a new reality in the distribution of new cases of dengue, especially in neighborhoods like Santo Antônio and Santa Delmira (north region), with 118 cases (14.8%), Conjunto Vingt-Rosado and Alto de São Manoel (east region), with 93 cases (11.7%) (Figure 1). More outlying neighborhoods, such as Lagoa do Mato and Aeroporto (west region), are also highlighted, with 99 cases (12.4%). The data revealed a change in the dynamics of the disease, possibly contributing to the recurrent cases of dengue.

Figure 1 - Density of dengue cases in Mossoró, Rio Grande do Norte, in the period from 2001 to 2007. 

Making an association of the epidemic peaks identified in the study with the rainfall indexes in the period of 2001 - 2007, there is a regular distribution regarding the disease's seasonality that occurs predominantly between the months of February and May, when the climatic conditions are favorable to the mosquito (Graph 2).

Graph 2 - Association between rainfall and the number of dengue cases in Mossoró, Rio Grande do Norte. 

According to Keating14, among other factors, temperature and rainfall affect the vector's survival, reproduction, changes in distribution and density. In Mossoró, the mean temperature and annual humidity are, respectively, 27°C and 69%, and the highest rainfall indexes occur between February and May, after which high numbers of people affected by dengue (Graph 2) can be observed. These abiotic climatic factors have shown to be associated with dengue cases. The seasonal pattern of the disease coincides with the summer due to the increased occurrence of rain and the higher temperature in this season15 , 16.

Among the cases reported, about 178 of them (20.5%) are recurrences, i.e., individuals who had dengue more than once. This is worrying data for the municipality that increases the risks of developing the most severe form of the disease, DHF. However, regarding the 18 confirmed cases of DHF, we observed an association with classic dengue cases (Figure 2) only in the neighborhoods of Santa Delmira and Presidente Costa e Silva.

Figure 2 - Distribution of recurrent dengue cases and DHF in Mossoró city. 

By assessing the geographic distribution of dengue in the municipality of São José do Rio Preto (SP) between 1990 and 2002, and using georeferencing through census sectors, Mondini17 found 29% of sectors had incidences lower than 100 cases per 100,000 inhabitants and 5% of them exceeded 5,000 cases.

Barcellos18 used GIS to do a point-to-point georeferencing when he studied dengue's transmission capabilities in the city of Porto Alegre associated with the presence of the vector and the environmental factors that characterized that location.

Most studies using GIS for dengue describe the georeferencing of cases per sanitary district or census sector, unlike the study conducted in Mossoró, which considered each separate case at the address provided. This type of data collection, point to point, enables a more realistic visualization of the distribution of the disease and allows the identification of sites where there are clusters of cases. According to Câmara19, this type of georeferencing aims to study the spatial distribution of these points, testing hypotheses on the observed pattern - whether it is random or regularly distributed in clusters, etc. This type of mapping can also identify the existence of possible environmental factors20 , 21.

The study of the spatial distribution of dengue in the city of Mossoró using GIS provided information that would not be visualized by working only with tabular data. The highest number of cases in the area comprising the north and east regions of the city was a widely known fact; however, as these regions occupy a large geographical area, there was no spatial visualization of the actual distribution of the disease. The georeferencing made possible to identify outbreaks, which showed the geographic "portrait" of the endemic, alerting researchers and the municipality's Health Management to the fact that dengue is in need of more attention from organizations that develop actions aiming at its elimination.


Dengue is advancing geographically throughout the Brazilian territory, reaching larger populations and causing more severe manifestations of the disease, especially in areas with simultaneous or sequential circulation of different serotypes.

The study of the spatial distribution of dengue in the municipality of Mossoró using GIS proved to be a usable and useful tool in epidemiological monitoring. With the visualization of the endemic, many doubts were clarified regarding the actual location of the cases, revealing that the disease is concentrated in more outlying areas, where the population of low socioeconomic standard resides, according to the findings in the field.

The results obtained point to the need for specific studies to clarify issues related to various factors involved in the transmission, such as the specific characteristics of the mosquito population, productivity of breeding sites and its role in the density of adult mosquitoes and their range of dispersal. Therefore, it is necessary to focus and facilitate actions of Mossoró's Public Health Surveillance, seeking strategies to fight this problem in priority areas.


To UERN (Universidade do Estado do Rio Grande do Norte) and FAPERN (Fundação de Apoio à Pesquisa do Estado do Rio Grande do Norte) for the financial support. We thank collaborators José Mairton Figueiredo de França, Executive Environmental Manager of Mossoró, Rio Grande do Norte, and Paulo César Ferreira Linhares, professor at UFERSA (Universidade Federal Rural do Semi-árido), for their help in the preparation of maps.


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Received: October 19, 2011; Revised: May 06, 2012; Accepted: October 31, 2012

Corresponding author: Wogelsanger Oliveira Pereira Rua Rodrigues Alves, 1410 Noêmia Chaves, 1201-A CEP: 59611-060 Mossoró, RN, Brazil Email:

Financial support: The research was funded by FAPERN - Fundação de Apoio à Pesquisa do Estado do Rio Grande do Norte (Announcement 03/2007 - First Projects Program, published in DOE No. 11,499, on 06/19/2007, Grant Application No. 041/2008).

Conflict of interests: nothing to declare.

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