Geospatial analysis applied to epidemiological studies of dengue: a systematic review

Análise geoespacial aplicada em estudos epidemiológicos de dengue: uma revisão sistemática

Maria Aparecida de Oliveira Helena Ribeiro Carlos Castillo-Salgado About the authors

Abstract

A systematic review of the geospatial analysis methods used in the dengue fever studies published between January 2001 and March 2011 was undertaken. In accordance with specific selection criteria thirty-five studies were selected for inclusion in the review. The aim was to assess the types of spatial methods that have been used to analyze dengue transmission. We found twenty-one different methods that had been used in dengue fever epidemiological studies in that period, three of which were most frequently used. The results show that few articles had applied spatial analysis methods in dengue fever studies; however, whenever they were applied they contributed to a better understanding of dengue fever geospatial diffusion.

Dengue; Spatial analysis; Geographic information systems; Geographic mapping; Time series studies; Medical geography


Introduction

People, place and time are the basic elements of epidemiological investigations. According to Moore and Carpenter 1 1. Moore DA, Carpenter TE. Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev 1999; 21(2): 143-61., the development of the Geographic Information System (GIS) has, over the last twenty years, boosted the development of the analysis of spatial patterns and processes in public health.

A general interest in spatial data analysis has developed rapidly over the last few decades, mainly because of the need for better public health tools. Greater interest and the subsequent improvements made have enabled researchers to tackle new urban diseases such as the dengue fever.

Geographical factors and information from different sources and formats can be spatially combined by GIS, both in epidemiology and public health - as, for example, in the studies of Briggs 2 2. Briggs DJ. Mapping environmental exposure. In: Geographical & Environmental Epidemiology. Methods for Small Area Studies. Ed. By P. Elliot. Great Britain: Oxford Medical publications; 1996. p.158-96., Albert et al. 3 3 . Albert PD, Gesler WM, Levergood B. Spatial Analysis, GIS, and Remote Sensing. Applications in the Health Sciences. Chelsea: Ann Arbor Press; 2001., Aron et al. 4 4. Aron JL, Patz JA. Ecosystem Change and Public Health: A Global Perspective. Baltimore: The Johns Hopkins University Press; 2001., Elliot et al. 5 5. Elliot P, Wakefield J, Best N, Briggs D. Spatial Epidemiology. Methods and applications. New York: Oxford University Press; 2006. and Khan et al. 6 6. Khan OA, Davenhall W, Ali M, Castillo-Salgado C, Vazquez-Prokopec G, Kitron U, et al. Geographical information systems and tropical medicine. Ann Trop Med Parasitol 2010; 104(4): 303-18..

With the growing number of studies such as these in public health research, new methods of geospatial analysis have been developed specifically for applications in epidemiological studies and have been incorporated in different analytical software packages around the world. Moreover, currently, it is possible to access several innovative geospatial analysis tools via Internet.

The objective of this review was to provide an overview regarding the types of geospatial methods that have been used to analyze epidemiological data for dengue transmission over the ten years quoted.

Methods

Selection of studies

In order to yield the largest number of articles utilizing spatial analysis, searches using the PubMed (http://www.ncbi.nim.nih.gov/pubmed), SciELO (http://www.scielo.br) and LILACS (http://lilacs.bvsalud.org/) databases were conducted using, in the first search, the keyword "spatial analysis". In the following searches, the keywords: "spatial autocorrelation dengue", "spatial clustering dengue", and "spatio-temporal clustering dengue" were used because they had appeared in the articles found in the first search.

We selected every article published between January 2001 and March 2011 that contained the keywords at some point in it. We selected studies published in English or Portuguese that focused on dengue fever and used spatial analysis methods. First, each abstract was appraised to determine whether the article could be included in the review. The criterion of inclusion was that the articles should have applied methods of spatial analysis to dengue data. The criteria adopted are presented in Figure 1.

Figure 1
Selection process used in a systematic review of Geospatial analysis applied to dengue epidemiological studies, 2001 - 2011.

The analysis focused on papers according to the unit of analysis of data, typology of representation, spatial methods applied, and the main results of the analysis of spatial data.

Results

Initially, 179 articles, published in either English or Portuguese, between 2001 and 2011, were selected. After reading the abstracts, 83 articles were excluded because they did not satisfy the inclusion criteria. Only 35 of the other 96 articles were found to meet the review criteria.

We found that some authors cited spatial analysis in the abstract but did not use a spatial method to analyze the data. For example, some articles had incorporated the term "spatial" but referred to micro-scale as it applies to genetics. A comparison of the spatial analysis methods used in the selected articles is given in Table 1.

Table 1
Review of the spatial analysis method used in the selected articles.

Year of studies and publication

Variations were reported in the period studied. Most published studies involved an analysis of data covering two years or more. Generally, the articles used data that had been collected four to ten years before publication. About 48% of the studies published used data collected for fewer than three years. Furthermore, more than 52% of the studies were published just four years after the event occurred, and only 8.5% of the studies used data collected less than one year before publication.

Not many papers using spatial analysis 6 6. Khan OA, Davenhall W, Ali M, Castillo-Salgado C, Vazquez-Prokopec G, Kitron U, et al. Geographical information systems and tropical medicine. Ann Trop Med Parasitol 2010; 104(4): 303-18.

7. Reiskind MH, Baisley KJ, Calampa C, Sharp TW, Watts DM, Wilson ML. Epidemiological and ecological characteristics of past dengue virus infection in Santa Clara, Peru. Trop Med Int Health 2001; 6(3): 212-8.

8. Getis A, Morrison AC, Gray K, Scott TW. Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. Am J Trop Med Hyg 2003; 69(5): 494-505.

9. Tran A, Deparis X, Dussart P, Morvan J, Rabarison P, Remy F, et al. Dengue spatial and temporal patterns, French Guiana, 2001. Emerg Infect Dis 2004; 10(4): 615-21.

10. Barcellos C, Pustai AK, Weber MA, Brito MR. Identification of places with potential transmission of dengue fever in Porto Alegre using Geographical Information Systems. Rev Soc Bras Med Trop 2005; 38(3): 246-50.

11. Vanwambeke SO, Van Benthem BH, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, et al. Multi-level analyses of spatial and temporal determinants for dengue infection. Int J Health Geogr 2006; 5: 5.

12. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ 2006; 332(7552): 1247-52.

13. Cheah WL, Chang MS, Wang YC. Spatial, environmental and entomological risk factors analysis on a rural dengue outbreak in Lundu District in Sarawak, Malaysia. Trop Biomed 2006; 23(1): 85-96.

14. Wen TH, Lin NH, Lin CH, King CC, Su MD. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 2006; 367(2-3): 631-40.
- 15 15. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103(1): 1-13. were published between 2001 and 2006, but since 2006 the number of papers based on geospatial studies has increased. Approximately 72% of the relevant papers were published after 2008 16 16. Barreto FR, Teixeira MG, Costa MC, Carvalho MS, Barreto ML. Spread pattern of the first dengue epidemic in the city of Salvador, Brazil. BMC Public Health 2008; 8: 51.

17. Nagao Y, Svasti P, Tawatsin A, Thavara U. Geographical structure of dengue transmission and its determinants in Thailand. Epidemiol Infect 2008; 136(6): 843-51.

18. Teixeira TR, Medronho RA. Socio-demographic factors and the dengue fever epidemic in 2002 in the State of Rio de Janeiro, Brazil. Cad Saude Publica 2008; 24(9): 2160-70.

19. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8: 361.

20. Mammen MP, Pimgate C, Koenraadt CJ, Rothman AL, Aldstadt J, Nisalak A, et al. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Med 2008; 5(11): e205.

21. Thammapalo S, Chongsuvivatwong V, Geater A, Dueravee M. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand. Epidemiol Infect 2008; 136(1): 135-43.

22. Almeida MC, Assunção RM, Proietti FA, Caiaffa WT. Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002. Cad Saúde Pública 2008; 24(10): 2385-95.

23. Galli B, Chiaravalloti Neto F. Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública 2008; 42(4): 656-63.

24. Lenhart A, Orelus N, Maskill R, Alexander N, Streit T, McCall PJ. Insecticide-treated bednets to control dengue vectors: preliminary evidence from a controlled trial in Haiti. Trop Med Int Health 2008; 13(1): 56-67.

25. Johansson MA, Dominici F, Glass GE. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Negl Trop Dis 2009; 3(2): e382.

26. Machado JP, Oliveira RM, Souza-Santos R. Spatial analysis of dengue occurrence and living conditions in Nova Iguaçu, Rio de Janeiro State, Brazil. Cad Saude Publica 2009; 25(5): 1025-34.

27. Schreiber MJ, Holmes EC, Ong SH, Soh HS, Liu W, Tanner L, et al. Genomic epidemiology of a dengue virus epidemic in urban Singapore. J Virol 2009; 83(9): 4163-73.

28. Lozano-Fuentes S, Fernandez-Salas I, Munoz ML, Garcia-Rejon J, Olson KE, Beaty BJ, et al. The neovolcanic axis is a barrier to gene flow among Aedes aegypti populations in Mexico that differ in vector competence for Dengue 2 virus. PLoS Negl Trop Dis 2009; 3(6): e468.

29. Honório NA, Nogueira RM, Codeço CT, Carvalho MS, Cruz OG, Magalhães MA, Almeida AS, et al. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro. Rev Saúde Pública 2009; 43(4): 666-73.

30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73.

31. San Pedro A, Souza-Santos R, Sabroza PC, Oliveira RM. Specific dengue transmission conditions at the local level: a study in Itaipu, Niterói, Rio de Janeiro State, Brazil. Cad Saúde Pública 2009; 25(9): 1937-46.

32. Jeffery JA, Thi Yen N, Nam VS, Nghia le T, Hoffmann AA, Kay BH, et al. Characterizing the Aedes aegypti population in a Vietnamese village in preparation for a Wolbachia-based mosquito control strategy to eliminate dengue. PLoS Negl Trop Dis 2009;3(11): e552.

33. Arboleda S, Jaramillo-O N, Peterson AT. Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. Int J Environ Res Public Health 2009; 6(12): 3040-55.

34. Flauzino RF, Souza-Santos R, Barcelllos C, Gracie R, Magalhães MA, Oliveira RM. Spatial heterogeneity of dengue fever in local studies, City of Niterói, Southeastern Brazil. Rev Saude Publica 2009; 43(6): 1035-43.

35. Barbosa GL, Lourenço, RW. Analysis on the spatial-temporal distribution of dengue and larval infestation in the municipality of Tupã, State of São Paulo. Rev Soc Bras Med Trop 2010; 43(2): 145-51.

36. Melo MS, Barreto FR, Costa MC, Morato VC, Teixeira MG. Progression of dengue virus circulation in the State of Bahia, Brazil, 1994-2000.Rev Soc Bras Med Trop 2010; 43(2): 139-44.

37. Wen TH, Lin NH, Chao DY, Hwang KP, Kan CC, Lin KC, et al. Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. Int J Infect Dis 2010; 14(4): e334-43.

38. Thai KT, Nagelkerke N, Phuong HL, Nga TT, Giao PT, Hung LQ, et al. Geographical heterogeneity of dengue transmission in two villages in southern Vietnam. Epidemiol Infect. 2010; 138(4): 585-91.

39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920.

40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74.

41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9.
- 42 42. Wong J, Stoddard ST, Astete H, Morrison AC, Scott TW. Oviposition site selection by the dengue vector Aedes aegypti and its implications for dengue control. PLoS Negl Trop Dis 2011; 5(4): e1015. (Table 2).

Table 2
Total articles by year and periodical.

Most of the studies were undertaken by Brazilian or American investigators. These countries are responsible for 50% of all the studies developed, followed by Thailand and Australia. However, it is noteworthy that, in the case of Brazil, the studies were carried out with the Brazilian database for dengue fever in Brazilian institutions. In the United States, on the other hand, the studies were undertaken using other countries' databases.

The articles were published in the various journals listed in Table 3, most of them being published in just four journals: PLoS Neglected Tropical Diseases, Cadernos de Saúde Pública, Revista de Saúde Pública and Revista Brasileira de Medicina Tropical. These journals published 78% of the articles which used spatial analysis methods in the investigation of dengue fever transmission.

Table 3
List the software used each year.

Nine of the studies included in this review applied spatial methods to analyze epidemiological and entomological information 7 7. Reiskind MH, Baisley KJ, Calampa C, Sharp TW, Watts DM, Wilson ML. Epidemiological and ecological characteristics of past dengue virus infection in Santa Clara, Peru. Trop Med Int Health 2001; 6(3): 212-8. , 8 8. Getis A, Morrison AC, Gray K, Scott TW. Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. Am J Trop Med Hyg 2003; 69(5): 494-505. , 10 10. Barcellos C, Pustai AK, Weber MA, Brito MR. Identification of places with potential transmission of dengue fever in Porto Alegre using Geographical Information Systems. Rev Soc Bras Med Trop 2005; 38(3): 246-50. , 20 20. Mammen MP, Pimgate C, Koenraadt CJ, Rothman AL, Aldstadt J, Nisalak A, et al. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Med 2008; 5(11): e205. , 21 21. Thammapalo S, Chongsuvivatwong V, Geater A, Dueravee M. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand. Epidemiol Infect 2008; 136(1): 135-43. , 24 24. Lenhart A, Orelus N, Maskill R, Alexander N, Streit T, McCall PJ. Insecticide-treated bednets to control dengue vectors: preliminary evidence from a controlled trial in Haiti. Trop Med Int Health 2008; 13(1): 56-67. , 26 26. Machado JP, Oliveira RM, Souza-Santos R. Spatial analysis of dengue occurrence and living conditions in Nova Iguaçu, Rio de Janeiro State, Brazil. Cad Saude Publica 2009; 25(5): 1025-34. , 35 35. Barbosa GL, Lourenço, RW. Analysis on the spatial-temporal distribution of dengue and larval infestation in the municipality of Tupã, State of São Paulo. Rev Soc Bras Med Trop 2010; 43(2): 145-51. , 36 36. Melo MS, Barreto FR, Costa MC, Morato VC, Teixeira MG. Progression of dengue virus circulation in the State of Bahia, Brazil, 1994-2000.Rev Soc Bras Med Trop 2010; 43(2): 139-44., and 23 others just analyzed epidemiological information 9 9. Tran A, Deparis X, Dussart P, Morvan J, Rabarison P, Remy F, et al. Dengue spatial and temporal patterns, French Guiana, 2001. Emerg Infect Dis 2004; 10(4): 615-21. , 11 11. Vanwambeke SO, Van Benthem BH, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, et al. Multi-level analyses of spatial and temporal determinants for dengue infection. Int J Health Geogr 2006; 5: 5.

12. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ 2006; 332(7552): 1247-52.

13. Cheah WL, Chang MS, Wang YC. Spatial, environmental and entomological risk factors analysis on a rural dengue outbreak in Lundu District in Sarawak, Malaysia. Trop Biomed 2006; 23(1): 85-96.

14. Wen TH, Lin NH, Lin CH, King CC, Su MD. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 2006; 367(2-3): 631-40.

15. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103(1): 1-13.

16. Barreto FR, Teixeira MG, Costa MC, Carvalho MS, Barreto ML. Spread pattern of the first dengue epidemic in the city of Salvador, Brazil. BMC Public Health 2008; 8: 51.

17. Nagao Y, Svasti P, Tawatsin A, Thavara U. Geographical structure of dengue transmission and its determinants in Thailand. Epidemiol Infect 2008; 136(6): 843-51.

18. Teixeira TR, Medronho RA. Socio-demographic factors and the dengue fever epidemic in 2002 in the State of Rio de Janeiro, Brazil. Cad Saude Publica 2008; 24(9): 2160-70.
- 19 19. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8: 361. , 22 22. Almeida MC, Assunção RM, Proietti FA, Caiaffa WT. Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002. Cad Saúde Pública 2008; 24(10): 2385-95. , 23 23. Galli B, Chiaravalloti Neto F. Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública 2008; 42(4): 656-63. , 25 25. Johansson MA, Dominici F, Glass GE. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Negl Trop Dis 2009; 3(2): e382. , 27 27. Schreiber MJ, Holmes EC, Ong SH, Soh HS, Liu W, Tanner L, et al. Genomic epidemiology of a dengue virus epidemic in urban Singapore. J Virol 2009; 83(9): 4163-73. , 30 30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73. , 31 31. San Pedro A, Souza-Santos R, Sabroza PC, Oliveira RM. Specific dengue transmission conditions at the local level: a study in Itaipu, Niterói, Rio de Janeiro State, Brazil. Cad Saúde Pública 2009; 25(9): 1937-46. , 33 33. Arboleda S, Jaramillo-O N, Peterson AT. Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. Int J Environ Res Public Health 2009; 6(12): 3040-55. , 34 34. Flauzino RF, Souza-Santos R, Barcelllos C, Gracie R, Magalhães MA, Oliveira RM. Spatial heterogeneity of dengue fever in local studies, City of Niterói, Southeastern Brazil. Rev Saude Publica 2009; 43(6): 1035-43. , 37 37. Wen TH, Lin NH, Chao DY, Hwang KP, Kan CC, Lin KC, et al. Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. Int J Infect Dis 2010; 14(4): e334-43.

38. Thai KT, Nagelkerke N, Phuong HL, Nga TT, Giao PT, Hung LQ, et al. Geographical heterogeneity of dengue transmission in two villages in southern Vietnam. Epidemiol Infect. 2010; 138(4): 585-91.

39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920.

40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74.
- 41 41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9.. Three articles included analysis of entomological information only 29 29. Honório NA, Nogueira RM, Codeço CT, Carvalho MS, Cruz OG, Magalhães MA, Almeida AS, et al. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro. Rev Saúde Pública 2009; 43(4): 666-73. , 32 32. Jeffery JA, Thi Yen N, Nam VS, Nghia le T, Hoffmann AA, Kay BH, et al. Characterizing the Aedes aegypti population in a Vietnamese village in preparation for a Wolbachia-based mosquito control strategy to eliminate dengue. PLoS Negl Trop Dis 2009;3(11): e552. , 42 42. Wong J, Stoddard ST, Astete H, Morrison AC, Scott TW. Oviposition site selection by the dengue vector Aedes aegypti and its implications for dengue control. PLoS Negl Trop Dis 2011; 5(4): e1015..

In terms of the geometric or shape representation of data, the studies primarily used polygons and points. The polygons were used to represent administrative frontiers, such as neighborhoods, districts, census tracts, or other administrative frontiers, and the points were used to represent cases of dengue, households, schools, or vector traps.

There is no predominant type related to the topology utilized because it was common to use more than one type in the articles. For example, often data are collected at household level, but for analysis purposes they are aggregated into areas.

Spatial units

In the articles selected, nine different primary units of analysis were identified. The most-used primary unit of analysis was the household, which was applied in ten articles, or around 28% of the published studies 7 7. Reiskind MH, Baisley KJ, Calampa C, Sharp TW, Watts DM, Wilson ML. Epidemiological and ecological characteristics of past dengue virus infection in Santa Clara, Peru. Trop Med Int Health 2001; 6(3): 212-8. , 8 8. Getis A, Morrison AC, Gray K, Scott TW. Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. Am J Trop Med Hyg 2003; 69(5): 494-505. , 11 11. Vanwambeke SO, Van Benthem BH, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, et al. Multi-level analyses of spatial and temporal determinants for dengue infection. Int J Health Geogr 2006; 5: 5.

12. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ 2006; 332(7552): 1247-52.
- 13 13. Cheah WL, Chang MS, Wang YC. Spatial, environmental and entomological risk factors analysis on a rural dengue outbreak in Lundu District in Sarawak, Malaysia. Trop Biomed 2006; 23(1): 85-96. , 19 19. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8: 361. , 20 20. Mammen MP, Pimgate C, Koenraadt CJ, Rothman AL, Aldstadt J, Nisalak A, et al. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Med 2008; 5(11): e205. , 24 24. Lenhart A, Orelus N, Maskill R, Alexander N, Streit T, McCall PJ. Insecticide-treated bednets to control dengue vectors: preliminary evidence from a controlled trial in Haiti. Trop Med Int Health 2008; 13(1): 56-67. , 29 29. Honório NA, Nogueira RM, Codeço CT, Carvalho MS, Cruz OG, Magalhães MA, Almeida AS, et al. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro. Rev Saúde Pública 2009; 43(4): 666-73. , 30 30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73..

The dengue case as primary unit was used in five studies 9 9. Tran A, Deparis X, Dussart P, Morvan J, Rabarison P, Remy F, et al. Dengue spatial and temporal patterns, French Guiana, 2001. Emerg Infect Dis 2004; 10(4): 615-21. , 15 15. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103(1): 1-13. , 16 16. Barreto FR, Teixeira MG, Costa MC, Carvalho MS, Barreto ML. Spread pattern of the first dengue epidemic in the city of Salvador, Brazil. BMC Public Health 2008; 8: 51. , 27 27. Schreiber MJ, Holmes EC, Ong SH, Soh HS, Liu W, Tanner L, et al. Genomic epidemiology of a dengue virus epidemic in urban Singapore. J Virol 2009; 83(9): 4163-73. , 33 33. Arboleda S, Jaramillo-O N, Peterson AT. Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. Int J Environ Res Public Health 2009; 6(12): 3040-55., and census tracts were used in four studies 10 10. Barcellos C, Pustai AK, Weber MA, Brito MR. Identification of places with potential transmission of dengue fever in Porto Alegre using Geographical Information Systems. Rev Soc Bras Med Trop 2005; 38(3): 246-50. , 23 23. Galli B, Chiaravalloti Neto F. Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública 2008; 42(4): 656-63. , 24 24. Lenhart A, Orelus N, Maskill R, Alexander N, Streit T, McCall PJ. Insecticide-treated bednets to control dengue vectors: preliminary evidence from a controlled trial in Haiti. Trop Med Int Health 2008; 13(1): 56-67. , 31 31. San Pedro A, Souza-Santos R, Sabroza PC, Oliveira RM. Specific dengue transmission conditions at the local level: a study in Itaipu, Niterói, Rio de Janeiro State, Brazil. Cad Saúde Pública 2009; 25(9): 1937-46.. Some studies used more than one unit of spatial analysis; for example, census tracts and cases were used in five studies 10 10. Barcellos C, Pustai AK, Weber MA, Brito MR. Identification of places with potential transmission of dengue fever in Porto Alegre using Geographical Information Systems. Rev Soc Bras Med Trop 2005; 38(3): 246-50. , 22 22. Almeida MC, Assunção RM, Proietti FA, Caiaffa WT. Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002. Cad Saúde Pública 2008; 24(10): 2385-95. , 34 34. Flauzino RF, Souza-Santos R, Barcelllos C, Gracie R, Magalhães MA, Oliveira RM. Spatial heterogeneity of dengue fever in local studies, City of Niterói, Southeastern Brazil. Rev Saude Publica 2009; 43(6): 1035-43. , 37 37. Wen TH, Lin NH, Chao DY, Hwang KP, Kan CC, Lin KC, et al. Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. Int J Infect Dis 2010; 14(4): e334-43. , 39 39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920.. Four studies 18 18. Teixeira TR, Medronho RA. Socio-demographic factors and the dengue fever epidemic in 2002 in the State of Rio de Janeiro, Brazil. Cad Saude Publica 2008; 24(9): 2160-70. , 25 25. Johansson MA, Dominici F, Glass GE. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Negl Trop Dis 2009; 3(2): e382. , 36 36. Melo MS, Barreto FR, Costa MC, Morato VC, Teixeira MG. Progression of dengue virus circulation in the State of Bahia, Brazil, 1994-2000.Rev Soc Bras Med Trop 2010; 43(2): 139-44. , 38 38. Thai KT, Nagelkerke N, Phuong HL, Nga TT, Giao PT, Hung LQ, et al. Geographical heterogeneity of dengue transmission in two villages in southern Vietnam. Epidemiol Infect. 2010; 138(4): 585-91. used the cities as the unit of analysis. Other studies used the block analysis unit 21 21. Thammapalo S, Chongsuvivatwong V, Geater A, Dueravee M. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand. Epidemiol Infect 2008; 136(1): 135-43. , 35 35. Barbosa GL, Lourenço, RW. Analysis on the spatial-temporal distribution of dengue and larval infestation in the municipality of Tupã, State of São Paulo. Rev Soc Bras Med Trop 2010; 43(2): 145-51. , 41 41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9.. In two studies, districts were used 26 26. Machado JP, Oliveira RM, Souza-Santos R. Spatial analysis of dengue occurrence and living conditions in Nova Iguaçu, Rio de Janeiro State, Brazil. Cad Saude Publica 2009; 25(5): 1025-34. , 30 30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73.. Towns or cities were applied in two cases 28 28. Lozano-Fuentes S, Fernandez-Salas I, Munoz ML, Garcia-Rejon J, Olson KE, Beaty BJ, et al. The neovolcanic axis is a barrier to gene flow among Aedes aegypti populations in Mexico that differ in vector competence for Dengue 2 virus. PLoS Negl Trop Dis 2009; 3(6): e468. , 40 40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74., administrative districts were used in one 17 17. Nagao Y, Svasti P, Tawatsin A, Thavara U. Geographical structure of dengue transmission and its determinants in Thailand. Epidemiol Infect 2008; 136(6): 843-51., and the planning unit was used in one41.

Methods of spatial analysis applied in dengue fever studies

Twenty-one different spatial methods used to analyze dengue data were found in the articles. However, some were more common than others. The methods used in selected papers are listed according to the topology of the data used.

Spatial analysis of points

In the analysis of point data, the method used most frequently, in 6 papers 16 16. Barreto FR, Teixeira MG, Costa MC, Carvalho MS, Barreto ML. Spread pattern of the first dengue epidemic in the city of Salvador, Brazil. BMC Public Health 2008; 8: 51. , 19 19. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8: 361. , 26 26. Machado JP, Oliveira RM, Souza-Santos R. Spatial analysis of dengue occurrence and living conditions in Nova Iguaçu, Rio de Janeiro State, Brazil. Cad Saude Publica 2009; 25(5): 1025-34. , 31 31. San Pedro A, Souza-Santos R, Sabroza PC, Oliveira RM. Specific dengue transmission conditions at the local level: a study in Itaipu, Niterói, Rio de Janeiro State, Brazil. Cad Saúde Pública 2009; 25(9): 1937-46. , 34 34. Flauzino RF, Souza-Santos R, Barcelllos C, Gracie R, Magalhães MA, Oliveira RM. Spatial heterogeneity of dengue fever in local studies, City of Niterói, Southeastern Brazil. Rev Saude Publica 2009; 43(6): 1035-43. , 35 35. Barbosa GL, Lourenço, RW. Analysis on the spatial-temporal distribution of dengue and larval infestation in the municipality of Tupã, State of São Paulo. Rev Soc Bras Med Trop 2010; 43(2): 145-51., was kernel density estimation. The Knox method43 was applied in three papers 9 9. Tran A, Deparis X, Dussart P, Morvan J, Rabarison P, Remy F, et al. Dengue spatial and temporal patterns, French Guiana, 2001. Emerg Infect Dis 2004; 10(4): 615-21. , 15 15. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103(1): 1-13. , 39 39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920.. The local Gi* statistic 44 44. Getis, A. Interaction Modeling Using Second-order Analysis. Environment and Planning 1984; 16: 173-83. was used in three papers 8 8. Getis A, Morrison AC, Gray K, Scott TW. Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. Am J Trop Med Hyg 2003; 69(5): 494-505. , 17 17. Nagao Y, Svasti P, Tawatsin A, Thavara U. Geographical structure of dengue transmission and its determinants in Thailand. Epidemiol Infect 2008; 136(6): 843-51. , 32 32. Jeffery JA, Thi Yen N, Nam VS, Nghia le T, Hoffmann AA, Kay BH, et al. Characterizing the Aedes aegypti population in a Vietnamese village in preparation for a Wolbachia-based mosquito control strategy to eliminate dengue. PLoS Negl Trop Dis 2009;3(11): e552.. Three papers 13 13. Cheah WL, Chang MS, Wang YC. Spatial, environmental and entomological risk factors analysis on a rural dengue outbreak in Lundu District in Sarawak, Malaysia. Trop Biomed 2006; 23(1): 85-96. , 20 20. Mammen MP, Pimgate C, Koenraadt CJ, Rothman AL, Aldstadt J, Nisalak A, et al. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Med 2008; 5(11): e205. , 36 36. Melo MS, Barreto FR, Costa MC, Morato VC, Teixeira MG. Progression of dengue virus circulation in the State of Bahia, Brazil, 1994-2000.Rev Soc Bras Med Trop 2010; 43(2): 139-44.used only the distance operations 45 45. Getis A, Ord JK. The analysis of spatial association by use of distance statistics. Geographical Analysis 1992; 24: 189-206., without spatial analysis methods.

Ripley's K statistic 44 44. Getis, A. Interaction Modeling Using Second-order Analysis. Environment and Planning 1984; 16: 173-83. was used in two studies 12 12. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ 2006; 332(7552): 1247-52. , 22 22. Almeida MC, Assunção RM, Proietti FA, Caiaffa WT. Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002. Cad Saúde Pública 2008; 24(10): 2385-95.. Bayes smoothing 46 46. Marshall RJ. Mapping disease and mortality rates using empirical Bayes estimators. J R Stat Soc Ser C Appl Stat 1991; 40(2): 283-94. was applied in two papers 40 40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74. , 41 41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9., while two papers 11 11. Vanwambeke SO, Van Benthem BH, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, et al. Multi-level analyses of spatial and temporal determinants for dengue infection. Int J Health Geogr 2006; 5: 5. , 34 34. Flauzino RF, Souza-Santos R, Barcelllos C, Gracie R, Magalhães MA, Oliveira RM. Spatial heterogeneity of dengue fever in local studies, City of Niterói, Southeastern Brazil. Rev Saude Publica 2009; 43(6): 1035-43. applied the Kulldorff analysis 47 47. Kuldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med 1995; 14(8): 799-810.. The Global K statistic 44 44. Getis, A. Interaction Modeling Using Second-order Analysis. Environment and Planning 1984; 16: 173-83. was applied in two studies 8 8. Getis A, Morrison AC, Gray K, Scott TW. Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. Am J Trop Med Hyg 2003; 69(5): 494-505. , 32 32. Jeffery JA, Thi Yen N, Nam VS, Nghia le T, Hoffmann AA, Kay BH, et al. Characterizing the Aedes aegypti population in a Vietnamese village in preparation for a Wolbachia-based mosquito control strategy to eliminate dengue. PLoS Negl Trop Dis 2009;3(11): e552.. In addition, two papers used only thematic maps such as the exploratory spatial analysis tool 25 25. Johansson MA, Dominici F, Glass GE. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Negl Trop Dis 2009; 3(2): e382. , 36 36. Melo MS, Barreto FR, Costa MC, Morato VC, Teixeira MG. Progression of dengue virus circulation in the State of Bahia, Brazil, 1994-2000.Rev Soc Bras Med Trop 2010; 43(2): 139-44..

Standard deviational ellipse48 was applied in two papers 10 10. Barcellos C, Pustai AK, Weber MA, Brito MR. Identification of places with potential transmission of dengue fever in Porto Alegre using Geographical Information Systems. Rev Soc Bras Med Trop 2005; 38(3): 246-50. , 40 40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74.. The generalized additive model 49 49. Hastie TJ, Tibshirani RJ. Generalized Additive Models. London: Chapman & Hall/CRC; 1990. was applied in two papers 19 19. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8: 361. , 29 29. Honório NA, Nogueira RM, Codeço CT, Carvalho MS, Cruz OG, Magalhães MA, Almeida AS, et al. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro. Rev Saúde Pública 2009; 43(4): 666-73. as well as the Monte Carlo simulation 7 7. Reiskind MH, Baisley KJ, Calampa C, Sharp TW, Watts DM, Wilson ML. Epidemiological and ecological characteristics of past dengue virus infection in Santa Clara, Peru. Trop Med Int Health 2001; 6(3): 212-8. , 14 14. Wen TH, Lin NH, Lin CH, King CC, Su MD. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 2006; 367(2-3): 631-40.. The Maxent algorithm 50 50. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol. Model 2006; 190: 231-59. was applied in one paper 33 33. Arboleda S, Jaramillo-O N, Peterson AT. Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia. Int J Environ Res Public Health 2009; 6(12): 3040-55.. Angular wavelet analysis 51 51. Rosenberg M, Kenkel N. Wavelet analysis for detecting anisotropy in point patterns. Journal of Vegetation Science 2004; 15: 277-84. was applied in one study 39 39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920., the local K-function 44 44. Getis, A. Interaction Modeling Using Second-order Analysis. Environment and Planning 1984; 16: 173-83. was applied in a separate study 39 39. Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 2010; 4(12): e920. and in another study 38 38. Thai KT, Nagelkerke N, Phuong HL, Nga TT, Giao PT, Hung LQ, et al. Geographical heterogeneity of dengue transmission in two villages in southern Vietnam. Epidemiol Infect. 2010; 138(4): 585-91., cluster analysis was applied. In one paper 27 27. Schreiber MJ, Holmes EC, Ong SH, Soh HS, Liu W, Tanner L, et al. Genomic epidemiology of a dengue virus epidemic in urban Singapore. J Virol 2009; 83(9): 4163-73., local K-means 44 44. Getis, A. Interaction Modeling Using Second-order Analysis. Environment and Planning 1984; 16: 173-83. was used.

The nearest neighbor statistic 51 51. Rosenberg M, Kenkel N. Wavelet analysis for detecting anisotropy in point patterns. Journal of Vegetation Science 2004; 15: 277-84. was applied in one 21 21. Thammapalo S, Chongsuvivatwong V, Geater A, Dueravee M. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand. Epidemiol Infect 2008; 136(1): 135-43., Fourier harmonic analysis 52 52. Rogerson PA. Statistical Methods for Geography. London: SAGE Publications; 2001. p. 161-4. was used in another 15 15. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103(1): 1-13. and, finally, inverse distance weighting 53 53. Crist EP, Kauth RJ. The tasselled cap demystified. Photogramm. Eng Rem S 1986; 52(1): 81-6. was applied in one study 28 28. Lozano-Fuentes S, Fernandez-Salas I, Munoz ML, Garcia-Rejon J, Olson KE, Beaty BJ, et al. The neovolcanic axis is a barrier to gene flow among Aedes aegypti populations in Mexico that differ in vector competence for Dengue 2 virus. PLoS Negl Trop Dis 2009; 3(6): e468..

Spatial analysis of area data

LISA (Local indicators of spatial association) 54 54. Anselin L. Local indicators of spatial association - LISA. Geogr Anal 1995; 27(2): 93-115. was the method used most often in analyzing polygon data 22 22. Almeida MC, Assunção RM, Proietti FA, Caiaffa WT. Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002. Cad Saúde Pública 2008; 24(10): 2385-95. , 23 23. Galli B, Chiaravalloti Neto F. Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública 2008; 42(4): 656-63. , 26 26. Machado JP, Oliveira RM, Souza-Santos R. Spatial analysis of dengue occurrence and living conditions in Nova Iguaçu, Rio de Janeiro State, Brazil. Cad Saude Publica 2009; 25(5): 1025-34. , 30 30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73. , 37 37. Wen TH, Lin NH, Chao DY, Hwang KP, Kan CC, Lin KC, et al. Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. Int J Infect Dis 2010; 14(4): e334-43. , 40 40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74. , 41 41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9.. Another method commonly used was the Global Moran Index 18 18. Teixeira TR, Medronho RA. Socio-demographic factors and the dengue fever epidemic in 2002 in the State of Rio de Janeiro, Brazil. Cad Saude Publica 2008; 24(9): 2160-70. , 21 21. Thammapalo S, Chongsuvivatwong V, Geater A, Dueravee M. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand. Epidemiol Infect 2008; 136(1): 135-43. , 30 30. Almeida AS, Medronho Rde A, Valencia LI. Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (Southeastern Brazil). Rev Saúde Pública 2009; 43(4): 666-73. , 40 40. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011; 8(1): 51-74.

41. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epidemiol Infect 2011; 139(3): 391-9.
- 42 42. Wong J, Stoddard ST, Astete H, Morrison AC, Scott TW. Oviposition site selection by the dengue vector Aedes aegypti and its implications for dengue control. PLoS Negl Trop Dis 2011; 5(4): e1015.. The Monte Carlo method was applied in one study 14 14. Wen TH, Lin NH, Lin CH, King CC, Su MD. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 2006; 367(2-3): 631-40., and the local Gi* statistic in another 17 17. Nagao Y, Svasti P, Tawatsin A, Thavara U. Geographical structure of dengue transmission and its determinants in Thailand. Epidemiol Infect 2008; 136(6): 843-51.(Table 1).

Software programs used for spatial analysis of dengue cases

Some articles did not report which software had been used to perform the spatial analysis of data. Further, in some cases, the method of spatial analysis was not referenced; instead, the focus was on the set of operations utilized. For example, it was clear in every article that different software had been used; in some cases, one software program was used to create the geographical coordinates (latitude and longitude), and another specifically to perform the spatial analysis.

The software programs used in the selected articles are given in Table 3. The most used were ArcGIS, GeoDa, TerraView and MapInfo. Several other software programs - for example, Satscan, Terrasee, Arc/Info, PPA, S-PLUS, and other customized ones - were used, but not as often.

Discussion

Despite place being a fundamental component of epidemiological investigations, the small number of papers found may indicate that the use of spatial analysis in studies of dengue is still uncommon. Among the possible reasons that may hinder the application of spatial analysis in the data analysis of dengue is the lack of health information systems that produce georeferenced entomological and epidemiological information, that is, the appropriate scales of analysis.

As from 2008, there was a significant increase in the number of articles that addressed the application of spatial analysis in studies of dengue. 72% of the published articles were found from that year on, perhaps due to the increased severity of the dengue epidemics the world observed during this period. The increase could also be a result of the need to develop new approaches to dengue fever research, to better understand the dynamics of the disease's transmission, and to formulate strategies to minimize its effects.

Among the works selected, there was a significant time interval between the occurrence of events (dengue cases), or even between the execution of serological and entomological surveys, and the publishing of the results of spatial analyses. This time lag might have been due to the natural flow of research, but it might also reflect the complexity and difficulties involved in conducting spatial analyses in countries where dengue is endemic.

The existence of such a long time interval between the collection of entomological and epidemiological information and the analysis and dissemination of results can lead to some bias against the early detection of epidemics and, therefore, to a reduction in the ability to identify the surveillance sites that require public health action.

Although most studies of dengue using spatial analysis were based on data from countries where the disease is endemic, the studies themselves were conducted elsewhere. The exception is Brazil, which has developed most of the spatial analyses, according to articles published in the countries themselves. Brazil and Thailand figured most among the countries studied - with 40% of the articles, followed by Peru and Vietnam.

The household has been the most commonly used spatial unit, followed by dengue cases and census tracts. This could be due to the actual characteristics of the entomological and epidemiological studies of dengue, which often focus on the areas surrounding the household because of the ecological characteristics associated with the spread of mosquitoes.

As can be observed in Table 1, twenty-one different methods of spatial analysis were found; however, only three of them were used frequently.

In analyzing the polygon data or areas, the most widely used indicator is LISA, and Moran's global statistic is the commonest way of measuring the degree of spatial autocorrelation in area data 52 52. Rogerson PA. Statistical Methods for Geography. London: SAGE Publications; 2001. p. 161-4.- perhaps because of the ease of application and interpretation of results, greater availability of free GIS software, such as GeoDa, or the lack of health information on more greatly detailed scales.

For the analysis of point data, the methods most used have been the intensity estimator kernel. These methods are essentially graphics 55 55. Fotheringham AS, Brunsdon C, Charlton M. Quantitative geography: perspectives on spatial analysis. London: Sage Publications; 2000.. Perhaps for this reason, only four studies used just spatial analysis models in the methodology of the article, while the remainder applied various statistical methods in conjunction with the methods of spatial analysis. There were also cases in which the authors did not use any method of spatial analysis. In other words, the spatial analysis of the article was based only on distances calculated using the GIS environment. These distances were used as independent variables in the regression models.

The other eighteen methods of analysis were each used by only one or two articles, and always as a complement to traditional methods of statistical analysis.

Some of the methods might have been more used due to their greater popularization, because of ease of access, as generally they are implemented in commercial software of great diffusion capacity with easy-to-handle friendly interfaces, as also in those of the public domain, with a larger number of courses and tutorials which assist the user in their use. On the other hand, although some of the methods least used may be available, both in software of the public domain as also in commercial software, they have as yet been little disseminated and present little didactic material, often having relatively unfriendly interfaces which make their manipulation on the part of users who have little familiarity with special data, difficult. Although they are commercial software, ArcView/ArcGIS and MapInfo are among the software programs most used, probably because they present more friendly interfaces than do the free software programs and count on a wide range of programs for the dissemination of and training in their use. On the other hand, although they are free (software) programs, GEODA and Terraview are also frequently used, probably because they make the methods commonly used in spatial analyses in public health available free, together with didactic material, and are widely represented at technical and scientific events.

Despite these considerations, it is pertinent to point out that regardless of the degree of sophistication of the method used, the results shown in the papers pointed to the great utility of spatial analysis for the understanding the epidemiology of dengue fever on different continents and in different geographical areas. Furthermore, results have shown that the methods such as Kernel Estimation, LISA and Moran's, can quickly produce efficient information regarding the location of clusters of dengue cases and of hot spot areas of transmission. Such information can be a powerful tool for monitoring dengue transmission at the local level.

Finally, despite the development of the methods of spatial analysis applied in epidemiological studies, they are rarely used in studies of dengue. However, the identification of spatial patterns in most of the articles discussed above confirms the usefulness of the application of these techniques and the need for development and application of advanced spatial analysis beyond the limits of visualization.

Some spatial analysis methods should be used in conjunction with conventional methods as, for example, in the control diagrams currently used by public health programs to identify dengue risk. The use of these methods to advance scientific knowledge on the dynamics of dengue transmission and its spatial diffusion could certainly be incorporated into current surveillance strategies and may contribute to reducing social costs, by incorporating both the individual and contextual variables associated with dengue transmission.

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History

  • Received
    30 Oct 2012
  • Reviewed
    22 July 2012
  • Accepted
    24 July 2013
  • Publication
    Dec 2013
Associação Brasileira de Pós -Graduação em Saúde Coletiva São Paulo - SP - Brazil
E-mail: revbrepi@usp.br