Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System

Land

Volumn 4, Issue 2, 2015, Pages 378-412

  Niaz Arifin, S M ^a   Arifin, Rumana Reaz ^a   Pitts, Dilkushi de Alwis ^b   Rahman, M Sohel ^c   Nowreen, Sara ^c   Madey, Gregory R ^a   Collins, Frank H ^a,b  

^a University of Notre Dame   (United States)

^b UNIVERSITY OF NOTRE DAME   (United States)

^c BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Bangladesh)

Author keywords

Agent based models; Hot spot analysis; Kriging; Landscape epidemiology; Modeling; Simulation; Spatial analysis

Indexed keywords

EID: 85010849759     PISSN: None     EISSN: 2073445X     Source Type: Journal    
DOI: 10.3390/land4020378     Document Type: Article

References (70)

1. Elliott, P.; Wakefield, J.C.; Best, N.G.; Briggs, D.J. Spatial Epidemiology: Methods and Applications; Oxford University Press: Oxford, UK, 2000.
2. Bithell, J.F. A classification of disease mapping methods. Stat. Med. 2000, 19, 2203–2215.
3. Pfeiffer, D.U.; Robinson, T.P.; Stevenson, M.; Stevens, K.B.; Rogers, D.J.; Clements, A.C.A. Spatial Analysis in Epidemiology; Oxford University Press: Oxford, UK, 2008.
4. Kitron, U. Landscape ecology and epidemiology of vector-borne diseases: Tools for spatial analysis. J. Med. Entomol. 1998, 35, 435–445.
5. Reisen, W.K. Landscape epidemiology of vector-borne diseases. Annu. Rev. Entomol. 2010, 55, 461–483.
6. Emmanuel, N.N.; Loha, N.; Okolo, M.O.; Ikenna, O.K. Landscape epidemiology: An emerging perspective in the mapping and modelling of disease and disease risk factors. Asian Pac. J. Trop. Dis. 2011, 1, 247–250.
7. Kitron, U. Risk maps: Transmission and burden of vector-borne diseases. Parasitol. Today 2000, 16, 324–325.
8. Meentemeyer, R.K.; Cunniffe, N.J.; Cook, A.R.; Filipe, J.A.; Hunter, R.D.; Rizzo, D.M.; Gilligan, C.A. Epidemiological modeling of invasion in heterogeneous landscapes: Spread of sudden oak death in California (1990–2030). Ecosphere 2011, 2, doi:10.1890/ES10-00192.1.
9. Jacquez, G.M. Spatial analysis in epidemiology: Nascent science or a failure of GIS? J. Geogr. Syst. 2000, 2, 91–97.
10. Arifin, S.M.N.; Arifin, R.R.; de Alwis Pitts, D.; Madey, G.R. Integrating an agent-based model of malaria mosquitoes with a geographic information system. In Proceedings of the 25th European Modeling and Simulation Symposium (EMSS), Athens, Greece, 25–27 September 2013.
11. World Health Organization (WHO). Malaria. 2014. Available online: http://www.who.int/mediacentre/factsheets/fs094/en/(accessed on 1 May 2015).
12. Zhou, Y.; Arifin, S.M.N.; Gentile, J.; Kurtz, S.J.; Davis, G.J.; Wendelberger, B.A. An agent-based model of the Anopheles gambiae mosquito life cycle. In Proceedings of the 2010 Summer Computer Simulation Conference, Ottawa, ON, Canada, 11–15 July 2010; pp. 201–208.
13. Arifin, S.M.N.; Davis, G.J.; Zhou, Y. A spatial agent-based model of malaria: Model verification and effects of spatial heterogeneity. Int. J. Agent Technol. Syst. 2011, 3, 17–34.
14. Arifin, S.M.N.; Davis, G.J.; Zhou, Y. Modeling space in an agent-based model of malaria: Comparison between non-spatial and spatial models. In Proceedings of the 2011 Workshop on Agent-Directed Simulation, Boston, MA, USA, 3–7 April 2011; pp. 92–99.
15. Arifin, S.M.N.; Madey, G.R.; Collins, F.H. Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool. Malar. J. 2013, 12, doi:10.1186/1475-2875-12-290.
16. Arifin, S.M.N.; Zhou, Y.; Davis, G.J.; Gentile, J.E.; Madey, G.R.; Collins, F.H. An agent-based model of the population dynamics of Anopheles gambiae. Malar. J. 2014, 13, doi:10.1186/1475-2875-13-424.
17. Peng, R.D. Reproducible research in computational science. Science 2011, 334, 1226–1227.
18. Jasny, B.R.; Chin, G.; Chong, L.; Vignieri, S. Data replication & reproducibility. Again, and again, and again …. Science 2011, 334, doi:10.1126/science.334.6060.1225.
19. ArcGIS Desktop: Release 9.3. Available online: http://www.esri.com/(accessed on May 2015).
20. Ross, R. The Prevention of Malaria; Dutton: New York, NY, USA, 1910.
21. Gimnig, J.E.; Ombok, M.; Otieno, S.; Kaufman, M.G.; Vulule, J.M.; Walker, E.D. Density-dependent development of Anopheles gambiae (Diptera: Culicidae) larvae in artificial habitats. J. Med. Entomol. 2002, 39, 162–172.
22. Koenraadt, C.J.M.; Takken, W. Cannibalism and predation among larvae of the Anopheles gambiae complex. Med. Vet. Entomol. 2003, 17, 61–66.
23. Sumba, L.; Okoth, K.; Deng, A.; Githure, J.; Knols, B.; Beier, J.; Hassanali, A. Daily oviposition patterns of the African malaria mosquito Anopheles gambiae Giles (Diptera: Culicidae) on different types of aqueous substrates. J. Circadian Rhythms 2004, 2, Art. 6.
24. Munga, S.; Minakawa, N.; Zhou, G.; Barrack, O.; Githeko, A.; Yan, G. Effects of larval competitors and predators on oviposition site selection of Anopheles gambiae sensu stricto. J. Med. Entomol. 2006, 43, 221–224.
25. Churcher, T.; Dawes, E.; Sinden, R.; Christophides, G.; Koella, J.; Basanez, M.G. Population biology of malaria within the mosquito: Density-dependent processes and potential implications for transmission-blocking interventions. Malar. J. 2010, 9, doi:10.1186/1475-2875-9-311.
26. Sumba, L.A.; Ogbunugafor, C.B.; Deng, A.L.; Hassanali, A. Regulation of oviposition in Anopheles gambiae ss: Role of inter-and intra-specific signals. J. Chem. Ecol. 2008, 34, 1430–1436.
27. Jannat, K.N.E.; Roitberg, B.D. Effects of larval density and feeding rates on larval life history traits in Anopheles gambiae s.s. (Diptera: Culicidae). J. Vector Ecol. 2013, 38, 120–126.
28. Himeidan, Y.; Temu, E.; Rayah, E.E.; Munga, S.; Kweka, E. Chemical cues for malaria vectors oviposition site selection: Challenges and opportunities. J. Insects 2013, 2013, doi:10.1155/2013/685182.
29. Java. Available online: http://www.java.com/en/(accessed on 1 May 2015).
30. The Eclipse Foundation. Available online: http://www.eclipse.org/(accessed on 1 May 2015).
31. Gu, W.; Novak, R.J. Agent-based modelling of mosquito foraging behaviour for malaria control. Trans. R. Soc. Trop. Med. Hyg. 2009, 103, 1105–1112.
32. Phillips-Howard, P.A.; Nahlen, B.L.; Alaii, J.A.; ter Kuile, F.O.; Gimnig, J.E.; Terlouw, D.J.; Kachur, S.P.; Hightower, A.W.; Lal, A.A.; Schoute, E.; et al. The efficacy of permethrin-treated bed nets on child mortality and morbidity in western Kenya I. Development of infrastructure and description of study site. Am. J. Trop. Med. Hyg. 2003, 68, 3–9.
33. Nahlen, B.L.; Clark, J.P.; Alnwick, D. Insecticide-treated bed nets. Am. J. Trop. Med. Hyg. 2003, 68, 1–2.
34. McElroy, P.D.; ter Kuile, F.O.; Hightower, A.W.; Hawley, W.A.; Phillips-Howard, P.A.; Oloo, A.J.; Lal, A.A.; Nahlen, B.L. All-cause mortality among young children in western Kenya. VI: The Asembo Bay Cohort Project. Am. J. Trop. Med. Hyg. 2001, 64, 18–27.
35. Kazura, J.W., Ed. The western Kenya insecticide-treated bed net trial. Am. J. Trop. Med. Hyg. 2003, 68, 1–173.
36. ESRI 2011. ArcGIS Desktop: Release 10. Available online: http://www.esri.com/(accessed on 1 May 2015).
37. Fillinger, U.; Lindsay, S. Larval source management for malaria control in Africa: Myths and reality. Malar. J. 2011, 10, doi:10.1186/1475-2875-10-353.
38. Killeen, G.F.; Smith, T.A. Exploring the contributions of bed nets, cattle, insecticides and excitorepellency to malaria control: A deterministic model of mosquito host-seeking behaviour and mortality. Trans. R. Soc. Trop. Med. Hyg. 2007, 101, 867–880.
39. Yakob, L.; Yan, G. Modeling the effects of integrating larval habitat source reduction and insecticide treated nets for malaria control. PLoS ONE 2009, 4, doi:10.1371/journal.pone.0006921.
40. Hancock, P.A. Combining fungal biopesticides and insecticide-treated bednets to enhance malaria control. PLoS Comput. Biol. 2009, 5, doi:10.1371/journal.pcbi.1000525.
41. Chitnis, N.; Schapira, A.; Smith, T.; Steketee, R. Comparing the effectiveness of malaria vector-control interventions through a mathematical model. Am. J. Trop. Med. Hyg. 2010, 83, 230–240.
42. Griffin, J.T.; Hollingsworth, T.D.; Okell, L.C.; Churcher, T.S.; White, M.; Hinsley, W.; Bousema, T.; Drakeley, C.J.; Ferguson, N.M.; Basáñez, M.G.; et al. Reducing Plasmodium falciparum malaria transmission in Africa: A model-based evaluation of intervention strategies. PLoS Med. 2010, 7, doi:10.1371/journal.pmed.1000324.
43. Eckhoff, P. A malaria transmission-directed model of mosquito life cycle and ecology. Malar. J. 2011, 10, doi:10.1186/1475-2875-10-303.
44. White, M.; Griffin, J.; Churcher, T.; Ferguson, N.; Basanez, M.G.; Ghani, A. Modelling the impact of vector control interventions on Anopheles gambiae population dynamics. Parasites Vectors 2011, 4, doi:10.1186/1756-3305-4-153.
45. Okumu, F.; Chipwaza, B.; Madumla, E.; Mbeyela, E.; Lingamba, G.; Moore, J.; Ntamatungro, A.; Kavishe, D.; Moore, S. Implications of bio-efficacy and persistence of insecticides when indoor residual spraying and long-lasting insecticide nets are combined for malaria prevention. Malar. J. 2012, 11, doi:10.1186/1475-2875-11-378.
46. Greenwood, B.M.; Fidock, D.A.; Kyle, D.E.; Kappe, S.H.; Alonso, P.L.; Collins, F.H.; Duffy, P.E. Malaria: Progress, perils, and prospects for eradication. J. Clin. Investig. 2008, 118, 1266–1276.
47. Phuc, H.K.; Andreasen, M.H.; Burton, R.S.; Vass, C.; Epton, M.J.; Pape, G.; Fu, G.; Condon, K.C.; Scaife, S.; Donnelly, C.A.; et al. Late-acting dominant lethal genetic systems and mosquito control. BMC Biol. 2007, 5, doi:10.1186/1741-7007-5-11.
48. Klassen, W. Introduction: Development of the sterile insect technique for African malaria vectors. Malar. J. 2009, 8, doi:10.1186/1475-2875-8-S2-I1.
49. Hancock, P.; Thomas, M.; Godfray, H. An age-structured model to evaluate the potential of novel malaria-control interventions: A case study of fungal biopesticide sprays. Proc. R. Soc. B Biol. Sci. 2009, 276, 71–80.
50. Marshall, J.M.; Taylor, C.E. Malaria control with transgenic mosquitoes. PLoS Med. 2009, 6, e1000020.
51. Carter, R. Transmission blocking malaria vaccines. Vaccine 2001, 19, 2309–2314.
52. Hawley, W.A.; ter Kuile, F.O.; Steketee, R.S.; Nahlen, B.L.; Terlouw, D.J.; Gimnig, J.E.; Shi, Y.P.; Vulule, J.M.; Alaii, J.A.; Hightower, A.W.; et al. Implications of the western Kenya permethrin-treated bed net study for policy, program implementation, and future research. Am. J. Trop. Med. Hyg. 2003, 68, 168–173.
53. ArcGIS: Hot Spot Analysis, 2014. Available online: http://resources.arcgis.com/en/help/main/10.2/index.html (accessed on 1 May 2015).
54. ESRI 2015. GIS Dictionary. Available online: http://support.esri.com/(accessed on 1 May 2015).
55. Diggle, P.; Moyeed, R.; Rowlingson, B.; Thompson, M. Childhood malaria in the Gambia: A case-study in model-based geostatistics. Appl. Stat. 2002, 51, 493–506.
56. Best, N.; Richardson, S.; Thomson, A. A comparison of Bayesian spatial models for disease mapping. Stat. Methods Med. Res. 2005, 14, 35–59.
57. Carrat, F.; Valleron, A.J. Epidemiologic mapping using the “Kriging” method: Application to an influenza-like epidemic in France. Am. J. Epidemiol. 1992, 135, 1293–1300.
58. Berke, O. Exploratory disease mapping: Kriging the spatial risk function from regional count data. Int. J. Health Geogr. 2004, 3, doi:10.1186/1476-072X-3-18.
59. Lai, P.C.; So, F.M.; Chan, K.W. Spatial Epidemiological Approaches in Disease Mapping and Analysis; CRC Press: Boca Raton, FL, USA, 2008.
60. De Carvalho Alves, M.; Pozza, E.A. Indicator Kriging modeling epidemiology of common bean anthracnose. Appl. Geomat. 2010, 2, 65–72.
61. Alexeeff, S.E.; Schwartz, J.; Kloog, I.; Chudnovsky, A.; Koutrakis, P.; Coull, B.A. Consequences of Kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data. J. Expo. Sci. Environ. Epidemiol. 2014, 25, 138–144.
62. Malaria Atlas Project. Available online: http://www.map.ox.ac.uk/(accessed on 1 May 2015).
63. Gething, P.W.; Patil, A.P.; Hay, S.I. Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation. PLoS Comput. Biol. 2010, 6, e1000724.
64. Howes, R.E.; Piel, F.B.; Patil, A.P.; Nyangiri, O.A.; Gething, P.W.; Dewi, M.; Hogg, M.M.; Battle, K.E.; Padilla, C.D.; Baird, J.K.; et al. G6PD deficiency prevalence and estimates of affected populations in malaria endemic countries: A geostatistical model-based map. PLoS Med. 2012, 9, e1001339.
65. Piel, F.B.; Patil, A.P.; Howes, R.E.; Nyangiri, O.A.; Gething, P.W.; Dewi, M.; Temperley, W.H.; Williams, T.N.; Weatherall, D.J.; Hay, S.I.; et al. Global epidemiology of sickle haemoglobin in neonates: A contemporary geostatistical model-based map and population estimates. Lancet 2013, 381, 142–151.
66. Gundogdu, K.S.; Guney, I. Spatial analyses of groundwater levels using universal Kriging. J. Earth Syst. Sci. 2007, 116, 49–55.
67. Batistella Pasini, M.P.; Dal’Col Lúcio, A.; Cargnelutti Filho, A. Semivariogram models for estimating fig fly population density throughout the year. Pesqui. Agropecu. Bras. 2014, 49, 493–505.
68. Arifin, S.M.N.; Davis, G.J.; Zhou, Y. Verification & validation by docking: A case study of agent-based models of Anopheles gambiae. In Proceedings of the Summer Computer Simulation Conference (SCSC), Ottawa, ON, Canada, 11–14 July 2010.
69. Arifin, S.M.N.; Davis, G.J.; Kurtz, S.J.; Gentile, J.E.; Zhou, Y. Divide and conquer: A four-fold docking experience of agent-based models. In Proceedings of the Winter Simulation Conference (WSC), Baltimore, MD, USA, 5–8 December 2010.
70. Helbing, D. Social Self-Organization; Springer: Berlin, Germany, 2012.