Big city, small world: Density, contact rates, and transmission of dengue across Pakistan

Journal of the Royal Society Interface

Volumn 12, Issue 111, 2015, Pages

  Kraemer, M U G ^a   Perkins, T A ^b,c   Cummings, D A T ^d   Zakar, R ^e   Hay, S I ^c,f,g   Smith, D L ^a,c,h   Reiner, R C ^c,i  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF NOTRE DAME   (United States)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

^d JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^e UNIVERSITY OF THE PUNJAB   (Pakistan)

^f UNIVERSITY OF OXFORD   (United Kingdom)

^g UNIVERSITY OF WASHINGTON   (United States)

^h SANARIA INC   (United States)

ⁱ INDIANA UNIVERSITY   (United States)

Author keywords

Dengue; Epidemiology; Heterogeneity; Mixing; Spatial accessibility; Spatial dynamics

Indexed keywords

EPIDEMIOLOGY; ESTIMATION; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; VIRUSES;

DENGUE; HETEROGENEITY; MAXIMUM LIKELIHOOD ESTIMATE; SPATIAL ACCESSIBILITY; SPATIAL DYNAMICS; TRANSMISSION INTENSITY; TRANSMISSION POTENTIAL; URBAN-RURAL GRADIENTS;

MIXING;

RAIN;

ARTICLE; DENGUE; DISEASE CARRIER; DISEASE TRANSMISSION; ENVIRONMENTAL FACTOR; HUMAN; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; MOSQUITO; PAKISTAN; POPULATION DENSITY DEPENDENCE; SEASONAL VARIATION; TEMPERATURE; TIME SERIES ANALYSIS; VEGETATION; CITY; COMMUNICABLE DISEASE CONTROL; DENGUE VIRUS; EPIDEMIC; GEOGRAPHY; POPULATION DYNAMICS; RURAL POPULATION; STATISTICAL MODEL; THEORETICAL MODEL; TRANSMISSION; URBAN POPULATION;

CITIES; COMMUNICABLE DISEASE CONTROL; DENGUE; DENGUE VIRUS; DISEASE OUTBREAKS; GEOGRAPHY; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, THEORETICAL; PAKISTAN; POPULATION DYNAMICS; RURAL POPULATION; URBAN POPULATION;

EID: 84945895822     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2015.0468     Document Type: Article

References (72)

1. Grenfell BT, Pybus OG, Gog JR, Wood JLN, Daly JM, Mumford JA, Holmes EC. 2004 Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303, 327-332. (doi:10.1126/science.1090727)
2. Stoddard ST, Morrison AC, Vazquez-Prokopec GM, Paz Soldan V, Kochel TJ, Kitron U, Elder JP, Scott TW. 2009 The role of human movement in the transmission of vector-borne pathogens. PLoS Negl. Trop. Dis. 3, e481. (doi:10.1371/journal.pntd.0000481)
3. Brockmann D, Helbing D. 2013 The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337-1342. (doi:10.1126/science.1245200)
4. Kilpatrick AM, Daszak P, Jones MJ, Marra PP, Kramer LD. 2006 Host heterogeneity dominates West Nile virus transmission. Proc. R. Soc. B 273, 2327-2333. (doi:10.1098/rspb.2006.3575)
5. Fenichel EP et al. 2011 Adaptive human behavior in epidemiological models. Proc. Natl Acad. Sci. USA 108, 6306-6311. (doi:10.1073/pnas.1011250108)
6. Weiss DJ, Bhatt S, Mappin B, Van Boecke1TP, Smith DL, Hay SI, Gething PW. 2014 Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction. Malar. J. 13, 171. (doi:10.1186/1475-2875-13-171)
7. Gilbert M et al. 2014 Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nat. Commun. 5, 4116. (doi:10.1038/ncomms5116)
8. Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M. 2010 Absolute humidity and the seasonal onset of influenza in the continental United States. PLoS Biol. 8, e1000316. (doi:10.1371/journal.pbio.1000316)
9. Anderson RM, May RM. 1991 Infectious diseases of humans: dynamics and control. Oxford, UK: Oxford University Press.
10. Finkenstadt BF, Grenfell BT. 2000 Time series modelling of childhood diseases: a dynamical systems approach. Appl. Stat. 49,187-205. (doi:10.1111/1467-9876.00187)
11. Pascual M, Levin SA. 1999 From individuals to population densities: searching for the scale of intermediate determinism. Ecology 80, 2225-2236. (doi:10.1890/0012-9658(1999)080[2225:FITPDS]2.0. C0;2)
12. Reich NG et al. 2013 Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity. J. R. Soc. Interface 10, 20130414. (doi :10.1098/rsif.2013.0414)
13. Bjornstad ON, Finkenstadt BF, Grenfell BT. 2002 Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecol. Monogr. 72, 169-184. (doi:10.1890/0012-9615(2002)072[0169:DOMEES]2.0.CO;2)
14. Koelle K, Pascual M. 2004 Disentangling extrinsic from intrinsic factors in disease dynamic: a nonlinear time series approach with an application to cholera. Am. Nat. 163, 901-913. (doi:10.1086/420798)
15. Metcalf CJE, Bjornstad ON, Ferrari MJ, Klepac P, Bharti N, Lopez-Gatell H, Grenfell BT. 2011 The epidemiology of rubella in Mexico: seasonality, stochasticity and regional variation. Epidemiol. Infect. 139, 1029-1038. (doi:10.1017/S0950268810002165.The)
16. Liu WM, Hethcote HW, Levin SA. 1987 Dynamical behavior of epidemiological models with nonlinear incidence rates.J. Math. Biol. 25, 359-380. (doi:10.1007/BF00277162)
17. Glass K, Xia Y, Grenfell BT. 2003 Interpreting time-series analyses for continuous-time biological models: measles as a case study. J. Theor. Biol. 223, 19-25. (doi:10.1016/S0022-5193(03)00031-6)
18. Bharti N, Tatem AJ, Ferrari MJ, Grais RF, Djibo A, Grenfell BT. 2011 Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science 334, 1424-1427. (doi:10.1126/science.1210554)
19. Lessler J et al. 2014 Estimating potential incidence of MERS-CoV associated with Hajj pilgrims to Saudi Arabia, 24 November 2014,1st edn (referring to: http://currents.plos.org/outbreaks/article/estimating-potential-incidence-of-mers-cov-associated-with-hajj-pilgrims-to-saudi-arabia-2014/). (doi:10.1371/currents.outbreaks.c5c9c9a bd636164a9b6fd4dbda974369)
20. Deville P, Linard C, Martin S, Gilbert M, Stevens FR, Gaughan AE, Blondel VD, Tatem AJ. 2014 Dynamic population mapping using mobile phone data. Proc. Natl Acad. Sci. USA 111, 15 888-15 893. (doi:10.1073/pnas.1408439111)
21. Salathe M, Kazandjieva M, Woo J, Levis P, Feldman MW, Jones JH. 2010 A high-resolution human contact network for infectious disease transmission. Proc. Natl Acad. Sci. USA 107, 22 020-22 025. (doi:10.1073/pnas.1009094108)
22. Vazquez-Prokopec GM et al. 2013 Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS ONE 8, e58802. (doi:10.1371/journal.pone.0058802)
23. Read JM, Lessler J, Riley S, Wang S, Tan LJ, Kwok KO, Guan Y, Jiang CQ, Cummings DAT. 2014 Social mixing patterns in rural and urban areas of southern China. Proc. R. Soc. B 281, 20140268. (doi:10.1098/rspb.2014.0268)
24. Background Paper for World Development Report 2009: Reshaping Economic Geography. Washington, DC: World Bank.
25. Simini F, Gonzalez MC, Maritan A, Barabasi A-L. 2012 A universal mode1for mobility and migration patterns. Nature 484, 96-100. (doi:10.1038/nature10856)
26. Perkins TA, Scott TW, Le Menach A, Smith DL. 2013 Heterogeneity, mixing, and the spatial scales of mosquito-borne pathogen transmission. PLoS Comput. Biol. 9, e1003327. (doi:10.1371/journal.pcbi.1003327)
27. Brady OJ et al. 2012 Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Imp. Dis. 6, e1760. (doi:10.1371/journal.pntd.0001760)
28. Bhatt S et al. 2013 The global distribution and burden of dengue. Nature 496, 504-507. (doi:10.1038/nature12060)
29. Morrison AC, Zielinski-Gutierrez E, Scott TW, Rosenberg R. 2008 Defining challenges and proposing solutions for control of the virus vector Aedes aegypti. PLoS Med. 5, e68. (doi:10.137V journal.pmed.0050068)
30. Harrington LC et al. 2005 Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am. J. Trop. Med. Hyg. 72, 209-220.
31. Akram W, Hafeez F, Ullah UN, Kim YK, Hussain A, Lee JJ. 2009 Seasonal distribution and species composition of daytime biting mosquitoes. Entomol. Res. 39, 107-113. (doi:10.1111/j.1748-5967.2009.00204.x)
32. Allicock OM et al. 2012 Phylogeography and population dynamics of dengue viruses in the Americas. Mol. Biol. Evol. 29, 1533-1543. (doi:10.1093J. Molbev/msr320)
33. Reiner RC, Stoddard ST, Scott TW. 2014 Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics 6, 30-36. (doi:10.1016/j.epidem.2013.12.003)
34. Stoddard ST et al. 2013 House-to-house human movement drives dengue virus transmission. Proc. Natl Acad. Sci. USA 110, 994-999. (doi:10.1073/pnas.1213349110)
35. Bhoomiboonchoo P et al. 2014 The spatial dynamics of dengue virus in Kamphaeng Phet, Thailand. PLoS Negl. Trop. Dis. 8, e3138. (doi:10.1371/journal.pntd.0003138)
36. Kraemer MUG et al. 2015 The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035. (doi:10.1038/sdata.2015.35)
37. Khan MA et al. 2013 Emergence and diversification | of dengue 2 cosmopolitan genotype in Pakistan, 2011. PLoS ONE 8, e56391. (doi:10.1371/journal.pone.0056391)
38. Kraemer MUG et al. 2015 The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347. (doi:10.7554/eLife.08347)
39. Kraemer MUG et al. 2015 Data from the global compendium of Aedes aegypti and Ae. albopictus occurrence. Dryad Digit. Repositry. (doi:10.5061/dryad.47v3c)
40. Sinka ME et al. 2012 A global map of dominant malaria vectors. Parasit. Vectors 5, 69. (doi:10.1186/1756-3305-5-69)
41. Gething PW, Patil AP, Smith DL, Guerra CA, Elyazar IRF, Johnston GL, Tatem AJ, Hay SI. 2011 A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar. J. 10, 378. (doi:10.1186/1475-2875-10-378)
42. Messina JP, Brady OJ, Pigott DM, Golding N, Kraemer MUG, Scott TW, Wint GRW, Smith DL, Hay SI. 2015 The many projected futures of dengue. Nat. Rev. Microbiol. 13, 230-239. (doi:10.1038/nrmicro3430)
43. Weiss DJ, Mappin B, Dalrymple U, Bhatt S, Cameron E, Hay SI, Gething PW. 2015 Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach. Malar. J. 14, 1-18. (doi:10.1186/sl2936-015-0574-x)
44. Eisen L, Lozano-Fuentes S. 2009 Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Negl. Trop. Dis. 3, e411. (doi:10.1371/journal.pntd.0000411)
45. Maciel-de-Freitas R, Marques WA, Peres RC, Cunha SP, De Oliveira RL. 2007 Variation in Aedes aegypti (Diptera: Culicidae) container productivity in a slum and a suburban district of Rio de Janeiro during dry and wet seasons. Mem. Inst. Oswaldo Cruz 102,489-496. (doi:10.1590/S0074-02762007005000056)
46. Weiss DJ, Atkinson PM, Bhatt S, Mappin B, Hay SI, Gething PW. 2014 An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS J. Photogramm. Remote Sens. 98, 106-118. (doi:10.1016/j.isprsjprs.2014.10.001)
47. Tatem AJ, Hemelaar J, Gray RR, Salemi M. 2012 Spatial accessibility and the spread of HIV-1 subtypes and recombinants. AIDS 26, 2351-2360. (doi:10.1097/QAD.0b013e328359a904)
48. CIESIN/IFPRI/WB/CIAT. Global Rural Urban Mapping Project (GRUMP): Gridded Population of the World, version 3. See http://sedac.ciesin.columbia.edu/gpw.
49. Hastie TJ, Tibshirani RJ. 1990 Generalized additive models, vol. 43. Chapman and Hall/CRC, CRC Press.
50. Dominici F, McDermott A, Zeger SL, Samet JM. 2002 On the use of generalized additive models in time-series studies of air pollution and health. Am. J. Epidemiol. 156,193-203. (doi:10.1093/aje/kwf062)
51. Wood SN. 2011 Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3-36. (doi:10.1111/j.1467-9868.2010.00749.x)
52. R Core Team. 2014 R: a language and environment for computing. Vienna, Austria: the R Foundation for Statistical Computing.
53. Patterson HD, Thompson R. 1971 Recovery of interblock information when block sizes are unequal. Biometrika 58, 545-554. (doi:10.1093/biomet/58.3.545)
54. Pigott DM, Kraemer MU. 2014 Enhancing infectious disease mapping with open access resources. Eurosurveillance 19, ppii: 20989.
55. Hu H, Nigmatulina K, Eckhoff P. 2013 The scaling of contact rates with population density for the infectious disease models. Math. Biosci. 244, 125-134. (doi:10.1016/j.mbs.2013.04.013)
56. Bartlett MS. 1960 The critical community size for measles in the U.S. J. R. Stat. Soc. A 123, 37-44. (doi:10.2307/2343186)
57. Keeling MJ, Eames KTD. 2005 Networks and epidemic models. J. R. Soc. Interface 2, 295-307. (doi:10.1098/rsif.2005.0051)
58. Kiss IZ, Green DM, Kao RR. 2008 The effect of network mixing patterns on epidemic dynamics and the efficacy of disease contact tracing. J. R. Soc. Interface 5, 791-799. (doi:10.1098/rsif.2007.1272)
59. Smith DL, Lucey B, Waller LA, Childs JE, Real LA. 2002 Predicting the spatial dynamics of rabies epidemics on heterogeneous landscapes. Proc. Natl Acad. Sci. USA 99, 3668-3672. (doi:10.1073/pnas.042400799)
60. Keeling MJ, Grenfell BT. 1997 Disease extinction and community size: modeling the persistence of measles. Science 275, 65-67. (doi:10.1126/science. 275.5296.65)
61. Yang Y, Herrera C, Eagle N, Gonzalez MC. 2014 Limits of predictability in commuting flows in the absence of data for calibration. Sci. Rep. 4, 5662. (doi:10.1038/srep05662)
62. Mills HL, Riley S. 2014 The spatial resolution of epidemic peaks. PLoS Comput. Biol. 10, e1003561. (doi:10.1371/journal.pcbi.1003561)
63. Ruberto I, Marques E, Burke DS, Van Panhuis WG. 2015 The availability and consistency of dengue surveillance data provided online by the World Health Organization. PLoS Negl. Trop. Dis. 9, e0003511. (doi:10.1371/journal.pntd.0003511)
64. Heesterbeek H et al. 2015 Modeling infectious disease dynamics in the complex landscape of global health. Science 347, paaa4339. (doi:10.1126/science.aaa4339)
65. Bauch CT, Galvani AP. 2013 Epidemiology. Social factors in epidemiology. Science 342, 47-49. (doi:10.1126/science.1244492)
66. Reiner RC, King AA, Emch M, Yunus M, Faruque ASG, Pascual M. 2012 Highly localized sensitivity to climate forcing drives endemic cholera in a megacity. Proc. Natl Acad. Sci. USA 109, 2033-2036. (doi:10.1073/pnas.1108438109)
67. Perkins TA, Metcalf CJE, Grenfell BT, Tatem AJ. 2015 Estimating drivers of autochthonous transmission of chikungunya virus in its invasion of the Americas. PLOS Currents Outbreaks, 10 February 2015,1st edn. (doi:10.1371/currents.outbreaks.a4c7b6ac10e0420b1788c9767946d1fc)
68. Johansson MA. 2015 Chikungunya on the move. Trends Parasitol. 31, 43-45. (doi:10.1016/j.pt.2014.12.008)
69. Cesare MDi, Bhatti Z, Soofi SB, Fortunato L, Ezzati M, Bhutta ZA. 2015 Geographical and socioeconomic inequalities in women and children's nutritional status in Pakistan in 2011: an analysis of data from a nationally representative survey. Lancet Glob. Health 3, e229-e239. (doi:10.1016/S2214-109X(15)70001-X)
70. Salje H et al. 2012 Revealing the microscale spatial signature of dengue transmission and immunity in an urban population. Proc. Natl Acad. Sci. USA 109, 9535-9538. (doi:10.1073/pnas.1120621109)
71. Grenfell BT, Bjurnstad ON, Kappey J. 2001 Travelling waves and spatial hierarchies in measles epidemics. Nature 414, 716-723. (doi:10.1038/414716a)
72. Schaffner F, Mathis A. 2014 Dengue and dengue vectors in the WHO European region: past, present, and scenarios for the future. Lancet. Infect. Dis. 14, 1271-1280. (doi:10.1016/S1473-3099(14) 70834-5)