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Bridging landscape ecology and urban science to respond to the rising threat of mosquito-borne diseases

Abstract

The prevalence of diseases borne by mosquitoes, particularly in the genus Aedes, is rising worldwide. This has been attributed, in part, to the dramatic rates of contemporary urbanization. While Aedes-borne disease risk varies within and between cities, few investigations use urban science-based approaches to examine how city structure and function contribute to vector or pathogen introduction and maintenance. Here, we integrate theories from complex adaptive systems, landscape ecology and urban geography to develop an urban systems framework for understanding Aedes-borne diseases. The framework establishes that cities comprise hierarchically structured patches of different land uses and characteristics. Properties of the patches (that is, composition) determine localized disease risk, while configuration and connectivity drive emergent patterns of pathogen spread. Complexity is added by incorporating individual and collective human social structures, considering how feedbacks among social actors and with the landscape drive risk and transmission. We discuss how these concepts apply to case studies of Aedes-borne disease from around the world. Ultimately, the framework strengthens existing theoretical and mixed qualitative–quantitative approaches, and advances considerations of how interventions including urban planning (for example, piped water provisioning) and emerging vector control strategies (for example, Wolbachia-infected mosquitoes) can be implemented to prevent and control the rising threat of Aedes-borne diseases.

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Fig. 1: Interactive properties of the urban system determine Aedes-borne disease risk.
Fig. 2: Landscape composition, configuration and connectivity determine risk and transmission.

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References

  1. World Urbanization Prospects: The 2018 Revision (UN Department of Economic and Social Affairs, 2018).

  2. Global Vector Control Response 2017–2030 (World Health Organization & UNICEF, 2017).

  3. Gubler, D. J. Dengue, urbanization and globalization: the unholy trinity of the 21st century. Trop. Med. Health 39, S3–S11 (2011).

    Article  Google Scholar 

  4. Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kraemer, M. U. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Brown, J. E. et al. Worldwide patterns of genetic differentiation imply multiple ‘domestications’ of Aedes aegypti, a major vector of human diseases. Proc. R. Soc. B Biol. Sci. 278, 2446–2454 (2011).

    Article  Google Scholar 

  7. Padmanabha, H., Durham, D., Correa, F., Diuk-Wasser, M. & Galvani, A. The interactive roles of Aedes aegypti super-production and human density in dengue transmission. PLoS Negl. Trop. Dis. 6, e1799 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Stewart-Ibarra, A. M. et al. Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC Infect. Dis. 14, 610 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cavany, S. M. et al. Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response. PLoS Comput. Biol. 16, e1007743 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Stefopoulou, Α et al. Reducing Aedes albopictus breeding sites through education: a study in urban area. PLoS ONE 13, e0202451 (2018).

    Article  Google Scholar 

  11. Lindsay, S. W., Wilson, A., Golding, N., Scott, T. W. & Takken, W.Improving the built environment in urban areas to control Aedes aegypti-borne diseases. Bull. World Health Organ. 95, 607–608 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Echaubard, P. et al. Fostering social innovation and building adaptive capacity for dengue control in Cambodia: a case study. Infect. Dis. Poverty 9, 126 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Vazquez-Prokopec, G. M., Lenhart, A. & Manrique-Saide, P. Housing improvement: a novel paradigm for urban vector-borne disease control? Trans. R. Soc. Trop. Med. Hyg. 110, 567–569 (2016).

    Article  PubMed  Google Scholar 

  14. Malone, R. W. et al. Zika virus: medical countermeasure development challenges. PLoS Negl. Trop. Dis. 10, e0004530 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Murdock, C. C., Evans, M. V., McClanahan, T. D., Miazgowicz, K. L. & Tesla, B. Fine-scale variation in microclimate across an urban landscape shapes variation in mosquito population dynamics and the potential of Aedes albopictus to transmit arboviral disease. PLoS Negl. Trop. Dis. 11, e0005640 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).

    Article  PubMed  Google Scholar 

  17. McDonald, R. I., Kareiva, P. & Forman, R. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).

    Article  Google Scholar 

  18. Ferraguti, M. et al. Effects of landscape anthropization on mosquito community composition and abundance. Sci. Rep. 6, 29002 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Juliano, S. A., Westby, K. M. & Ower, G. D. Know your enemy: effects of a predator on native and invasive container mosquitoes. J. Med. Entomol. 56, 320–328 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Mahendra, A. & Seto, K. C. Upward and Outward Growth: Managing Urban Expansion for More Equitable Cities in the Global South (World Resources Institute, 2019).

  21. Moretto, L. et al. Challenges of water and sanitation service co-production in the global South. Environ. Urban. 30, 425–443 (2018).

    Article  Google Scholar 

  22. Seto, K. C., Sánchez-Rodríguez, R. & Fragkias, M. The new geography of contemporary urbanization and the environment. Annu. Rev. Environ. Resour. 35, 167–194 (2010).

    Article  Google Scholar 

  23. Estallo, E. L. et al. A decade of arbovirus emergence in the temperate southern cone of South America: dengue, Aedes aegypti and climate dynamics in Córdoba, Argentina. Heliyon 6, e04858 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kaufman, M. G. & Fonseca, D. M.Invasion biology of Aedes japonicus japonicus (Diptera: Culicidae). Annu. Rev. Entomol. 59, 31–49 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kache, P. A. et al. Environmental determinants of Aedes albopictus abundance at a northern limit of its range in the United States. Am. J. Trop. Med. Hyg. 102, 436–447 (2020).

    Article  PubMed  Google Scholar 

  26. Eskew, E. A. & Olival, K. J. De-urbanization and zoonotic disease risk. EcoHealth 15, 707–712 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Biehler, D. et al. in The Palgrave Handbook of Critical Physical Geography 295–318 (Springer, 2018).

  28. Stoddard, S. T. et al. The role of human movement in the transmission of vector-borne pathogens. PLoS Negl. Trop. Dis. 3, e481 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and Chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wong, P. P.-Y., Lai, P.-C., Low, C.-T., Chen, S. & Hart, M. The impact of environmental and human factors on urban heat and microclimate variability. Build. Environ. 95, 199–208 (2016).

    Article  Google Scholar 

  31. Rey, J. R. & O’Connell, S. M. Oviposition by Aedes aegypti and Aedes albopictus: influence of congeners and of oviposition site characteristics. J. Vector Ecol. 39, 190–196 (2014).

    Article  PubMed  Google Scholar 

  32. Leisnham, P. T. & Juliano, S. Spatial and temporal patterns of coexistence between competing Aedes mosquitoes in urban Florida. Oecologia 160, 343–352 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Paploski, I. A. D. et al. Storm drains as larval development and adult resting sites for Aedes aegypti and Aedes albopictus in Salvador, Brazil. Parasit. Vectors 9, 419 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zainon, N., Rahim, F. A. M., Roslan, D. & Abd Samat, A. H. Prevention of Aedes breeding habitats for urban high-rise building in Malaysia. Plan. Malay. 14, 115–128 (2016).

    Google Scholar 

  35. Kenneson, A. et al. Social-ecological factors and preventive actions decrease the risk of dengue infection at the household-level: results from a prospective dengue surveillance study in Machala, Ecuador. PLoS Negl. Trop. Dis. 11, e0006150 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Harrington, L. C. et al. Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am. J. Trop. Med. Hyg. 72, 209–220 (2005).

    Article  PubMed  Google Scholar 

  37. Vavassori, L., Saddler, A. & Müller, P. Active dispersal of Aedes albopictus: a mark–release–recapture study using self-marking units. Parasit. Vectors 12, 583 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Ren, H., Wu, W., Li, T. & Yang, Z. Urban villages as transfer stations for dengue fever epidemic: a case study in the Guangzhou, China. PLoS Negl. Trop. Dis. 13, e0007350 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Charron, D. F. in Ecohealth Research in Practice 255–271 (Springer, 2012).

  40. Lippi, C. A. et al. Exploring the utility of social–ecological and entomological risk factors for dengue infection as surveillance indicators in the dengue hyper-endemic city of Machala, Ecuador. PLoS Negl. Trop. Dis. 15, e0009257 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wijayanti, S. P. et al. The importance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indonesia. PLoS Negl. Trop. Dis. 10, e0004964 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zellweger, R. M. et al. Socioeconomic and environmental determinants of dengue transmission in an urban setting: an ecological study in Nouméa, New Caledonia. PLoS Negl. Trop. Dis. 11, e0005471 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ryan, S. J. et al. Socio-ecological factors associated with dengue risk and Aedes aegypti presence in the Galápagos Islands, Ecuador. Int. J. Environ. Res. Public Health 16, 682 (2019).

    Article  PubMed Central  Google Scholar 

  44. Roiz, D. et al. Integrated Aedes management for the control of Aedes-borne diseases. PLoS Negl. Trop. Dis. 12, e0006845 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sanchez, L. et al. Aedes aegypti larval indices and risk for dengue epidemics. Emerg. Infect. Dis. 12, 800–806 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cromwell, E. A. et al. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection. PLoS Negl. Trop. Dis. 11, e0005429 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Honório, N. A. et al. Spatial evaluation and modeling of dengue seroprevalence and vector density in Rio de Janeiro, Brazil. PLoS Negl. Trop. Dis. 3, e545 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Chadee, D. Dengue cases and Aedes aegypti indices in Trinidad, West Indies. Acta Trop. 112, 174–180 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Fustec, B. et al. Complex relationships between Aedes vectors, socio-economics and dengue transmission—lessons learned from a case-control study in northeastern Thailand. PLoS Negl. Trop. Dis. 14, e0008703 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat. Commun. 10, 898 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Batty, M. in Encyclopedia of Complexity and Systems Science (ed. Meyers, R.) 1041–1071 (Springer, 2009).

  52. McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).

  53. Rus, K., Kilar, V. & Koren, D. Resilience assessment of complex urban systems to natural disasters: a new literature review. Int. J. Disaster Risk Reduct. 31, 311–330 (2018).

    Article  Google Scholar 

  54. Bettencourt, L. M. Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems (MIT Press, 2021).

  55. Handbook for Integrated Vector Management (World Health Organization, 2012).

  56. Kolimenakis, A. et al. The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit—a systematic review. PLoS Negl. Trop. Dis. 15, e0009631 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Evans, M. V., Bhatnagar, S., Drake, J. M., Murdock, C. C. & Mukherjee, S.Socio‐ecological dynamics in urban systems: an integrative approach to mosquito‐borne disease in Bengaluru, India. People Nat. 4, 730–743 (2022).

    Article  Google Scholar 

  58. Cook, E. M., Hall, S. J. & Larson, K. L. Residential landscapes as social–ecological systems: a synthesis of multi-scalar interactions between people and their home environment. Urban Ecosyst. 15, 19–52 (2012).

    Article  Google Scholar 

  59. Bai, X., McAllister, R. R., Beaty, R. M. & Taylor, B. Urban policy and governance in a global environment: complex systems, scale mismatches and public participation. Curr. Opin. Environ. Sustain. 2, 129–135 (2010).

    Article  Google Scholar 

  60. Batty, M. Inventing Future Cities (MIT Press, 2018).

  61. McPhearson, T. et al. Advancing urban ecology toward a science of cities. BioScience 66, 198–212 (2016).

    Article  Google Scholar 

  62. Grimm, N. B., Cook, E. M., Hale, R. L. & Iwaniec, D. M. in The Routledge Handbook of Urbanization and Global Environmental Change 227–236 (Routledge, 2015).

  63. Haase, D. et al. A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. Ambio 43, 413–433 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Filatova, T., Parker, D. & Van der Veen, A. Agent-based urban land markets: agent’s pricing behavior, land prices and urban land use change. J. Artif. Soc. Soc. Simul. 12, 3 (2009).

    Google Scholar 

  65. Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nat. Sustain. 1, 2–4 (2018).

    Article  Google Scholar 

  66. Collins, M. & Kapucu, N. Early warning systems and disaster preparedness and response in local government. Disaster Prev. Manag. 17, 587–600 (2008).

    Article  Google Scholar 

  67. Ahern, J. From fail-safe to safe-to-fail: sustainability and resilience in the new urban world. Landsc. Urban Plan. 100, 341–343 (2011).

    Article  Google Scholar 

  68. Gordon-Larsen, P., Nelson, M. C., Page, P. & Popkin, B. M. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117, 417–424 (2006).

    Article  PubMed  Google Scholar 

  69. Zhou, S. & Lin, R. Spatial–temporal heterogeneity of air pollution: the relationship between built environment and on-road PM2.5 at micro scale. Transp. Res. D Transp. Environ. 76, 305–322 (2019).

    Article  Google Scholar 

  70. Frank, L. D. & Engelke, P. Multiple impacts of the built environment on public health: walkable places and the exposure to air pollution. Int. Reg. Sci. Rev. 28, 193–216 (2005).

    Article  Google Scholar 

  71. Diuk-Wasser, M. A., VanAcker, M. C. & Fernandez, M. P. Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases. J. Med. Entomol. 58, 1546–1564 (2021).

    Article  PubMed  Google Scholar 

  72. Sengupta, U., Rauws, W. S. & De Roo, G.Planning and complexity: engaging with temporal dynamics, uncertainty and complex adaptive systems. Environ. Plann. B Plann. Des. 43, 970–974 (2016).

    Article  Google Scholar 

  73. Shi, Y. et al. Assessment methods of urban system resilience: from the perspective of complex adaptive system theory. Cities 112, 103141 (2021).

    Article  Google Scholar 

  74. Holland, J. H. Signals and Boundaries: Building Blocks for Complex Adaptive Systems (MIT Press, 2012).

  75. Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23, 46–61 (2018).

    Article  Google Scholar 

  76. Levin, S. et al. Social–ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18, 111–132 (2013).

    Article  Google Scholar 

  77. Waldrop, M. M. Complexity: The Emerging Science at the Edge of Order and Chaos (Simon and Schuster, 1993).

  78. Nel, D., du Plessis, C. & Landman, K. Planning for dynamic cities: introducing a framework to understand urban change from a complex adaptive systems approach. Int. Plan. Stud. 23, 250–263 (2018).

    Article  Google Scholar 

  79. Sharifi, A. Resilient urban forms: a macro-scale analysis. Cities 85, 1–14 (2019).

    Article  Google Scholar 

  80. Borgström, S. T., Elmqvist, T., Angelstam, P. & Alfsen-Norodom, C. Scale mismatches in management of urban landscapes. Ecol. Soc. 11, 16 (2006).

    Article  Google Scholar 

  81. Walker, B. H., Carpenter, S. R., Rockstrom, J., Crépin, A.-S. & Peterson, G. D. Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17, 30 (2012).

    Article  Google Scholar 

  82. Carpenter, S. R. & Turner, M. G. Hares and tortoises: interactions of fast and slow variables in ecosystems. Ecosystems 3, 495–497 (2000).

    Article  Google Scholar 

  83. Peters, D. P., Bestelmeyer, B. T. & Turner, M. G. Cross-scale interactions and changing pattern–process relationships: consequences for system dynamics. Ecosystems 10, 790–796 (2007).

    Article  Google Scholar 

  84. Crépin, A.-S. Using fast and slow processes to manage resources with thresholds. Environ. Resour. Econ. 36, 191–213 (2007).

    Article  Google Scholar 

  85. Soranno, P. A. et al. Cross‐scale interactions: quantifying multi‐scaled cause–effect relationships in macrosystems. Front. Ecol. Environ. 12, 65–73 (2014).

    Article  Google Scholar 

  86. Pickett, S. T. et al. Theoretical perspectives of the Baltimore Ecosystem Study: conceptual evolution in a social–ecological research project. BioScience 70, 297–314 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Gunderson, L. H., Holling, C. S. & Light, S. S. Barriers and Bridges to the Renewal of Ecosystems and Institutions (Columbia Univ. Press, 1995).

  88. Turner, M. G., Dale, V. H. & Gardner, R. H. Predicting across scales: theory development and testing. Landsc. Ecol. 3, 245–252 (1989).

    Article  Google Scholar 

  89. Wu, J. & Loucks, O. L. From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q. Rev. Biol. 70, 439–466 (1995).

    Article  Google Scholar 

  90. Flores, A., Pickett, S. T., Zipperer, W. C., Pouyat, R. V. & Pirani, R. Adopting a modern ecological view of the metropolitan landscape: the case of a greenspace system for the New York City region. Landsc. Urban Plan. 39, 295–308 (1998).

    Article  Google Scholar 

  91. Fauchald, P. & Tveraa, T. Hierarchical patch dynamics and animal movement pattern. Oecologia 149, 383–395 (2006).

    Article  PubMed  Google Scholar 

  92. Linton, J. & Budds, J. The hydrosocial cycle: defining and mobilizing a relational–dialectical approach to water. Geoforum 57, 170–180 (2014).

    Article  Google Scholar 

  93. Knox, P. & Pinch, S. Urban Social Geography: an Introduction (Routledge, 2014).

  94. Geels, F. W. From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res. Policy 33, 897–920 (2004).

    Article  Google Scholar 

  95. West, S., Haider, L. J., Stålhammar, S. & Woroniecki, S. A relational turn for sustainability science? Relational thinking, leverage points and transformations. Ecosyst. People 16, 304–325 (2020).

    Article  Google Scholar 

  96. Jones, M. Phase space: geography, relational thinking, and beyond. Prog. Hum. Geogr. 33, 487–506 (2009).

    Article  Google Scholar 

  97. Wohl, S. Considering how morphological traits of urban fabric create affordances for complex adaptation and emergence. Prog. Hum. Geogr. 40, 30–47 (2016).

    Article  Google Scholar 

  98. Herold, M., Scepan, J. & Clarke, K. C. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A 34, 1443–1458 (2002).

    Article  Google Scholar 

  99. Morrison, A. C. et al. Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol. 41, 1123–1142 (2004).

    Article  PubMed  Google Scholar 

  100. LaCon, G. et al. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru. PLoS Negl. Trop. Dis. 8, e3038 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Lai, S. et al. Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005–2015. PLoS Negl. Trop. Dis. 12, e0006743 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Gergel, S. E. & Turner, M. G. Learning Landscape Ecology: a Practical Guide to Concepts and Techniques (Springer, 2017).

  103. Hosseini, P. R. et al. Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk. Phil. Trans. R. Soc. B Biol. Sci. 372, 20160129 (2017).

    Article  Google Scholar 

  104. LaDeau, S. L., Allan, B. F., Leisnham, P. T. & Levy, M. Z. The ecological foundations of transmission potential and vector‐borne disease in urban landscapes. Funct. Ecol. 29, 889–901 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Rowley, W. A. & Graham, C. L. The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. J. Insect Physiol. 14, 1251–1257 (1968).

    Article  CAS  PubMed  Google Scholar 

  106. Evans, M. V. et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in urban areas. Am. J. Trop. Med. Hyg. 101, 362–370 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Alto, B. W. & Juliano, S. A. Temperature effects on the dynamics of Aedes albopictus (Diptera: Culicidae) populations in the laboratory. J. Med. Entomol. 38, 548–556 (2001).

    Article  CAS  PubMed  Google Scholar 

  108. Streutker, D. R. A remote sensing study of the urban heat island of Houston, Texas. Int. J. Remote Sens. 23, 2595–2608 (2002).

    Article  Google Scholar 

  109. Fikrig, K. et al. Sugar feeding patterns of New York Aedes albopictus mosquitoes are affected by saturation deficit, flowers, and host seeking. PLoS Negl. Trop. Dis. 14, e0008244 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Samson, D. M. et al. Resting and energy reserves of Aedes albopictus collected in common landscaping vegetation in St. Augustine, Florida. J. Am. Mosq. Control Assoc. 29, 231–236 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Grove, J. M., Locke, D. H. & O’Neil-Dunne, J. P. An ecology of prestige in New York City: examining the relationships among population density, socio-economic status, group identity, and residential canopy cover. Environ. Manag. 54, 402–419 (2014).

    Article  Google Scholar 

  112. Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14, 20180082 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Aronson, M. F. et al. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196 (2017).

    Article  Google Scholar 

  114. Hemme, R. R., Thomas, C. L., Chadee, D. D. & Severson, D. W. Influence of urban landscapes on population dynamics in a short-distance migrant mosquito: evidence for the dengue vector Aedes aegypti. PLoS Negl. Trop. Dis. 4, e634 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  115. García-Betancourt, T., Higuera-Mendieta, D. R., González-Uribe, C., Cortés, S. & Quintero, J. Understanding water storage practices of urban residents of an endemic dengue area in Colombia: perceptions, rationale and socio-demographic characteristics. PLoS ONE 10, e0129054 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Plummer, R., de Loë, R. & Armitage, D. A systematic review of water vulnerability assessment tools. Water Resour. Manag. 26, 4327–4346 (2012).

    Article  Google Scholar 

  117. Ledogar, R. J. et al. Mobilising communities for Aedes aegypti control: the SEPA approach. BMC Public Health 17, 103–114 (2017).

    Article  Google Scholar 

  118. Michalos, A. C. Encyclopedia of Quality of Life and Well-being Research (Springer Netherlands, 2014).

  119. Reiner, R. C. Jr, Stoddard, S. T. & Scott, T. W. Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics 6, 30–36 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Whiteford, L. M. The ethnoecology of dengue fever. Med. Anthropol. Q. 11, 202–223 (1997).

    Article  CAS  PubMed  Google Scholar 

  121. Ibarra, A. M. S. et al. A social–ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health 14, 1135 (2014).

    Article  Google Scholar 

  122. Mitchell-Foster, K. L. Interdisciplinary Knowledge Translation and Evaluation Strategies for Participatory Dengue Prevention in Machala, Ecuador. PhD thesis, Univ. British Columbia (2013).

  123. Kropf, K.Aspects of urban form. Urban Morphol. 13, 105–120 (2009).

    Article  Google Scholar 

  124. Rose, L. A. Topographical constraints and urban land supply indexes. J. Urban Econ. 26, 335–347 (1989).

    Article  Google Scholar 

  125. Liu, F. Interrupted Development”: The Effects of Blighted Neighborhoods and Topographic Barriers on Cities. PhD thesis, George Washington Univ. (2006).

  126. Durand-Lasserve, A. & Selod, H. in Urban Land Markets 101–132 (Springer, 2009).

  127. Talen, E. City Rules: How Regulations Affect Urban Form (Island Press, 2012).

  128. Scheer, B. C. The Evolution of Urban Form: Typology for Planners and Architects (Routledge, 2017).

  129. Dimoudi, A., Kantzioura, A., Zoras, S., Pallas, C. & Kosmopoulos, P. Investigation of urban microclimate parameters in an urban center. Energy Build. 64, 1–9 (2013).

    Article  Google Scholar 

  130. Middel, A., Häb, K., Brazel, A. J., Martin, C. A. & Guhathakurta, S. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 122, 16–28 (2014).

    Article  Google Scholar 

  131. Honório, N. A. et al. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem. Inst. Oswaldo Cruz 98, 191–198 (2003).

    Article  PubMed  Google Scholar 

  132. Seto, K. C. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 923–1000 (Cambridge Univ. Press, 2014).

  133. Romeo-Aznar, V., Freitas, L. P., Cruz, O. G., King, A. & Pascual, M. Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics. Nat. Commun. 13, 996 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Lafferty, K. D. et al. Local extinction of the Asian tiger mosquito (Aedes albopictus) following rat eradication on Palmyra Atoll. Biol. Lett. 14, 20170743 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Rodríguez, M. C., Dupont-Courtade, L. & Oueslati, W. Air pollution and urban structure linkages: evidence from European cities. Renew. Sustain. Energy Rev. 53, 1–9 (2016).

    Article  Google Scholar 

  136. Venter, Z. S., Krog, N. H. & Barton, D. N. Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Sci. Total Environ. 709, 136193 (2020).

    Article  CAS  PubMed  Google Scholar 

  137. Little, E., Barrera, R., Seto, K. C. & Diuk-Wasser, M. Co-occurrence patterns of the dengue vector Aedes aegypti and Aedes mediovitattus, a dengue competent mosquito in Puerto Rico. EcoHealth 8, 365–375 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Pereira dos Santos, T. et al. Potential of Aedes albopictus as a bridge vector for enzootic pathogens at the urban–forest interface in Brazil. Emerg. Microbes Infect. 7, 191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Cardoso, J. et al. Yellow fever virus in Haemagogus leucocelaenus and Aedes serratus mosquitoes, southern Brazil, 2008. Emerg. Infect. Dis. 16, 1918–1924 (2010).

    Article  PubMed Central  Google Scholar 

  140. Grobbelaar, A. A. et al. Resurgence of yellow fever in Angola, 2015–2016. Emerg. Infect. Dis. 22, 1854–1855 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Tonkiss, F. Cities by Design: the Social Life of Urban Form (John Wiley & Sons, 2014).

  142. Hillier, B., Greene, M. & Desyllas, J. Self-generated neighbourhoods: the role of urban form in the consolidation of informal settlements. Urban Des. Int. 5, 61–96 (2000).

    Article  Google Scholar 

  143. Li, X., Mou, Y., Wang, H., Yin, C. & He, Q. How does polycentric urban form affect urban commuting? Quantitative measurement using geographical big data of 100 cities in China. Sustainability 10, 4566 (2018).

    Article  Google Scholar 

  144. Wen, T.-H., Lin, M.-H., Teng, H.-J. & Chang, N.-T. Incorporating the human–Aedes mosquito interactions into measuring the spatial risk of urban dengue fever. Appl. Geogr. 62, 256–266 (2015).

    Article  Google Scholar 

  145. Achee, N. L. et al. A critical assessment of vector control for dengue prevention. PLoS Negl. Trop. Dis. 9, e0003655 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Scott, T. W. & Morrison, A. C. Vector dynamics and transmission of dengue virus: implications for dengue surveillance and prevention strategies: vector dynamics and dengue prevention. Curr. Top. Microbiol. Immunol. 338, 115–128 (2010).

    PubMed  Google Scholar 

  147. Delmelle, E., Kim, C., Xiao, N. & Chen, W. Methods for space–time analysis and modeling: an overview. Int. J. Appl. Geospat. Res. 4, 1–18 (2013).

    Article  Google Scholar 

  148. Kua, K. P. & Lee, S. W. H. Randomized trials of housing interventions to prevent malaria and Aedes-transmitted diseases: a systematic review and meta-analysis. PLoS ONE 16, e0244284 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Chareonviriyaphap, T. et al. The use of an experimental hut for evaluating the entering and exiting behavior of Aedes aegypti (Diptera: Culicidae), a primary vector of dengue in Thailand. J. Vector Ecol. 30, 344–346 (2005).

    PubMed  Google Scholar 

  150. Maneerat, S. & Daudé, E. A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas. Ecol. Model. 333, 66–78 (2016).

    Article  Google Scholar 

  151. Barbu, C. M. et al. The effects of city streets on an urban disease vector. PLoS Comput. Biol. 9, e1002801 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Stewart Ibarra, A. M. et al. Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control. PLoS ONE 8, e78263 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Mesch, G. S. & Manor, O. Social ties, environmental perception, and local attachment. Environ. Behav. 30, 504–519 (1998).

    Article  Google Scholar 

  154. Matthews, L. & Haydon, D. Introduction. Cross-scale influences on epidemiological dynamics: from genes to ecosystems. J. R. Soc. Interface 4, 763–765 (2007).

    Article  PubMed Central  Google Scholar 

  155. Strauss, A. T., Shoemaker, L. G., Seabloom, E. W. & Borer, E. T. Cross‐scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens. Ecology 100, e02836 (2019).

    Article  PubMed  Google Scholar 

  156. Schreiber, S. J. et al. Cross-scale dynamics and the evolutionary emergence of infectious diseases. Virus Evol. 7, veaa105 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  157. Ramalho, C. E. & Hobbs, R. J. Time for a change: dynamic urban ecology. Trends Ecol. Evol. 27, 179–188 (2012).

    Article  PubMed  Google Scholar 

  158. Waggoner, J. J. et al. Homotypic dengue virus reinfections in Nicaraguan children. J. Infect. Dis. 214, 986–993 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Ezeakacha, N. F. & Yee, D. A. The role of temperature in affecting carry-over effects and larval competition in the globally invasive mosquito Aedes albopictus. Parasit. Vectors 12, 123 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  160. Evans, M. V. et al. Carry-over effects of urban larval environments on the transmission potential of dengue-2 virus. Parasit. Vectors 11, 426 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. Lancet Planet. Health 5, e209–e219 (2021).

    Article  PubMed  Google Scholar 

  162. Chen, S.-C. et al. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Sci. Total Environ. 408, 4069–4075 (2010).

    Article  CAS  PubMed  Google Scholar 

  163. Elsinga, J. et al. Knowledge, attitudes, and preventive practices regarding dengue in Maracay, Venezuela. Am. J. Trop. Med. Hyg. 99, 195–203 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  164. Wong, L. P., Shakir, S. M. M., Atefi, N. & AbuBakar, S. Factors affecting dengue prevention practices: nationwide survey of the Malaysian public. PLoS ONE 10, e0122890 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Des Roches, S. et al. Socio‐eco‐evolutionary dynamics in cities. Evol. Appl. 14, 248–267 (2021).

    Article  PubMed  Google Scholar 

  166. Pickett, S. T. et al. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu. Rev. Ecol. Syst. 32, 127–157 (2001).

    Article  Google Scholar 

  167. Combs, M. A. et al. Socio‐ecological drivers of multiple zoonotic hazards in highly urbanized cities. Glob. Change Biol. 28, 1705–1724 (2022).

    Article  CAS  Google Scholar 

  168. Zhou, Q.A review of sustainable urban drainage systems considering the climate change and urbanization impacts. Water 6, 976–992 (2014).

    Article  Google Scholar 

  169. Stewart-Ibarra, A. M. et al. Co-developing climate services for public health: stakeholder needs and perceptions for the prevention and control of Aedes-transmitted diseases in the Caribbean. PLoS Negl. Trop. Dis. 13, e0007772 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  170. Hastings, A. Timescales, dynamics, and ecological understanding. Ecology 91, 3471–3480 (2010).

    Article  PubMed  Google Scholar 

  171. Lippi, C. A. et al. A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador. Int. J. Health Geogr. 19, 3 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  172. Projection of the Ecuadorian Population, per Calendar Years, by Cantons 2010–2020 (National Institute of Statistics and Census, 2012).

  173. Pertumbuhan Ekonomi Indonesia Triwulan II (Badann Pusat Statistik, 2021).

  174. Rašić, G. et al. Aedes aegypti has spatially structured and seasonally stable populations in Yogyakarta, Indonesia. Parasit. Vectors 8, 610 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  175. Schmidt, T. L. et al. Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian tiger mosquito, Aedes albopictus. PLoS Negl. Trop. Dis. 11, e0006009 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  176. Schmidt, T. L., Filipović, I., Hoffmann, A. A. & Rašić, G. Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia. Heredity 120, 386–395 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Tantowijoyo, W. et al. Stable establishment of wMel Wolbachia in Aedes aegypti populations in Yogyakarta, Indonesia. PLoS Negl. Trop. Dis. 14, e0008157 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  178. Telle, O. et al. The spread of dengue in an endemic urban milieu—the case of Delhi, India. PLoS ONE 11, e0146539 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  179. Telle, O. et al. Social and environmental risk factors for dengue in Delhi city: a retrospective study. PLoS Negl. Trop. Dis. 15, e0009024 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Stokes, E. C. & Seto, K. C. Characterizing and measuring urban landscapes for sustainability. Environ. Res. Lett. 14, 045002 (2019).

    Article  Google Scholar 

  181. Jackson-Smith, D. B. et al. Differentiating urban forms: a neighborhood typology for understanding urban water systems. Cities Environ. 9, 5 (2016).

    Google Scholar 

  182. Population Census by Age (Department of Provincial Administration, accessed March 2022); https://stat.bora.dopa.go.th/new_stat/webPage/statByAge.php

  183. Salje, H. et al. Revealing the microscale spatial signature of dengue transmission and immunity in an urban population. Proc. Natl Acad. Sci. USA 109, 9535–9538 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Salje, H. et al. Reconstructing unseen transmission events to infer dengue dynamics from viral sequences. Nat. Commun. 12, 1810 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Chai, B. & Seto, K. C. Conceptualizing and characterizing micro-urbanization: a new perspective applied to Africa. Landsc. Urban Plan. 190, 103595 (2019).

    Article  Google Scholar 

  186. Zhu, G., Liu, J., Tan, Q. & Shi, B. Inferring the spatio-temporal patterns of dengue transmission from surveillance data in Guangzhou, China. PLoS Negl. Trop. Dis. 10, e0004633 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  187. Ab Hamid, N. et al. Vertical infestation profile of Aedes in selected urban high-rise residences in Malaysia. Trop. Med. Infect. Dis. 5, 114 (2020).

    Article  PubMed Central  Google Scholar 

  188. Sun, H. et al. Spatio-temporal analysis of the main dengue vector populations in Singapore. Parasit. Vectors 14, 41 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  189. Ho, C.-M. et al. Surveillance for dengue fever vectors using ovitraps at Kaohsiung and Tainan in Taiwan. Formos. Entomol. 25, 159–174 (2005).

    Google Scholar 

  190. McKenzie, D. & Ray, I. Urban water supply in India: status, reform options and possible lessons. Water Policy 11, 442–460 (2009).

    Article  Google Scholar 

  191. Qian, S. S., Cuffney, T. F., Alameddine, I., McMahon, G. & Reckhow, K. H. On the application of multilevel modeling in environmental and ecological studies. Ecology 91, 355–361 (2010).

    Article  PubMed  Google Scholar 

  192. Parham, P. E. et al. Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Phil. Trans. R. Soc. B Biol. Sci. 370, 20130551 (2015).

    Article  Google Scholar 

  193. Slocum, M. G., Beckage, B., Platt, W. J., Orzell, S. L. & Taylor, W. Effect of climate on wildfire size: a cross-scale analysis. Ecosystems 13, 828–840 (2010).

    Article  Google Scholar 

  194. Chiu, C.-H., Wen, T.-H., Chien, L.-C. & Yu, H.-L. A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method. PLoS ONE 9, e106334 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  195. Higuera-Mendieta, D. R., Cortés-Corrales, S., Quintero, J. & González-Uribe, C. KAP surveys and dengue control in Colombia: disentangling the effect of sociodemographic factors using multiple correspondence analysis. PLoS Negl. Trop. Dis. 10, e0005016 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  196. Zhang, J. H., Yuan, J. & Wang, T. Direct cost of dengue hospitalization in Zhongshan, China: associations with demographics, virus types and hospital accreditation. PLoS Negl. Trop. Dis. 11, e0005784 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Tam, C. C. et al. Estimates of dengue force of infection in children in Colombo, Sri Lanka. PLoS Negl. Trop. Dis. 7, e2259 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  198. Lee, S. & Castillo-Chavez, C. The role of residence times in two-patch dengue transmission dynamics and optimal strategies. J. Theor. Biol. 374, 152–164 (2015).

    Article  PubMed  Google Scholar 

  199. Adams, B. & Kapan, D. D. Man bites mosquito: understanding the contribution of human movement to vector-borne disease dynamics. PLoS ONE 4, e6763 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  200. Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J. Theor. Biol. 251, 450–467 (2008).

    Article  PubMed  Google Scholar 

  201. Otero, M., Schweigmann, N. & Solari, H. G. A stochastic spatial dynamical model for Aedes aegypti. Bull. Math. Biol. 70, 1297–1325 (2008).

    Article  PubMed  Google Scholar 

  202. Otero, M. & Solari, H. G. Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito. Math. Biosci. 223, 32–46 (2010).

    Article  CAS  PubMed  Google Scholar 

  203. Li, X. & Liu, X. Embedding sustainable development strategies in agent‐based models for use as a planning tool. Int. J. Geogr. Inf. Sci. 22, 21–45 (2008).

    Article  Google Scholar 

  204. Mozaffaree Pour, N. & Oja, T. Urban expansion simulated by integrated cellular automata and agent-based models; an example of Tallinn, Estonia. Urban Sci. 5, 85 (2021).

    Article  Google Scholar 

  205. Gilbert, N. Agent-Based Models Vol. 153 (Sage Publications, 2019).

  206. Roster, K. & Rodrigues, F. A. Neural networks for dengue prediction: a systematic review. Preprint at https://arxiv.org/abs/2106.12905 (2021).

  207. Zhao, N. et al. Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl. Trop. Dis. 14, e0008056 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  208. Zhai, Y. et al. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. Int. J. Geogr. Inf. Sci. 34, 1475–1499 (2020).

    Article  Google Scholar 

  209. Verma, D. & Jana, A. LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: evidence from Mumbai. Preprint at https://arxiv.org/abs/1909.09774 (2019).

  210. Djenontin, I. N. S. & Meadow, A. M. The art of co-production of knowledge in environmental sciences and management: lessons from international practice. Environ. Manag. 61, 885–903 (2018).

    Article  Google Scholar 

  211. Meschede, C. & Mainka, A. Including citizen participation formats for drafting and implementing local sustainable development strategies. Urban Sci. 4, 13 (2020).

    Article  Google Scholar 

  212. Mansfield, R. G., Batagol, B. & Raven, R. “Critical agents of change?”: opportunities and limits to children’s participation in urban planning. J. Plan. Lit. 36, 170–186 (2021).

    Article  Google Scholar 

  213. Curtis, A., Quinn, M., Obenauer, J. & Renk, B. M. Supporting local health decision making with spatial video: dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua. Appl. Geogr. 87, 197–206 (2017).

    Article  Google Scholar 

  214. Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).

    Article  Google Scholar 

  215. Dickens, L. & Butcher, M. Going public? Re‐thinking visibility, ethics and recognition through participatory research praxis. Trans. Inst. Br. Geogr. 41, 528–540 (2016).

    Article  Google Scholar 

  216. Wallerstein, N. et al. Power dynamics in community-based participatory research: a multiple-case study analysis of partnering contexts, histories, and practices. Health Educ. Behav. 46, 19S–32S (2019).

    Article  PubMed  Google Scholar 

  217. Parra, C. et al. Synergies between technology, participation, and citizen science in a community-based dengue prevention program. Am. Behav. Sci. 64, 1850–1870 (2020).

    Article  Google Scholar 

  218. Lozano–Fuentes, S. et al. Cell phone-based system (Chaak) for surveillance of immatures of dengue virus mosquito vectors. J. Med. Entomol. 50, 879–889 (2013).

    Article  PubMed  Google Scholar 

  219. Kelvin, A. A. et al. ZIKATracker: a mobile app for reporting cases of ZIKV worldwide. J. Infect. Dev. Ctries. 10, 113–115 (2016).

    Article  PubMed  Google Scholar 

  220. Fernandez, M. P. et al. Usability and feasibility of a smartphone app to assess human behavioral factors associated with tick exposure (The Tick App): quantitative and qualitative study. JMIR mHealth uHealth 7, e14769 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  221. Hamer, S. A., Curtis-Robles, R. & Hamer, G. L. Contributions of citizen scientists to arthropod vector data in the age of digital epidemiology. Curr. Opin. Insect Sci. 28, 98–104 (2018).

    Article  PubMed  Google Scholar 

  222. Van Leeuwen, J. P., Hermans, K., Jylhä, A., Quanjer, A. J. & Nijman, H. Effectiveness of virtual reality in participatory urban planning: a case study. In Proc. Media Architecture Biennale 128–136 (Association for Computing Machinery, 2018).

  223. Kahila-Tani, M. Reshaping the Planning Process Using Local Experiences: Utilising PPGIS in Participatory Urban Planning. PhD thesis, Aalto Univ. (2015).

  224. Iwaniec, D. M. et al. The co-production of sustainable future scenarios. Landsc. Urban Plan. 197, 103744 (2020).

    Article  Google Scholar 

  225. Dickin, S. K., Schuster-Wallace, C. J. & Elliott, S. J. Mosquitoes & vulnerable spaces: mapping local knowledge of sites for dengue control in Seremban and Putrajaya Malaysia. Appl. Geogr. 46, 71–79 (2014).

    Article  Google Scholar 

  226. Chircop, A., Bassett, R. & Taylor, E. Evidence on how to practice intersectoral collaboration for health equity: a scoping review. Crit. Public Health 25, 178–191 (2015).

    Article  Google Scholar 

  227. Gamache, S., Diallo, T. A., Shankardass, K. & Lebel, A. The elaboration of an intersectoral partnership to perform health impact assessment in urban planning: the experience of Quebec City (Canada). Int. J. Environ. Res. Public Health 17, 7556 (2020).

    Article  PubMed Central  Google Scholar 

  228. Herdiana, H., Sari, J. F. K. & Whittaker, M. Intersectoral collaboration for the prevention and control of vector borne diseases to support the implementation of a global strategy: a systematic review. PLoS ONE 13, e0204659 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  229. Lee, S. A., Economou, T., de Castro Catão, R., Barcellos, C. & Lowe, R. The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil. PLoS Negl. Trop. Dis. 15, e0009773 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  230. Johansson, M. A., Cummings, D. A. & Glass, G. E. Multiyear climate variability and dengue—El Nino southern oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med. 6, e1000168 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  231. Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl. Trop. Dis. 5, e1378 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  232. Hess, G. Disease in metapopulation models: implications for conservation. Ecology 77, 1617–1632 (1996).

    Article  Google Scholar 

  233. Hanski, I. Metapopulation dynamics: does it help to have more of the same? Trends Ecol. Evol. 4, 113–114 (1989).

    Article  CAS  PubMed  Google Scholar 

  234. Masui, H. et al. Assessing potential countermeasures against the dengue epidemic in non-tropical urban cities. Theor. Biol. Med. Model. 13, 12 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  235. Stone, C. M., Schwab, S. R., Fonseca, D. M. & Fefferman, N. H. Contrasting the value of targeted versus area-wide mosquito control scenarios to limit arbovirus transmission with human mobility patterns based on different tropical urban population centers. PLoS Negl. Trop. Dis. 13, e0007479 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  236. O’Reilly, K. M. et al. Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis. BMC Med. 16, 180 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  237. Santé, I., García, A. M., Miranda, D. & Crecente, R. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc. Urban Plan. 96, 108–122 (2010).

    Article  Google Scholar 

  238. Yang, J., Gong, J., Tang, W. & Liu, C. Patch-based cellular automata model of urban growth simulation: integrating feedback between quantitative composition and spatial configuration. Comput. Environ. Urban Syst. 79, 101402 (2020).

    Article  Google Scholar 

  239. Rozos, E., Butler, D. & Makropoulos, C. An integrated system dynamics–cellular automata model for distributed water-infrastructure planning. Water Sci. Technol. Water Supply 16, 1519–1527 (2016).

    Article  Google Scholar 

  240. Enduri, M. K. & Jolad, S. Dynamics of dengue disease with human and vector mobility. Spat. Spatiotemporal Epidemiol. 25, 57–66 (2018).

    Article  PubMed  Google Scholar 

  241. Medeiros, L. C. et al. Modeling the dynamic transmission of dengue fever: investigating disease persistence. PLoS Negl. Trop. Dis. 5, e942 (2011).

    Article  PubMed Central  Google Scholar 

  242. Ali, A. M., Shafiee, M. E. & Berglund, E. Z. Agent-based modeling to simulate the dynamics of urban water supply: climate, population growth, and water shortages. Sustain. Cities Soc. 28, 420–434 (2017).

    Article  Google Scholar 

  243. Philippon, D. et al. in Multi-Agent Based Simulation XVII. MABS 2016. Lecture Notes in Computer Science Vol 10399 (eds Nardin, L. & Antunes, L.) 111–127 (Springer, 2016).

  244. Agyemang, F. S., Silva, E. & Fox, S.Modelling and simulating ‘informal urbanization’: an integrated agent-based and cellular automata model of urban residential growth in Ghana. Urban Anal. City Sci. 0, 1–15 (2022).

    Google Scholar 

  245. Chouhan, S. S., Kaul, A. & Singh, U. P. Image segmentation using computational intelligence techniques. Arch. Comput. Methods Eng. 26, 533–596 (2019).

    Article  Google Scholar 

  246. Andersson, V. O., Birck, M. A. F. & Araujo, R. M. Towards predicting dengue fever rates using convolutional neural networks and street-level images. Proc. 2018 Int. Jt Conf. Neural Netw. 1–8 (IEEE, 2018).

  247. Chrysler, A., Gunarso, R., Puteri, T. & Warnars, H. A Literature Review of Crowd-Counting System on Convolutional Neural Network 012029 (IOP Conference Series: Earth and Environmental Science Volume 729, IOP Publishing, 2021).

  248. Bharambe, A., Chandorkar, A. A. & Kalbande, D. A deep learning approach for dengue tweet classification. Proc. 3rd Int. Conf. Invent. Res. Comput. Appl. 1043–1047 (IEEE, 2021).

  249. Kumar, A. & Garg, G. Sentiment analysis of multimodal twitter data. Multimed. Tools Appl. 78, 24103–24119 (2019).

    Article  Google Scholar 

  250. Marin, A. & Wellman, B. in The SAGE Handbook of Social Network Analysis Ch. 2 (2011).

  251. Snijders, T. A. & Steglich, C. E. Representing micro–macro linkages by actor-based dynamic network models. Sociol. Methods Res. 44, 222–271 (2015).

    Article  PubMed  Google Scholar 

  252. Warren, C. R., Burton, R., Buchanan, O. & Birnie, R. V. Limited adoption of short rotation coppice: the role of farmers’ socio-cultural identity in influencing practice. J. Rural Stud. 45, 175–183 (2016).

    Article  Google Scholar 

  253. Beal Cohen, A. A., Muneepeerakul, R. & Kiker, G. Intra-group decision-making in agent-based models. Sci. Rep. 11, 17709 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  254. Frederiks, E. R., Stenner, K. & Hobman, E. V. Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain. Energy Rev. 41, 1385–1394 (2015).

    Article  Google Scholar 

  255. Spiegel, J. et al. Barriers and bridges to prevention and control of dengue: the need for a social–ecological approach. EcoHealth 2, 273–290 (2005).

    Article  Google Scholar 

  256. Arellano, C. et al. Knowledge and beliefs about dengue transmission and their relationship with prevention practices in Hermosillo, Sonora. Front. Public Health 3, 142 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  257. Gertler, M. S. & Wolfe, D. A. Local social knowledge management: community actors, institutions and multilevel governance in regional foresight exercises. Futures 36, 45–65 (2004).

    Article  Google Scholar 

  258. Brown, R. R., Farrelly, M. A. & Loorbach, D. A. Actors working the institutions in sustainability transitions: the case of Melbourne’s stormwater management. Glob. Environ. Change 23, 701–718 (2013).

    Article  Google Scholar 

  259. Castilla-Rho, J. C., Mariethoz, G., Rojas, R., Andersen, M. S. & Kelly, B. F. An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems. Environ. Model. Softw. 73, 305–323 (2015).

    Article  Google Scholar 

  260. Sabatier, P. A. Toward better theories of the policy process. PS Polit. Sci. Polit. 24, 147–156 (1991).

    Article  Google Scholar 

  261. Abrantes, P. et al. Modelling urban form: a multidimensional typology of urban occupation for spatial analysis. Environ. Plan. B Urban Anal. City Sci. 46, 47–65 (2019).

    Article  Google Scholar 

  262. McGarigal, K. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure Vol. 351 (US Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1995).

  263. Vazquez-Prokopec, G. M. et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS ONE 8, e58802 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  264. Ligtenberg, A., van Lammeren, R. J., Bregt, A. K. & Beulens, A. J. Validation of an agent-based model for spatial planning: a role-playing approach. Comput. Environ. Urban Syst. 34, 424–434 (2010).

    Article  Google Scholar 

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Acknowledgements

This publication was supported in part by the National Science Foundation Geography and Spatial Sciences Doctoral Dissertation Research Improvement Grant and Cooperative Agreement Number U01CK000509-01, funded by the Centers for Disease Control and Prevention. Its contents are solely our responsibility and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. Select graphics icons were designed using resources from Microsoft PowerPoint, Iconfinder.com and Flaticon.com. We are grateful to the multiple colleagues who contributed to the development of these ideas, including D. Ruiz Carrascal, J. Cascante Vega, M. Combs, M. del Pilar Fernandez, J. Schoen and M. VanAcker.

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P.A.K., M.S.-V. and M.A.D.-W. conceptualized the study. P.A.K. wrote the original draft of the manuscript. P.A.K., M.S.-V., A.M.S.-I., E.M.C., K.C.S. and M.A.D.-W. edited and revised the manuscript. All authors gave final approval for publication and agreed to be held accountable for the work performed.

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Correspondence to Maria A. Diuk-Wasser.

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Kache, P.A., Santos-Vega, M., Stewart-Ibarra, A.M. et al. Bridging landscape ecology and urban science to respond to the rising threat of mosquito-borne diseases. Nat Ecol Evol 6, 1601–1616 (2022). https://doi.org/10.1038/s41559-022-01876-y

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