Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. USA 118, e2023483118 (2021).
Google Scholar
Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).
Google Scholar
Kuipers, K. J. J. et al. Habitat fragmentation amplifies threats from habitat loss to mammal diversity across the world’s terrestrial ecoregions. One Earth 4, 1505–1513 (2021).
Google Scholar
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
Google Scholar
Watson, J. E. M. & Venter, O. Mapping the continuum of humanity’s footprint on land. One Earth 1, 175–180 (2019).
Google Scholar
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
Google Scholar
Glidden, C. K. et al. Human-mediated impacts on biodiversity and the consequences for zoonotic disease spillover. Curr. Biol. 31, R1342–R1361 (2021).
Google Scholar
Grobbelaar, A. A. et al. Resurgence of yellow fever in Angola, 2015-2016. Emerg. Infect. Dis. 22, 1854–1855 (2016).
Google Scholar
Gubler, D. J. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 10, 100–103 (2002).
Google Scholar
Hotez, P. J. Neglected tropical diseases in the Anthropocene: the cases of Zika, Ebola, and other infections. PLoS Negl. Trop. Dis. 10, e0004648 (2016).
Google Scholar
Paixão, E. S., Teixeira, M. G. & Rodrigues, L. C. Zika, chikungunya and dengue: the causes and threats of new and re-emerging arboviral diseases. BMJ Glob. Health 3, e000530 (2018).
Google Scholar
Rosenberg, R. et al. Vital signs: trends in reported vectorborne disease cases – United States and territories, 2004-2016. Morb. Mortal. Wk. Rep. 67, 496–501 (2018).
Google Scholar
World Malaria Report 2020: 20 Years of Global Progress and Challenges (WHO, 2020); https://apps.who.int/iris/handle/10665/337660
Lambin, E. F., Tran, A., Vanwambeke, S. O., Linard, C. & Soti, V. Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts. Int. J. Health Geogr. 9, 54 (2010).
Google Scholar
Shocket, M. S. et al. Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 °C and 26 °C. eLife 9, e58511 (2020).
Google Scholar
Kilpatrick, A. M. & Randolph, S. E. Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. Lancet 380, 1946–1955 (2012).
Google Scholar
Franklinos, L. H. V., Jones, K. E., Redding, D. W. & Abubakar, I. The effect of global change on mosquito-borne disease. Lancet Infect. Dis. 19, e302–e312 (2019).
Google Scholar
Keys, P. W., Barnes, E. A. & Carter, N. H. A machine-learning approach to human footprint index estimation with applications to sustainable development. Environ. Res. Lett. 16, 044061 (2021).
Google Scholar
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).
Google Scholar
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
Google Scholar
Hill, J. E., DeVault, T. L., Wang, G. & Belant, J. L. Anthropogenic mortality in mammals increases with the human footprint. Front. Ecol. Environ. 18, 13–18 (2020).
Google Scholar
Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Topography and human pressure in mountain ranges alter expected species responses to climate change. Nat. Commun. 11, 1974 (2020).
Google Scholar
Su, J., Yin, H. & Kong, F. Ecological networks in response to climate change and the human footprint in the Yangtze River Delta urban agglomeration, China. Landsc. Ecol. 36, 2095–2112 (2021).
Google Scholar
Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).
Google Scholar
Dos Santos, C. V. B., da Paixão Sevá, A., Werneck, G. L. & Struchiner, C. J. Does deforestation drive visceral leishmaniasis transmission? A causal analysis. Proc. R. Soc. B 288, 20211537 (2021).
Google Scholar
MacDonald, A. J. & Mordecai, E. A. Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing. Proc. Natl. Acad. Sci. USA 116, 22212–22218 (2019).
Google Scholar
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).
Google Scholar
Rodrigues, N. B. et al. Brazilian Aedes aegypti as a competent vector for multiple complex arboviral coinfections. J. Infect. Dis. 224, 101–108 (2021).
Google Scholar
Weinstein, J. S., Leslie, T. F. & von Fricken, M. E. Spatial associations between land use and infectious disease: Zika virus in Colombia. Int. J. Environ. Res. Public Health 17, E1127 (2020).
Google Scholar
Heukelbach, J., Alencar, C. H., Kelvin, A. A., de Oliveira, W. K. & Pamplona de Góes Cavalcanti, L. Zika virus outbreak in Brazil. J. Infect. Dev. Countr. 10, 116–120 (2016).
Google Scholar
Lowe, R. et al. The Zika virus epidemic in Brazil: from discovery to future implications. Int. J. Environ. Res. Public Health 15, E96 (2018).
Google Scholar
Alves, M. C. G. P., de Matos, M. R., de Lourdes Reichmann, M. & Dominguez, M. H. Estimation of the dog and cat population in the State of São Paulo. Rev. Saude Publica 39, 891–897 (2005).
Google Scholar
Mordecai, E. A. et al. Thermal biology of mosquito-borne disease. Ecol. Lett. 22, 1690–1708 (2019).
Google Scholar
Gage, K. L., Burkot, T. R., Eisen, R. J. & Hayes, E. B. Climate and vectorborne diseases. Am. J. Prev. Med. 35, 436–450 (2008).
Google Scholar
Doenças e Agravos de Notificação – 2007 em Diante (SINAN) (DATASUS, Ministério da Saúde do Brasil, 2021); https://datasus.saude.gov.br/acesso-a-informacao/doencas-e-agravos-de-notificacao-de-2007-em-diante-sinan/
SIVEP – MALÁRIA Notificação de Casos (Ministério da Saúde do Brasil, 2021); http://200.214.130.44/sivep_malaria/
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020); https://www.R-project.org/
Sorichetta, A. et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci. Data 2, 150045 (2015).
Google Scholar
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar
Souza at. al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sens. 12, https://doi.org/10.3390/rs12172735 (2020).
Fountain-Jones, N. M. et al. How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure. J. Anim. Ecol. 88, 1447–1461 (2019).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar
Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recogn. Lett. 31, 2225–2236 (2010).
Google Scholar
Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).
Ratner, B. The correlation coefficient: its values range between +1/−1, or do they? J. Target. Meas. Anal. Mark. 17, 139–142 (2009).
Google Scholar
Ishwaran, H. & Kogalur, U. B. Fast unified random forests for survival, regression, and classification (RF-SRC) (2019).
O’Brien, R. & Ishwaran, H. A random forests quantile classifier for class imbalanced data. Pattern Recognit. 90, 232–249 (2019).
Google Scholar
Silge, J. & Mahoney, M. spatialsample: spatial resampling infrastructure. R version 0.2.1 (2023).
Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).
Google Scholar
Weaver, S. C. & Forrester, N. L. Chikungunya: evolutionary history and recent epidemic spread. Antivir. Res. 120, 32–39 (2015).
Google Scholar
Puntasecca, C. J., King, C. H. & LaBeaud, A. D. Measuring the global burden of chikungunya and Zika viruses: a systematic review. PLoS Negl. Trop. Dis. 15, e0009055 (2021).
Google Scholar
Baeza, A., Santos-Vega, M., Dobson, A. P. & Pascual, M. The rise and fall of malaria under land-use change in frontier regions. Nat. Ecol. Evol. 1, 108 (2017).
Google Scholar
de Araújo Pedrosa, F. & de Alencar Ximenes, R. A. Sociodemographic and environmental risk factors for American cutaneous leishmaniasis (ACL) in the State of Alagoas, Brazil. Am. J. Trop. Med. Hyg. 81, 195–201 (2009).
Google Scholar
Gonçalves, N. V. et al. Cutaneous leishmaniasis: spatial distribution and environmental risk factors in the state of Pará, Brazilian Eastern Amazon. J. Infect. Dev. Countr. 13, 939–944 (2019).
Google Scholar
Leishmaniasis (Pan American Health Organization, 2022); https://www.paho.org/en/topics/leishmaniasis
Harhay, M. O., Olliaro, P. L., Costa, D. L. & Costa, C. H. N. Urban parasitology: visceral leishmaniasis in Brazil. Trends Parasitol. 27, 403–409 (2011).
Google Scholar
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