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Abstract
Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.
Here, using a dynamic modelling approach, the authors find that the spread of dengue through Mexico and Brazil is shaped by specific interactions between human mobility, climate, and the environment. Their models can also be applied to predict future spread in these geographic areas.
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1 University of Toronto, Temerty Faculty of Medicine, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Dalla Lana School of Public Health, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); Vector Institute for Artificial Intelligence, Toronto, Canada (GRID:grid.494618.6) (ISNI:0000 0005 0272 1351)
2 London School of Hygiene & Tropical Medicine, Centre for the Mathematical Modelling of Infectious Diseases, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene & Tropical Medicine, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene & Tropical Medicine, Centre on Climate Change and Planetary Health, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X)
3 Imperial College London, Medical Research Council Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics and Department of Infectious Disease Epidemiology, School of Public Health, London, UK (GRID:grid.7445.2) (ISNI:0000 0001 2113 8111); Universidade Federal do Rio de Janeiro, Departamento de Genética, Rio de Janeiro, Brazil (GRID:grid.8536.8) (ISNI:0000 0001 2294 473X)
4 London School of Hygiene & Tropical Medicine, Centre for the Mathematical Modelling of Infectious Diseases, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene & Tropical Medicine, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); London School of Hygiene & Tropical Medicine, Centre on Climate Change and Planetary Health, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); University College London, Department of Genetics, Evolution and Environment, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)
5 University of Oxford, Department of Biology, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948)
6 BlueDot, Toronto, Canada (GRID:grid.507904.f)
7 University of Washington, Institute for Health Metrics and Evaluation, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); University of Washington, Department of Health Metrics Sciences, School of Medicine, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
8 University of Notre Dame, Department of Biological Sciences, Notre Dame, USA (GRID:grid.131063.6) (ISNI:0000 0001 2168 0066); University of Notre Dame, Eck Institute for Global Health, Notre Dame, USA (GRID:grid.131063.6) (ISNI:0000 0001 2168 0066)
9 Telethon Kids Institute, Geospatial Health and Development, Nedlands, Australia (GRID:grid.414659.b) (ISNI:0000 0000 8828 1230); Curtin University, Faculty of Health Sciences, Perth, Australia (GRID:grid.1032.0) (ISNI:0000 0004 0375 4078)
10 University of Toronto, Temerty Faculty of Medicine, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University Health Network, Divisions of General Internal Medicine and Infectious Diseases, Toronto General Hospital, Toronto, Canada (GRID:grid.231844.8) (ISNI:0000 0004 0474 0428)
11 Emory University, Department of Environmental Sciences, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0004 1936 7398)
12 Autonomous University of Yucatan, Merida, Mexico (GRID:grid.412864.d) (ISNI:0000 0001 2188 7788)
13 State Secretary of Health of São Paulo, Pasteur Institute, São Paulo, Brazil (GRID:grid.412864.d) (ISNI:0000 0005 0955 754X)
14 Universidade de São Paulo, Institute of Tropical Medicine, Faculdade de Medicina, São Paulo, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722)
15 University of Toronto, Temerty Faculty of Medicine, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Dalla Lana School of Public Health, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); BlueDot, Toronto, Canada (GRID:grid.507904.f); Unity Health Toronto, Division of Infectious Diseases, St. Michael’s Hospital, Toronto, Canada (GRID:grid.415502.7); Unity Health Toronto, Li Ka Shing Knowledge Institute, Toronto, Canada (GRID:grid.415502.7)
16 Universidade Federal do Rio de Janeiro, Departamento de Genética, Rio de Janeiro, Brazil (GRID:grid.8536.8) (ISNI:0000 0001 2294 473X); Universidade de São Paulo, Institute of Tropical Medicine, Faculdade de Medicina, São Paulo, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722)
17 Centro Nacional de Programas Preventivos y Control de Enfermedades (CENAPRECE) Secretaria de Salud Mexico, Ciudad de Mexico, Mexico (GRID:grid.415745.6) (ISNI:0000 0004 1791 0836)
18 University of São Paulo, Department of Epidemiology, School of Public Health, São Paulo, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722)