The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok
Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dh...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Brown, Tyler S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: P517: Vitamin D deficiency and frailty criteria - Mejri, M.E.D. ELSEVIER, 2014, the journal of infectious disease dynamics, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:35 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.epidem.2021.100441 |
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| 245 | 1 | 4 | |a The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok |
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| 520 | |a Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. | ||
| 520 | |a Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. | ||
| 650 | 7 | |a Mobile phone data |2 Elsevier | |
| 650 | 7 | |a SARS-CoV-2 dynamics |2 Elsevier | |
| 650 | 7 | |a Cities |2 Elsevier | |
| 650 | 7 | |a Human mobility |2 Elsevier | |
| 700 | 1 | |a Engø-Monsen, Kenth |4 oth | |
| 700 | 1 | |a Kiang, Mathew V. |4 oth | |
| 700 | 1 | |a Mahmud, Ayesha S. |4 oth | |
| 700 | 1 | |a Maude, Richard J. |4 oth | |
| 700 | 1 | |a Buckee, Caroline O. |4 oth | |
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10.1016/j.epidem.2021.100441 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001443.pica (DE-627)ELV054316561 (ELSEVIER)S1755-4365(21)00004-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Brown, Tyler S. verfasserin aut The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Mobile phone data Elsevier SARS-CoV-2 dynamics Elsevier Cities Elsevier Human mobility Elsevier Engø-Monsen, Kenth oth Kiang, Mathew V. oth Mahmud, Ayesha S. oth Maude, Richard J. oth Buckee, Caroline O. oth Enthalten in Elsevier Mejri, M.E.D. ELSEVIER P517: Vitamin D deficiency and frailty criteria 2014 the journal of infectious disease dynamics Amsterdam [u.a.] (DE-627)ELV022707530 volume:35 year:2021 pages:0 https://doi.org/10.1016/j.epidem.2021.100441 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_130 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 35 2021 0 |
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10.1016/j.epidem.2021.100441 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001443.pica (DE-627)ELV054316561 (ELSEVIER)S1755-4365(21)00004-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Brown, Tyler S. verfasserin aut The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Mobile phone data Elsevier SARS-CoV-2 dynamics Elsevier Cities Elsevier Human mobility Elsevier Engø-Monsen, Kenth oth Kiang, Mathew V. oth Mahmud, Ayesha S. oth Maude, Richard J. oth Buckee, Caroline O. oth Enthalten in Elsevier Mejri, M.E.D. ELSEVIER P517: Vitamin D deficiency and frailty criteria 2014 the journal of infectious disease dynamics Amsterdam [u.a.] (DE-627)ELV022707530 volume:35 year:2021 pages:0 https://doi.org/10.1016/j.epidem.2021.100441 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_130 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 35 2021 0 |
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10.1016/j.epidem.2021.100441 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001443.pica (DE-627)ELV054316561 (ELSEVIER)S1755-4365(21)00004-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Brown, Tyler S. verfasserin aut The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Mobile phone data Elsevier SARS-CoV-2 dynamics Elsevier Cities Elsevier Human mobility Elsevier Engø-Monsen, Kenth oth Kiang, Mathew V. oth Mahmud, Ayesha S. oth Maude, Richard J. oth Buckee, Caroline O. oth Enthalten in Elsevier Mejri, M.E.D. ELSEVIER P517: Vitamin D deficiency and frailty criteria 2014 the journal of infectious disease dynamics Amsterdam [u.a.] (DE-627)ELV022707530 volume:35 year:2021 pages:0 https://doi.org/10.1016/j.epidem.2021.100441 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_130 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 35 2021 0 |
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10.1016/j.epidem.2021.100441 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001443.pica (DE-627)ELV054316561 (ELSEVIER)S1755-4365(21)00004-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Brown, Tyler S. verfasserin aut The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Mobile phone data Elsevier SARS-CoV-2 dynamics Elsevier Cities Elsevier Human mobility Elsevier Engø-Monsen, Kenth oth Kiang, Mathew V. oth Mahmud, Ayesha S. oth Maude, Richard J. oth Buckee, Caroline O. oth Enthalten in Elsevier Mejri, M.E.D. ELSEVIER P517: Vitamin D deficiency and frailty criteria 2014 the journal of infectious disease dynamics Amsterdam [u.a.] (DE-627)ELV022707530 volume:35 year:2021 pages:0 https://doi.org/10.1016/j.epidem.2021.100441 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_130 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 35 2021 0 |
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10.1016/j.epidem.2021.100441 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001443.pica (DE-627)ELV054316561 (ELSEVIER)S1755-4365(21)00004-9 DE-627 ger DE-627 rakwb eng 610 VZ 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Brown, Tyler S. verfasserin aut The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Mobile phone data Elsevier SARS-CoV-2 dynamics Elsevier Cities Elsevier Human mobility Elsevier Engø-Monsen, Kenth oth Kiang, Mathew V. oth Mahmud, Ayesha S. oth Maude, Richard J. oth Buckee, Caroline O. oth Enthalten in Elsevier Mejri, M.E.D. ELSEVIER P517: Vitamin D deficiency and frailty criteria 2014 the journal of infectious disease dynamics Amsterdam [u.a.] (DE-627)ELV022707530 volume:35 year:2021 pages:0 https://doi.org/10.1016/j.epidem.2021.100441 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_130 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 35 2021 0 |
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The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok |
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Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. |
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Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. |
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Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. |
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The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok |
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