Spatial scales of COVID-19 transmission in Mexico.

Autor:	Klein B; Network Science Institute, Northeastern University, Boston, MA 02115, USA.; Laboratory for the Modeling of Biological & Socio-technical Systems, Northeastern University, Boston, MA 02115, USA.; Institute for Experiential AI, Northeastern University, Boston, MA 02115, USA., Hartle H; Network Science Institute, Northeastern University, Boston, MA 02115, USA.; Santa Fe Institute, Santa Fe, NM 87501, USA., Shrestha M; Network Science Institute, Northeastern University, Boston, MA 02115, USA., Zenteno AC; Healthcare Systems Engineering, Massachusetts General Hospital, Boston, MA 02114, USA., Barros Sierra Cordera D; Prestaciones Económicas y Sociales, Instituto Mexicano del Seguro Social, Ciudad de México, 06600, México., Nicolás-Carlock JR; Instituto de Física, Universidad Nacional Autónoma de México, Ciudad de México, 04510, México., Bento AI; Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA., Althouse BM; Information School, University of Washington, Seattle, WA 98105, USA.; Department of Biology, New Mexico State University, Las Cruces, NM 88003, USA., Gutierrez B; Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.; Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito 170136, Ecuador.; Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Consejo Nacional de Ciencia y Tecnología, Ciudad de México, 03940, México.; Pandemic Sciences Institute, University of Oxford, Oxford OX3 7BN, United Kingdom., Escalera-Zamudio M; Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.; Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), Consejo Nacional de Ciencia y Tecnología, Ciudad de México, 03940, México., Reyes-Sandoval A; The Jenner Institute, University of Oxford, Oxford OX3 7DQ, United Kingdom.; Instituto Politécnico Nacional, IPN, Ciudad de México, 07738, México., Pybus OG; Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.; Pandemic Sciences Institute, University of Oxford, Oxford OX3 7BN, United Kingdom.; Department of Pathobiology and Population Science, Royal Veterinary College, London AL9 7TA, United Kingdom., Vespignani A; Network Science Institute, Northeastern University, Boston, MA 02115, USA.; Laboratory for the Modeling of Biological & Socio-technical Systems, Northeastern University, Boston, MA 02115, USA., Díaz-Quiñonez JA; Health Emergencies Department, Pan American Health Organization, Washington, DC 20037, USA.; Instituto de Ciencias de la Salud, Universidad Autónoma del Estado de Hidalgo, Pachuca Hgo, 42160, México., Scarpino SV; Network Science Institute, Northeastern University, Boston, MA 02115, USA.; Institute for Experiential AI, Northeastern University, Boston, MA 02115, USA.; Santa Fe Institute, Santa Fe, NM 87501, USA., Kraemer MUG; Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.; Pandemic Sciences Institute, University of Oxford, Oxford OX3 7BN, United Kingdom.
Jazyk:	angličtina
Zdroj:	PNAS nexus [PNAS Nexus] 2024 Jul 31; Vol. 3 (9), pp. pgae306. Date of Electronic Publication: 2024 Jul 31 (Print Publication: 2024).
DOI:	10.1093/pnasnexus/pgae306
Abstrakt:	During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March-June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general. (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu