Using Big Data and Network Theory to Inform Decision-making on COVID-19 in Bogotá

Autor: Alejandro Feged-Rivadeneira, Felipe González-Casabianca, Andrea Parra-Salazar, Juana Salcedo-Ortiz, Federico Andrade-Rivas, Pablo Cárdenas, Álvaro Morales, Juliana Damelines-Pareja, Diana Rios-Oliveros, Carolina Salazar, Santiago Usma, Marina Muñoz, Luz Patiño, Nathalia Ballesteros, Juan Ramírez, Andrés Ángel, Tomás Rodríguez, Jaime Cascante Vega, Hector Galindo-Silva, Stephanie Majerowicz, Vladimir Corredor
Rok vydání: 2022
DOI: 10.21203/rs.3.rs-2148358/v1
Popis: After the initial year of the pandemic (2020), a need for Non-Pharmaceutical Interventions (NPIs) that did not imply lockdowns became evident, particularly in locations where human mobility was greatly restricted like in South America. In this research, we propose a multidisciplinary framework to combine findings from diverse academic fields (epidemiology, public health, urban studies, molecular biology) to inform decision making in public health. Furthermore, we designed and implemented NPIs that minimized the effect on human mobility while mitigating viral transmission in Bogota, a city of ~10 million people in a middle-income country. Our results suggest that near real time information can and should be used to design, assess and optimize the effectiveness of public health interventions to reduce disease burden while minimizing socioeconomic disturbances.
Databáze: OpenAIRE