Forecasting of dengue importation risk in Brazil using deep learning and mobility networks
Authors:
Open Access
- 4 May 2026
- journal article
- research article
- Published by Springer Nature in npj Digital Public Health
- Vol. 1 (1) , 11
Abstract
No abstract availableKeywords
Funding Information
- The Letten Prize
This publication has 43 references indexed in Scilit:
- Dengue virus importation risks in Africa: a modelling studyThe Lancet Planetary Health, 2024
- Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation studyThe Lancet Digital Health, 2024
- Human movement and environmental barriers shape the emergence of dengueNature Communications, 2024
- DengueThe Lancet, 2024
- Weather integrated multiple machine learning models for prediction of dengue prevalence in IndiaInternational Journal of Biometeorology, 2022
- Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in MadagascarEpidemics, 2022
- Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling studyThe Lancet Infectious Diseases, 2020
- Infodengue: A nowcasting system for the surveillance of arboviruses in BrazilRevue D'Épidémiologie Et de Santé Publique, 2018
- Time series analysis of dengue surveillance data in two Brazilian citiesActa Tropica, 2018
- Other Adaptations2014