A New Approach to Monitoring Dengue Activity

Overview

Journal PLoS Negl Trop Dis

Specialties Infectious Diseases
Tropical Medicine

Date 2011 Jun 8

PMID 21647309

Citations 18

Authors

Lawrence C Madoff

David N Fisman

Taha Kass-Hout

Affiliations

Soon will be listed here.

Citing Articles

Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis.

Laranjeira C, Pereira M, Oliveira R, Barbosa G, Fernandes C, Bermudi P PLoS Negl Trop Dis. 2024; 18(6):e0011811.

PMID: 38829905 PMC: 11192312. DOI: 10.1371/journal.pntd.0011811.

Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil.

Koplewitz G, Lu F, Clemente L, Buckee C, Santillana M PLoS Negl Trop Dis. 2022; 16(1):e0010071.

PMID: 35073316 PMC: 8824328. DOI: 10.1371/journal.pntd.0010071.

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.

Sylvestre E, Joachim C, Cecilia-Joseph E, Bouzille G, Campillo-Gimenez B, Cuggia M PLoS Negl Trop Dis. 2022; 16(1):e0010056.

PMID: 34995281 PMC: 8740963. DOI: 10.1371/journal.pntd.0010056.

Investigating the utility of Google trends for Zika and Chikungunya surveillance in Venezuela.

Strauss R, Lorenz E, Kristensen K, Eibach D, Torres J, May J BMC Public Health. 2020; 20(1):947.

PMID: 32546159 PMC: 7298838. DOI: 10.1186/s12889-020-09059-9.

Using Google Trends to Examine the Spatio-Temporal Incidence and Behavioral Patterns of Dengue Disease: A Case Study in Metropolitan Manila, Philippines.

Ho H, Carvajal T, Bautista J, Capistrano J, Viacrusis K, Hernandez L Trop Med Infect Dis. 2018; 3(4).

PMID: 30423898 PMC: 6306840. DOI: 10.3390/tropicalmed3040118.

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