Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1009791
Title: Fine-scale estimation of effective reproduction numbers for dengue surveillance
Authors: Ong, Janet
Soh, Stacy
Ho, Soon Hoe
Seah, Annabel
Dickens, Borame Sue 
Tan, Ken Wei 
Koo, Joel Ruihan 
Cook, Alex R 
Richards, Daniel R
Gaw, Leon Yan-Feng 
Ng, Lee Ching
Lim, Jue Tao 
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
VIRUS-INFECTION
VECTOR CONTROL
SEROEPIDEMIOLOGY
SINGAPORE
Issue Date: 1-Jan-2022
Publisher: PUBLIC LIBRARY SCIENCE
Citation: Ong, Janet, Soh, Stacy, Ho, Soon Hoe, Seah, Annabel, Dickens, Borame Sue, Tan, Ken Wei, Koo, Joel Ruihan, Cook, Alex R, Richards, Daniel R, Gaw, Leon Yan-Feng, Ng, Lee Ching, Lim, Jue Tao (2022-01-01). Fine-scale estimation of effective reproduction numbers for dengue surveillance. PLOS COMPUTATIONAL BIOLOGY 18 (1). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1009791
Abstract: The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2 EpiEstim = 0.95, R2 EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.
Source Title: PLOS COMPUTATIONAL BIOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/230838
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1009791
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