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Fine-scale estimation of effective reproduction numbers for dengue surveillance.

Authors :
Ong J
Soh S
Ho SH
Seah A
Dickens BS
Tan KW
Koo JR
Cook AR
Richards DR
Gaw LY
Ng LC
Lim JT
Source :
PLoS computational biology [PLoS Comput Biol] 2022 Jan 20; Vol. 18 (1), pp. e1009791. Date of Electronic Publication: 2022 Jan 20 (Print Publication: 2022).
Publication Year :
2022

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 (R2EpiEstim = 0.95, R2EpiFilter = 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.<br />Competing Interests: The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
1553-7358
Volume :
18
Issue :
1
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
35051176
Full Text :
https://doi.org/10.1371/journal.pcbi.1009791