Name
Abstract | ||
|---|---|---|
The effective reproduction number R-t 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 R-t as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of R-t for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated R-t by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of R-t from each proposed method and determined exogenous temporal and spatial drivers for R-t in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R-EpiEstim(2) = 0.95, R-EpiFilter(2) = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE (EpiEstim) = 1.23, MASE(EpiFilter) = 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 R-t as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of R-t at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response. Author summaryThe effective reproduction number R-t 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 R-t as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of R-t for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated R-t by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of R-t from each proposed method and determined exogenous temporal and spatial drivers for R-t in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved high accuracy, but disparities in performance were large at finer spatial scales when case counts are low. This study demonstrates the potential of R-t as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of R-t at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1371/journal.pcbi.1009791 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 18 | 1 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janet Ong | 1 | 0 | 0.34 |
| Stacy Soh | 2 | 0 | 0.34 |
| Soon Hoe Ho | 3 | 0 | 0.34 |
| Annabel Seah | 4 | 0 | 0.34 |
| Borame Sue Dickens | 5 | 0 | 0.68 |
| Ken Wei Tan | 6 | 0 | 0.68 |
| Joel Ruihan Koo | 7 | 0 | 0.68 |
| Alex R Cook | 8 | 5 | 2.04 |
| Daniel R Richards | 9 | 0 | 0.34 |
| Leon Yan-Feng Gaw | 10 | 0 | 0.34 |
| Lee Ching Ng | 11 | 0 | 0.34 |
| Jue Tao Lim | 12 | 0 | 0.34 |