Abstract
Background : Dengue fever has become a big public concern in Guangzhou after the unprecedented dengue outbreak in 2014. However, there was no efficient early warning system to help public health authorities to control dengue outbreaks several months in advance. Methods : Here, we built a spatio-temporal early warning system based on the Bayesian spatio-temporal probabilistic forecasts. A spatio-temporal Bayesian hierarchical model was formulated, using monthly dengue cases, from 2011 to 2017, for 167 townships as the response variable. Explanatory variables included temperature anomaly (averaged over the preceding 3 months), relative humidity, road density and dengue relative risk lagged by 3 months. To assess the performance of this model, we compared the observed and predicted dengue incidence rate (DIR) in each township in September, 2014 (high risk period) and September, 2015 (low risk period). We calculated the probability of dengue incidence falling into predefined categories. We defined the low, medium, and high risk categories as dengue case is lower than 1, between 1 and 10, and higher than 10. To assess the performance of warning system, we calculate the rank probability skill score (RPSS), which measures the improvement of the probabilistic forecast skill relative to the skill of a benchmark forecast. We calculated the benchmark probability by long-term average distribution of dengue cases in Guangzhou, September, 2011-2017. Results : Similar spatial pattern was found between observed DIR and predicted DIR, both in high and low epidemic risk period. R-square value (0.783) indicate good agreement between observed and predicted log DIR. Skill was high in townships in the downtown area with high risk of dengue, indicating warning system performed well. Conclusions : This early warning system may be useful to prevent the next dengue outbreak, not only before the peak dengue season each year, but also to assign medical resources accurately at fine geographic scale.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
