Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China.
BACKGROUND
Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China.


METHODS
We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters.


CONCLUSIONS
By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures.
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China.
Guanghu
http://orcid.org/0000-0002-1416-1476
G
Zhu
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Jiming
J
Liu
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Qi
Q
Tan
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.
Benyun
B
Shi
D000330:Aedes / Q000254:growth & development; D000818:Animals; D002681:China / Q000453:epidemiology; D002980:Climate; D003715:Dengue / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious; D062665:Epidemiological Monitoring; D005544:Forecasting; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D011157:Population Dynamics; D062211:Spatio-Temporal Analysis