Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis

Abstract

Background: Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods: Moran’s I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results: Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature (q = 0.33) was the dominant factor of dengue fever, followed by precipitation (q = 0.24), road density (q = 0.24), and water body area (q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions: Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou.

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

“…We estimated the relative contributions of local conditions and importation in driving inter-annual variation in dengue epidemics in Guangzhou, China, which has recently been subject to seasonal epidemics ranging four orders of magnitude in size. Other studies [19][20][21][22][23][24] have investigated the same 11-year time series, either in whole or in part, but arrived at differing conclusions and did not take full advantage of the exceptional level of detail in this data set (Table S1). By leveraging these data more fully and using a modeling framework that blends elements of mechanistic and statistical modeling, we showed that local conditions and importation patterns jointly determined epidemic size in most years and that anomalies in unexplained conditions affecting local transmission were responsible for one anomalously large epidemic.…”

“…Two studies concluded that weather conditions were the primary driver of DENV transmission 19,20 , whereas others concluded that importation patterns, delayed outbreak response, or both importation patterns and delayed outbreak response were causal drivers of the 2014 epidemic [21][22][23] . Still others found that neither weather conditions nor importation were key drivers of transmission, but instead that urbanization was pivotal 24,25 . Two analyses 19,20 that used incidence data aggregated at a monthly time scale for 2005-2015 showed high predictive capability at one-month lead times but did not facilitate clear interpretation of how importation interacts with local conditions to result in high inter-annual variation in transmission.…”

“…Two analyses 19,20 that used incidence data aggregated at a monthly time scale for 2005-2015 showed high predictive capability at one-month lead times but did not facilitate clear interpretation of how importation interacts with local conditions to result in high inter-annual variation in transmission. Mechanistic models applied to date have used daily or weekly data, but only for 2013-2014, and therefore only considered years with anomalously high transmission 21,[23][24][25] . As a result, it is unclear how well those models could explain the strikingly low incidence observed in years other than 2013-2014.…”

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

“…The factor detector quantifies the impact of ecological and anthropogenic factors on an observed spatial PM 2.5 pattern. The interaction detector probes whether two impact factors taken together enhance or weaken each other, or whether they affect PM 2.5 concentrations independently [37].…”

Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever

“…The link between environments and DF diffusion has been discussed for decades. Previous studies have shown that the meteorological environment, social-economic environment, and built environment could facilitate local infections [7][8][9][10][11][12][13][14][15]. Due to the limited flight range of mosquitos, which is usually less than 400 m [16], human mobility is considered and proven to be another important driver for DF transmissions [16][17][18][19][20][21][22][23][24][25].…”