Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China

Abstract

Background: Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale.

Methodology and principal findings: In this study, the relationships between reported dengue fever (DF) epidemics during 2012-2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16.53%-18.07% of residential area and 16.84%-18.02% of population, UVs possessed 28.55%-38.24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1.81-3.13 and 1.82-3.06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations were observed between gridded DF incidence rates and UV area (r = 0.33, P = 0.000), the number of bus stops (r = 0.49, P = 0.000) and subway stations (r = 0.27, P = 0.000), and road density (r = 0.39, P = 0.000). About 60% of spatial variations in the gridded DF incidence rates were interpreted by the different variables of GDP, UVs, and bus stops integrated in GWR models.

Conclusions: UVs likely acted as special transfer stations, receiving and/or exporting DF cases during epidemics. This work increases our understanding of the influences of UVs on vector-borne diseases in highly urbanized areas, supplying valuable clues to local authorities making targeted interventions for the prevention and control of DF epidemics.

Fig 1. Study areas and GF-2 satellite data coverage of the central areas in Guangzhou.

Fig 2. Spatial distribution and temporal variations of different data.

(A): DF cases in 2012~2014 and 2017; (B): Spatial distribution of land use types, GDP and population density; (C): bus stations, subway stations and road network in 2017.

Fig 3. Spatial distribution of UVs across the central districts in Guangzhou during 2012–2017.

Fig 4. Spatial distribution of the gridded DF epidemic in 2012~ 2017.

This is the spatial distribution of the ratio of DF cases in each infected unit to the mean value of all the infected units.

Fig 5. The aggregation effect of UVs on the DF cases across the central region in Guangzhou City.

AVG is the average results of four years. Cases in buffer represent that DF cases count in different buffer radius. Cases between buffer indicate the number of DF cases between different buffer zones. Percent in buffer indicate the proportion of DF cases in different buffer radius. Increasing slope indicate the increasing rates of DF cases in different buffer radius.

Fig 6. Standardized residual (StdResid) values and local coefficients of selected variables.

This is derived from the GWR model with GDP, UVs and bus stops.