Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China

Environmental Research 150 (2016) 299–305

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Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China Huaiyu Tian a,1, Shanqian Huang a,1, Sen Zhou b,c,1, Peng Bi d,1, Zhicong Yang e,n,1, Xiujun Li f,1, Lifan Chen a, Bernard Cazelles g,h, Jing Yang a, Lei Luo e, Qinlong Jing e, Wenping Yuan i, Yao Pei b, Zhe Sun b, Tianxiang Yue j, Mei-Po Kwan k, Qiyong Liu l, Ming Wang e, Shilu Tong m, John S. Brownstein c,n, Bing Xu a,b,n,n a

State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China Ministry of Education Key Laboratory for Earth System Modelling, Center for Earth System Science, Tsinghua University, Beijing, China Department of Pediatrics, Harvard Medical School, Boston, MA, USA d Discipline of Public Health, University of Adelaide, Adelaide, Australia e Guangzhou Center for Disease Control and Prevention, Guangzhou, China f School of Public Health, Shandong University, Jinan, China g UMMISCO, UMI 209 IRD – UPMC, 93142 Bondy, France h Eco-Evolutionary Mathematic, IBENS UMR 8197, ENS, 75230 Paris Cedex 05, France i State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China j State Key Laboratory of Resources and Environment Information System, Chinese Academy of Sciences, Beijing, China k Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA l State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China m School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4059, Australia n Department of Geography, University of Utah, Salt Lake City, UT 84112, USA b c

art ic l e i nf o

a b s t r a c t

Article history: Received 5 November 2015 Received in revised form 19 May 2016 Accepted 20 May 2016 Available online 21 June 2016

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment–mosquito–urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world. & 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Climate Remote sensing Urban dengue outbreak Water surface area Guangzhou

1. Introduction

n

Corresponding authors. E-mail addresses: [email protected] (Z. Yang), [email protected] (J.S. Brownstein), [email protected] (B. Xu). 1 These authors contributed equally to this manuscript.

Dengue is a severe mosquito-borne viral disease of humans, with an estimated about 96 million symptomatic dengue infections and nearly 390 million overall infections every year worldwide (Bhatt et al., 2013), mainly in tropical and subtropical regions. Dengue is caused by a single-stranded positive-sense RNA virus belonging to the genus Flavivirus and existing as four genetically distinct serotypes (DENV-1 to DENV-4) (Gubler and Clark,

http://dx.doi.org/10.1016/j.envres.2016.05.039 0013-9351/& 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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1995; Twiddy et al., 2003). The main transmission cycle is identified for dengue, which is largely caused by the urban adapted Aedes aegypti mosquito, and along with some other species such as Aedes albopictus (Raghwani et al., 2011). The transmission of dengue is driven by multiple factors, and those that enhance the contact between vector and host probably favor an increase in dengue transmission (Monath, 1997). The immune status of hosts (Endy et al., 2004), virus traits (Cologna et al., 2005; Hang et al., 2010; Libraty et al., 2002), and environmental variables (Paaijmans et al., 2009; Scott et al., 2000) could influence its transmission patterns as well. For example, rapid urbanization, especially in developing countries, results in urban population growth, crowd housing, and inadequate water supply and waste management systems that may lead to the emerging and re-emerging of dengue fever (DF) (Jones et al., 2008; Resurgence, 2008). The urban environment is an important but poorly understood contributor to disease transmission dynamics. Understanding the fluctuation of dengue epidemics in urban areas is important for designing and implementing effective vector control activities and minimizing the health risks of urbanization processes, especially for developing countries (Dickin et al., 2013; Khormi and Kumar, 2011; Schmidt et al., 2011). In 2014, Guangzhou had the largest outbreak of dengue – more than 35,000 reported dengue cases within 3 months, with DENV-1 dominant (Li et al., 2014). The number of cases in 2014 was more than twice the total accumulated cases in the city since the first reported case in 1978. To better understand the dynamics of dengue transmission, we extracted all records of dengue cases (1978–2014), entomological surveillance data (2006–2014) showing the dominance of Aedes albopictus, weather data (1978–2014), remote sensing images (1978–2014), and gene sequence data (1978–2014), to explore the possible inter-relations of the environment, vector, and pathogen with dengue transmission in the urban areas of Guangzhou.

2. Materials and methods 2.1. Background and data collection Guangzhou, the biggest city and the economic, political and cultural center in South China, is located between 22°26′–23°56′N and 112°57′–114°3′E (Fig. 1) with approximately 12 million persons (according to the 2010 National Census). Notified dengue cases in Guangzhou between January 1978 and October 2014, and entomological data between January 2006 and September 2014 were retrieved from the Notifiable Infectious Disease Report System (NIDRS) at the Guangzhou Center for Disease Control and Prevention (Guangzhou CDC) (Li et al., 2014; Luo et al., 2012). Aedes albopictus is the sole transmission vector in Guangzhou city (Luo et al., 2012), and the Breteau Index (BI) was used to measure mosquito density levels (The location of the sample sites are shown in Supplementary Fig. 1). Complete DENV envelope (E) gene sequences with a known date and location of sampling were collected from GenBank. This resulted in a final data set of 193 sequences isolated in Guangzhou from 1978 to 2014. Supplementary Table 1 shows the number, date of isolation, and origin of the included isolates. Daily records of climatic variables were obtained from local meteorological stations, and were used to calculate monthly average values. There were no missing values during the study period (January 1978–December 2014). 2.2. Spatiotemporal reflectance blending in urban environment Time series Landsat scenes from 1978 to 2014, covering the

Fig. 1. Time series dynamics of DF outbreaks in Guangzhou, South China. (A) Time series of annual DF cases and annual rainfall, 1978–2014. (B) Mean number of monthly reported DF cases in Guangzhou from 1995 to 2004, and the mean monthly Breteau Index (red). Shaded regions give 72 standard deviations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

entire city area of Guangzhou (113.2644°E, 23.1292°N) were used to detect land cover changes, especially changes in water area. Landsat standard data products (cloud o10%) were acquired from the Earth Explorer (http://earthexplorer.usgs.gov/), processed through the Level 1 Product Generation System (LPGS), and carefully selected to be close to the dates of peak dengue activity. The path/row of Landsat images used in geo-referenced mosaicking were following the WRS-1 (the Worldwide Reference System) as 131/44 and 131/43, or WRS-2 as 122/44 and 122/43 (see Supplementary Table 2). To reduce the possible influence of seasonal change of water area, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) (Zhu et al., 2010)was applied to generate the fusion images in summer time, based on the Landsat images in winter time (with the resolution of 30 m) and MODIS images in summer time (extracted from MOD09GA, with the resolution of 500 m). Considering the cloud cover and the strong striping noise in the scenes, we developed CESTARFM (Customized Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to improve the method by adding temporal weight interpolation of coarse resolution images before the first step of the ESTARFM (Michishita et al., 2015, 2012). Iterative self-organizing data analysis techniques algorithm (ISODATA) with fixed numbers of clusters were applied to classify five land cover types, including water, urban, cropland, bare land, and forest. The sites of field record collection were determined by visual interpretation with reference to high-resolution images in Google Earth. The accuracy assessment of land cover classification using ground truth images by region of interest tools (ROI) indicated an overall accuracy of 89.97% and a Kappa coefficient of

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0.85. Data processing was completed with ENVI version 4.8 (Exelis Visual Information Solutions, Boulder, CO, USA). 2.3. Phylogenetic analysis Nucleotide sequences were aligned using Muscle 3.8.31 program (Edgar, 2004) and later manually edited in BioEdit (v7.0) (Hall, 1999). Phylogenetic relationships of the 193 DENV sequences in Guangzhou were constructed using the neighbor-joining (NJ) approach and the GTR þIþ Γ4 model of nucleotide substitution by PAUP 4.0. Tree topology reliabilities were tested with 1000 bootstrap replicates. RDP3 was used to detect recombination in dengue viruses (Martin et al., 2010). To generate temporal phylogenies using time-stamped sequence data, we applied a relaxed-clock Bayesian Markov chain Monte Carlo (MCMC) method as implemented in BEAST v1.7 (Drummond et al., 2012). All analyses were executed with a codon-structured SDR06 substitution model (Shapiro et al., 2006), a relaxed uncorrelated lognormal clock (Drummond et al., 2006; Twiddy et al., 2003), the GTR model with Gamma-distributed rate variation (γ), and a proportion of invariable sites (I). This method allows for variable nucleotide substitution rates among lineages, and incorporates phylogenetic uncertainty by sampling phylogenies and parameter estimates in proportion to their posterior probability. Five independent MCMC runs of five chains each were run for 50–100 million generations. These analyses were combined after the removal of an appropriate burn-in (10% of the samples in most cases), with 5000 generations sampled from each run for a total of 25,000 trees and parameter estimates. Convergence of the chains was inspected using Tracer.v.1.5. The annual changing patterns of viral population sizes, defined as the effective number of infections, were estimated by Bayesian skyride analysis (Minin et al., 2008), which uses a piecewise demographic model with a Gaussian Markov random field (GMRF) smoothing prior. 2.4. Environment-based dengue transmission model Using environmental conditions, vector density, transmission probability, and viral abundance, the dengue transmission dynamics in Guangzhou was explored. In the absence of host recovery, the general model structure, which models the dengue infection force that arises if a mosquito bites one infected person and is introduced into susceptible hosts, and used in the dengue epidemic analysis, was:

dy ⎛ aA ⎞⎛ γ EIP M η ⎞ ⎟mS − γy ⎟⎜ =⎜ ⎝ N ⎠⎝ − ln( γ ) N ⎠ dt

(1)

dm = ap( y + I )( M − m) − μm dt

(2)

p = T ( 0.001 + δ1)( T − 12.28 + δ2) 32.46 − T + δ3

(3)

where M is the Breteau Index, N is human population size and S is susceptible population, α represents daily probability of a vector biting a human host and A represents water surface area in the city, γ is the per capita rate of human recovery from infection, μ is the per capita rate of mosquito mortality, and η allows for nonlinearities in contact rates. EIP is the duration of the pathogen extrinsic incubation period within the mosquito (Kramer and Ebel, 2003), y is the number of dengue cases, I is imported cases and m is the infected mosquitoes, δ is the error term, and p is the transmission probability. We modeled DENV transmission probability (pt), defined as the proportion of midgut-infected mosquitoes transmitting virus

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(Kramer and Ebel, 2003), using the result of previous experimental studies at various constant temperatures (Dohm et al., 2002; Dohm and Turell, 2001; Jupp, 1974; Kay et al., 1979, 1989a, 1989b; Kilpatrick et al., 2008; Reisen et al., 1993; Richards et al., 2007; Sardelis et al., 2001; Turell et al., 2001). The relationship between pt and temperature was described using a non-monotonic function (Lambrechts et al., 2011). Because the average monthly atmospheric temperature used was different from the experimental conditions, we have then re-estimated the parameters based on our data set and empirical reports. Mosquito-borne viruses need a period of time to complete their extrinsic incubation within the vector, and this incubation is temperature sensitive (Bates and Roca-García 1946; Chamberlain and Sudia, 1955; Kilpatrick et al., 2008; Kramer et al., 1983; Reisen et al., 2006; Watts et al., 1987). Here, we used an established enzyme kinetics model (Focks et al., 1995) to characterize the incubation period (see Eq. (3)). Parameters were estimated by sampling the posterior distributions, using the Metropolis-Hastings Markov Chain Monte Carlo algorithm. Five chains with different initial conditions were performed to check for convergence of posterior distribution estimates. The prior distributions for the parameters were Gaussian (with a mean of 0 and a variance of 105). We discarded 10,000 initial burn-in iterations, followed by 500,000 iterations that were used for inferences. The medians of posterior distributions and 95% credible intervals were used to present the results.

3. Results 3.1. Seasonality and dynamics of dengue in Guangzhou Contrary to the common patterns of dengue outbreaks in Southeast Asia and South Pacific (Cazelles et al., 2005; Hales et al., 1999), our data over the last 36 yr suggest that the DF outbreaks were not associated with annual rainfall in Guangzhou (Fig. 1A). Dengue outbreaks have occurred in 3–5 year cycles in Guangzhou, with an increasing magnitude since its first recorded outbreak in 1978. The outbreak in 2014 was by far the largest (Fig. 1A). The vector density peaked in July and August, and the seasonal DF cases were clustered from August to October, which were preceded by mosquito density peak with 1-month lag (Fig. 1B). 3.2. The impact of water area on population genetics of DENV All four dengue virus serotypes (DENV-1–4) were identified from patients in Guangzhou from 1978 to 2014 (Fig. 2A), with DENV-1 being predominant (Luo et al., 2012). Bayesian “skyride” reconstructions (Minin et al., 2008) of DENV diversity demonstrated a fluctuation from 1978 to 2014; an increasing relative genetic diversity was observed before 1995, with lower levels of relative genetic diversity thereafter (Fig. 2B). It is interesting to note that a similar fluctuation in surface water areas was found throughout the timeframe, which was extracted based on the land cover classification. From 1978 to 1995, water areas increased slowly, but from 1996 to 2012 they were sharply decreased. We assume that the strength of natural selection, virus mutation rates, the number of imported cases and related viral migration, and generation times are broadly similar through the time period in Guangzhou, such that any differences observed between years likely represent differences in viral population size. Hence, these results suggested that DENV population size may vary with water area. Specifically, larger water area may lead to a higher virus relative diversity, and vice versa.

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Fig. 2. Viral population size, water area and DF outbreak in Guangzhou, 1978–2014. (A) Temporally structured maximum clade credibility phylogenetic tree of the envelope (E) gene of dengue virus isolated in Guangzhou, where the branches are colored by viral serotypes. (B) Viral population size and water area variations in Guangzhou. Blue bars represent water area variation, and unfilled bars represent the fusion results. Smooth black line shows the estimated effective population size (Ne). (C) Relationships between annual DF cases and water area in the second half of the year, 1978–2014. Blue points represent the water area variation, and circles represent fusion results. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Enlarging surface water area and dengue epidemic. (A) Surface water distribution and the land cover change of area from orchard to artificial lake–Haizhu Lake, red box was shown as an example. A total of 16 artificial lakes are under construction. (B) DF cases, surface water area and rainfall in recent years. Dotted line indicates the starting of Artificial Lake Project. Relationship between annual water area and DF cases, 2006–2014, R¼ 0.86, Po 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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3.3. Surface water areas and dengue epidemics in Guangzhou The number of DF cases was found to be associated with the surface water areas (Fig. 2C, Fig. 3B). In 2014, Guangzhou had the largest surface water area in history, and simultaneously had its largest outbreak of dengue (Fig. 3). Since the Artificial Lake Project was started, water area continued to expand in the city (Fig. 3A). Before the project, rainfall could influence the surface water area significantly. The heavy rainfall caused an increased in the surface water area in 2006 and 2008, while less rainfall decreased the surface water area in 2007. During 2010–2014, the surface water area was not associated with rainfall, the surface water area increased even while the amount of rainfall decreased (Fig. 3B). We speculate that this phenomenon may change contact opportunities between vector and host. A larger surface water area could provide more breeding habitats for A. albopictus, which would possibly lead to a higher contact opportunity. A vast number of mosquitoes could serve as reservoirs, providing sufficient population size for accumulating such mutations. The phylogenetic analysis strongly supports that the virus population shift occurred simultaneously with changes in the surface water area and dengue outbreaks. 3.4. Disease transmission models Empirical studies have indicated that temperature can affect both the extrinsic incubation period (EIP) and transmission probability (Lambrechts et al., 2011). We assessed the effect of temperature on the EIP and transmission probability over a range of conditions using an Markov Chain Monte Carlo (MCMC) method to remove the possible offset (δ) caused by monthly data differences (see Supplementary Figs. 2 and 3, Table 1). As shown in Fig. 4, the simulated series capture qualitatively the major epidemic dynamics over time. This visual observation has been confirmed by a high pseudo-R2 value of 0.71. Trace plots indicated convergence. Our results indicated that the surface water area in Guangzhou may have promoted and influenced the amplitude of urban DF outbreaks with A. albopictus as vectors. The artificial lake construction in Guangzhou might have increased the number of DF cases by about 30–50% in 2014 (Fig. 4B).

4. Discussion Our results indicate that the DENV transmission and urban outbreaks in Guangzhou may be influenced by variations in the amount of surface area covered by water. We have identified significant linkages among the urban environment, DENV population, and dengue transmission in Guangzhou. With increasing water surface area in the city, the contact between vector (A. albopictus) Table 1. Posterior estimates, standard deviations (S.D.), and 95% credible intervals (CI) for the parameters. Variables Estimate S.D.

Definition

α η γ μ T1

Mosquito attack rate Nonliearities in contact rates Human recovery rate from infection Mosquito mortality rate Temperature threshold-1 for transmission probability Temperature threshold-2 for transmission probability Temperature threshold-3 for transmission probability

0.008 5.66 0.95 0.98 0.002

0.001 0.21 0.01 0.03 0.0001

T2

11.91

0.04

T3

31.16

0.06

T1 ¼ 0.001þ δ1; T2 ¼ 12.28 þ δ2; T3 ¼ 32.46 þ δ3.

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and host is enhanced, which leads to an increase in DENV transmission, and then determines the population genetics of DENV. These results may help to explicate the dengue dynamics in Guangzhou where there is no simple relationship between DF epidemic and weather factors could be explained. The research findings may provide important policy implications for dengue control and prevention in urban area, especially for city planning and community health education. Remote sensing is an important tool to detect environmental changes, especially for understanding the transmission of vectorborne diseases including dengue fever (Rogers et al., 2002). However, previous studies were limited by the trade-off between spatial resolution and revisiting intervals of satellite remote sensors (Michishita et al., 2015). Besides, peak dengue season usually coincides with the rainy season, as a result, cloud contaminations may degrade satellite imagery, and hamper deeper investigation of the mechanism of urban environmental changes and disease transmission dynamics. In this study, by using a spatiotemporal reflectance blending model, multi-temporal satellite imagery has aided in better understanding of the changes in urban environment, which making us possible to detect the potential mosquito distribution patterns in high spatial resolution and identify the different levels of transmission. Temperature has been used to model vector behavior, EIP, and transmission probability, and the mean temperature has been shown to be an important predictor of for DENV transmission via affecting vectors (Watts et al., 1987). We used Bayesian framework to estimate the effect of environmental temperature on DENV transmission, based on experimental studies of temperature and the disease transmission, although the mechanism is largely unexplored in an urban area. This method could be used to understand the potential influence of climate change on DF transmission. Higher temperatures could reduce the EIP of arboviruses (Cross and Hyams, 1996). Furthermore, relatively high temperatures from June–October 2014 in Guangzhou (see Supplementary Fig. 4) may be an important contributor to the 2014 dengue outbreak, because of the shortened EIP. However, published studies indicated that the transmission probability, the proportion of midgut-infected mosquitoes transmitting virus, decreased at higher temperatures (Briere et al., 1999; Kilpatrick et al., 2008; Reisen et al., 1993). In addition, the optimal temperature range for the transmission may be various due to different combinations of vector and virus genotypes (Lambrechts et al., 2009). These increased the complexity in the study of the potential influence of climate change on DF transmission. The urban environment offers favorable grounds for the spread of DENV because of high human population densities and mismanagement of waste water (Alirol et al., 2011), especially in developing countries. In many regions of China, urbanization is being implemented with the potential for disease epidemic. In Guangzhou, for example, dengue has been circulated for more than three decades (Wu et al., 2010). The amount of surface water area in urban could be list as one of factors that influence DENV transmission and dengue outbreaks, although the underlying mechanism and mathematical relationships between water area and vector population are yet to be understood in Guangzhou. This is because standardized mosquito surveillance data were very limited in the past decade–systematic historical vector data were not available. However, DF transmission is influenced by the contact between vector and host, which is directly related to environmental condition. With the assumption that the pattern is stable over the study period, the relationships among the surface water area, contact rate, and DF cases may still be established. It has been noted that several dengue outbreaks in South China, especially in Guangzhou, were caused by imported cases (Chen, 2011). Imported cases were considered as initial facilitators for

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Fig. 4. Time series plots of the human DF cases and simulations, 2006–2014. (A) The DF cases with the deterministic model (r2 ¼ 0.71). The red indicates observations; the green is the deterministic prediction from the dynamic model. The gray envelopes suggest the 95% credible intervals of the model fit, and blue line is the surface water area over the same period. (B) Residuals between DF cases in different urban water area scenarios and observed DF cases. Black circle shows the 2014 DF outbreak and the red shaded area represents residuals between simulated DF epidemics under the scenario of constant water area and observed DF outbreak in 2014. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

dengue transmission (Shang et al., 2010) and could enhance the infection force through the infected mosquito. Along with the globalization trend, increased air traffic could also amplify the potential risk of dengue outbreaks (see Supplementary Fig. 5). This study has several limitations. First, there have been no vector surveillance data in early years. Further study is needed to validate our results in other populations. Second, the virus population was estimated using an uneven number of strains among years, the estimations maybe sensitive and complex demographic trends maybe ignored when sampling is unevenly distributed on a temporal scale. This uncertainty could be reduced by increasing sample size and frequency in future studies. Third, due to the limited remote sensing images in early years, we are unable to evaluate higher spatial resolution of water surfaces at the housebased level.

5. Conclusions To our best knowledge, the results from this study revealed a significant role of urban environmental change in the transmission dynamics for dengue in metropolitan regions. We propose an ecoepidemiological link among environment, vector, pathogen, and dengue transmission dynamics. Our research highlights the need for a better understanding of the roles of environmental and biological determinants in the disease transmission, and provides evidence for the relationship between environmental changes and disease transmission. The inclusion of surface water areas as a factor for dengue control will provide an extra opportunity to develop effective strategies to prevent dengue outbreaks.

Competing interests All authors declare that they have no competing interests.

Acknowledgements This research was supported by the Ministry of Science and Technology, China, National Research Program (2012CB955501, 2012AA12A407 and 2013AA122003), the National Natural Science Foundation of China (41271099), and the Fundamental Research Funds for the Central Universities (2015NT06).

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2016.05. 039.

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