Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue
Graphical Abstract
Introduction
The transmission of mosquito-borne infectious diseases (MIDs), such as malaria, Zika and dengue, is a spatiotemporal dynamic process, affected by multiple factors involved in vectors, pathogen and humans. These factors, including but not limited to environmental, demographic, socioeconomic, behavioural and genetic elements, largely determine the disease transmission patterns (Ostfeld et al., 2005, Lambrechts et al., 2011, Sanna and Hsieh, 2017, Tang et al., 2017). Previous studies claimed that temperature and human mobility are important driving force leading to the temporal and spatial transmissions of MIDs (Searle et al., 2017, Stoddard et al., 2009, Xu et al., 2017), in that temperature affects mosquito entomology and pathogen activity (Mordecai et al., 2017, Morin et al., 2013), and human mobility modifies their exposure to mosquitoes and pathogens (Stoddard et al., 2009). On the other hand, due to the lack of effective therapeutics and applicable vaccination for many MIDs, mosquito control has been the most commonly used strategy to fight against these diseases. The above-mentioned factors couple together, and play as activators or inhibitors in shaping the spatiotemporal distributions of MID incidences. In view of this, it would be desirable and essential to systematically assess the integrated impacts of related factors on the space-time variations of MID incidences (Liu et al., 2012). Doing so will help better understand the transmission patterns and further guide public health authorities to plan control strategies.
Technically speaking, many methods have been developed to analyze the heterogeneous distributions of MID incidences, and identify the risk factors underlying disease transmission (Racloz et al., 2012, Guzman and Harris, 2015, Reiner et al., 2013). Mapping techniques and statistical methods, such as geographic information systems (Wu et al., 2009), time-series regression (Xu et al., 2017), and spatial scan statistics (Li et al., 2012), are widely used to identify the hot spots and factors' effects, and estimate the dispersion process (Racloz et al., 2012). However, pure statistical and mapping analysis methods are limited in exploring the real dynamics of disease transmission (Shi et al., 2015). On the other side, following the classical Ross-Macdonald theory, various transmission models have been developed to describe the underlying epidemiological process (Mordecai et al., 2017, Reiner et al., 2013, Smith et al., 2014). Unfortunately, due to the intrinsic complexity of MID transmission, there are still some other factors, such as mosquito control and heterogeneity of mosquito-host behaviors, the combined effects of which have not been explicitly interpreted from the perspective of spatiotemporal dynamic mechanism (Reiner et al., 2013).
This paper aims to provide a modeling framework which systematically incorporates the impact factors allowed for exploring the spatiotemporal transmission patterns of MIDs. It focuses on the 2014 dengue outbreak in the Pearl River Delta (PRD) in southern China. One reason that dengue is chosen as a case study lies in that it is now regarded as the most prevalent and rapidly spreading MIDs among humans (WHO, 2012). Dengue is spreading across 120 countries and causes half the world's population being at risk of infection. The global incidence has increased 30-fold compared with 50 years ago (WHO, 2012), with an estimation of 390 million cases each year (Bhatt et al., 2013). In China, dengue infection in recent years also exhibited upward trend both in extent and severity (Lai et al., 2015). During 1991–2013, China recorded about 21,532 dengue cases with 620 deaths (Lai et al., 2015). The incidence reached an unprecedented level in 2014, where 46,864 cases were reported (Xiao et al., 2016). Another important reason is that the situation of dengue transmission in the PRD is complicated. First, the PRD is located in subtropical regions and is one of the most urbanized and densely populated regions in the world. Such conditions favor the survival and activity of mosquitoes as well as the transmission of dengue virus. Indeed, the number of dengue cases in the PRD always ranked first in China. In 2014, about 92.3% dengue cases were reported in the PRD. Second, the PRD has convenient transportation and is a transport hub in China, which potentially leads to the spatial spread of dengue in this region (Zhu et al., 2018, Qi et al., 2015). Third, mosquito control strategies have been widely employed in the PRD in 2014. Lin et al. (2016) claimed that the intervention program adopted in Guangzhou led to a 70.47% reduction of dengue cases. However, the control effects incorporating spatial connection by human mobility has not been systematically evaluated in the 7 cities of the PRD. Fourth, transmission potential of dengue virus in different regions of the PRD varies significantly due to the variations of influence factors (e.g., climatic, environmental, and socioeconomic elements) and demographic profiles (e.g., population density and human mobility) (Zhu et al., 2018, Qi et al., 2015, Li et al., 2017).
To clarify the underlying cause of geographic differences of dengue incidence and to identify the spatiotemporal dynamics of dengue transmission, in this paper, the following three technological challenges are addressed.
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How to systematically integrate the vector-virus-host interactions with the impact of risk factors in a transmission model and thus to simulate the dynamic diffusion process?
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How to characterize the roles of the dominant factors in activating or inhibiting the spatiotemporal transmission of dengue?
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How to develop computational methods to quantify geographic variations by fitting model parameters to observed dengue incidences?
Here, mathematical model and machine learning method are employed to tackle these challenges. Specifically, a new dengue transmission model is built based on the notions of Ross-Macdonald theory (Reiner et al., 2013, Smith et al., 2014) and meta-population system (Bichara and Castillochavez, 2016, Ruktanonchai et al., 2016). The model characterizes dengue transmission potential and epidemic evolution by integrating the dynamical changes of virus activity, and the ecology and behaviour of both humans and mosquitoes. Thereinto, (1) temperature is incorporated by affecting the transmission elements involved in viruses and mosquitoes (namely, the rates of oviposition, mortality, aquatic transition, incubation, mosquito bites and transmission probability); (2) mosquito control effect (measured by the change of vector indices) is taken into account by reducing the abundance of mosquito population; and (3) human mobility (simulated by residence time matrix) modifies the force of infections. Furthermore, to quantitatively assess geographic variations of dengue incidences, machine learning methods (such as the Markov chain Monte Carlo (MCMC) method; Shi et al., 2015, Lopes et al., 2008 can be used by fit modeling results to real-world observations.
To evaluate the performance of the proposed model, a real-world study is carried out to investigate the spatiotemporal transmission of dengue among the 7 cities in the PRD, China. By implementing the proposed model, it is able to (1) identify the contributions of temperature and human mobility to the geographic variations of dengue incidences; (2) evaluate the effects of control strategies on stopping dengue transmission; (3) clarify the underlying transmission dynamics of dengue.
Section snippets
Modeling framework
Inspired by recently-developed mathematical models (Reiner et al., 2013, Smith et al., 2014, Bichara and Castillochavez, 2016, Ruktanonchai et al., 2016, Rodriguez and Torressorando, 2001, Iggidr et al., 2016), and based on the feature of dengue transmission as shown in Fig. 1, a new dengue transmission model is established in this section. In this model, certain geographic area (such as city, province or country) is viewed as a patch, and its permanent residents and vectors constitute
Application
In this section, the proposed model was applied to analyze the spatiotemporal transmission of dengue in the PRD between July and December in 2014.
Discussion
Dengue is posing an increasing threat to public health in the tropical and subtropical regions of the world. To control and prevent dengue transmission, it is widely recommended the development of effective and efficient surveillance systems for timely and accurate implementation of risk assessment and control strategies (Racloz et al., 2012, WHO, 2009). This requires clear information about epidemiologic features and transmission patterns of the disease. In reality, dengue spread is a hidden
Acknowledgments
This work was jointly supported by the National Key R&D Program of China (2018YFA0606202), the National Natural Science Foundation of China (11661026 and81773497), the Natural Science Foundation of Guangdong Province (2017A030313699), and Guangxi Natural Science Foundation (2017GXNSFAA198235), as well as Guangdong Provincial Medical Research Foundation (A2017474). The authors would like to thank the handling Editor and the anonymous reviewers for their constructive comments.
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