Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue

Li, Ruiyun; Xu, Lei; Bjørnstad, Ottar N.; Liu, Keke; Song, Tie; Chen, Aifang; Xu, Bing; Liu, Qiyong; Stenseth, Nils Christian

doi:10.1073/pnas.1806094116

Cited by 118 publications

(134 citation statements)

References 21 publications

(25 reference statements)

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“…However, the effects of pre-summer rainfall on dengue incidence around southern China are often ignored since the rainfall season normally has a lead time up to three to five months before the outbreaks. These lag effects were often not considered in many of the previous studies in this region [22][23][24][25] .…”

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confidence: 99%

The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study

Yuan

Liang

Lin

et al. 2020

Sci Rep

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In recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong's subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.Dengue is one of the most prevalent vector-borne arboviruses, transmitted between humans by Aedes mosquitoes. The number of dengue cases reported to the World Health Organization (WHO) has increased steadily from an average of less than a thousand cases per year globally in the 1950s to over 3.34 million in 2016 1,2 . Moreover, a recent study estimated that 390 million dengue infections occur each year 3 while another indicated that 3.9 billion people in 128 countries are at risk of infection 4 . The number of cases is still increasing as the disease spreads to new areas and explosive outbreaks occur. This global expansion of dengue virus (DENV) can be due to several causes, including environmental and climate factors. Climate is an important determinant of vector-borne disease epidemics as it directly influences the abundance and distribution of the vector. Together with environmental factors such as the degree of urbanisation, climate variations may result in the geographic expansion of mosquitoes and dengue into new areas 5 .Studies have shown that a positive association exists between temperature and dengue transmission 6-9 . One possible reason is that warmer temperatures lead to a higher abundance of Aedes mosquitoes by increasing their survival and development rates 10,11 . Furthermore, the extrinsic incubation period (EIP; the time required for viruses to become transmis...

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confidence: 99%

The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study

Yuan

Liang

Lin

et al. 2020

Sci Rep

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“…Our study develops statistical analysis though the multiyear time series [8,23,27], built from the previous research that links dengue with meteorological variables [14–17] and climate change [39]. Our findings suggest that the advance prediction of dengue trends is achieved through neural network models using combinations of meteorological and disease surveillance variables.…”

Section: Discussionmentioning

confidence: 98%

“…Xu et al used Zero Inflated Generalized Additive Models (ZIGAM) to successfully demonstrate the effects of climatic conditions on the spread of mosquito and dengue transmission rate [8]. In addition, Li et al provide accurate dengue prediction by applying the Susceptible Infected Recovered (SIR) Epidemic Model in the mainland of China [23].…”

Section: Introductionmentioning

confidence: 99%

Developing a dengue forecast model using Long Short Term Memory neural networks method

et al. 2019

Preprint

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22Background 23 Dengue Fever (DF) is a tropical mosquito-borne disease that threatens public health 24 and causes enormous economic burdens worldwide. In China, DF expanded from 25 coastal region to inner land, and the incidence sharply increased in the last few years. 26In this study, we conduct the analysis of dengue using the Long Short Term Memory 27 (LSTM) recurrent neural networks. This is an artificial intelligence technology, to 28 develop a precise dengue forecast model. 29 Methodology/Principal Findings 30The model is developed from monthly dengue cases and local meteorological data of 31 2005-2018 among top 20 Chinese cities with a record of the highest dengue 32 incidence. The first 13 year data were used to construct the LSTM and to predict the 33 dengue outbreaks in 2018. The results are compared with the estimated dengue cases 34 of other previously published models. Model performance and prediction accuracy 35 were assessed using Root Mean Square Error (RMSE). With the LSTM method, the 36 prediction measurements of average RMSE drop by 54.79% and 34.76% as compared 37 with the Susceptible Infected Recovered (SIR) model and Zero Inflated Generalized 38 Additive Model (ZIGAM). Our results showed that if only local data were used to 39 develop forecast models, the LSTM neural networks would fail to capture the 40 transmission characteristics of dengue virus in areas with fewer dengue cases. 41 Contrarily, transfer learning (TL) can improve the accuracy of prediction of the 42 LSTM neural network model in areas with fewer dengue incidences. 3 43 Conclusion and significance 44 The LSTM model is beneficial in predicting dengue incidence as compared with other 45 previously published forecasting models. The findings provide a more precise forecast 46 dengue model, which can help the local government and health-related departments 47 respond early to dengue epidemics. 48 49 Author summary 50In China, DF is a public health concern that poses a great economic burden on local 51 governments. However, the incidence has sharply increased in recent years with 52 growth in the sub-regions. With this issue, it will be challenging to develop an 53 accurate and timely dengue forecast model. LSTM recurrent neural networks, deep 54 learning methods and virus propagation rules by learning from observational data 55 offer more advantages in predicting the prevalence of infectious disease dynamics 56 than the traditional statistical model. The 2005-2017 data of the top 20 Chinese cities 57 with the highest dengue incidence were used to construct the LSTM model, 58 advantageous in predicting dengue in most cities. Moreover, the model helped to 59 predict the dengue outbreaks in 2018 and used to compare the estimated dengue cases 60 with the RMSE results of other previously published models. A thorough search of 61 the literature shows that this is the first established dengue forecast model using the 62 LSTM method, which is effective in predicting the trend of dengue dynamics. 63 4 64

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“…Climate has been shown to play a key role in arboviral transmission dynamics as annual oscillations in temperature, precipitation and humidity induce seasonal fluctuations in vector suitability and virus transmissibility (Caminade et al, 2017;Johansson et al, 2009;Li et al, 2019). Increased temperatures have been associated with faster virus replication rates and shorter extrinsic incubation periods for Zika (Tesla et al, 2018), Chikungunya (Mbaika et al, 2016), dengue (Mordecai et al, 2017;Xiao et al, 2014), and yellow fever (Johansson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Inferring the ecological drivers of arboviral outbreaks

Tennant

McKinley

Recker

2019

Preprint

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The emergence and wide-spread circulation of mosquito-transmitted viral diseases, such as dengue, Zika and Chikungunya, is a global public health concern. In the absence of effective vaccines, current control measures are mostly targeted against the mosquito vector and have so far only shown limited success. The reliance on mosquitoes for transmission also imposes strong ecological constraints that can introduce significant spatial and temporal variations in disease incidence. However, the way that epidemiological and ecological factors interact and determine population-level disease dynamics is only partially understood. Here we fit a spatially-explicit individual based model defined within a Bayesian framework to Zika incidence data from Feira de Santana, allowing us to more precisely quantify the relationships between socioecological factors and arboviral outbreaks. Our results further demonstrated that the virus was likely introduced into multiple spatially segregated locations at the start of the outbreak, highlighting the benefits that spatio-temporal incidence data would bring in making modelling approaches more realistic for public health planning.where t is the individual's age in days, L is the location parameter of where infant and adult mortality coincide, {a H , b H } and {c H , d H } are the shape and scale parameters of infant and adult human mortality, respectively, and {c V , d V } are the shape and scale parameters of adult mosquito mortality, respectively.An arbovirus was introduced into the system by allowing every human individual to be either susceptible (S H ), exposed (E H ), infectious (I H ), or have recovered from (R H ) the disease. Similarly, individual mosquitoes were either susceptible (S V ), exposed (E V ) or infectious (I V ) with the virus. Mosquitoes bit at a constant rate β and infectious mosquitoes transmitted the virus with probability p H . Infected humans became infectious after 1/ H days and recovered after 1/γ days. Mosquitoes were then infected with probability p V given a bite on an infectious human, and became infectious after 1/ V days. To account for the introduction of the disease into the system, humans were also infected at an external infection rate, ι.We modelled the influence of temperature, humidity and rainfall on these transmission parameters similarly to Lourenço et al. (2017).

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Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue

Cited by 118 publications

References 21 publications

The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study

The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study

Developing a dengue forecast model using Long Short Term Memory neural networks method

Inferring the ecological drivers of arboviral outbreaks

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