The application of meteorological data and search index data in improving the prediction of HFMD: A study of two cities in Guangdong Province, China

Chen, Shaoxing; Liu, Xiaojian; Wu, Yongsheng; Xu, Guangyao; Zhang, Xubin; Mei, Shujiang; Zhang, Zhen; O’Meara, Michael; O'Gara, Mary Clare; Tan, Xuerui; Li, Liping

doi:10.1016/j.scitotenv.2018.10.304

Cited by 32 publications

(31 citation statements)

References 44 publications

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“…The Geographic Detector Model (GDM) is a set of statistical methods to identify spatial differentiation among the geographical elements. This method can quantitatively analyze the driving mechanisms of geographical phenomena and is widely used to determine the explanatory power of driving factors and the interaction between factors without too many hypothetical conditions [ 73 , 74 , 75 ].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Distribution and Influencing Factors of Ecosystem Vulnerability on Qinghai-Tibet Plateau

Song

2021

IJERPH

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As the “Third Pole”, the Qinghai-Tibet Plateau is threatened by environmental changes. Ecosystem vulnerability refers to the sensitivity and resilience of ecosystems to external disturbances. However, there is a lack of relevant studies on the driving factors of ecosystem vulnerability. Therefore, based on spatial principal components analysis and geographic detectors methods, this paper evaluates the ecosystem vulnerability and its driving factors on the Qinghai-Tibet Plateau from the years 2005 to 2015. The results were as follows: (1) The ecosystem vulnerability index (EVI) of the Qinghai-Tibet Plateau is mainly heavy and extreme, showing a gradually increasing trend from southeast to northwest. (2) The spatial heterogeneity of the EVI is significant in the southeast and northwest, but not in the southwest and central parts. (3) Analysis of influencing factors shows that environmental factors have more significant effects on EVI than socioeconomic variables, facilitating the proposal of adequate policy implications. More efforts should be devoted to ecological protection and restoration to prevent grassland degradation and desertification in the high-EVI areas in northwest. The government is also urged to improve the ecological compensation mechanisms and balance ecological protection and residents’ development needs in the southeast.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Distribution and Influencing Factors of Ecosystem Vulnerability on Qinghai-Tibet Plateau

Song

2021

IJERPH

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“…For example, the 'FluSight' challenge 25 of the US evaluates the proposed models on future incidence prediction, peak intensity prediction, peak week prediction and onset week prediction, because these error indicators are directly related to the development of control measures by the public health department. Previous HFMD prediction [12][13][14][26][27][28][29] didn't use similar indicators. In our study, we evaluated our models on future point prediction, peak intensity prediction and peak month prediction, and these error indicators may facilitate deep learning models to be more widely used in the practice of epidemic prediction.…”

Section: Discussionmentioning

confidence: 99%

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

Wang¹,

Cao²,

Zeng³

et al. 2020

Sci Rep

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Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0-3, 3-6 and > 6 years old) from 2011 to 2018 using residualconvolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups. HFMD is a mild gastrointestinal disease, mainly caused by EV-A, EV-B and EV-C species, while the EV-A71 subtype is prone to more serious complications 1. Spatial and temporal patterns of HFMD incidence are strongly correlated with climatic factors 2-4 , e.g. high-level humidity and middle-level temperatures. Social factors also affect the spread of HFMD, e.g. contact amongst children in school 5. Under the influences of multi-type pathogenic viruses, complex climatic and social factors, HFMD presents a periodic outbreak in the Asia-Pacific region 1,5. In China, the incidence and mortality of HFMD have been leading the type C infectious diseases since its severe outbreak in 2008 and the situation is getting worse. Beijing city, the capital of China, is also vastly threatened by HFMD 6. Studies have explored the predominant virus 6,7 , weather factors 8,9 and space-time patterns 10 of HFMD in Beijing. Vaccines that prevent EV71-associated HFMD have been developed, but HFMD wouldn't be eliminated because of many other pathogens. Therefore, forecasting the prevalence of HFMD in Beijing is still essential for public health. Many previous studies used regressive models to predict the incidence of HFMD 11-16. In addition to the HFMD incidence data, search index, temperature records, air quality and other exogenous variables were applied to fit the regressive models. Although these methods had achieved acceptable prediction accuracies, there are still some limitations. First, these works only focused on the total number of cases. They ignored the intrinsic age groups in children. In fact, children aged 0 to 6 are the most susceptible to HFMD, while children over the age of 6 have stronger immunities to HFMD. So, under the effect of epidemic transmission dynamics and immunity, there are connections among inc...

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“…Apart from that, several studies have shown that the incidence of HFMD significantly increases as the temperature increases ( Thanh, 2016 ; Kim et al., 2016 ; Wahid et al., 2020 ). Nevertheless, it is contrary to Chen et al. (2019) and Song et al.…”

Section: Introductionmentioning

confidence: 88%

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Wahid

Suhaila

Rahman

2021

Infectious Disease Modelling

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Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents, including malaria, cholera, dengue fever, hand, foot, and mouth disease (HFMD), and the recent Corona-virus pandemic. HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s. The outbreaks may result from a combination of rapid population growth, climate change, socioeconomic changes, and other lifestyle changes. However, the modeling of climate variability and HFMD remains unclear, particularly in statistical theory development. The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling. When dealing with time-series data with clustered variables such as HFMD with clustered states, the independence principle of both modeling approaches may be violated. Thus, a Generalized Additive Mixed Model (GAMM) is used to investigate the relationship between HFMD and climate factors in Malaysia. The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect. This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation. The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia. The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm. Besides, a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances. The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon. The study's outcomes can be used by public health officials and the general public to raise awareness, and thus, implement effective preventive measures.

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The application of meteorological data and search index data in improving the prediction of HFMD: A study of two cities in Guangdong Province, China

Cited by 32 publications

References 44 publications

Spatiotemporal Distribution and Influencing Factors of Ecosystem Vulnerability on Qinghai-Tibet Plateau

Spatiotemporal Distribution and Influencing Factors of Ecosystem Vulnerability on Qinghai-Tibet Plateau

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

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