Effect of meteorological conditions on occurrence of hand, foot and mouth disease in Wuwei City, Northwestern China

Phan

2019

WPSAR

Background: Hand, foot and mouth disease (HFMD) is a public health problem in Viet Nam, and studies have reported seasonal fluctuation in the occurrence of HFMD. This study sought to describe the occurrence of HFMD and its associated meteorological factors in Dak Lak province, Viet Nam. Methods: Monthly data on HFMD cases were collected from all commune health stations in Dak Lak province from 2012 through 2013. An HFMD case was defined as a brief febrile illness accompanied by a typical skin rash with or without mouth ulcers. Average temperature, maximum temperature, minimum temperature, humidity, rainfall, evaporation, sunshine duration and wind speed were recorded monthly at five local meteorological stations throughout Dak Lak. Data were aggregated at the district level, and the association between these meteorological factors and HFMD cases were examined by Poisson regression. Results: In 2012 through 2013, there were 7128 HFMD patients in Dak Lak. The number of HFMD cases increased during the rainy season. An increased risk of HFMD was associated with higher average temperature (risk ratio and 95% confidence interval: 1.06; 1.03–1.08 per 1 °C increase), higher rainfall (1.19; 1.14–1.24 per 200 mm increase) and longer sunshine duration (1.14; 1.07–1.22 per 60 hours increase). The risk of HFMD was inversely associated with wind speed (0.77; 0.73–0.81 per 1 m/s increase). Conclusion: This study suggests that there is a significant association between HFMD occurrence and climate. Temperature, rainfall, wind speed and sunshine duration could be used as meteorological predictors of HFMD occurrence in Viet Nam’s Central Highlands region. Intensified surveillance for HFMD during the rainy season is recommended.

Section: Discussionmentioning

confidence: 99%

Meteorological factors associated with hand, foot and mouth disease in a Central Highlands province in Viet Nam: an ecological study

Phan

2019

WPSAR

International Journal of Infectious Diseases

“…[7][8][9] The relationship between meteorological factors and HFMD has been investigated in a number of studies. [10][11][12][13][14] For instance, Hu et al 10 found that climate factors were potential determinants of the incidence of HFMD in most areas in China, using geographically weighted regression. A study by Huang et al 12 provided evidence that the incidence of HFMD in children was associated with the high average temperature and high relative humidity in Guangzhou.…”

Section: Introductionmentioning

confidence: 99%

Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models

Wang

Xian-jun

et al. 2015

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)

“…By using correlation and regression analysis method, Morabito finds that cooling and high temperature can cause the onset of myocardial infarction and high temperature lasting more than 9 hours increases the rate of admission rate [4]. Shan Zheng used correlation analysis, multiple linear regression method and the exponential curve method to investigate meteorological conditions of different effects on the incidence of hand foot mouth disease in Wuwei City [5]. Although the kind of method can realize the prediction of the disease, but accuracy is low.…”

Section: Introductionmentioning

confidence: 99%

A disease forecasting algorithm based on single factor correlation analysis and the JacUOD algorithm

Guo

Sun

Xie

2015

There is a close relationship between the occurrence of a variety of diseases and meteorological factors. However, the typical disease forecasting methods are based on history data and the requirement of initial data is strict. To solve these problems, we proposed a disease forecasting algorithm to adapt to real-time data. The proposed algorithm has two contributions: (1) It uses the single factor correlation analysis methods when selecting meteorological factors that affect disease (2) It introduces a new method to calculate disease prediction to build date _number _meteorological factor matrix and use JacUOD algorithm to evaluate the similarity of meteorological factors between the target dates and past ones. To find out the top-N dates are of the maximum similarity with the target one, therefore, we could forecast the number combining the similarity value and the N date's patient number. Obviously, the number of patient is obtained by calculating the similarity of different dates' meteorological factors. Experiments show that the algorithm generates a better accuracy than the traditional algorithms in disease prediction.