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Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

Kwak J, Kim S, Kim G, Singh VP, Hong S, Kim HS - Int J Environ Res Public Health (2015)

Bottom Line: The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus.Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012.Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

View Article: PubMed Central - PubMed

Affiliation: Forecast and Control Division, Nakdong River Flood Control Office, Busan 604-851, Korea. firstsword@korea.kr.

ABSTRACT
Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

No MeSH data available.


Related in: MedlinePlus

Weighted average meteorological data from Korea [44].
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Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4515655&req=5

ijerph-12-07254-f002: Weighted average meteorological data from Korea [44].

Mentions: We collected 24 meteorological observatory (2001 to 2007) and 25 meteorological observatory (2008 to 2013) meteorological data, and these data were weighted averaged into one, which considered the spatial distributions (C.I. number of L. scutellare in Figure 1) of L. scutellare as in study by [39]. Meteorological data included in the analysis were monthly average, maximum and minimum air temperatures (°C), precipitation (mm), relative humidity (%), wind speed (m/s), duration of sunshine (hours), and cloud amount, which are known as responsible factors for scrub typhus [31,40,41,42]. Also, some studies have indicated that land use affects scrub typhus [29], but Jin et al. [43] showed the land use changes did not affect Scrub typhus. Hence, it was not taken into account in this study. The collected meteorological data are shown in Figure 2 and scrub typhus in Figure 3.


Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

Kwak J, Kim S, Kim G, Singh VP, Hong S, Kim HS - Int J Environ Res Public Health (2015)

Weighted average meteorological data from Korea [44].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4515655&req=5

ijerph-12-07254-f002: Weighted average meteorological data from Korea [44].
Mentions: We collected 24 meteorological observatory (2001 to 2007) and 25 meteorological observatory (2008 to 2013) meteorological data, and these data were weighted averaged into one, which considered the spatial distributions (C.I. number of L. scutellare in Figure 1) of L. scutellare as in study by [39]. Meteorological data included in the analysis were monthly average, maximum and minimum air temperatures (°C), precipitation (mm), relative humidity (%), wind speed (m/s), duration of sunshine (hours), and cloud amount, which are known as responsible factors for scrub typhus [31,40,41,42]. Also, some studies have indicated that land use affects scrub typhus [29], but Jin et al. [43] showed the land use changes did not affect Scrub typhus. Hence, it was not taken into account in this study. The collected meteorological data are shown in Figure 2 and scrub typhus in Figure 3.

Bottom Line: The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus.Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012.Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

View Article: PubMed Central - PubMed

Affiliation: Forecast and Control Division, Nakdong River Flood Control Office, Busan 604-851, Korea. firstsword@korea.kr.

ABSTRACT
Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

No MeSH data available.


Related in: MedlinePlus