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The relationship between meteorological factors and mumps based on Boosted regression tree model
Dandan Zhang1, Yuming Guo2, Shannon Rutherford3
1Department of Biostatistics, School of Public Health, Shandong University, Jinan, China.
The Science of the Total Environment
|August 19, 2019
Summary
Mumps outbreaks are linked to weather patterns. Daily mean temperature, relative humidity, and sunshine duration significantly influence mumps incidence, with specific thresholds identified for early warning systems.
Area of Science:
- Epidemiology
- Environmental Health
- Public Health
Background:
- Mumps is a significant global public health concern.
- Previous studies on meteorological factors and mumps lacked comprehensive analysis of nonlinear relationships, delayed effects, and collinearity.
- Accurate estimation of these associations is crucial for effective mumps control.
Purpose of the Study:
- To investigate the relationship between meteorological factors and mumps incidence.
- To account for nonlinearity, delayed effects, and collinearity in the analysis.
- To identify key meteorological predictors and their impact on mumps risk.
Main Methods:
- Utilized daily mumps case data and meteorological data from Jining City, China (2007-2016).
- Employed a Boosted Regression Tree (BRT) model to determine optimal lag times for meteorological factors.
- Analyzed nonlinear relationships and identified thresholds for significant factors.
Main Results:
- A total of 15,064 mumps cases were reported, with peak prevalence in children aged 5-14 years.
- Optimal lag time for meteorological factors was found to be 10 days.
- Daily mean temperature (24.4%), relative humidity (19.9%), and sunshine duration (18.3%) were the most significant predictors.
Conclusions:
- Daily mean temperature, relative humidity, and sunshine duration are significantly associated with mumps incidence in Jining.
- Understanding the nonlinear relationships and thresholds of these factors is vital.
- This knowledge can inform the development of effective early warning systems for mumps prevention and control.
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