Implementation of generalized additive models in environmental epidemiology research in Python

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Author: Xiang-Ying LI Pei-Zheng LI Jing WANG Chen-Xi LUO Qing-Yu ZHANG  Lu MA

Affiliation: School of Public Health, Wuhan University, Wuhan 430071, China

Keywords: Python Generalized additive models Time series data Environmental epidemi-ology

DOI: 10.12173/j.issn.1004-4337.202302032

Reference: Li XY, Li PZ, Wang J, Luo CX, Zhang QY, Ma L. Implementation of generalized additive models in environmental epidemiology research in Python[J]. Journal of Mathematical Medicine, 2023, 36(3): 241-245. DOI: 10.12173/j.issn.1004-4337.202302032[Article in Chinese]  Copied

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Abstract

Objective To explore the common time series data in the field of environmental ep-idemiology, using Python and other statistical softwares to realize the modeling of generalized additive models (GAM), and to compare the similarities and differences of the modeling process and results of each software.

Method A study of the relationship between PM2.5 and the number of hospital admissions of respiratory diseases was taken as an example. Python software used statsmodles library, R software used mgcv library, SAS software used proc gam syntax to build GAM models, and the differences in codes, parameter settings, and parameter estimates were compared.

Results The modeling logic of 3 programs is similar, but there are differences in the built-in function fitting process, code using and callable spline function. The outputs are basically consistent.

Conclusion Python software can build GAM by using third-party libraries. It provides a reference for further expanding its application in the field of epide-miological scientific research.

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