Driving role of climatic and socioenvironmental factors on human brucellosis in China: machine-learning-based predictive analyses

Table 3 Sunshine and humidity copula model performance, 2014–2020

Climatic variables	Function	2014	2015	2016	2017	2018	2019	2020	2014–2020
Sunshine edge distribution \({R}^{2}\)	Frechet	0.978	0.977	0.976	0.977	0.965	0.956	0.992	0.979
	Gumbel	0.979	0.980	0.976	0.977	0.965	0.965	0.991	0.979
	Weibull	0.995	0.997	0.994	0.997	0.991	0.986	0.997	0.997
Humidity edge distribution \({R}^{2}\)	Frechet	0.899	0.886	0.887	0.915	0.890	0.917	0.894	0.906
	Gumbel	0.899	0.886	0.893	0.915	0.891	0.927	0.894	0.903
	Weibull	0.975	0.970	0.965	0.973	0.958	0.985	0.984	0.975
Copula Joint distribution function AIC (\({10}^{4}\))	Clayton	−1.050	−0.943	−0.980	−1.150	−1.035	−1.271	−0.753	−7.786
	Frank	−1.486	−1.333	−1.338	−1.387	−1.365	−2.053	−1.308	−10.312
	Gumbel	−1.314	−1.253	−1.256	−1.316	−1.322	−1.913	−1.270	−9.648

ISSN: 2049-9957