Climate change and Aedes albopictus risks in China: current impact and future projection

Table 1 CHAID univariate analysis of the correlation between climatic variable and Aedes albopictus prevalence

Climatic variable	Month	Ae. albopictus prevalence by month
Climatic variable	Month	Jan	Feb	Mar	Apr	May	Jun–Sep	Oct	Nov	Dec
Mean temperature	Jan	1	0.99	0.94	0.88	0.77
Mean temperature	Feb		0.94	0.91	0.85	0.76
	Mar			0.88	0.84	0.77
	Apr				0.7	0.72
	Oct	0.91	0.9					0.63	0.78	0.94
	Nov	1	0.92	0.84					0.9	0.92
	Dec	0.95	0.97	0.92	0.88					0.97
Maximum temperature	Jan	0.91	0.89	0.91	0.85	0.79
Maximum temperature	Feb		0.85	0.93	0.8	0.74
	Mar			0.88	0.68	0.72
	Apr				0.59	0.65
	Oct	0.92	0.93					0.56	0.7	0.94
	Nov	0.94	1	0.93					0.88	0.92
	Dec	0.94	0.95	0.97	0.92					0.97
Minimum temperature	Jan	0.87	0.22	0.15	0.29	0.47
Minimum temperature	Feb		0.95	0.85	0.88	0.75
	Mar			0.79	0.87	0.75
	Apr				0.85	0.83
	Oct	0.82	0.95					0.57	0.84	0.94
	Nov	0.94	0.88	0.85					0.88	0.92
	Dec	0.84	0.95	0.84	0.95					0.97
Precipitation	Jan	0.16	0.36	0.34	0.79	0.62
	Feb		0.3	0.39	0.91	0.67	0.64
	Mar			0.33	0.7	0.5	0.15
	Apr				0.54	0.76	0.83
	May					0.7	0.57
	Jun–Sep	-0.05					0.55	-0.03	0.15	0.16
	Oct	0.74	0.54					0.44	0.58	0.48
	Nov	0.49	0.26	0.2					0.56	0.3
	Dec	0.2	0.33	0.33	0.87					0.32

Numbers represent Yale’s correlation coefficient between Ae. albopictus prevalence and climatic variables by prevalence at different months against climatic variables at different months. Empty cell means not examined because we assume that climatic effects lag up to four months. Since Ae. albopictus has been detected from June to September everywhere where it was found, therefore, May to September temperature is assumed to be perfect for Ae. albopictus, thus they were not included in the analyses

ISSN: 2049-9957