Table 3.

Cross-autocorrelation of climate variables with leishmaniasis cases and integration to the optimal model

Area	Climate variable	Most associated lag (month)*	r	P	AIC of the multivariate model
Sri Lanka	Relative humidity	− 3	0.230	< 0.05	30.80#
Hambanthota	Maximum temperature	− 3	0.555	< 0.05	124.24#
	Relative humidity	− 3	0.255	< 0.05	122.32#
Anuradhapura	Average temperature	− 9	− 0.267	< 0.05	70.41
	Relative humidity	− 3	0.267	< 0.05	70.34#
	Minimum temperature	− 9	− 0.281	< 0.05	71.02
	Maximum temperature	− 4	− 0.319	< 0.05	70.48
Polonnaruwa	Rainfall	− 6	− 0.239	< 0.05	432.00#
Matara	Relative humidity	− 1	0.489	< 0.05	101.04

*Number of months taken to reflect the climate condition in the number of patients (for instance, if the relative humidity is higher in the current month, we can expect an increase in the number of patients three months later

^#AIC values are lower than the univariate models. Thus, the integration of these climate factors increases the model accuracy, thus likely to affect disease incidence