Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic

BMC Public Health

Table 1 Output for ZIKV predictive models

		Effect Estimate	Standardized Effect Estimate	Standard Error	P-Value	Model R²
Florida Models
Multivariate	Intercept	0.2750	0.0013	0.6350	0.6670	0.74
	ZIKV_t-1	− 0.6993	−0.6993	0.1352	< 0.0001	–
	ZIKV_t-2	−0.6271	−0.6271	0.1432	< 0.0001	–
	ZIKV_t-3	−0.4264	−0.4264	0.1373	0.0033	–
	Tweet_t-1	0.0626	0.4104	0.0136	< 0.0001	–
Univariate	Intercept	0.2711	0.0007	2.1455	0.9000	0.60
	Tweet_t-1	0.0443	0.2903	0.0211	0.0408	–
Univariate	Intercept	0.2720	−0.0002	1.5694	0.8631	0.61
	ZIKV_t-1	−0.3282	−0.3281	0.1350	0.0187	–
U.S. Models
Multivariate	Intercept	1.0587	0.0107	3.4241	0.7586	0.70
	ZIKV_t-1	−0.5221	−0.5221	0.1402	0.0005	–
	ZIKV_t-2	−0.3806	−0.3806	0.1457	0.0120	–
	Tweet_t-1	0.0242	0.2622	0.0114	0.0392	–
Univariate	Intercept	0.4715	0.0001	7.2653	0.9485	0.63
	Tweet_t-1	0.0325	0.3517	0.0123	0.0114	–
Univariate	Intercept	0.2720	0.0023	1.5694	0.8631	0.64
	ZIKV_t-1	−0.3282	−0.3756	0.1350	0.0187	–

*Note, effect estimates represent the effects of covariates after first-order differencing; thus explaining the negative coefficients of AR terms that are otherwise positively auto-correlated

ISSN: 1471-2458