Evaluation of the effectiveness of surveillance policies to control the COVID-19 pandemic in São Paulo, Brazil

Table 2 Estimated Coefficients for the Segmented Regressions for RT-PCR Testing Policies in São Paulo (Brazil)

Dependent variables of RT-PCR testing outcome:	I. Log (RT-PCR tests per 100,000)		II.\(\frac{RT - PCR Tests}{Cases}\)		III. Log (positivity rate)
Dependent variables of RT-PCR testing outcome:	Coefficient	Newey standard error	Coefficient	Newey standard error	Coefficient	Newey standard error
Intercept	1.948	(0.158)***	8.961	(3.988)**	0.043	(0.090)
Time	0.023	(0.003)***	− 0.129	(0.069)*	0.000	(0.002)
Testing policy II	0.710	(0.278)**	4.018	(2.576)	− 0.090	(0.081)
Time*post testing policy II	− 0.018	(0.005)***	0.128	(0.069)*	− 0.001	(0.002)
Testing policy III	− 0.392	(0.188)**	0.481	(0.394)	− 0.045	(0.047)
Timepost testing policy IIpost testing policy III	− 0.000	(0.004)	0.008	(0.006)	0.002	(0.001)**
Post intervention linear trend
Testing policy II (β1 + β3)	0.005	(0.004)	− 0.001	(0.002)	− 0.000	(0.001)
Testing policy III (β1 + β3 + β5)	0.005	(0.001)**	0.007	(0.006)	0.002	(0.001)**
Number of observations	39		39		39

From March 29, 2020 to December 26, 2020 (aggregated data by week)
For each dependent variable, the following segmented regression model was estimated: Y_t = β0 + β1 time + β2Testing Policy II_t + β3 time*post Testing Policy II_t + β4 Testing Policy III_t + β5 time*post Testing Policy IIIt + ε_t
***p-value < 0.001, **p-value < 0.05, *p-value < 0.1

ISSN: 2397-0642