What are the drivers of the SARC-CoV-2 spread on Catalonia?
Date: 2020-02-29
Date: 2020-10-16
Days: 29
Charts with information from Catalonia Open Data sources.
Evolution of the rates by age.
Evolution of the rates by age smooth by loess
Prevalence analysis, to obtain the percent of detection in each age range.
Number of tests done, global and by age.
The percent of detection to obtain the real cases are based on the prevalence. We apply if for the different rounbds, being the Round I to III almost the same
The percent of detection to obtain the real cases are based on the prevalence. We apply if for the different rounbds, being the Round I to III almost the same
Evolution of the rates of real cases by age smooth by loess
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. See https://en.wikipedia.org/wiki/Granger_causality
Phillips-Ouliaris Cointegration Test
data: bfx
Phillips-Ouliaris demeaned = -113.83, Truncation lag parameter = 2,
p-value = 0.01
Phillips-Ouliaris Cointegration Test
data: bfx
Phillips-Ouliaris demeaned = -40.074, Truncation lag parameter = 2,
p-value = 0.01| Lag | gr49by19 | gr49by09 | gr39by19 | gr39by09 | gr19by49 | gr09by49 | gr19by39 | gr09by39 |
|---|---|---|---|---|---|---|---|---|
| Lag 1 | 0.211 | 0.802 | 0.427 | 0.461 | 0.112 | 0.018 | 0.074 | 0.002 |
| Lag 2 | 0.594 | 0.443 | 0.690 | 0.424 | 0.440 | 0.060 | 0.246 | 0.009 |
| Lag 3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.101 | 0.089 | 0.193 | 0.004 |
| Lag 4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.120 | 0.094 | 0.191 | 0.011 |
| Lag 5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Lag 6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Lag 7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.003 | 0.000 | 0.000 |
| Lag 8 | 0.000 | 0.005 | 0.002 | 0.055 | 0.254 | 0.031 | 0.073 | 0.000 |
| Lag 9 | 0.000 | 0.004 | 0.011 | 0.015 | 0.076 | 0.006 | 0.025 | 0.000 |
| Lag 10 | 0.000 | 0.007 | 0.016 | 0.016 | 0.080 | 0.011 | 0.040 | 0.000 |
| Lag | gr49by19 | gr49by09 | gr39by19 | gr39by09 | gr19by49 | gr09by49 | gr19by39 | gr09by39 |
|---|---|---|---|---|---|---|---|---|
| Lag 1 | 0.018 | 0.096 | 0.077 | 0.026 | 0.390 | 0.026 | 0.174 | 0.023 |
| Lag 2 | 0.057 | 0.044 | 0.107 | 0.037 | 0.039 | 0.000 | 0.013 | 0.001 |
| Lag 3 | 0.152 | 0.053 | 0.081 | 0.104 | 0.075 | 0.001 | 0.006 | 0.001 |
| Lag 4 | 0.313 | 0.111 | 0.096 | 0.146 | 0.155 | 0.004 | 0.024 | 0.004 |
| Lag 5 | 0.134 | 0.150 | 0.012 | 0.220 | 0.139 | 0.005 | 0.009 | 0.005 |
| Lag 6 | 0.095 | 0.019 | 0.023 | 0.156 | 0.124 | 0.008 | 0.000 | 0.009 |
| Lag 7 | 0.003 | 0.042 | 0.005 | 0.055 | 0.072 | 0.048 | 0.002 | 0.021 |
| Lag 8 | 0.110 | 0.077 | 0.276 | 0.030 | 0.000 | 0.002 | 0.000 | 0.000 |
| Lag 9 | 0.013 | 0.103 | 0.063 | 0.044 | 0.001 | 0.000 | 0.000 | 0.000 |
| Lag 10 | 0.011 | 0.090 | 0.069 | 0.041 | 0.003 | 0.000 | 0.000 | 0.000 |
Notice that no modification due to the use of the taxes (modifying both series the same), we add this just for validation purposes.
| Lag | gr49by19 | gr49by09 | gr39by19 | gr39by09 | gr19by49 | gr09by49 | gr19by39 | gr09by39 |
|---|---|---|---|---|---|---|---|---|
| Lag 1 | 0.018 | 0.096 | 0.077 | 0.026 | 0.390 | 0.026 | 0.174 | 0.023 |
| Lag 2 | 0.057 | 0.044 | 0.107 | 0.037 | 0.039 | 0.000 | 0.013 | 0.001 |
| Lag 3 | 0.152 | 0.053 | 0.081 | 0.104 | 0.075 | 0.001 | 0.006 | 0.001 |
| Lag 4 | 0.313 | 0.111 | 0.096 | 0.146 | 0.155 | 0.004 | 0.024 | 0.004 |
| Lag 5 | 0.134 | 0.150 | 0.012 | 0.220 | 0.139 | 0.005 | 0.009 | 0.005 |
| Lag 6 | 0.095 | 0.019 | 0.023 | 0.156 | 0.124 | 0.008 | 0.000 | 0.009 |
| Lag 7 | 0.003 | 0.042 | 0.005 | 0.055 | 0.072 | 0.048 | 0.002 | 0.021 |
| Lag 8 | 0.110 | 0.077 | 0.276 | 0.030 | 0.000 | 0.002 | 0.000 | 0.000 |
| Lag 9 | 0.013 | 0.103 | 0.063 | 0.044 | 0.001 | 0.000 | 0.000 | 0.000 |
| Lag 10 | 0.011 | 0.090 | 0.069 | 0.041 | 0.003 | 0.000 | 0.000 | 0.000 |
The Chow test, proposed by econometrician Gregory Chow in 1960, is a test of whether the true coefficients in two linear regressions on different data sets are equal. In econometrics, it is most commonly used in time series analysis to test for the presence of a structural break at a period which can be assumed to be known a priori (for instance, a major historical event such as a war). In program evaluation, the Chow test is often used to determine whether the independent variables have different impacts on different subgroups of the population. Source: https://en.wikipedia.org/wiki/Chow_test
We plot all the p.values for the Chow test to find the periods where the trends between the 10-19 serie and the 40-49 serie differs, when the p.value is over the critical value of 0.05 implies that the series moves along.
Day: 198 -> 4.02184409626916e-05
The dataset needed to do this analysis. Accesed thorugt Open Data.
Data: Date casosPositiusData: All new cases detected PCRsData: All tests done Sub9 to Sub90: All new cases by age PCRsSub9 to PCRsAvisData: All PCRs by age casosSub15 to casosAvis: All new cases by other range age totalPond: All real cases (prevalence) Sub09Pond to Sub90Pond: Real cases by age (prevalence) Taxa: All cases rate taxaSub09 to taxaSub90 : Rates by age taxaPond: All real cases rate (prevalence) taxaSub09 to taxaSub90Pond: All real cases rate by age (prevalence) percPos percPosSub15 to percPosSub00: Percent of positivity by age