Granger causality testing mobility variables predictive of 14-day CI for different lag orders.

Granger causality testing mobility variables predictive of 14-day CI for different lag orders.

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We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only...

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Context 1
... interest is to examine whether mobility time series helps to predict future values of 14-day CI, controlling for lags. Table 6 reports Granger causality test outcomes for different lag orders analysing whether past values of mobility variables provide additional information about 14-day CI beyond past values of 14-day CI. ...
Context 2
... the results in Table 6, the effect of lags of mobility variables retail and recreation, parks and public transport on 14-day CI is highly significant whatever the number of lags is. The stationarity of the variables was previously checked using the Augmented Dickey-Fuller test via the adf.test function in R. Bearing this in mind, according to WHO, the incubation period of COVID-19 is on average 5-6 days but can be as long as 14 days, lags have been considered varying from 5 to 14 days. ...

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... For example, contact tracing can help in predicting the evolution of the COVID-19 infections so that Fig. 4 The course of the hospital COVID-19 caseload in Kuwait if the lockdown starts 5, 10, or 15 days before the peak and lasts for 15, 30, or 45 days. The uncertainty is shown in the gray shaded areas, while the solid black curve shows the mean of the simulation results predictions of the peak of the epidemic becomes easier [25]. ...
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