Table 1.
Description of the five NPIs used in this study.
Fig 1.
Schematic presentation of the study design.
Vertical lines represent days. We denote day (in red) as the day when NPI k was enacted, considering that this NPI was not active the day before (i.e., event date). The treatment group for NPI k consists of
across all countries. The control group are the remaining days (in blue) outside the time interval
, where w is a lag period.
Fig 2.
The study can be divided into three main parts: data collection and pre-processing, outcome prediction and causal effect estimation.
Fig 3.
Distribution of NPIs and frequency of their co-existence.
(A) Cumulative number of events over time. An event is defined as the day when an intervention was enacted in a country, considering that this NPI was not active the day before. Until June 1st 2020 (dashed vertical line), 60% of all events had already happened. (B) Co-occurrence matrix of events in the limited period until June 1st 2020. The diagonal values represent the number of events. Cell values indicate the sum of times that NPI i (x-axis) and NPI j (y-axis) were enacted together within a period of 2 weeks in the same country or US state.
Fig 4.
Important predictors of outcome and accuracy of prediction for different time-lags.
(A) Identification of top-10 predictive variables affecting Rt estimation. (B) Top-10 most contributing features for residential mobility estimation. SHAP analysis in A and B is based on predictions using a time-lag of 7 days. Each dot represents a single data sample in the validation set (i.e., country at a date). The dot color represents the feature value (red = high, blue = low). The farther a dot is from 0 on the x-axis, the more effect (positive or negative) this feature had on the prediction model for this particular sample. (C) Model performance (log of normalized MSE) of Rt (solid dark blue line) and residential mobility (solid green lines) in the validation set for different time lags. The dashed lines of the same color correspond to random prediction derived by permutation tests with their respective models. Both models significantly outperformed random prediction.
Fig 5.
Estimated causal effects of NPIs over time on residential mobility (left) and reproduction number rate (right).
In both plots, the opacity of the markers represents the ability of the balancing weights method to balance the treatment groups: the smaller the ASMD is, the more opaque the markers are. Fully opaque markers indicate an ASMD <0.1, half-transparent markers indicate 0.1 ≤ ASMD ≤ 0.25 and most transparent ones represent ASMD > 0.25. Apart from mask wearing mandates, all NPIs caused a significant increase in time spent at home. Of those, school and cultural closures were the most effective. Closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction in Rt after their initiation, which was not observed with the other social distancing measures. Code used for generating figure is available at https://github.com/barakm-ki/symptoms-dynamics-of-COVID-19-infection/blob/master.