Our models have been updated to include the data from Ireland, Italy and the US for the week just ended. Trends and parameters are stable in all regions. We do not adjust R0 in any region (this was fitted to the early 'exponential' data) but we refit the 'mixing' profiles each week and values have been steady for some time. Since 11 April, the models also predict death rates and the related fitted parameters are also steady.
Of the three regions, R0 may have been highest in the US (3.92) and lowest in Italy (2.94), with Ireland in between (3.52). We estimate that R_effective (or Rt) after adoption of restrictions is now 1.1 in the US, 0.98 in Italy and 0.84 in Ireland. These figures translate to mixing levels of 28%, 33% and 24% of baseline respectively in the periods of most severe 'lockdown'.
In order to predict the safest and most beneficial ways to release restrictions, data on the incremental effectiveness of each separate measure (e.g. social distancing, wearing masks, cocooning the vulnerable, staying at home, closing shops and places of work) would be extremely useful but are not yet available. We may learn these parameters as the outbreak progresses and be better able to predict exit strategies.
In the meantime, Google and Apple are providing anonymized data related to movement, recorded when our devices talk to their servers. In general, these data trend in line with the mixing percentages we estimate from the predictive model. Results are shown below for the United States as an example. Though the mobility data are scattered, the trends align with the predictive model and indicate that mobility data are helpful indicators of progress.
Of the three regions, R0 may have been highest in the US (3.92) and lowest in Italy (2.94), with Ireland in between (3.52). We estimate that R_effective (or Rt) after adoption of restrictions is now 1.1 in the US, 0.98 in Italy and 0.84 in Ireland. These figures translate to mixing levels of 28%, 33% and 24% of baseline respectively in the periods of most severe 'lockdown'.
In order to predict the safest and most beneficial ways to release restrictions, data on the incremental effectiveness of each separate measure (e.g. social distancing, wearing masks, cocooning the vulnerable, staying at home, closing shops and places of work) would be extremely useful but are not yet available. We may learn these parameters as the outbreak progresses and be better able to predict exit strategies.
In the meantime, Google and Apple are providing anonymized data related to movement, recorded when our devices talk to their servers. In general, these data trend in line with the mixing percentages we estimate from the predictive model. Results are shown below for the United States as an example. Though the mobility data are scattered, the trends align with the predictive model and indicate that mobility data are helpful indicators of progress.
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| Mobility data from Apple devices compared to the mixing variable fitted to COVID-19 case data for the US. Though the data are scattered, the trends align with the predictive model. |
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