Mobility: Optimising the Balance
Mobility as an Early-Warning Signal
Forecasting the Spread of Covid-19
The Early-Warning Model as a Policy Tool
Limitations and Developments
- ^ MRC Centre for Global Disease Analysis, Imperial College London, Planning tools https://mrc-ide.github.io/covid19estimates/#/details/United_Kingdom
- ^ Ibid.
- ^ Coronavirus disease 2019 (COVID-19) Situation Report 73, World Health Organisation (2020).
- ^ Lavezzo E. et al, Suppression of COVID-19 outbreak in the municipality of Vo, Italy, MedRxiv (2020).
- ^ See for example MRC Biostatistics Unit, Cambridge University https://www.mrc-bsu.cam.ac.uk/tackling-covid-19/nowcasting-and-forecasting-of-covid-19/
- ^ See https://support.google.com/covid19-mobility/answer/9824897?hl=en&ref_topic=9822927
- ^ Capacity limits in schools, on public transport or in shops are, on this definition suppression measures rather than containment, and should show up in reduced mobility.
- ^ Indeed if the herd immunity threshold were to be reached – either through the spread of the virus or a vaccine – then the association between mobility and acceleration would drop to zero.
- ^ Coronavirus (COVID-19) in the UK: https://coronavirus.data.gov.uk/#countries
- ^ Coronavirus (COVID-19): Scaling up our testing programs, Department of Health and Social Care (2020).
- ^ Google COVID-19 Community Mobility Reports: https://www.google.com/covid19/mobility/
- ^ Ainslie K. et al., Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment, Wellcome Open Research (2020).
- ^ For our purposes it does not matter whether the acceleration window used in the model matched the period for which people are infectious. However, the five-day acceleration rate can be converted into and implied R0 by the following relationship: Acceleration=2.3^(n-1)/2.3^(n-2) -1=2.3-1=1.3=130%, where n is the number of five-day intervals since the beginning of the virus outbreak and 2.3^(n-1) the number of new cases anticipated at that day for a reproduction number of 2.3.
- ^ a. Flaxman S. et al., Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries, Imperial College London (2020). b. Bryant P., Elofsson A., Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries, MedRxiv (2020).
- ^ Vollmer M. et al., Using mobility to estimate the transmission intensity of COVID-19 in Italy: a subnational analysis with future scenarios. Imperial College London (2020).
- ^ Bryant P., Elofsson A., Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries, MedRxiv (2020).
- ^ A smoothing window of 44 means a moving average of 4 lags and 4 leads whereas a smoothing window of 40 denotes a moving average of 4 lags and no leads. To illustrate: Moving Average x,44=(xt-4+xt-3+⋯+xt+3+xt+4)/8
- ^ Kao C., Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics (1999) 90: 1–44.
- ^ Pedroni P., Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics (1999) 61: 653–670., Pedroni P., Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory (2004) 20: 597–625.
- ^ Westerlund J., New simple tests for panel cointegration. Econometric Reviews (2005) 24: 297–316.