Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps

Abstract

The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling. Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020. Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number ( $R_0$ ). We developed a travel network density parameter ${\beta }_t$ to provide an estimate of NPI impact on mobility patterns. Given the infection-fatality ratio and time lag (onset-to-death), a Bayesian probabilistic model is adapted to calculate the change in epidemic development with ${\beta }_t$ . Results showed that the change in ${\beta }_t$ clearly impacted $R_0$ . The three lockdowns strongly affected the growth of transmission rate and collectively reduced $R_0$ by 78% before the restrictions were eased. The model forecasted daily infections and deaths by 2% and 3% fractional errors. It also projected what-if scenarios for different implementation protocols of each NPI. The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants, as well as for future pandemics.

Keywords: COVID-19; Epidemiological modelling; Mobility maps; Non-pharmaceutical interventions.

Fig. 1

A section of the mobility directional map for Abu Dhabi City on Apr 26, 2020

Fig. 2

Trend analysis results of changes in population mobility and travel network density metric (${\beta }_t$) due to NPIs.

Fig. 3

Correlation between daily deaths and NPI-induced changes in travel network density “${\beta }_t$”

Fig. 4

R-hat statistics for the modelling parameters in the simulation. Simulation convergence = values of 1.

Fig. 5

Model results for changes in $R_{e,t}$ due to the implementation of NPIs based on travel network density.${\beta }_t$

Fig. 6

Model results for NPIs–implementation impact on a) daily, and b) cumulative infections based on travel network density.${\beta }_t$

Fig. 7

Model results for NPIs–implementation impact on a) daily, and b) cumulative deaths based on travel network density.${\beta }_t$

Fig. 8

Forecasting results for NPIs–implementation impact on daily a) infections, and b) deaths based on travel network density.${\beta }_t$

Fig. 9

Mean relative change in travel network density ${\beta }_t$ with 95% CIs for each NPI compared to the pre-epidemic condition.

Fig. 10

Comparison of the R_e,t values and 50% and 95% CIs between different NPI implementation scenarios for the analysed country of UAE at the baseline time horizon (Jan 29, 2020 to Oct 18, 2020) predicted by changes in network travel density.${\beta }_t$