However, although the connection between mobility and disease has been known for centuries, the detailed quantitative study of this relationship has been difficult. That movement affects disease transmission and incidence has been shown in numerous studies. More recently, measures restricting social interaction and movement have been used in response to the SARS and MERS epidemics which occurred in the last decades. The implementation of such measures in response to infectious disease outbreaks is not new methods aiming to reduce social contact and limit mobility being used for centuries. Such recommendations were followed by more formal, more stringent and often legally imposed governmental restrictions on personal movement which have included ‘stay at home’ orders, closure of non-essential retail units and schools, and banning of sports and entertainment gatherings. Recommendations have included the practising of social distancing, self-isolation or quarantine, and increasing levels of personal hygiene. Given the highly infectious nature of COVID-19, reducing levels of social interaction and community movement have been seen as key in reducing the rates of COVID-19 transmission. Those affected are infectious prior to exhibiting symptoms of illness, or remain unaware of infection because they experience only mild symptoms or are asymptomatic factors which promote further transmission of the disease. The level of COVID-19 transmissibility is greater than for other closely related conditions, such as the SARS virus. The observed relationship between CMR data and case incidence, and its ability to enhance model quality and prediction suggests data related to community mobility could prove of use in future COVID-19 modelling.ĬOVID-19 is a highly infectious viral infection, and the main route of transmission is thought to be through respiratory droplets. The predictions made with the distributed lag model significantly outperformed all other models. When modelling, CMR-expanded models proved superior to the model without CMR.

Continent-wide examination found a negative correlation for all continents with the exception of South America. Noticeable were negative correlations between CMR data and case incidence for prominent industrialised countries of Western Europe and the North Americas. Models using numerical date, contemporaneous and distributed lag CMR data were contrasted using Bayesian Information Criteria. Models were fitted to explain case numbers of each country's epidemic. CMR data for countries worldwide were cross-correlated with corresponding COVID-19-confirmed case numbers. The objective was to examine whether an association between COVID-19-confirmed case numbers and levels of mobility was apparent, and if so then to examine whether such data enhance disease modelling and prediction. They thus offer the unique opportunity to examine the relationship between mobility and disease incidence. Google's ‘Community Mobility Reports’ (CMR) detail changes in activity and mobility occurring in response to COVID-19.

Google mobility data