Radius of Gyration as predictor of COVID-19 deaths trend with three-weeks offset

Total and perimetral lockdowns were the strongest nonpharmaceutical interventions to fight against Covid-19, as well as the with the strongest socioeconomic collateral effects. Lacking a metric to predict the effect of lockdowns in the spreading of COVID-19 with, authorities and decision-makers opted for preventive measures that showed either too strong or not strong enough after a period of two to three weeks, once data about hospitalizations and deaths was available. We present here the radius of gyration as a candidate predictor of the trend in deaths by COVID-19 with an offset of three weeks. Indeed, the radius of gyration aggregates the most relevant microscopic aspects of human mobility into a macroscopic value, very sensitive to temporary trends and local effects, such as lockdowns and mobility restrictions. We use mobile phone data of more than 13 million users in Spain during a period of one year (from January 6th 2020 to January 10th 2021) to compute the users' daily radius of gyration and compare the median value of the population with the evolution of COVID-19 deaths: we find that for all weeks where the radius of gyration is above a critical value (70% of its pre-pandemic score) the number of weekly deaths increases three weeks after. The reverse also stands: for all weeks where the radius of gyration is below the critical value, the number of weekly deaths decreased after three weeks. This observation leads to two conclusions: i) the radius of gyration can be used as a predictor of COVID-19-related deaths; and ii) partial mobility restrictions are as effective as a total lockdown as far the radius of gyration is below this critical value.

[1]  A. L. Schmidt,et al.  Economic and social consequences of human mobility restrictions under COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[2]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[3]  R. Sampson,et al.  Urban mobility and neighborhood isolation in America’s 50 largest cities , 2018, Proceedings of the National Academy of Sciences.

[4]  Y. Teo,et al.  Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe , 2020, The Lancet.

[5]  Marco De Nadai,et al.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.

[6]  W. Edmunds,et al.  Delaying the International Spread of Pandemic Influenza , 2006, PLoS medicine.

[7]  Yves Zenou,et al.  Why is central Paris rich and downtown Detroit poor?: An amenity-based theory , 1999 .

[8]  Johannes Zierenberg,et al.  Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions , 2020, Science.

[9]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[10]  C. E. WHO Coronavirus Disease (COVID-19) Dashboard , 2020 .

[11]  Siddharth Gupta,et al.  The TimeGeo modeling framework for urban mobility without travel surveys , 2016, Proceedings of the National Academy of Sciences.

[12]  Sandro Galea,et al.  Population health in an era of rising income inequality: USA, 1980–2015 , 2017, The Lancet.

[13]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Cécile Viboud,et al.  Air Travel and the Spread of Influenza: Important Caveats , 2006, PLoS medicine.

[15]  V. Isham,et al.  Five challenges for spatial epidemic models , 2015, Epidemics.

[16]  A. Plastino,et al.  Social inequalities in human mobility during the Spanish lockdown and post-lockdown in the Covid-19 pandemic of 2020 , 2020, medRxiv.

[17]  Alessandro Vespignani,et al.  influenza A(H1N1): a Monte Carlo likelihood analysis based on , 2009 .

[18]  A. Tatem,et al.  Effect of non-pharmaceutical interventions to contain COVID-19 in China , 2020, Nature.

[19]  Mirza Lucía Flores Mori La educación odontológica en tiempos de pandemia por covid 19 , 2021 .

[20]  Equipo de trabajo multidisciplinario de la Revista de Odo Latinoamericana Lineamiento técnico de atención para procedimientos con sedación durante la etapa de confinamiento y posterior declarados por la pandemia por COVID-19 , 2021 .

[21]  Alessandro Vespignani,et al.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.

[22]  P. Bajardi,et al.  COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.

[23]  Malte Jochum,et al.  Trade-offs between multifunctionality and profit in tropical smallholder landscapes , 2020, Nature Communications.

[24]  Alessandro Vespignani,et al.  Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic , 2011, PloS one.

[25]  Dominic Winter,et al.  Comprehensive draft of the mouse embryonic fibroblast lysosomal proteome by mass spectrometry based proteomics , 2020, Scientific Data.

[26]  J. Ramasco,et al.  Effects of mobility and multi-seeding on the propagation of the COVID-19 in Spain , 2020, medRxiv.

[27]  Caroline O Buckee,et al.  The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology , 2020, Nature Communications.

[28]  N. Ferguson,et al.  Will travel restrictions control the international spread of pandemic influenza? , 2006, Nature Medicine.

[29]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.