Lessons from being challenged by COVID-19

We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus onthe megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 millioninhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelersfrom abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modificationsof the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous modelsused to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporatingmobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the officialnumber of cases with minimal parameter fitting and fine-tuning.  We discuss the strengths and limitations of the proposed models,focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in theinterpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Balenzuela, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Travizano, M.. Grandata Labs; Estados UnidosFil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Sequences of purchases in credit card data reveal lifestyles in urban populations

This is the final version. Available from Nature Research via the DOI in this record.Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Gates FoundationUnited Nations FoundationNewton International FellowshipThe Royal SocietyThe British AcademyAcademy of Medical Science