Efficient team structures in an open-ended cooperative creativity experiment

Understanding how to best form teams to perform creative tasks is a fascinating although elusive problem. Here we propose an experimental setting for studying the performances of a population of individuals committed to an open-ended cooperative creativity task, namely the construction of LEGO artworks. The real-time parallel monitoring of the growth of the artworks and the structure and composition of the dynamically working teams allow identifying the key ingredients of successful teams. Large teams composed of committed and influential people are more effectively building. Also, there exists an optimal fraction of weak ties in the working teams, i.e., an optimal ratio exploit/explore that maximizes the building efficiency.Creativity is progressively acknowledged as the main driver for progress in all sectors of humankind{ extquoteright}s activities: arts, science, technology, business, and social policies. Nowadays, many creative processes rely on many actors collectively contributing to an outcome. The same is true when groups of people collaborate in the solution of a complex problem. Despite the critical importance of collective actions in human endeavors, few works have tackled this topic extensively and quantitatively. Here we report about an experimental setting to single out some of the key determinants of efficient teams committed to an open-ended creative task. In this experiment, dynamically forming teams were challenged to create several artworks using LEGO bricks. The growth rate of the artworks, the dynamical network of social interactions, and the interaction patterns between the participants and the artworks were monitored in parallel. The experiment revealed that larger working teams are building at faster rates and that higher commitment leads to higher growth rates. Even more importantly, there exists an optimal number of weak ties in the social network of creators that maximizes the growth rate. Finally, the presence of influencers within the working team dramatically enhances the building efficiency. The generality of the approach makes it suitable for application in very different settings, both physical and online, whenever a creative collective outcome is required

Human mobility and epidemics

La mobilité humaine affecte la probabilité d'entrer en contact avec des individus infectés et donc la probabilité de propagation de la maladie. Dans les dernières décennies, les données relatives aux téléphones portables ont été utilisées pour suivre les comportements individuels à des fins épidémiologiques. Dans les situations d'urgence, les individus peuvent modifier leurs comportements en raison des restrictions de mobilité mises en place par les gouvernements, ou de comportements individuels d'adaptation à l'épidémie, comme l'aversion au risque. La COVID-19 a mis en évidence la nécessité de disposer de données sur la mobilité en temps réel pour contribuer à atténuer la diffusion virale. Les opérateurs de réseau mobile et les entreprises ont donc fait des efforts pour partager rapidement leurs données dans le cadre d'accords conformes au respect de la vie privée. Les informations massives et détaillées sur la mobilité ont ainsi ouvert de nouveaux défis sur la quantification des variations de mobilité et sur l'intégration de ces données dans les modèles. Ma thèse de doctorat a répondu à cette question en traitant des travaux de recherche théorique et appliquée visant à intégrer les données de mobilité en temps réel à différentes échelles spatiales dans des modèles mathématiques pour des applications de santé publique, en particulier sur l’épidémie de covid-19 en France.Human mobility affects the mixing among populations and thus crucially alter the probability of coming in contact with infected individuals and the likelihood of disease propagation. To date, mobile phone data have been largely used to track individual behaviours within countries. During emergencies, individuals may change their behaviours due to mobility restrictions put in place by governments to mitigate the epidemic activity; or individual adaptive behaviours to the epidemic, like risk aversion. COVID-19 pandemic has underlined the necessity of real-time mobility data to help mitigate the viral diffusion. Network operators and companies across the world made thus huge efforts to quickly share their data through privacy compliant agreements. The massive and detailed information of human mobility has thus opened new challenges on i) quantifying the impact mobility restrictions in terms of mobility changes; ii) integrating real-time mobility data into models, in order to increase their predictive power by accounting for mobility changes. My doctoral thesis answered this question by dealing with theoretical and applied research work aimed at integrating real-time mobility data on different spatial scales into mathematical models for public heath applications. In particular, I focused on the context of COVID-19 epidemic in France

Mobilité humaine et propagation des épidémies

Human mobility affects the mixing among populations and thus crucially alter the probability of coming in contact with infected individuals and the likelihood of disease propagation. To date, mobile phone data have been largely used to track individual behaviours within countries. During emergencies, individuals may change their behaviours due to mobility restrictions put in place by governments to mitigate the epidemic activity; or individual adaptive behaviours to the epidemic, like risk aversion. COVID-19 pandemic has underlined the necessity of real-time mobility data to help mitigate the viral diffusion. Network operators and companies across the world made thus huge efforts to quickly share their data through privacy compliant agreements. The massive and detailed information of human mobility has thus opened new challenges on i) quantifying the impact mobility restrictions in terms of mobility changes; ii) integrating real-time mobility data into models, in order to increase their predictive power by accounting for mobility changes. My doctoral thesis answered this question by dealing with theoretical and applied research work aimed at integrating real-time mobility data on different spatial scales into mathematical models for public heath applications. In particular, I focused on the context of COVID-19 epidemic in France.La mobilité humaine affecte la probabilité d'entrer en contact avec des individus infectés et donc la probabilité de propagation de la maladie. Dans les dernières décennies, les données relatives aux téléphones portables ont été utilisées pour suivre les comportements individuels à des fins épidémiologiques. Dans les situations d'urgence, les individus peuvent modifier leurs comportements en raison des restrictions de mobilité mises en place par les gouvernements, ou de comportements individuels d'adaptation à l'épidémie, comme l'aversion au risque. La COVID-19 a mis en évidence la nécessité de disposer de données sur la mobilité en temps réel pour contribuer à atténuer la diffusion virale. Les opérateurs de réseau mobile et les entreprises ont donc fait des efforts pour partager rapidement leurs données dans le cadre d'accords conformes au respect de la vie privée. Les informations massives et détaillées sur la mobilité ont ainsi ouvert de nouveaux défis sur la quantification des variations de mobilité et sur l'intégration de ces données dans les modèles. Ma thèse de doctorat a répondu à cette question en traitant des travaux de recherche théorique et appliquée visant à intégrer les données de mobilité en temps réel à différentes échelles spatiales dans des modèles mathématiques pour des applications de santé publique, en particulier sur l’épidémie de covid-19 en France

Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study

International audienceBackground: On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities.Methods: For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18-64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours.Findings: Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Île-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown).Interpretation: Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events. Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions. We also identified socioeconomic and demographic constraints to the efficacy of restrictions. The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response.Agence Nationale de la Recherche, EU, REACTing

Modelling safe protocols for reopening schools during the COVID-19 pandemic in France

International audienceAs countries in Europe implement strategies to control the COVID-19 pandemic, different options are chosen regarding schools. Through a stochastic age-structured transmission model calibrated to the observed epidemic in Île-de-France in the first wave, we explored scenarios of partial, progressive, or full school reopening. Given the uncertainty on children's role, we found that reopening schools after lockdown may increase COVID-19 cases, yet protocols exist to keep the epidemic controlled. Under a scenario with stable epidemic activity if schools were closed, reopening pre-schools and primary schools would lead to up to 76% [67, 84]% occupation of ICU beds if no other school level reopened, or if middle and high schools reopened later. Immediately reopening all school levels may overwhelm the ICU system. Priority should be given to pre- and primary schools allowing younger children to resume learning and development, whereas full attendance in middle and high schools is not recommended for stable or increasing epidemic activity. Large-scale test and trace is required to keep the epidemic under control. Ex-post assessment shows that progressive reopening of schools, limited attendance, and strong adoption of preventive measures contributed to a decreasing epidemic after lifting the first lockdown

Impact of January 2021 curfew measures on SARS-CoV-2 B.1.1.7 circulation in France

International audienceFollowing the spread of the SARS-CoV-2 B.1.1.7 variant, social distancing was strengthened in France in January 2021. Using a two-strain mathematical model calibrated on genomic surveillance, we estimated that curfew measures allowed hospitalisations to plateau by decreasing transmission of the historical strains while B.1.1.7 continued to grow. School holidays appear to have further slowed down progression in February. Without progressively strengthened social distancing, a rapid surge of hospitalisations is expected, despite the foreseen increase in vaccination rhythm

Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020

International audienceAs at 27 January 2020, 42 novel coronavirus (2019-nCoV) cases were confirmed outside China. We estimate the risk of case importation to Europe from affected areas in China via air travel. We consider travel restrictions in place, three reported cases in France, one in Germany. Estimated risk in Europe remains high. The United Kingdom, Germany and France are at highest risk. Importation from Beijing and Shanghai would lead to higher and widespread risk for Europe

Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

International audienceBackground: In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases.Methods and findings: Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.Conclusions: Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown