Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks

Potential health and economic impacts of dexamethasone treatment for patients with COVID-19

Acknowledgements We thank all members of the COVID-19 International Modelling Consortium and their collaborative partners. This work was supported by the COVID-19 Research Response Fund, managed by the Medical Sciences Division, University of Oxford. L.J.W. is supported by the Li Ka Shing Foundation. R.A. acknowledges funding from the Bill and Melinda Gates Foundation (OPP1193472).Peer reviewedPublisher PD

Optimizing COVID-19 surveillance using historical electronic health records of influenza infections.

Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly

Optimizing COVID-19 surveillance using historical electronic health records of influenza infections

Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly

Disproportionate impacts of COVID-19 in a large US city.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited