Lenzen M, Li M, Malik A, Pomponi F, Sun Y-Y, Wiedmann T, et al. (2020) Global socio-economic losses and environmental gains from the Coronavirus pandemic. PLoS ONE 15(7): e0235654. https://doi.org/10.1371/journal.pone.0235654

 

On 3 April 2020, the Director-General of the WHO stated: “[COVID-19] is much more than a health crisis. We are all aware of the profound social and economic consequences of the pandemic (WHO, 2020)”. Such consequences are the result of counter-measures such as lockdowns, and world-wide reductions in production and consumption, amplified by cascading impacts through international supply chains. Using a global multi-regional macro-economic model, we capture direct and indirect spill-over effects in terms of social and economic losses, as well as environmental effects of the pandemic. Based on information as of May 2020, we show that global consumption losses amount to 3.8$tr, triggering significant job (147 million full-time equivalent) and income (2.1$tr) losses. Global atmospheric emissions are reduced by 2.5Gt of greenhouse gases, 0.6Mt of PM2.5, and 5.1Mt of SO2 and NOx. While Asia, Europe and the USA have been the most directly impacted regions, and transport and tourism the immediately hit sectors, the indirect effects transmitted along international supply chains are being felt across the entire world economy. These ripple effects highlight the intrinsic link between socio-economic and environmental dimensions, and emphasise the challenge of addressing unsustainable global patterns. How humanity reacts to this crisis will define the post-pandemic world.

Source: journals.plos.org

Scarpino SV, Scott JG, Eggo RM, Clements B, Dimitrov NB, Meyers LA (2020) Socioeconomic bias in influenza surveillance. PLoS Comput Biol 16(7): e1007941. https://doi.org/10.1371/journal.pcbi.1007941

 

Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.

Source: journals.plos.org

Xiaowen Dong, Alfredo J. Morales, Eaman Jahani, Esteban Moro, Bruno Lepri, Burcin Bozkaya, Carlos Sarraute, Yaneer Bar-Yam & Alex Pentland
EPJ Data Science volume 9, Article number: 20 (2020)

 

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications.

Source: epjdatascience.springeropen.com

Jonas L. Juul, Kaare Græsbøll, Lasse Engbo Christiansen, Sune Lehmann

 

Across the world, scholars are racing to predict the spread of the novel coronavirus, COVID-19. Such predictions are often pursued by numerically simulating epidemics with a large number of plausible combinations of relevant parameters. It is essential that any forecast of the epidemic trajectory derived from the resulting ensemble of simulated curves is presented with confidence intervals that communicate the uncertainty associated with the forecast. Here we argue that the state-of-the-art approach for summarizing ensemble statistics does not capture crucial epidemiological information. In particular, the current approach systematically suppresses information about the projected trajectory peaks. The fundamental problem is that each time step is treated separately in the statistical analysis. We suggest using curve-based descriptive statistics to summarize trajectory ensembles. The results presented allow researchers to report more representative confidence intervals, resulting in more realistic projections of epidemic trajectories and — in turn — enable better decision making in the face of the current and future pandemics.

Source: arxiv.org

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana

 

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

Source: arxiv.org