Urban sensing as a random search process

Kevin O’Keeffe, Paolo Santi, Brandon Wang, CarloRatti

Physica A: Statistical Mechanics and its Applications
Volume 562, 15 January 2021, 125307

We study a new random search process: the taxi drive. The motivation for this process comes from urban sensing in which sensors are mounted on moving vehicles such as taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi drive is composed of both random and regular parts: passengers are brought to randomly chosen locations via deterministic (i.e. shortest paths) routes. We show through a numerical study that this hybrid motion endows the taxi drive with advantageous spreading properties. In particular, on certain graph topologies it offers reduced cover times compared to random walks and persistent random walks.

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Adapting to the challenges of warming | Science

Steven C. Sherwood
Science 13 Nov 2020:
Vol. 370, Issue 6518, pp. 782-783

Heat extremes on Earth have reached a disturbing new level in recent years. The July 2020 temperatures soared across Siberia and reached a record-breaking 38°C inside the Arctic Circle, continuing a line of record heat events globally. “Event attribution” calculations, which are an endeavor to apportion blame for extreme events through quantitative modeling, suggest that some events would have been nearly impossible without human-induced global warming. This includes the recent Siberian summer and the 2018 heat wave in Japan, which killed more than a thousand people (1, 2). Rising heat is creating new challenges for humanity that will require new adaptation and protection measures. Smart implementation requires careful calculation of how further global temperature rises will translate into short-term regional heat events and how these will translate into impacts on human health and activities, food supply, infrastructure, and ecosystems.

Read the full article at: science.sciencemag.org

Mobility network models of COVID-19 explain inequities and inform reopening

Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky & Jure Leskovec
Nature (2020)

The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

Read the full article at: www.nature.com

Heterogeneity in social and epidemiological factors determines the risk of measles outbreaks

Paolo Bosetti, Piero Poletti, Massimo Stella, Bruno Lepri, Stefano Merler, and Manlio De Domenico
PNAS

The recent increase in large-scale migration trends generates several concerns about public health in destination countries, especially in the presence of massive incoming human flows from countries with a disrupted healthcare system. Here, we analyze the flow of 3.5 M Syrian refugees toward Turkey to quantify the risk of measles outbreaks. Our results suggest that heterogeneity in immunity, population distribution, and human-mobility flows is mostly responsible for such a risk: In fact, adequate policies of social integration and vaccine campaigns provide the most effective strategies to reduce measles disease risks for both migrant and hosting populations.

Read the full article at: www.pnas.org