No Arabic abstract
We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, $Re^{bus}$, and in the city, $Re^{city}$, followed a compatible behavior during the first wave of the local outbreak. Moreover, by distinguishing the close contacts of healthcare workers in the buses, we discovered that their transmission, $Re^{health}$, during the same period, was systematically higher than $Re^{bus}$. This result reinforces the need for special public transportation policies for highly exposed groups of people.
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiotemporal epidemiological model that combines a spatial Cellular Automaton (CA) with a temporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared with the existing time risk prediction models, the proposed CA-SUIR model shows the multi-level risk of the county to the government and coronavirus transmission patterns under different policies. This new toolbox is first utilized to the projection of the multi-level COVID-19 prevalence over 412 Landkreis (counties) in Germany, including t-day-ahead risk forecast and the risk assessment to the travel restriction policy. As a practical illustration, we predict the situation at Christmas where the worst fatalities are 34.5 thousand, effective policies could contain it to below 21 thousand. Such intervenable evaluation system could help decide on economic restarting and public health policies making in pandemic.
The Covid-19 pandemic has radically changed our lives. Under different circumstances, people react to it in various ways. One way is to work-from-home since lockdown has been announced in many regions around the world. For some places, however, we dont know if people really work from home due to the lack of information. Since there are lots of uncertainties, it would be helpful for us to understand what really happen in these places if we can detect the reaction to the Covid-19 pandemic. Working from home indicates that people have changed the way they interact with the Internet. People used to access the Internet in the company or at school during the day. Now it is more likely that they access the Internet at home in the daytime. Therefore, the network usage changes in one place can be used to indicate if people in this place actually work from home. In this work, we reuse and analyze Trinocular outages data (around 5.1M responsive /24 blocks) over 6 months to find network usage changes by a new designed algorithm. We apply the algorithm to sets of /24 blocks in several cities and compare the detected network usage changes with real world covid-19 events to verify if the algorithm can capture the changes reacting to the Covid-19 pandemic. By applying the algorithm to all measurable /24 blocks to detect network usages changes, we conclude that network usage can be an indicator of the reaction to the Covid-19 pandemic.
This paper continues to highlight trends in mobility and sociability in New York City (NYC), and supplements them with similar data from Seattle, WA, two of the cities most affected by COVID-19 in the U.S. Seattle may be further along in its recovery from the pandemic and ensuing lockdown than NYC, and may offer some insights into how travel patterns change. Finally, some preliminary findings from cities in China are discussed, two months following the lifting of their lockdowns, to offer a glimpse further into the future of recovery.
Identifying highly susceptible individuals in spreading processes is of great significance in controlling outbreaks. In this paper, we explore the susceptibility of people in susceptible-infectious-recovered (SIR) and rumor spreading dynamics. We first study the impact of community structure on peoples susceptibility. Despite that the community structure can reduce the infected population given same infection rates, it will not deterministically affect nodes susceptibility. We find the susceptibility of individuals is sensitive to the choice of spreading dynamics. For SIR spreading, since the susceptibility is highly correlated to nodes influence, the topological indicator k-shell can better identify highly susceptible individuals, outperforming degree, betweenness centrality and PageRank. In contrast, in rumor spreading model, where nodes susceptibility and influence have no clear correlation, degree performs the best among considered topological measures. Our finding highlights the significance of both topological features and spreading mechanisms in identifying highly susceptible population.
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation models results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.