830 research outputs found
Interplay between population density and mobility in determining the spread of epidemics in cities
The increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns
COVID-19 and Income Profile: How People in Different Income Groups Responded to Disease Outbreak, Case Study of the United States
Due to immature treatment and rapid transmission of COVID-19, mobility
interventions play a crucial role in containing the outbreak. Among various
non-pharmacological interventions, community infection control is considered to
be a quite promising approach. However, there is a lack of research on
improving community-level interventions based on a community's real conditions
and characteristics using real-world observations. Our paper aims to
investigate the different responses to mobility interventions between
communities in the United States with a specific focus on different income
levels. We produced six daily mobility metrics for all communities using the
mobility location data from over 100 million anonymous devices on a monthly
basis. Each metric is tabulated by three performance indicators: "best
performance," "effort," and "consistency." We found that being high-income
improves social distancing behavior after controlling multiple confounding
variables in each of the eighteen scenarios. In addition to the reality that it
is more difficult for low-income communities to comply with social distancing,
the comparisons between scenarios raise concerns on the employment status,
working condition, accessibility to life supplies, and exposure to the virus of
low-income communities
Data-Centric Epidemic Forecasting: A Survey
The COVID-19 pandemic has brought forth the importance of epidemic
forecasting for decision makers in multiple domains, ranging from public health
to the economy as a whole. While forecasting epidemic progression is frequently
conceptualized as being analogous to weather forecasting, however it has some
key differences and remains a non-trivial task. The spread of diseases is
subject to multiple confounding factors spanning human behavior, pathogen
dynamics, weather and environmental conditions. Research interest has been
fueled by the increased availability of rich data sources capturing previously
unobservable facets and also due to initiatives from government public health
and funding agencies. This has resulted, in particular, in a spate of work on
'data-centered' solutions which have shown potential in enhancing our
forecasting capabilities by leveraging non-traditional data sources as well as
recent innovations in AI and machine learning. This survey delves into various
data-driven methodological and practical advancements and introduces a
conceptual framework to navigate through them. First, we enumerate the large
number of epidemiological datasets and novel data streams that are relevant to
epidemic forecasting, capturing various factors like symptomatic online
surveys, retail and commerce, mobility, genomics data and more. Next, we
discuss methods and modeling paradigms focusing on the recent data-driven
statistical and deep-learning based methods as well as on the novel class of
hybrid models that combine domain knowledge of mechanistic models with the
effectiveness and flexibility of statistical approaches. We also discuss
experiences and challenges that arise in real-world deployment of these
forecasting systems including decision-making informed by forecasts. Finally,
we highlight some challenges and open problems found across the forecasting
pipeline.Comment: 67 pages, 12 figure
- …