相关论文: Modelling exposure between populations using netwo…

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated…

The availability of cellphone geolocation data provides a remarkable opportunity to study human mobility patterns and how these patterns are affected by the recent pandemic. Two simple centrality metrics allow us to measure two different…

This study develops a new method to quantify neighborhood activity levels at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social distancing policies vary with socioeconomic and…

The COVID-19 pandemic has changed human life. To mitigate the pandemic's impacts, different regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven…

The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic…

Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic the regular contact patterns of the population has been disrupted due to social distancing both imposed by…

COVID-19 significantly has changed the lifestyle in the urban areas. Urban parks reemerged as a savior of leisure activities and social joints under strict social-distancing measures. Thus, there have been significant changes in the…

In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the…

Social contact patterns are key drivers of infectious disease transmission. During the COVID-19 pandemic, differences between pre-COVID and COVID-era contact rates were widely attributed to non-pharmaceutical interventions such as…

During the COVID-19 pandemic, governments faced difficulties in implementing mobility restriction measures, as no clear quantitative relationship between human mobility and infection spread in large cities is known. We developed a model…

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors. This study used large-scale mobile device location data in…

The COVID-19 pandemic and the resulting economic recession negatively affected many people's physical, social, and psychological health and has been shown to change population-level mobility, but little attention has been given to park…

The novel coronavirus (COVID-19) pandemic has posed unprecedented challenges for the utilities and grid operators around the world. In this work, we focus on the problem of load forecasting. With strict social distancing restrictions, power…

What predicts a neighborhood's resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel…

We present a collection of networks that describe the travel patterns between municipalities in Mexico between 2020 and 2021. Using anonymized mobile device geo-location data we constructed directed, weighted networks representing the…

Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer…

Commuting patterns are a central component of urban dynamics and many societal activities. Exogenous shocks, such as a pandemic, might drastically modify them inducing heterogeneous variations across socioeconomic strata. Here, we quantify…

This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five…

In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare systems and economies. Using Italian data at different…

We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the…