How Human Mobility Models Can Help to Deal with COVID-19

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.

Download Full-text

Human mobility models for opportunistic networks

IEEE Communications Magazine ◽

10.1109/mcom.2011.6094021 ◽

2011 ◽

Vol 49 (12) ◽

pp. 157-165 ◽

Cited By ~ 134

Author(s):

Dmytro Karamshuk ◽

Chiara Boldrini ◽

Marco Conti ◽

Andrea Passarella

Keyword(s):

Human Mobility ◽

Opportunistic Networks ◽

Mobility Models

Download Full-text

The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030996 ◽

2021 ◽

Vol 18 (3) ◽

pp. 996

Author(s):

Yun Li ◽

Moming Li ◽

Megan Rice ◽

Haoyuan Zhang ◽

Dexuan Sha ◽

...

Keyword(s):

Growth Rate ◽

Human Mobility ◽

Public Information ◽

Key Factors ◽

Information Campaigns ◽

Rapid Spread ◽

The Us ◽

Policy Measures ◽

The Impact ◽

At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

Download Full-text

Human mobility models reveal the underlying mechanism of seasonal movements across China

International Journal of Modern Physics C ◽

10.1142/s0129183122500541 ◽

2021 ◽

Author(s):

Bing Song ◽

Xiao-Yong Yan ◽

Suoyi Tan ◽

Bin Sai ◽

Shengjie Lai ◽

...

Keyword(s):

Traffic Engineering ◽

Human Mobility ◽

Underlying Mechanism ◽

Gravity Models ◽

Mobility Models ◽

Temporal And Spatial Distribution ◽

Population Movements ◽

Different Types ◽

Before And After ◽

Time Periods

Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow” effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

Download Full-text

On modeling and impact of geographic restrictions for human mobility in opportunistic networks

Performance Evaluation ◽

10.1016/j.peva.2018.12.002 ◽

2019 ◽

Vol 130 ◽

pp. 17-31 ◽

Cited By ~ 3

Author(s):

Matthias Schwamborn ◽

Nils Aschenbruck

Keyword(s):

Human Mobility ◽

Opportunistic Networks

Download Full-text

Mobility Models for Opportunistic Networks

Mobility Models for Next Generation Wireless Networks ◽

10.1002/9781118344774.part6 ◽

2012 ◽

pp. 215-215

Keyword(s):

Opportunistic Networks ◽

Mobility Models

Download Full-text

Performance Analysis of A∗-Based Hop Selection Technique in Opportunistic Networks Through Movement Mobility Models

Lecture Notes on Data Engineering and Communications Technologies - 2nd International Conference on Wireless Intelligent and Distributed Environment for Communication ◽

10.1007/978-3-030-11437-4_6 ◽

2019 ◽

pp. 61-74

Author(s):

Pragya Kuchhal ◽

Satbir Jain

Keyword(s):

Performance Analysis ◽

Opportunistic Networks ◽

Mobility Models ◽

Selection Technique

Download Full-text

Key Factors Influencing Italian Mothers’ Intention to Vaccinate Sons against HPV: The Influence of Trust in Health Authorities, Anticipated Regret and Past Behaviour

Sustainability ◽

10.3390/su11236879 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6879 ◽

Cited By ~ 3

Author(s):

Caso ◽

Carfora ◽

Starace ◽

Conner

Keyword(s):

Theory Of Planned Behaviour ◽

Hpv Vaccination ◽

Anticipated Regret ◽

Key Factors ◽

Transmitted Infection ◽

Sexually Transmitted ◽

Integrated Theory ◽

Health Authorities ◽

Different Types ◽

Tpb Model

The Human Papillomavirus (HPV), which is the most common sexually transmitted infection in the world, is associated with different types of cancer, especially cervical cancer. In Italy in 2017 the Italian Ministry of Health introduced a free HPV vaccination programme for 12-years-old boys. In this study we tested an integrated theory of planned behaviour (TPB) model to explain Italian mothers’ intention to vaccinate their sons against HPV. TPB variables plus past behaviour, trust in healthy authorities and anticipated regret were measured by a questionnaire in a sample of 333 Italian mothers of 6–11 years old boys, who were not yet vaccinated against HPV. Results showed that subjective norm was the strongest predictor of mothers’ intention followed by anticipated regret, trust in health authorities, perceived behavioural control and attitude. Mediation analysis found that past behaviour influenced intention via attitude. The discussion considers the relevance of these factors in promoting mothers’ adherence.

Download Full-text

Barriers to and solutions for addressing insufficient professional interpreter use in primary healthcare

BMC Health Services Research ◽

10.1186/s12913-019-4628-6 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Fabienne N. Jaeger ◽

Nicole Pellaud ◽

Bénédicte Laville ◽

Pierre Klauser

Keyword(s):

Primary Care ◽

Primary Care Physicians ◽

Primary Care Providers ◽

Care Providers ◽

Key Factors ◽

Cross Sectional ◽

Interpreter Use ◽

Health Authorities ◽

Professional Interpreter ◽

Paediatric Primary Care

Abstract Background The aim of this nationwide study was to investigate barriers to adequate professional interpreter use and to describe existing initiatives and identify key factors for successful interpreter policies in primary care, using Switzerland as a case study. Methods Adult and paediatric primary care providers were invited to participate in an online cross-sectional questionnaire-based study. All accredited regional interpreter agencies were contacted first by email and, in the absence of a reply, by mail and then by phone. Local as well as the national health authorities were asked about existing policies. Results 599 primary care physicians participated. Among other reasons, physicians identified cumbersome organization (58.7%), absent financial coverage (53.7%) and lack of knowledge on how to arrange interpreter interventions (44%) as main barriers. The odds of organising professional interpreters were 6.6-times higher with full financial coverage. Some agencies confirmed difficulties providing professional interpreters for certain languages at a timely manner. Degrees of coverage of professional interpreter costs (full coverage to none) and organization varied between regions resulting in different levels of unmet needs. Conclusions Professional interpreter use can be improved through the following points: increase awareness and knowledge of primary care providers on interpreter use and organization, ensure financial coverage, as well as address organizational aspects. Examples of successful interventions exist.

Download Full-text

A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy

Microorganisms ◽

10.3390/microorganisms8060911 ◽

2020 ◽

Vol 8 (6) ◽

pp. 911

Author(s):

Lara Savini ◽

Luca Candeloro ◽

Paolo Calistri ◽

Annamaria Conte

Keyword(s):

Census Data ◽

Human Mobility ◽

Industrial Area ◽

Disease Spread ◽

Geographical Patterns ◽

Italian Government ◽

Health Authorities ◽

Vulnerability Map ◽

Different Levels ◽

Emergency Measures

In February 2020, Italy became the epicenter for COVID-19 in Europe, and at the beginning of March, the Italian Government put in place emergency measures to restrict population movement. Aim of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy, using commuting data at a high spatial resolution, characterizing the territory in terms of vulnerability. We used a Susceptible–Infectious stochastic model and we estimated a municipality-specific infection contact rate (β) to capture the susceptibility to the disease. We identified in Lombardy, Veneto and Emilia Romagna regions (52% of all Italian cases) significant clusters of high β, due to the simultaneous presence of connections between municipalities and high population density. Local simulated spreading in regions, with different levels of infection observed, showed different disease geographical patterns due to different β values and commuting systems. In addition, we produced a vulnerability map (in the Abruzzi region as an example) by simulating the epidemic considering each municipality as a seed. The result shows the highest vulnerability values in areas with commercial hubs, close to the highest populated cities and the most industrial area. Our results highlight how human mobility can affect the epidemic, identifying particular situations in which the health authorities can promptly intervene to control the disease spread.

Download Full-text