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

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.

scholarly journals A municipality-based approach using commuting census data to characterise the vulnerability to influenza-like epidemic: the COVID-19 application in Italy.

2020 ◽  
Author(s):  
Lara Savini ◽  
Luca Candeloro ◽  
Paolo Calistri ◽  
Annamaria Conte
Keyword(s):  
Census Data ◽  
Human Mobility ◽  
National Level ◽  
Decision Makers ◽  
Disease Spread ◽  
Use Of Data ◽  
Italian Government ◽  
Health Authorities ◽  
Specific Contact ◽  
Emergency Measures

In February 2020, Italy became the epicentre for COVID-19 in Europe and at the beginning of March, in response to the growing epidemic, the Italian Government put in place emergency measures to restrict the movement of the population. Human mobility represents a crucial element to be considered in modelling human infectious diseases. In this paper, we examined the mechanisms underlying COVID-19 propagation using a Susceptible-Infected stochastic model (SI) driven mainly by commuting network in Italy. We modelled a municipality-specific contact rate to capture the disease permeability of each municipality, considering the population at different times of the day and describing the characteristic of the municipalities as attractors of commuters or places that make their workforce available elsewhere. The purpose of our analysis is to provide a better understanding of the epidemiological context of COVID-19 in Italy and to characterize the territory in terms of vulnerability at local or national level. The use of data at such a high spatial resolution allows highlighting particular situations on which the health authorities can promptly intervene to control the disease spread. Our approach provides decision-makers with useful geographically detailed metrics to evaluate those areas at major risk for infection spreading and for which restrictions of human mobility would give the greatest benefits, not only at the beginning of the epidemic but also in the last phase, when the risks deriving from the gradual lockdown exit strategies must be carefully evaluated.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

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

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Enrique Hernández-Orallo ◽  
Antonio Armero-Martínez
Keyword(s):  
Human Mobility ◽  
Opportunistic Networks ◽  
Mobility Models ◽  
Key Factors ◽  
Mobile Computer ◽  
Heat Map ◽  
Health Authorities ◽  
Spatial Risk ◽  
Management Plans ◽  
Human Infections

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.

The Crisis of Social Institutions and Police Homicides: The Adverse Effects of Low Institutional Control

2021 ◽  
pp. 108876792110184
Author(s):  
Kamali’ilani T. E. Wetherell ◽  
Terance D. Miethe
Keyword(s):  
Public Policy ◽  
Adverse Effects ◽  
Census Data ◽  
Social Institutions ◽  
Crime Rates ◽  
Future Research ◽  
Homicide Rates ◽  
Institutional Control ◽  
Different Levels

Using U.S. census data and a multi-source database on officer-involved killings, the current study extends previous research by exploring the influence of measures of weak social control in economic, educational, and familial institutions on state rates of police homicide. States with lower levels of institutional control are found to have higher overall rates of police homicides and police killings involving Black, Hispanic, and White decedents. The significant effects of institutional control on these police homicide rates are generally found to exhibit contextual invariance across different levels of various control variables (e.g., comparisons of states with low or high violent crime rates, low vs high economic inequality, low vs high levels of urbanization). These results and the limitations of this study are discussed in terms of implications for future research and public policy on police homicides and the role of social institutions in minimizing the occurrence of these incidents.

scholarly journals Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics

2020 ◽  
Author(s):  
Viktor Jirsa ◽  
Spase Petkoski ◽  
Huifang Wang ◽  
Marmaduke Woodman ◽  
Jan Fousek ◽  
...  
Keyword(s):  
Human Mobility ◽  
Disease Spread ◽  
Vulnerable Groups ◽  
Psychosocial Variables ◽  
Cross Sectional ◽  
Societal Factors ◽  
Spread Model ◽  
Careful Handling ◽  
Diverse Groups ◽  
The Impact

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

scholarly journals Spatial Dynamics of Internal Migration Flows of the Skilled and Unskilled in Mexico

2020 ◽  
Author(s):  
Miguel Flores Segovia ◽  
Eliud Silva
Keyword(s):  
Internal Migration ◽  
Census Data ◽  
Relative Concentration ◽  
Spatial Dynamics ◽  
Unskilled Labor ◽  
Geographical Patterns ◽  
Economic Agglomeration ◽  
Skilled And Unskilled Labor ◽  
Migratory Patterns ◽  
Relationship Of

ABSTRACT: The dynamics of the internal migration is a crucial element in the composition of the workforce of a certain region, so its analysis contributes to the better understanding of labor markets and sociodemographic changes in a region. In order to characterize the most recent patterns of migratory flows of skilled and unskilled labor, census data are considered for the periods 1995-2000, 2005-2010 and 2010-2015. The analysis considers different indicators that describe the intensity and relative concentration of interstate migration. Changes in migratory patterns are evident; a lower concentration of internal migration whose effect is more marked for unskilled labor. That is, it is observed that the number of states that play a preponderant role in the redistribution of labor in Mexico has increased. The relationship of domestic labor mobility is evident to the regional transformation as a result of new geographical patterns of location of investment, production and economic agglomeration.

scholarly journals Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

2021 ◽  
Author(s):  
Tetsuya Yamada ◽  
Shoi Shi
Keyword(s):  
Time Series ◽  
Infectious Diseases ◽  
Time Series Data ◽  
Human Mobility ◽  
Emerging Infectious Diseases ◽  
Human Movement ◽  
Data Driven ◽  
Disease Spread ◽  
Series Data ◽  
Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

Bigdata-based investigation of heterogeneous migration and population distribution

2021 ◽  
Author(s):  
Zhiwei Fan ◽  
L. Xiong ◽  
Bo Zheng
Keyword(s):  
Census Data ◽  
Population Distribution ◽  
Model Simulation ◽  
Human Mobility ◽  
Economic Systems ◽  
Migration Patterns ◽  
Demographic Trend ◽  
Education Levels ◽  
The Uk ◽  
Migration Probability

Abstract Human mobility is very important in understanding complex social and economic systems. With massive empirical datasets from the China Household Finance Survey and the National Statistics in the UK, we construct a migration probability matrix, and analyze the heterogeneous migration patterns. We then develop a random walk model to dynamically simulate the population distribution. In the stationary state, the resulting population distribution is in good agreement with the real statistical data. For comparison, simulations with an optimized gravity model and other datasets such as the census data in China are also performed. Further, the model simulation is applied to predict the demographic trend with different education levels. Our method could be generally extended to other real communities and internet worlds.

scholarly journals Changing travel patterns in China during the early stages of the COVID-19 pandemic

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamish Gibbs ◽  
◽  
Yang Liu ◽  
Carl A. B. Pearson ◽  
Christopher I. Jarvis ◽  
...  
Keyword(s):  
Public Health ◽  
Human Mobility ◽  
Transportation Network ◽  
Mainland China ◽  
Disease Spread ◽  
Travel Patterns ◽  
Network Analyses ◽  
Early Stages ◽  
Travel Restrictions ◽  
Spatio Temporal

Abstract Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigate the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020, and discuss their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower healthcare capacity. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and did not lead to structural reorganisation of the transportation network during the study period.

scholarly journals Genetics, Egypt, and History: Interpreting Geographical Patterns of Y Chromosome Variation

2005 ◽  
Vol 32 ◽  
pp. 221-246 ◽  
Author(s):  
S.O.Y. Keita ◽  
A.J. Boyce
Keyword(s):  
Y Chromosome ◽  
Population Biology ◽  
Time Frame ◽  
Nile Valley ◽  
Geographical Patterns ◽  
History Of ◽  
Modern Egypt ◽  
Vantage Points ◽  
Different Levels ◽  
Insight Into

Modern Egypt, the site of Africa's earliest state, lies near the crossroads of two other continents, and has had historic interactions with all its neighboring regions. This alone would make it an ideal place to study historical population biology. Egypt can also be conceptualized as a linear oasis in the eastern Sahara, one that traverses several regions of Africa. An oasis can be a way station or serve as a refugium, as well as be a place of settlement with its own special biological and cultural adaptive strategies. Both of these perspectives—crossroads and oasis/refugium—can be expected to provide insight into the processes that could have affected the Nile valley's populations/peoples. From these vantage points this presentation will examine aspects of what might be called the historical genetics of the Nile valley, with a focus on the Y chromosome. The time-frame is the late pleistocene through holocene; within this there are different levels of biocultural history. Of special interest here is patterns of north-south variation in the Egyptian Nile valley.

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