scholarly journals Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

2019 ◽  
Vol 11 (4) ◽  
pp. 92 ◽  
Author(s):  
Jürgen Hackl ◽  
Thibaut Dubernet
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Urban Setting ◽  
Disease Spread ◽  
Model Parameters ◽  
Epidemic Spreading ◽  
Agent Based ◽  
Spread Model ◽  
Spread Dynamics

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

scholarly journals A Linear Dynamical Perspective on Epidemiology: Interplay Between Early COVID-19 Outbreak and Human Mobility

2021 ◽  
Author(s):  
Shakib Mustavee ◽  
Shaurya Agarwal ◽  
Suddhasattwa Das ◽  
Chinwendu Enyioha
Keyword(s):  
Human Mobility ◽  
Dynamic Mode Decomposition ◽  
Disease Spread ◽  
Dynamic Mode ◽  
Control Input ◽  
Mobility Patterns ◽  
Spread Model ◽  
Spread Dynamics ◽  
The Impact ◽  
Locally Linear

Abstract This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and the input-output characteristics as a locally linear controlled dynamical system. The approach solely relies on the snapshots of spatiotemporal data and does not require any knowledge of the system’s physical laws. We exploit the Koopman operator framework by utilizing the Hankel Dynamic Mode Decomposition with Control (HDMDc) algorithm to obtain a linear disease spread model incorporating human mobility as a control input. The study demonstrated that the proposed methodology could capture the impact of local mobility on the early dynamics of the ongoing global pandemic. The obtained locally linear model can accurately forecast the number of new infections for various prediction windows ranging from two to four weeks. The study corroborates a leader-follower relationship between mobility and disease spread dynam-

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 Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals Optimal Lockdown Strategies: All about Time

2021 ◽  
Author(s):  
Anagh Pathak ◽  
Varun Madan Mohan ◽  
Arpan Banerjee
Keyword(s):  
Large Scale ◽  
Economic Cost ◽  
Mitigation Strategies ◽  
Disease Spread ◽  
Small Scale ◽  
Mass Model ◽  
Agent Based ◽  
Effective Reduction ◽  
Limiting Case ◽  
Infection Spread

Abstract Lockdowns are disease mitigation strategies that aim to contain the spread of an infection by restricting the interactions of its carriers. Lockdowns can thus have a considerable economic cost, which makes the identification of optimal lockdown windows that minimize both infection spread and economic disruption imperative. A well-known feature of complex dynamical systems is their sensitivity to the timing of external inputs. Hence, we hypothesized that the timing and duration of lockdowns should dictate lockdown outcomes. We demonstrate this concept computationally from two perspectives - Firstly, a stochastic "small-scale" Agent Based Model (ABM) of a Susceptible-Infected-Recovered (SIR) disease spread and secondly, a deterministic "large-scale" perspective using a multi-group SIR mass model with parameters determined from the SARS-CoV2 data in India. Lockdowns were implemented as an effective reduction of interaction probabilities in both models. This allowed us to evaluate the parametric variations of lockdown intensity and duration onto the dynamical properties of the infection spread over different connection topologies. We definitively show that the lockdown outcomes in both the stochastic small-scale and deterministic large-scale perspectives depend sensitively on the timing of its imposition and that it is possible to minimize lockdown duration while limiting case loads to numbers below hospitalization thresholds.

scholarly journals Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

2020 ◽  
Vol 68 ◽  
pp. 541-570
Author(s):  
Raquel Rosés ◽  
Cristina Kadar ◽  
Charlotte Gerritsen ◽  
Chris Rouly
Keyword(s):  
Human Activity ◽  
Urban Areas ◽  
Large Scale ◽  
Complex Dynamics ◽  
Open Data ◽  
Human Movement ◽  
Urban Structure ◽  
Real World Data ◽  
Activity Data ◽  
Agent Based

In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

scholarly journals Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

2020 ◽  
Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning
Keyword(s):  
Public Transportation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Tracking System ◽  
Human Mobility ◽  
Transportation Systems ◽  
Mobility Patterns ◽  
Systematic Analysis ◽  
Long Term Study

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

scholarly journals Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Urban Areas ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Epidemic Spread ◽  
Long Distance ◽  
Mobility Data ◽  
The World ◽  
Travel Restrictions ◽  
Long Distance Movement

AbstractIn response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

scholarly journals Interplay between population density and mobility in determining the spread of epidemics in cities

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Surendra Hazarie ◽  
David Soriano-Paños ◽  
Alex Arenas ◽  
Jesús Gómez-Gardeñes ◽  
Gourab Ghoshal
Keyword(s):  
Population Density ◽  
Urban Areas ◽  
Human Mobility ◽  
The United States ◽  
Mitigation Strategies ◽  
Mobility Patterns ◽  
Epidemic Spreading ◽  
Optimal Control Strategy ◽  
Global Trend ◽  
Data Driven Approach

AbstractThe 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.

scholarly journals Rodent Control a Tool for Prevention of Leptospirosis - A Success Story on Human Leptospirosis with Gujarat State

2020 ◽  
Vol 52 (03) ◽  
pp. 38-42
Author(s):  
AMK Mohan Rao ◽  
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Health Management ◽  
Rattus Rattus ◽  
Pilot Scale ◽  
Disease Prevalence ◽  
Disease Spread ◽  
Health Departments ◽  
Rodent Control ◽  
Tropical Country

Leptospirosis is a debilitating zoonosis for humans as well as domestic animals worldwide and widespread in tropical and sub tropical areas with high rainfall. India being a tropical country is potential for this debilitating disease. Among mammals, rodents are most important and widely distributed reservoirs of leptospiral infection. Four species of rodents i.e., Rattus norvegicus, Rattus rattus, Bandicota indica and Bandicota bengalensis, are known vectors for this disease and transmit the infection in rural and urban areas. Hence, rodent control in larger areas is recommended for preventing the spread of this disease. Gujarat is one of the States having established leptospirosis endemic pockets, particularly in Valsad, Navsari, Tapi and Surat districts. Hence, the Departments of Health and Agriculture, Gujarat conducted coordinated rodent vector control campaigns to prevent the disease spread. All activities were guided by National Institute of Plant Health Management, Hyderabad and funded under National Plan on Rodent Pest Management of Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India. The initial pilot scale rodent control campaigns conducted in 114 villages covering 5,127 hectares jointly by Agriculture and Health Departments of the state in 2009 brought a reduction of 61% leptospirosis disease prevalence. Following this success, large scale community based rodent control campaigns were organized during 2012 in these districts in 1822 villages covering 4,70,782 hectares and obtained 82% reduction in the disease prevalence. It was observed that the inter-departmental coordination of Agriculture and Health Departments brought these significant results, which can be replicated and adopted elsewhere to prevent leptospirosis in the country.

scholarly journals Epidemic Overdispersion Strengthens the Effectiveness of Mobility Restrictions

2021 ◽  
Author(s):  
Gerrit Großmann ◽  
Michael Backenköhler ◽  
Verena Wolf
Keyword(s):  
Simulation Study ◽  
Branching Process ◽  
Branching Processes ◽  
Human Mobility ◽  
Transmission Dynamics ◽  
Epidemic Spreading ◽  
Interaction Structure ◽  
Agent Based ◽  
Model Combining ◽  
Spatial Branching Process

AbstractHuman mobility is the fuel of global pandemics. In this simulation study, we analyze how mobility restrictions mitigate epidemic processes and how this mitigation is influenced by the epidemic’s degree of dispersion.We find that (even imperfect) mobility restrictions are generally efficient in mitigating epidemic spreading. Notably, the effectiveness strongly depends on the dispersion of the offspring distribution associated with the epidemic. We also find that mobility restrictions are useful even when the pathogen is already prevalent in the whole population. However, also a delayed implementation is more efficient in the presence of overdispersion. Conclusively, this means that implementing green zones is easier for epidemics with overdispersed transmission dynamics (e.g., COVID-19). To study these relationships at an appropriate level of abstraction, we propose a spatial branching process model combining the flexibility of stochastic branching processes with an agent-based approach allowing a conceptualization of locality, saturation, and interaction structure.

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