Modeling geographical spread of COVID-19 in India using network-based approach (Preprint)

2020 ◽  
Author(s):  
Ankush Kumar
Keyword(s):  
Human Mobility ◽  
Uttar Pradesh ◽  
Decisive Role ◽  
Northeast India ◽  
North India ◽  
Mobility Patterns ◽  
Large Country ◽  
Geographical Spread ◽  
Internal Mobility ◽  
The Impact

BACKGROUND COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. OBJECTIVE This study aims to propose a framework for predicting the geographical spread of COVID-19 based on human mobility, by incorporating migration and transport statistics. The motivation of the research is to identify the locations, which can be at higher level COVID -19 spread risk, during migrants transfer and transportation activities. METHODS We use reported COVID-19 cases, census migration data, and monthly airline data of passengers. RESULTS We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger's data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in the northeast India and in some south India are relatively safe. CONCLUSIONS A model is developed for systematically understanding the effect of migration and transport on the spreading of COVID-19, and predetermining the hotspots on real time basis. Through the model, we identified the airports and states that are at higher level of COVID-19 risk. The study can guide policymakers in prior planning of transport and estimate the required medical and quarantine facilities to minimize the impact of COVID-19.

scholarly journals Modeling geographical spread of COVID-19 in India using network-based approach

2020 ◽  
Author(s):  
Ankush Kumar
Keyword(s):  
Human Mobility ◽  
Uttar Pradesh ◽  
Decisive Role ◽  
Northeast India ◽  
North India ◽  
Mobility Patterns ◽  
Large Country ◽  
Geographical Spread ◽  
Internal Mobility ◽  
Global Concern

COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. Herein, we propose a framework for predicting the geographical spread of COVID-19, using reported COVID-19 cases, census migration data, and monthly airline data of passengers. We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger’s data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in northeast India and in some south India are relatively safe. The detailed analysis in this direction will guide policymakers for prior planning of transport, and minimize the spread of COVID-19.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

Coalition Politics in North India

1968 ◽  
Vol 62 (4) ◽  
pp. 1174-1191 ◽  
Author(s):  
Paul R. Brass
Keyword(s):  
Structural Characteristics ◽  
Uttar Pradesh ◽  
North India ◽  
Indian States ◽  
Indian Politics ◽  
Dominant Party ◽  
Coalition Politics ◽  
Party Development ◽  
General Elections ◽  
The Impact

The years 1964 and 1967 stand as two crucial landmarks in the democratic development of India's political systems both at the center and in the several states. In the three years since Nehru's death in May, 1964, Indian politics entered fully into a major test of legitimacy. Since 1964, the national leadership of the Indian National Congress has three times demonstrated its ability to handle smoothly the first stage of India's process of legitimizing democratic political authority—that of transferring power from a charismatic leader to his successors within the dominant party. After the 1967 General Elections, Indian politics moved to a second stage to confront the problems of transferring power from the previously dominant Congress to diverse parties and party coalitions in more than half the Indian states.The purpose of this paper is to examine the implications for party development in India of the ways in which power has been transferred from the Congress to multi-group coalitions in the three north Indian states of Bihar, Uttar Pradesh, and Punjab. Specifically, I am concerned with the structural characteristics of the developing party systems in the three states; with the roles played in the systems by parties, factions, and individuals; and with the impact of the ways in which the systems function upon government formation and stability. I will argue that north Indian political parties operate in systems in which inter-party ideological divisions are less decisive in the formation and breakup of governments than intra-party divisions.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals The impact of dengue illness on social distancing and caregiving behavior

2021 ◽  
Vol 15 (7) ◽  
pp. e0009614
Author(s):  
Kathryn L. Schaber ◽  
Amy C. Morrison ◽  
William H. Elson ◽  
Helvio Astete-Vega ◽  
Jhonny J. Córdova-López ◽  
...  
Keyword(s):  
Human Mobility ◽  
Social Contact ◽  
Febrile Illness ◽  
Well Being ◽  
Denv Infection ◽  
Transmission Dynamics ◽  
Mobility Patterns ◽  
Social Contacts ◽  
Infectious Individual ◽  
The Impact

Background Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. Methodology and principal findings Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low “health-related quality of well-being” during illness (Fisher’s Exact, p = 0.01). Conclusions/Significance Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual’s exposure to virus or a presymptomatic/clinically inapparent individual’s contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

scholarly journals Human mobility and COVID-19 initial dynamics

2020 ◽  
Vol 101 (3) ◽  
pp. 1901-1919 ◽  
Author(s):  
Stefano Maria Iacus ◽  
Carlos Santamaria ◽  
Francesco Sermi ◽  
Spyros Spyratos ◽  
Dario Tarchi ◽  
...  
Keyword(s):  
Coming Out ◽  
Human Mobility ◽  
Mobile Network ◽  
Systematic Analysis ◽  
Mobile Data ◽  
Eu Member States ◽  
Mobility Data ◽  
Internal Mobility ◽  
The Impact ◽  
Excess Deaths

Abstract Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14–20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

Impact of Human Mobility Trends on the Outbreak of COVID-19 Across Different States of India

2022 ◽  
pp. 241-255
Author(s):  
Swati Ahiirao ◽  
Shraddha Phansalkar ◽  
Nikhil Matta ◽  
Ketan Kotecha
Keyword(s):  
Public Health ◽  
Population Density ◽  
Statistical Methods ◽  
Death Rate ◽  
Human Mobility ◽  
Decisive Role ◽  
Mobility Patterns ◽  
Public Health Infrastructure ◽  
Health Infrastructure ◽  
Regulatory Bodies

The explosion of coronavirus has posed challenges to public health infrastructure in India. This pandemic can be contained with social distancing and isolation. The analysis of human mobility trends plays a decisive role in the spread of the pandemic. These movement patterns are extracted from Google COVID-19 Community Mobile Reports. These reports help to analyze the human mobility trends to various frequently visited places across different states of India. This work focuses on analyzing mobility trends in India and their effect on the spread of pandemic in terms of number of active cases and death rate. The mobility patterns, number of tests conducted, population density across different states in India are explored to understand their effect on the severity of epidemic. These features are correlated using statistical methods. This study lays the foundation in building a framework to contain the contributors for the spread of pandemics and provide insights to the regulatory bodies to strategize enforcing or revoking lockdown restrictions across regions in the country.

scholarly journals Coordinating Measurements in Uncertain Participatory Sensing Settings

2018 ◽  
Vol 61 ◽  
pp. 433-474 ◽  
Author(s):  
Alexandros Zenonos ◽  
Sebastian Stein ◽  
Nicholas R. Jennings
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Participatory Sensing ◽  
Sufficient Information ◽  
Mobility Patterns ◽  
Computationally Efficient ◽  
The Impact

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

scholarly journals The duration of travel impacts the spatial dynamics of infectious diseases

2020 ◽  
Vol 117 (36) ◽  
pp. 22572-22579 ◽  
Author(s):  
John R. Giles ◽  
Elisabeth zu Erbach-Schoenberg ◽  
Andrew J. Tatem ◽  
Lauren Gardner ◽  
Ottar N. Bjørnstad ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Human Mobility ◽  
Trip Duration ◽  
Disease Dynamics ◽  
Mobility Patterns ◽  
Spatial And Temporal Scales ◽  
The Impact ◽  
Spatial Transmission

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

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