Leveraging Network Science for Social Distancing to Curb Pandemic Spread

Roy, Samit; Cherevko, Andrii; Chakraborty, Sayak; Ghosh, Nirnay; Ghosh, Preetam

doi:10.1109/access.2021.3058206

Cited by 13 publications

(12 citation statements)

References 30 publications

(37 reference statements)

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“…The non-pharmaceutical intervention of social distancing has been a primary policy to reduce contagion for a big part of the pandemic. Block et al [28] and Roy et al [29] proposed social distancing measures using network science concepts of clustering and homophily to enable individuals in making local decisions to curb spread. With regard to vaccines, there has been scepticism on the part of the public to accept the emerging vaccinations [30].…”

Section: ) New Interdiction and Vaccine Distribution Strategiesmentioning

confidence: 99%

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

2021

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COVID-19 is an infectious disease that has been declared as a global public health emergency by the World Health Organization. Besides claiming over 3 million lives worldwide, COVID-19 led to unprecedented disruption in industrial productivity, trading, and global food supply, resulting in loss of livelihood. Despite initial success in curbing the spread of diseases through a lockdown and rapid vaccine development, human lives are threatened by sudden outbreaks from new strains of the virus. This motivates the conceptualization of effective interdiction policies to inform human mobility in a manner that the damage to lives as well as the economy could be minimized. In this work, we present three interdiction policies that employ machine learning-based network inference on daily infected cases to infer the influence of contagion between neighboring zones. The proposed policies leverage network science concepts such as coloring and clustering to attain time-varying partial or complete travel restrictions. Through extensive simulation experiments, we show that these strategies yield lower infection spread than greedy and random migration-based tie elimination approaches as well as a balance between contagion mitigation and economic gain.

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Section: ) New Interdiction and Vaccine Distribution Strategiesmentioning

confidence: 99%

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

2021

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“…Roy et al utilized regression studies to report pre-lockdown factors that impact post-lockdown contagion [14] and topic modeling to find the least and most affected economic sectors in the US [15]. They also proposed dynamic lockdown and mobility management strategies to curb contagion on the basis of economic and epidemiological profiles [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends

Roy

Ghosh

2021

Healthcare

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COVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US.

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“…Machine learning-based prediction models leverage epidemiological and clinical data to identify vulnerable individuals 8 , 9 , trace the trends in infection dynamics 10 and measure the long-term effects of testing in identifying affected individuals 11 . We proposed a time-varying linear optimization-based approach, which incorporated epidemiological factors, like population density, susceptible count and infected ratio as well as transportation costs, to distribute vaccines among zones 12 and optimization measures based on network science to guide human mobility and restrict contact of susceptible and infective individuals 13 . Regression and topic models have been used to pinpoint socioeconomic factors controlling contagion and the economic sectors affected by it 14 , 15 , while reinforcement learning has been employed to design a dynamic pandemic lockdown strategy to control mobility of individuals within zones based on its healthcare resource budget 16 .…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal tracing of pandemic spread from infection data

Roy

Biswas

Ghosh

2021

Sci Rep

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COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.

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Leveraging Network Science for Social Distancing to Curb Pandemic Spread

Cited by 13 publications

References 30 publications

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends

Spatiotemporal tracing of pandemic spread from infection data

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