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Article ; Online: Promote to protect

Sayantari Ghosh / Saumik Bhattacharya / Shagata Mukherjee / Sujoy Chakravarty

Scientific Reports, Vol 14, Iss 1, Pp 1-

data-driven computational model of peer influence for vaccine perception

2024 Volume 15

Abstract: Abstract Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed ... ...

Abstract	Abstract Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed or open-ended questionnaires. Commonly used simple statistical tools do not do justice to the richness of this data. Considering the gradual development of vaccine acceptance in a society driven by multiple local/global factors as a compartmental contagion process, we propose a novel methodology where drivers and barriers of these dynamics are detected from survey participants’ responses, instead of heuristic arguments. Applying rigorous natural language processing analysis to the survey responses of participants from India, who are from various socio-demographics, education, and perceptions, we identify and categorize the most important factors as well as interactions among people of different perspectives on COVID-19 vaccines. With a goal to achieve improvement in vaccine perception, we also analyze the resultant behavioral transitions through platforms of unsupervised machine learning and natural language processing to derive a compartmental contagion model from the data. Analysis of the model shows that positive peer influence plays a very important role and causes a bifurcation in the system that reflects threshold-sensitive dynamics.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Promote to protect: data-driven computational model of peer influence for vaccine perception.

Ghosh, Sayantari / Bhattacharya, Saumik / Mukherjee, Shagata / Chakravarty, Sujoy

Scientific reports

2024 Volume 14, Issue 1, Page(s) 306

Abstract: Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed or open- ... ...

Abstract	Vaccine hesitancy and acceptance, driven by social influence, is usually explored by most researchers using exhaustive survey-based studies, which investigate public preferences, fundamental values, beliefs, barriers, and drivers through closed or open-ended questionnaires. Commonly used simple statistical tools do not do justice to the richness of this data. Considering the gradual development of vaccine acceptance in a society driven by multiple local/global factors as a compartmental contagion process, we propose a novel methodology where drivers and barriers of these dynamics are detected from survey participants' responses, instead of heuristic arguments. Applying rigorous natural language processing analysis to the survey responses of participants from India, who are from various socio-demographics, education, and perceptions, we identify and categorize the most important factors as well as interactions among people of different perspectives on COVID-19 vaccines. With a goal to achieve improvement in vaccine perception, we also analyze the resultant behavioral transitions through platforms of unsupervised machine learning and natural language processing to derive a compartmental contagion model from the data. Analysis of the model shows that positive peer influence plays a very important role and causes a bifurcation in the system that reflects threshold-sensitive dynamics.
MeSH term(s)	Humans ; COVID-19 Vaccines ; Peer Influence ; Vaccines ; Educational Status ; Perception ; Vaccination
Chemical Substances	COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2024-01-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-50756-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Quantitative Modelling of Diffusion-driven Pattern Formation in microRNA-regulated Gene Expression

Chakraborty, Priya / Ghosh, Sayantari

2023

Abstract: MicroRNAs are extensively known for post-transcriptional gene regulation and pattern formation in the embryonic developmental stage. We explore the origin of these spatio-temporal patterns mathematically, considering three different motifs here. For ... ...

Abstract	MicroRNAs are extensively known for post-transcriptional gene regulation and pattern formation in the embryonic developmental stage. We explore the origin of these spatio-temporal patterns mathematically, considering three different motifs here. For three scenarios, (1) simple microRNA-based mRNA regulation with a graded response in output, (2) microRNA-based mRNA regulation resulting in bistability in the dynamics, and (3) a coordinated response of microRNA (miRNA), simultaneously regulating the mRNAs of two different pools, detailed dynamical analysis, as well as the reaction-diffusion scenario have been considered and analyzed in the steady state and for the transient dynamics further. We have observed persistent-temporal patterns, as a result of the dynamics of the motifs, that explain spatial gradients and relevant patterns formed by related proteins in development and phenotypic heterogenetic aspects in biological systems. Competitive effects of miRNA regulation have also been found to be capable to cause spatio-temporal patterns, persistent enough to direct developmental decisions. Under coordinated regulation, miRNAs are found to generate spatio-temporal patterning even from complete homogeneity in concentration of target protein, which may have impactful insights in choice of cell-fates. Comment: 31 pages
Keywords	Quantitative Biology - Quantitative Methods
Subject code	612
Publishing date	2023-07-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Emergent correlations in gene expression dynamics as footprints of resource competition.

Chakraborty, Priya / Ghosh, Sayantari

The European physical journal. E, Soft matter

2021 Volume 44, Issue 10, Page(s) 131

Abstract: Genetic circuits need a cellular environment to operate in, which naturally couples the circuit function with the overall functionality of gene regulatory network. To execute their functions, all gene circuits draw resources in the form of RNA ... ...

Abstract	Genetic circuits need a cellular environment to operate in, which naturally couples the circuit function with the overall functionality of gene regulatory network. To execute their functions, all gene circuits draw resources in the form of RNA polymerases, ribosomes, and tRNAs. Recent experiments pointed out that the role of resource competition on synthetic circuit outputs could be immense. However, the effect of complexity of the circuit architecture on resource sharing dynamics is yet unexplored. In this paper, we employ mathematical modelling and in-silico experiments to identify the sources of resource trade-off and to quantify its impact on the function of a genetic circuit, keeping our focus on regulation of immediate downstream proteins, which are often used as protein read-outs. We show that estimating gene expression dynamics from readings of downstream protein data might be unreliable when the resource is limited and ribosome affinities are asymmetric. We focus on the impact of mRNA copy number and ribosome binding site (RBS) strength on the nonlinear isocline that emerges with two regimes, prominently separated by a tipping point, and study how correlation and competition dominate each other depending on various circuit parameters. Focusing further on genetic toggle circuit, we have identified major effects of resource competition in this model motif and quantified the observations. The observations are testable in wet-lab experiments, as all the parameters chosen are experimentally relevant.
MeSH term(s)	Binding Sites ; Gene Expression ; Gene Regulatory Networks ; RNA, Messenger/genetics ; Ribosomes/genetics
Chemical Substances	RNA, Messenger
Language	English
Publishing date	2021-10-25
Publishing country	France
Document type	Journal Article
ZDB-ID	2004003-9
ISSN	1292-895X ; 1292-8941
ISSN (online)	1292-895X
ISSN	1292-8941
DOI	10.1140/epje/s10189-021-00122-w
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Article ; Online: Computational Model on COVID-19 Pandemic Using Probabilistic Cellular Automata.

Ghosh, Sayantari / Bhattacharya, Saumik

SN computer science

2021 Volume 2, Issue 3, Page(s) 230

Abstract: Since March, 2020, Coronavirus disease (COVID-19) has been designated as a pandemic by World Health Organization. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, ... ...

Abstract	Since March, 2020, Coronavirus disease (COVID-19) has been designated as a pandemic by World Health Organization. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, governments are adopting nationwide interventions, like lockdown, containment and quarantine, restrictions on travel, cancelling social events and extensive testing. To understand the effects of these measures on the control of the epidemic in a data-driven manner, we propose a probabilistic cellular automata (PCA) based epidemiological model. The transitions associated with the model is driven by data available on chronology, symptoms, pathogenesis and transmissivity of the virus. By arguing that the lattice-based model captures the features of the dynamics along with the existing fluctuations, we perform rigorous computational analyses of the model to take into account of the spatial dynamics of social distancing measures imposed on the people. Considering the probabilistic behavioral aspects associated with mitigation strategies, we study the model considering factors like population density and testing efficiency. Using the model, we focus on the variability of epidemic dynamics data for different countries, and point out the reasons behind these contrasting observations. To the best of our knowledge, this is the first attempt to model COVID-19 spread using PCA that gives us both spatial and temporal variations of the infection spread with the insight about the contributions of different infection parameters.
Language	English
Publishing date	2021-04-22
Publishing country	Singapore
Document type	Journal Article
ISSN	2661-8907
ISSN (online)	2661-8907
DOI	10.1007/s42979-021-00619-3
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Article: How surface and fomite infection affect contagion dynamics: a study with self-propelled particles.

Ghosh, Sayantari / Chakraborty, Arijit / Bhattacharya, Saumik

The European physical journal. Special topics

2022 , Page(s) 1–14

Abstract: Self-propelled particles have been a tool of choice for many studies for understanding spatial interaction of people and propagation of infectious diseases. Other than the direct contagion process through face-to-face contacts with an infected agent, in ... ...

Abstract	Self-propelled particles have been a tool of choice for many studies for understanding spatial interaction of people and propagation of infectious diseases. Other than the direct contagion process through face-to-face contacts with an infected agent, in some diseases, like COVID-19, the disease can spread by indirect ways, through contaminated object surfaces and puff-clouds created by the infected individual. However, this dual spreading process and the impact of these indirect infections in the entire dynamics are not properly explored. In this work, we consider epidemic spreading in an artificial society, with realistic parameters and movements of people, along with the possibilities of indirect exposure through contaminated surfaces and puff-clouds. This particular simulation based infectious disease dynamics is associated with the movements of some self-propelled free agents executing random motion which is investigated in conjunction with the rules of a realistic contagion process. With mathematical formulation and extensive computational studies, we have accommodated the indirect infection possibilities into the dynamics by incorporating an infectious 'tail' with the infected individuals. Analytical expressions of survival distance and infection probability of individuals have been explicitly calculated and reported. Results of precise and comparative simulation study have revealed the seriousness of indirect infections in connection with several dynamical parameters. Using this framework, interpretation of multiple waves in local as well as global scenarios have been established for COVID-19 infection statistics. Furthermore, the importance of indirect infections are also pointed out through data fitting, showing that ignoring this component might cause a misinterpretation of the dynamical parameters, like, imposed restrictions.
Language	English
Publishing date	2022-01-12
Publishing country	France
Document type	Journal Article
ZDB-ID	2267176-6
ISSN	1951-6401 ; 1951-6355
ISSN (online)	1951-6401
ISSN	1951-6355
DOI	10.1140/epjs/s11734-022-00431-x
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Book ; Online: Resource allocation determines alternate cell fate in Bistable Genetic Switch

Chakraborty, Priya / Ghosh, Sayantari

2022

Abstract: Living cells need a constant availability of certain resources to have a sustained gene expression process. Limited availability of cellular resources for gene expression, like ribosomes, along with a variation of resource affinity, significantly ... ...

Abstract	Living cells need a constant availability of certain resources to have a sustained gene expression process. Limited availability of cellular resources for gene expression, like ribosomes, along with a variation of resource affinity, significantly modifies the system dynamics. Factors like the variation in rate of binding, or variation in efficiency of the recruited resource have the potential to affect crucial dynamical phenomena like cell fate determination. In this paper, we have taken a very important motif, a bistable genetic toggle switch, and explored the effect of resource imbalance in this circuit in terms of the bifurcations taking place. We show that initial asymmetric biasing to resource via resource affinity or gene copy number, significantly modifies the cell fate transition, both in pitchfork and saddle node type bifurcation. Our study establishes that in a limited resource environment, controlled resource allocation can be an important factor for robust functioning of the synthetic or cellular genetic switches. Comment: 11 pages, 5 figures
Keywords	Quantitative Biology - Quantitative Methods
Subject code	612
Publishing date	2022-05-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Hesitancy, Awareness and Vaccination

Mallick, Dibyajyoti / Ray, Aniruddha / Das, Ankita / Ghosh, Sayantari

A Computational Analysis on Complex Networks

2023

Abstract: Considering the global pandemic of coronavirus disease 2019 (COVID-19), around the world several vaccines are being developed. Till now, these vaccines are the most effective way to reduce the high burden on the global health infrastructure. However, the ...

Abstract	Considering the global pandemic of coronavirus disease 2019 (COVID-19), around the world several vaccines are being developed. Till now, these vaccines are the most effective way to reduce the high burden on the global health infrastructure. However, the public acceptance towards vaccination is a crucial and pressing problem for health authorities. This study has been designed to determine the parameters affecting the decisions of common individuals towards COVID-19 vaccine. In our study, using the platforms of compartmental model and network simulation, we categorize people and observe their motivation towards vaccination in a mathematical social contagion process. In our model, we consider peer influence as an important factor in this dynamics, and study how individuals are influencing each other for vaccination. The efficiency of the vaccination process is estimated by the period of time required to vaccinate a substantial fraction of total population. We discovered the major barriers and drivers of this dynamics, and concluded that it is required to formulate specific strategies by the healthcare workers which could be more effective for the undecided and vaccine hesitant group of people. Comment: 12 pages, 6 figures
Keywords	Physics - Physics and Society
Subject code	306
Publishing date	2023-02-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata.

Ghosh, Sayantari / Bhattacharya, Saumik

Applied soft computing

2020 Volume 96, Page(s) 106692

Abstract: COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is ... ...

Abstract	COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection spreading dynamics is becoming crucial to minimize impact and lifetime of COVID-19 and any future pandemic. In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries. This study proposes that for an accurate data-driven modelling of this infection spread, cellular automata provides an excellent platform, with a sequential genetic algorithm for efficiently estimating the parameters of the dynamics. To the best of our knowledge, this is the first attempt to understand and interpret COVID-19 data using optimized cellular automata, through genetic algorithm. It has been demonstrated that the proposed methodology can be flexible and robust at the same time, and can be used to model the daily active cases, total number of infected people and total death cases through systematic parameter estimation. Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed, with markedly divergent time evolution of the infection spreading because of demographic and socioeconomic factors. The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.
Keywords	covid19
Language	English
Publishing date	2020-08-29
Publishing country	United States
Document type	Journal Article
ISSN	1568-4946
ISSN	1568-4946
DOI	10.1016/j.asoc.2020.106692
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Article ; Online: A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata

Ghosh, Sayantari / Bhattacharya, Saumik

Applied Soft Computing

2020 Volume 96, Page(s) 106692

Keywords	Software ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	1568-4946
DOI	10.1016/j.asoc.2020.106692
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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