arXiv subjects

Samit Bhattacharyya

Publications and source records attributed to Samit Bhattacharyya.

7 recordsLinked to original sources

arXiv2024

Insecticide treated bed net use and elimination of malaria in Sub-Saharan African countries: Assessing the Global Technical Strategy (GTS) using an evolutionary game approach

The WHO 2021 malaria report revealed that its Global Technical Strategy (GTS) 2020 milestones for morbidity and mortality, based on the 2015 baseline, have not been achieved globally -- the world is off-track by 42% and, can be extended up to 91% in 2030. Most of the Sub-Saharan African (SSA) countries failed to achieve GTS 2020 -- only 4 out of 40 highest burden countries met the goals. By fitting evolutionary game modeling to the malaria case and Insecticide-Treated Nets (ITN) usages data, we identify factors contributing to the GTS 2020 failures of 38 SSA countries. We use optimized projection of our model to evaluate further the potential achievement of GTS 2025 and 2030 objectives and discuss strategies for attaining goals in situations where these milestones seem unattainable. Our findings categorize all 38 countries based on the possibility to achieve their future milestone, either through increased campaigns, or by economic assistance, or through enhancing the efficacy of ITNs at minimal expense.

q-bio.PE↗

arXiv2024

Estimating the epidemic threshold under individual vaccination behaviour and adaptive social connections: A game-theoretic complex network model

Information dissemination intricately intertwines with the dynamics of infectious diseases in the contemporary interconnected world. Recognizing the critical role of public awareness, individual vaccination choices appear to be an essential factor in collective efforts against emerging health threats. This study aims to characterize disease transmission dynamics under evolving social connections, information sharing, and individual vaccination decisions. To address this important problem, we present an integrated behaviour-prevalence model on an adaptive multiplex network. While the physical layer (layer-II) focuses on disease transmission under vaccination, the virtual layer (layer-I), representing individuals' social contacts, is adaptive and deals with information dissemination, resulting in the dynamics of vaccination choice in a socially influenced environment. Utilizing the microscopic Markov Chain Method (MMCM), we derive analytical expressions of the epidemic threshold for populations with different levels of perceived vaccine risk. It indicates that the adaptive nature of social contacts contributes to the higher epidemic threshold compared to non-adaptive scenarios, and numerical simulations also support that. The network topology, such as the power-law exponent of a scale-free network, also significantly influences the spreading of infections in the network population. We also observe that vaccine uptake increases proportionately with the number of individuals with a higher perceived infection risk or a higher sensitivity of an individual to their non-vaccinated neighbours. As a result, our findings provide insights for public health officials in developing vaccination programs in light of the evolution of social connections, information dissemination, and vaccination choice in the digital era.

physics.soc-ph↗

arXiv2024

An evolutionary game theory approach to modeling behavioral interaction in disclosing infection begins with an outbreak: COVID-19 as an example

The global impact of the COVID-19 pandemic on the livelihoods of people worldwide prompted the implementation of a range of preventive measures at local, national, and international levels. Early in the outbreak, before the vaccine became accessible, voluntary quarantine and social isolation emerged as crucial strategies to curb the spread of infection. In this research, we present a game-theoretic model to elucidate the voluntary disclosure of exposure to infected individuals within communities. By employing a fractional derivative approach to illustrate disease propagation within the compartmental model, we determine the minimum level of voluntary disclosure required to disrupt the chain of transmission and allow the epidemic to fade. Our findings suggest that higher transmission rates and increased perceived severity of infection change the externality of disclosing infected exposure, thereby contributing to a rise in the proportion of individuals opting for quarantine and reducing disease incidence. We estimate behavioral parameters and transmission rates by fitting the model to hospitalized cases in Chile, South America. Results from our paper underscore the potential for public health authorities to influence and regulate voluntary disclosure of infection during emerging outbreaks through effective risk communication, emphasizing the severity of the disease, and providing accurate information about hospital capacity to the public.

physics.soc-ph↗

arXiv2021

Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic

Since the recent introduction of several viable vaccines for SARS-CoV-2, vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. We argue that game theory and social network models should be used to guide decisions pertaining to vaccination programmes for the best possible results. In the months following the introduction of vaccines, their availability and the human resources needed to run the vaccination programmes have been scarce in many countries. Vaccine hesitancy is also being encountered from some sections of the general public. We emphasize that decision-making under uncertainty and imperfect information, and with only conditionally optimal outcomes, is a unique forte of established game-theoretic modelling. Therefore, we can use this approach to obtain the best framework for modelling and simulating vaccination prioritization and uptake that will be readily available to inform important policy decisions for the optimal control of the COVID-19 pandemic.

cs.GT↗

arXiv2017

Parameter Estimation in Models with Complex Dynamics

Mathematical models of real life phenomena are highly nonlinear involving multiple parameters and often exhibiting complex dynamics. Experimental data sets are typically small and noisy, rendering estimation of parameters from such data unreliable and difficult. This paper presents a study of the Bayesian posterior distribution for unknown parameters of two chaotic discrete dynamical systems conditioned on observations of the system. The study shows how the qualitative properties of the posterior are affected by the intrinsic noise present in the data, the representation of this noise in the parameter estimation process, and the length of the data-set. The results indicate that increasing length of dataset does not significantly increase the precision of the estimate, and this is true for both periodic and chaotic data. On the other hand, increasing precision of the measurements leads to significant increase in precision of the estimated parameter in case of periodic data, but not in the case of chaotic data. These results are highly useful in designing laboratory and field-based studies in biology in general, and ecology and conservation in particular.

nlin.CD↗

arXiv2016

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

physics.soc-ph↗

arXiv2015

Role of word-of-mouth for programs of voluntary vaccination: A game-theoretic approach

We propose a model describing the synergetic feedback between word-of-mouth (WoM) and epidemic dynamics controlled by voluntary vaccination. We combine a game-theoretic model for the spread of WoM and a compartmental model describing $SIR$ disease dynamics in the presence of a program of voluntary vaccination. We evaluate and compare two scenarios, depending on what WoM disseminates: (1) vaccine advertising, which may occur whether or not an epidemic is ongoing and (2) epidemic status, notably disease prevalence. Understanding the synergy between the two strategies could be particularly important for organizing voluntary vaccination campaigns. We find that, in the initial phase of an epidemic, vaccination uptake is determined more by vaccine advertising than the epidemic status. As the epidemic progresses, epidemic status become increasingly important for vaccination uptake, considerably accelerating vaccination uptake toward a stable vaccination coverage.

q-bio.PE↗