Coupling Fear and Contagion for Modeling Epidemic Dynamics

Jain, Kirti; Bhatnagar, Vasudha; Prasad, Sadanand; Kaur, Sharanjit

doi:10.1109/tnse.2022.3187775

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Our modelling rests on three main strands of literature SIR Models (Kermack/McKendrick, 1927, or Martcheva, 2010 for a modern exposition), Economic Decision Models (von Neumann, 1947), and Social Welfare Analysis (d'Aspermont in Arrow et al, 2002). Related to our model are models that take fear into account (Mandal et al, 2020, Juga et al, 2021, Mpeshe/Nyerere, 2021, Epstein et al, 2021, Jain et al, 2022, Retzlaff et al, 2022, which in our model can be quantified as the expectation of a decrease in well being. A helpful pedagogical example in designing the model was (Tuckwell/Williams, 2007).…”

Section: Consequences Of Biasmentioning

confidence: 99%

The Impact on Well-Being of Cognitive Bias about Infectious Diseases

Pestow

2024

Preprint

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We investigate the relationship between bias about infectious disease and well-being. First, we empirically establish the existence and the causes of bias, specifically during the COVID-19 pandemic. After that we investigate theoretically the effects of bias on well-being. In order to do this, we present a behavioral-epidemiological differential equation model derived from an agent-based model that combines rational choice behavior with infectious disease dynamics. In addition the model is evaluated normatively by an axiomatically characterized model of an ethical, impartial, eudaimonistic and individualist observer. These assumptions imply a new proof for the utilitarian principle. The result is that while increased fear improves purely epidemiological outcomes, the social welfare outcome shows mixed results; which shows that it is not enough to take only epidemiological measures into account when generating policy recommendations. Finally, we draw some practical consequences from the model, argue, as it pertains to the topic, for protective rights against psychological control by the state, and give some outlooks for future research.

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Section: Consequences Of Biasmentioning

confidence: 99%

The Impact on Well-Being of Cognitive Bias about Infectious Diseases

Pestow

2024

Preprint

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“…In addition, some scholars considered that different individuals have different impacts on the perception of information and disease, and may also produce different emotional changes; therefore, they further studied its impact on the disease transmission process with individual heterogeneity and individual emotional changes. [31][32][33][34][35][36][37][38][39] Xu et al [31] considered the effects of individual heterogeneity on disease transmission and analyzed the effects of heterogeneity on the degree of local and global disease perception within layers; the results showed that individual heterogeneity has a large effect on the proportion of disease transmission. Du et al [32] developed a threelayer network model to analyze how social media and human behavior affect disease transmission.…”

Section: Introductionmentioning

confidence: 99%

“…The most effective protective measure when an epidemic breaks out is vaccination, which is more efficient at reducing the probability of infection and the proportion of disease transmission. [30,[34][35][36][37][40][41][42][43][44] Zuo et al [35] proposed a new SEIR/V-AI model to study the interaction between vaccination behavior and hip-hop, and the results showed that individual sensitivity to information affects individual vaccination behavior. Jain et al [37] analyzed the effects of fear generated by individuals during disease transmission on information diffusion.…”

Section: Introductionmentioning

confidence: 99%

“…[30,[34][35][36][37][40][41][42][43][44] Zuo et al [35] proposed a new SEIR/V-AI model to study the interaction between vaccination behavior and hip-hop, and the results showed that individual sensitivity to information affects individual vaccination behavior. Jain et al [37] analyzed the effects of fear generated by individuals during disease transmission on information diffusion. Kabri et al [39] considered the effects of vaccination strategies on disease transmission to better understand actual disease transmission patterns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Huo 霍,

Wu 武

2024

Chinese Phys. B

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Nowadays, with the rapid development of the Internet, a large amount of information often accompanies the rapid transmission of disease outbreaks, and more and more scholars are studying the relationship between information and the disease transmission process on the basis of complex networks. In fact, the disease transmission process is very complex, beside the information of this factor, often will be individual behavioral measures and other factors together, most of the previous research to establish a two-layer network model to consider the impact of information on the transmission process of disease, divided into information and behavior respectively on the transmission of diseases less research, in order to more in-depth analyze disease transmission process and the intrinsic influence mechanism, this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamics co-evolution of information diffusion, vaccination behavior, and disease transmission is proposed considering four influential relationships between adjacent layers in multilayer networks. In the information layer, the diffusion process of negative information is described, and the feedback effects of the local and global vaccination are considered; in the behavioral layer, the individual vaccination behavior is described, and the probability of individual receiving vaccination behavior is influenced by two factors:one is the influence of negative information, and the other is the influence of local and global disease severity; in the disease layer, individual susceptibility is considered to be influenced by vaccination behavior. The state transition equations are derived by using the Micro Markov Chain Approach (MMCA), and disease prevalence thresholds are obtained. It was demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior, and is mainly influenced by global vaccination behavior; the vaccination behavior is mainly influenced by local disease conditions, is less influenced by global disease conditions; the disease transmission threshold increases with increasing vaccination rate; the scale of disease transmission increases with increasing negative information diffusion rate and decreases with increasing vaccination rate. Finally, it is found that when individual vaccination behavior considers both the influence of negative information and disease, it can increase the disease transmission threshold and reduce the scale of disease transmission, so we should resist the diffusion of negative information, increase vaccination proportion, and take appropriate protective measures in time.

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“…Further studies have been reported that investigate the dynamics of epidemiological waves, thus addressing the complexity of viral spread [31, 32]. Despite notable advances in the development of machine learning-based algorithms for the early identification of high-risk patients [33], it is crucial to highlight the importance of conducting prospective studies that validate the effectiveness and accuracy of these algorithms in real-world situations.…”

Section: Introductionmentioning

confidence: 99%

Prospective study of machine learning for identification of high-risk COVID-19 patients

Frausto-Avila,

León-Montiel,

Quiroz-Juárez

et al. 2024

Preprint

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The Coronavirus Disease 2019 (COVID-19) pandemic constituted a public health crisis with a devastating effect in terms of its death toll and effects on the world economy. Notably, machine learning methods have played a pivotal role in devising novel technological solutions designed to tackle challenges brought forth by this pandemic. In particular, tools for the rapid identification of high-risk COVID-19 patients have been developed to aid in the effective allocation of hospital resources and for containing the spread of the virus. A comprehensive validation of such intelligent technological approaches is needed to ascertain their clinical utility; importantly, it may help develop future strategies for efficient patient classification to be used in future viral outbreaks. Here we present a prospective study to evaluate the performance of state-of-the-art machine-learning models proposed in PloS one 16, e0257234 (2021), which we developed for the identification of high-risk COVID-19 patients across four identified clinical stages. The model relies on artificial neural networks trained with historical patient data from Mexico. To assess their predictive capabilities across the six, registered, epidemiological waves of COVID-19 infection in Mexico, we measure the accuracy within each wave without retraining the neural networks. We then compare their performance against neural networks trained with cumulative historical data up to the end of each wave. Our findings indicate that models trained using early historical data exhibit strong predictive capabilities, which allows us to accurately identify high-risk patients in subsequent epidemiological waves—under clearly varying vaccination, prevalent viral strain, and medical treatment conditions. These results show that artificial intelligence-based methods for patient classification can be robust throughout an extended period characterized by constantly evolving conditions, and represent a potentially powerful tool for tackling future pandemic events, particularly for clinical outcome prediction of individual patients.

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Coupling Fear and Contagion for Modeling Epidemic Dynamics

Cited by 10 publications

References 43 publications

The Impact on Well-Being of Cognitive Bias about Infectious Diseases

The Impact on Well-Being of Cognitive Bias about Infectious Diseases

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Prospective study of machine learning for identification of high-risk COVID-19 patients

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