Universality and herd immunity threshold: Revisiting the SIR model for COVID-19

Chowdhury, Sourav; Roychowdhury, Suparna; Chaudhuri, Indranath

doi:10.1142/s012918312150128x

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Once the herd immunity threshold is met, the spread of the infectious disease is kept under control, current 9 outbreaks will extinguish and endemic transmission of the pathogen will be interrupted. Earlier estimates of the threshold found values of about 60 − 70% of the population [41,42,43]. In reality, highly transmissible strains tend to increase the threshold value, possibly keeping this goal out of reach.…”

Section: Herd Immunity and New Covid-19 Variantsmentioning

confidence: 98%

Modeling the effect of the vaccination campaign on the Covid-19 pandemic

Angeli,

Neofotistos,

Mattheakis

et al. 2021

Preprint

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Population-wide vaccination is critical for containing the SARS-CoV-2 (Covid-19) pandemic when combined with restrictive and prevention measures. In this study we introduce SAIVR, a mathematical model able to forecast the Covid-19 epidemic evolution during the vaccination campaign. SAIVR extends the widely used Susceptible-Infectious-Removed (SIR) model by considering the Asymptomatic (A) and Vaccinated (V) compartments. The model contains several parameters and initial conditions that are estimated by employing a semi-supervised machine learning procedure. After training an unsupervised neural network to solve the SAIVR differential equations, a supervised framework then estimates the optimal conditions and parameters that best fit recent infectious curves of 27 countries. Instructed by these results, we performed an extensive study on the temporal evolution of the pandemic under varying values of roll-out daily rates, vaccine efficacy, and a broad range of societal vaccine hesitancy/denial levels. The concept of herd immunity is questioned by studying future scenarios which involve different vaccination efforts and more infectious Covid-19 variants.

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Section: Herd Immunity and New Covid-19 Variantsmentioning

confidence: 98%

Modeling the effect of the vaccination campaign on the Covid-19 pandemic

Angeli,

Neofotistos,

Mattheakis

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Estimation of the model parameters by learning-based methods are considered and verified by real data in [13], [14], [15], [16], [17], [18]. Other attributes such as lockdown policy [19], multi-wave prediction [20], herd immunity threshold [21] are also considered by data-driven experiments. These works mostly focus on the estimation of model parameters thorough real data, and aim to make a more accurate prediction of the spread.…”

Section: Introductionmentioning

confidence: 99%

Containing a spread through sequential learning: to exploit or to explore?

Chen¹,

Nikpey²,

Kim³

et al. 2023

Preprint

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The spread of an undesirable contact process, such as an infectious disease (e.g. , is contained through testing and isolation of infected nodes. The temporal and spatial evolution of the process (along with containment through isolation) render such detection as fundamentally different from active search detection strategies. In this work, through an active learning approach, we design testing and isolation strategies to contain the spread and minimize the cumulative infections under a given test budget. We prove that the objective can be optimized, with performance guarantees, by greedily selecting the nodes to test. We further design reward-based methodologies that effectively minimize an upper bound on the cumulative infections and are computationally more tractable in large networks. These policies, however, need knowledge about the nodes' infection probabilities which are dynamically changing and have to be learned by sequential testing. We develop a message-passing framework for this purpose and, building on that, show novel tradeoffs between exploitation of knowledge through rewardbased heuristics and exploration of the unknown through a carefully designed probabilistic testing. The tradeoffs are fundamentally distinct from the classical counterparts under active search or multi-armed bandit problems (MABs). We provably show the necessity of exploration in a stylized network and show through simulations that exploration can outperform exploitation in various synthetic and realdata networks depending on the parameters of the network and the spread.

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“…There are models which have proposed various intervention and vaccination strategies to prevent and control the spread of the disease [ 8 – 12 ]. Also, some authors have tried to predict different inherent properties of this pandemic like herd immunity and its chaotic nature [ 13 – 17 ]. These temporal models can give us much valuable information, however, most of these models assume that a population is homogeneously mixed and cannot describe any spatial behavior.…”

Section: Introductionmentioning

confidence: 99%

Cellular automata in the light of COVID-19

Chowdhury

Roychowdhury

Chaudhuri

2022

Eur. Phys. J. Spec. Top.

Self Cite

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Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability ( q ) which helps us to measure the infectivity of a disease and (ii) exponent ( n ) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction.

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Universality and herd immunity threshold: Revisiting the SIR model for COVID-19

Cited by 12 publications

References 22 publications

Modeling the effect of the vaccination campaign on the Covid-19 pandemic

Modeling the effect of the vaccination campaign on the Covid-19 pandemic

Containing a spread through sequential learning: to exploit or to explore?

Cellular automata in the light of COVID-19

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