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The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rate worldwide. Unfortunately, this unprecedented level of…

应用统计 · 统计学 2024-01-31 Yuansan Liu , Saransh Srivastava , Zuo Huang , Felisa J. Vázquez-Abad

Background. The paper concerns the SARS-CoV2 (COVID-19) pandemic that, starting from the end of February 2020, began spreading along the Italian peninsula, by first attacking small communities in north regions, and then extending to the…

种群与进化 · 定量生物学 2020-11-25 Luigi Brugnano , Felice Iavernaro , Paolo Zanzottera

With the covid-19 pandemic still ongoing and an enormous amount of test data available, the lessons learned over the last two years need to be developed to a point where they can provide understanding for tackling new variants and future…

种群与进化 · 定量生物学 2022-06-09 Adam Mielke

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated…

定量方法 · 定量生物学 2025-04-08 Shuai Han , Lukas Stelz , Horst Stoecker , Lingxiao Wang , Kai Zhou

A model of reactive social distancing in epidemics is proposed, in which the infection rate changes with the number infected. The final-size equation for the total number that the epidemic will infect can be derived analytically, as can the…

种群与进化 · 定量生物学 2020-03-19 Rose Baker

In this paper, we develop an extension of standard epidemiological models, suitable for COVID-19. This extension incorporates the transmission due to pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model also…

种群与进化 · 定量生物学 2020-11-20 Anirban Ghatak , Shivshanker Singh Patel , Soham Bonnerjee , Subhrajyoty Roy

Social distancing strategies have been adopted by governments to manage the COVID-19 pandemic, since the first outbreak began. However, further epidemic waves keep out the return of economic and social activities to their standard levels of…

最优化与控制 · 数学 2021-06-07 J. E. Sereno , A. D' Jorge , A. Ferramosca , E. A. Hernandez-Vargas , A. H. Gonzalez

Susceptible-Invective-Recovered (SIR) mathematical models are in high demand due to the COVID-19 pandemic. They are used in their standard formulation, or through the many variants, trying to fit and hopefully predict the number of new…

种群与进化 · 定量生物学 2020-05-19 Ben-Hur Francisco Cardoso , Sebastián Gonçalves

A class of multiple-timescale asymptotic solutions to the equations of the susceptible-infected-recovered (SIR) model is presented for the case of high basic reproduction number, with the inverse of the latter employed as the expansion…

种群与进化 · 定量生物学 2025-12-25 Oleg B. Shiryaev

Mathematical models of epidemics often use compartmental models dividing the population into several compartments. Based on a microscopic setting describing the temporal evolution of the subpopulation sizes in the compartments by stochastic…

种群与进化 · 定量生物学 2025-03-11 Florent Ouabo Kamkumo , Ibrahim Mbouandi Njiasse , Ralf Wunderlich

We consider the SEIRS compartment epidemiology model suitable for predicting the evolution of the COVID-19 pandemy in the extreme limiting case of no acquired immunity. The disease-free and endemic fixed points are found and their stability…

种群与进化 · 定量生物学 2020-12-15 J. M. Ilnytskyi

We propose a mathematical model with five compartments for the SARS-CoV-2 transmission: susceptible $S$, undetected infected asymptomatic $A$, undetected infected symptomatic $I$, confirmed positive and isolated $L$, and recovered $ R$, for…

最优化与控制 · 数学 2020-09-29 Gabriela Marinoschi

This paper is concerned with a stochastic model for the spread of an SEIR (susceptible -> exposed (=latent) -> infective -> removed) epidemic with a contact tracing scheme, in which removed individuals may name some of their infectious…

概率论 · 数学 2015-12-08 Frank G Ball , Edward S Knock , Philip D O'Neill

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first…

We propose an epidemiological model using an adaptive dynamic three compartment (with four states) SIR(D) model. Our approach is similar to non-parametric curve fitting in spirit and automatically adapts to key external factors, such as…

应用统计 · 统计学 2020-09-14 Madhuchhanda Bhattacharjee , Arup Bose

The COVID-19 pandemic (SARS-CoV-2 virus) is the defying global health crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be…

种群与进化 · 定量生物学 2020-06-30 Saulo B. Bastos , Marcelo M. Morato , Daniel O. Cajueiro anda Julio E Normey-Rico

When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by…

种群与进化 · 定量生物学 2020-05-25 Timoteo Carletti , Duccio Fanelli , Francesco Piazza

In a previous article [1] we have described the temporal evolution of the Sars- Cov-2 in Italy in the time window February 24-April 1. As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and…

种群与进化 · 定量生物学 2020-08-26 Gabriele Martelloni , Gianluca Martelloni

The current COVID-19 pandemic and subsequent lockdowns have highlighted the close and delicate relationship between a country's public health and economic health. Macroeconomic models that use preexisting epidemic models to calculate the…

物理与社会 · 物理学 2020-07-01 Erhan Bayraktar , Asaf Cohen , April Nellis

We present a simple analytical model to describe the fast increase of deaths produced by the corona virus (COVID-19) infections. The 'D' (deaths) model comes from a simplified version of the SIR (susceptible-infected-recovered) model known…

种群与进化 · 定量生物学 2020-04-01 J. E. Amaro