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Motivated by modelling epidemics like COVID-19, this paper proposes a generalized chain binomial process which integrates two types of infectives, those with symptoms and those without. Testing of infectives and vaccination of susceptibles are then incorporated as preventive protective measures. Our interest relates to the distribution of the state of the population at the end of infection and to the reproduction number [Formula: see text] with the associated extinction condition. The method uses the construction of a family of martingales and a branching approximation for large populations, respectively. A more general branching process for epidemics is also constructed and studied. Finally, some results obtained are illustrated by numerical examples., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper discusses a generalized SIR epidemic model that incorporates infectives with or without symptoms, allows arbitrary distributions for infectious periods and assumes infection rates depending on the current size of susceptibles. Our interest lies in the joint distribution of the state of the population and the severity of the disease at the end of infection. The approach is based on a formulation of the epidemic as a so-called collective model. First, a set of martingales is constructed which provides the distribution of this final epidemic outcome. Then, its corresponding distributional transform is expressed in terms of a family of pseudo-polynomials of Abel-Gontcharoff type. Some of the results obtained are illustrated numerically. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of possible ruin when providing insurance coverage for an epidemic. The model studied is an SIS type epidemic which generalizes the well-known logistic model. Contractually, the premiums are paid by susceptible people while the care costs are reimbursed to infected people via an annuity or a lump-sum benefit. Our goal is to determine the distribution of the main statistics of the ruin when it occurs during the epidemic. The case where the reserve alternates between normal and epidemic episodes is also discussed using a Brownian modeling of the reserve. Finally, some of the results are illustrated for two particular SIS epidemic models. [ABSTRACT FROM AUTHOR]

This paper proposes a block-structured Markov process to describe the spread of epidemics of Susceptible-Infected-Removed (SIR) type. Our purpose is to determine the distribution of the final state of the process and of some other interesting measures of the dimension of the epidemic. The followed modeling approach proves to be simple and systematic. Its flexibility is underlined by the presentation of several specific models that extend the classical general epidemic. Finally, two numerical examples illustrate some of the results obtained. [ABSTRACT FROM AUTHOR]

This paper considers a general epidemic model that the spread of which is partially controlled by a health organization. Our primary goal is to show that standard life insurance principles can be applied to guarantee the insurance coverage of such an epidemic. This leads us to determine the expected value of the final population state and of a total path integral for the process. A more general transform of the epidemic outcomes is then obtained using a special family of bivariate pseudopolynomials. [ABSTRACT FROM AUTHOR]

A block-structured transient Markov process is introduced to describe an epidemic spreading within two linked populations, of carriers and susceptibles. The epidemic terminates as soon as there are no more carriers or susceptibles present in the population. Our purpose is to determine the distribution of the final susceptible and carrier states, and of any integral path for the susceptible process. The transient epidemic state is also briefly discussed. Then, the model is extended to allow the recovery of infected individuals. Finally, several particular models, some known, are used for illustration. [ABSTRACT FROM AUTHOR]

In this paper we aim to apply simple actuarial methods to build an insurance plan protecting against an epidemic risk in a population. The studied model is an extended SIR epidemic in which the removal and infection rates may depend on the number of registered removals. The costs due to the epidemic are measured through the expected epidemic size and infectivity time. The premiums received during the epidemic outbreak are measured through the expected susceptibility time. Using martingale arguments, a method by recursion is developed to calculate the cost components and the corresponding premium levels in this extended epidemic model. Some numerical examples illustrate the effect of removals and the premium calculation in an insurance plan. [ABSTRACT FROM AUTHOR]

This paper is concerned with two families of multivariate polynomials: the Appell polynomials and the Abel-Gontcharoff polynomials. Both families are well-known in the univariate case, but their multivariate version is much less standard. We first provide a simple interpretation of these polynomials through particular constrained random walks on a lattice. We then derive nice analytical results for two special cases where the parameters of the polynomials are randomized. Thanks to the interpretation and randomization of the polynomials, we can derive new results and give other insights for the study of two different risk problems: the ruin probability in a multiline insurance model and the size distribution in a multigroup epidemic. [ABSTRACT FROM PUBLISHER]