Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals' behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents' behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents' delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie's classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents' behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

Epidemic Models and Estimation of the Spread of SARS-CoV-2: Case Study Portoviejo-Ecuador

The mathematical models can help to characterize, quantify, summarize, and determine the severity of the outbreak of the Coronavirus, the estimation of the dynamics of the pandemic helps to identify the type of measures and interventions that can be taken to minimize the impact by classified information. In this work, we propose four epidemiological models to study the spread of SARS-CoV-2. Specifically, two versions of the SIR model (Susceptible, Infectious, and Recovered) are considered, the classical Crank-Nicolson method is used with a stochastic version of the Beta-Dirichlet state-space models. Subsequently, the SEIR model (Susceptible, Exposed, Infectious, and Recovered) is fitted, the Euler method and a stochastic version of the Beta-Dirichlet state-space model are used. In the results of this study (Portoviejo-Ecuador), the SIR model with the Beta-Dirichlet state-space form determines the maximum point of infection in less time than the SIR model with the Crank-Nicolson method. Furthermore, the maximum point of infection is shown by the SEIR model, that is reached during the first two weeks where the virus begins to spread, more efficient is shown by this model. To measure the quality of the estimation of the algorithms, we use three measures of goodness of fit. The estimated errors are negligible for the analyzed data. Finally, the evolution of the spread is predicted, that can be helpful to prevent the capacity of the country’s health system. © 2022, Springer Nature Switzerland AG.

Incentives, lockdown, and testing: from Thucydides' analysis to the COVID-19 pandemic.

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In particular, we model the interplay between the government and the population as a principal-agent problem with moral hazard, à la Cvitanic et al. (Finance Stoch 22(1):1-37, 2018), while an epidemic is spreading according to dynamics given by compartmental stochastic SIS or SIR models, as proposed respectively by Gray et al. (SIAM J Appl Math 71(3):876-902, 2011) and Tornatore et al. (Phys A Stat Mech Appl 354(15):111-126, 2005). More precisely, to limit the spread of a virus, the population can decrease the transmission rate of the disease by reducing interactions between individuals. However, this effort-which cannot be perfectly monitored by the government-comes at social and monetary cost for the population. To mitigate this cost, and thus encourage the lockdown of the population, the government can put in place an incentive policy, in the form of a tax or subsidy. In addition, the government may also implement a testing policy in order to know more precisely the spread of the epidemic within the country, and to isolate infected individuals. In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax. The government's optimisation problems then boils down to solving an Hamilton-Jacobi-Bellman equation. Numerical results confirm that if a tax policy is implemented, the population is encouraged to significantly reduce its interactions. If the government also adjusts its testing policy, less effort is required on the population side, individuals can interact almost as usual, and the epidemic is largely contained by the targeted isolation of positively-tested individuals.

Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.When faced with a potential outbreak, decision-makers need to be able to trust mathematical models for their decision-making processes. One of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions, or even in different individuals, which can be a sign of uncertain and accidental behavior in the disease outbreak. This trait reflects the existence of the capacity of transmitting perturbations across its domains. We construct a stochastic environment because of parameters random essence and introduce a stochastic version of theReservoir-Peoplemodel. Then we prove the uniqueness and existence of the solution on the stochastic model. Moreover, the equilibria of the system are considered. Also, we establish the extinction of the disease under some suitable conditions. Finally, some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model. © 2022 Tech Science Press. All rights reserved.