A Novel Room-Based Epidemic Model: Quarantine, Testing, and Vaccination Strategies

Epidemic outbreaks pose significant challenges to public health and socio-economic stability, necessitating a comprehensive understanding of disease transmission dynamics and effective control strategies. This article discusses the limitations of traditional compartmental and network-based models and, inspired by the opinion formation models, introduces a room-based model that incorporates social gatherings and intuitive quarantine measures. Through simulations and analysis, we examine the impact of various model parameters, and confinement measures like quarantine and preventive measures like testing, and vaccination on disease spread. Additionally, we explore centrality-based testing and immunization strategies, demonstrating their effectiveness in reducing the spread of diseases compared to a random approach. Finally, we propose a combined strategy, that outperforms the existing strategies. It takes both global and local properties of the network structure into account, highlighting the potential for integrated control measures in epidemic management. This research not only contributes to a deeper understanding of epidemic models, but also provides insights into devising successful intervention strategies, including quarantine measures, testing methodologies, and vaccine programs to combat emerging epidemics and pandemic

Controlling species densities in structurally perturbed intransitive cycles with higher-order interactions

The persistence of biodiversity of species is a challenging proposition in ecological communities in the face of Darwinian selection. The present article investigates beyond the pairwise competitive interactions and provides a novel perspective for understanding the influence of higher-order interactions on the evolution of social phenotypes. Our simple model yields a prosperous outlook to demonstrate the impact of perturbations on intransitive competitive higher-order interactions. Using a mathematical technique, we show how alone the perturbed interaction network can quickly determine the coexistence equilibrium of competing species instead of solving a large system of ordinary differential equations. It is possible to split the system into multiple feasible cluster states depending on the number of perturbations. Our analysis also reveals the ratio between the unperturbed and perturbed species is inversely proportional to the amount of employed perturbation. Our results suggest that nonlinear dynamical systems and interaction topologies can be interplayed to comprehend species' coexistence under adverse conditions. Particularly our findings signify that less competition between two species increases their abundance and outperforms others.Comment: 17 pages, 10 figure

Response of a three-species cyclic ecosystem to a short-lived elevation of death rate

Abstract A balanced ecosystem with coexisting constituent species is often perturbed by different natural events that persist only for a finite duration of time. What becomes important is whether, in the aftermath, the ecosystem recovers its balance or not. Here we study the fate of an ecosystem by monitoring the dynamics of a particular species that encounters a sudden increase in death rate. For exploration of the fate of the species, we use Monte-Carlo simulation on a three-species cyclic rock-paper-scissor model. The density of the affected (by perturbation) species is found to drop exponentially immediately after the pulse is applied. In spite of showing this exponential decay as a short-time behavior, there exists a region in parameter space where this species surprisingly remains as a single survivor, wiping out the other two which had not been directly affected by the perturbation. Numerical simulations using stochastic differential equations of the species give consistency to our results