Modeling the Interplay Between Seasonal Flu Outcomes and Individual Vaccination Decisions

Modelling the Interplay between Responsive Individual Vaccination Decisions and the Spread of SARS-CoV-2

The uptake of COVID-19 vaccines remains low despite their high effectiveness. Epidemic models that represent decision-making psychology can provide insight into the potential impact of vaccine promotion interventions in the context of the COVID-19 pandemic. We coupled a network-based mathematical model of SARS-CoV-2 transmission in Georgia, USA with a social-psychological vaccination decision-making model in which vaccine side effects, post-vaccination infections, and other unidentified community-level factors could "nudge" individuals towards vaccine resistance while hospitalization spikes could nudge them towards willingness. Combining an increased probability of hospitalization-prompted resistant-to-willing switches with a decreased probability of willing-to-resistant switches prompted by unidentified community-level factors increased vaccine uptake and decreased SARS-CoV-2 incidence by as much as 30.7% and 24.0%, respectively. The latter probability had a greater impact than the former. This illustrates the disease prevention potential of vaccine promotion interventions that address community-level factors influencing decision-making and anticipate the case curve instead of reacting to it.

Propagation thresholds in a diffusive epidemic model with latency and vaccination

This paper studies the propagation thresholds in a diffusive epidemic model with latency and vaccination. When the initial condition satisfies proper exponential decaying behavior, we present the spatial expansion feature of the infected. Different leftward and rightward spreading speeds are obtained with respect to different decaying initial values. Moreover, the convergence in the sense of compact open topology is also studied when the spreading speeds are finite. Finally, we show that the minimal spreading speed is the minimal wave speed of traveling wave solutions, which also presents the precisely asymptotic behavior of traveling wave solutions for the infected branch at the disease-free side. Here, the asymptotic behavior plays an important role that distinguishes the minimal spreading speed from all possible spreading speeds. From the definition of possible spreading speeds, we may find some factors affecting the spatial expansion ability, which includes that the vaccination could decrease the spatial expansion ability of the disease.

Social dilemmas of sociality due to beneficial and costly contagion

Levels of sociality in nature vary widely. Some species are solitary; others live in family groups; some form complex multi-family societies. Increased levels of social interaction can allow for the spread of useful innovations and beneficial information, but can also facilitate the spread of harmful contagions, such as infectious diseases. It is natural to assume that these contagion processes shape the evolution of complex social systems, but an explicit account of the dynamics of sociality under selection pressure imposed by contagion remains elusive. We consider a model for the evolution of sociality strategies in the presence of both a beneficial and costly contagion. We study the dynamics of this model at three timescales: using a susceptible-infectious-susceptible (SIS) model to describe contagion spread for given sociality strategies, a replicator equation to study the changing fractions of two different levels of sociality, and an adaptive dynamics approach to study the long-time evolution of the population level of sociality. For a wide range of assumptions about the benefits and costs of infection, we identify a social dilemma: the evolutionarily-stable sociality strategy (ESS) is distinct from the collective optimum—the level of sociality that would be best for all individuals. In particular, the ESS level of social interaction is greater (respectively less) than the social optimum when the good contagion spreads more (respectively less) readily than the bad contagion. Our results shed light on how contagion shapes the evolution of social interaction, but reveals that evolution may not necessarily lead populations to social structures that are good for any or all.

Analysis of COVID-19 in India using a vaccine epidemic model incorporating vaccine effectiveness and herd immunity

COVID-19 will be a continuous threat to human population despite having a few vaccines at hand until we reach the endemic state through natural herd immunity and total immunization through universal vaccination. However, the vaccine acts as a practical tool for reducing the massive public health problem and the emerging economic consequences that the continuing COVID -19 epidemic is causing worldwide, while the vaccine efficacy wanes. In this work, we propose and analyze an epidemic model of Susceptible-Exposed-Infected-Recovered-Vaccinated population taking into account the rate of vaccination and vaccine waning. The dynamics of the model has been investigated, and the condition for a disease-free endemic equilibrium state is obtained. Further, the analysis is extended to study the COVID-19 spread in India by considering the availability of vaccines and the related critical parameters such as vaccination rate, vaccine efficacy and waning of vaccine's impact on deciding the emerging fate of this epidemic. We have also discussed the conditions for herd immunity due to vaccinated individuals among the people. Our results highlight the importance of vaccines, the effectiveness of booster vaccination in protecting people from infection, and their importance in epidemic and pandemic modeling.