An Epidemic Model with Pro and Anti-vaccine Groups.

Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective.

A complex network model for a society with socioeconomic classes.

People's attitudes and behaviors are partially shaped by the socioeconomic class to which they belong. In this work, a model of scale-free graph is proposed to represent the daily personal contacts in a society with three social classes. In the model, the probability of having a connection between two individuals depends on their social classes and on their physical distance. Numerical simulations are performed by considering sociodemographic data from France, Peru, and Zimbabwe. For the complex networks built for these three countries, average values of node degree, shortest-path length, clustering coefficient, closeness centrality, betweenness centrality, and eigenvector centrality are computed. These numerical results are discussed by taking into account the propagation of information about COVID-19.

The co-circulation of two infectious diseases and the impact of vaccination against one of them

An epidemiological model based on probabilistic cellular automaton is proposed to investigate the dynamics of two co-circulating infections. In the model, one of these two diseases compromises the immune response to future infections;however, there is vaccine against this immunosuppressive disease. The goal is to evaluate the impact of the vaccination coverage on the prevalence and on the cumulative deaths associated with both contagious diseases. The performed numerical simulations highlight the importance of vaccination on decreasing morbidity and mortality. The results are discussed from a public health standpoint, by taking into account outbreaks of measles and COVID-19.

The negative impact of technological advancements on mental health: An epidemiological approach

Internet and smartphone are inventions that have brought significant benefits to humanity. However, many individuals have become addicted to using these technologies and, as a consequence, they experience negative mental effects. The home confinement due to the COVID-19 pandemic may have worsened this situation. Here, an epidemic model is proposed to represent the spread of the problematic technology use. The model is written as a set of differential equations, which describes the time evolution of the numbers of non-users, light/moderate users, heavy users, and problematic users. This model presents a single endemic steady-state, which is asymptotically stable. This result is illustrated by numerical simulations and its relevance is examined from a public health perspective.

On the spread of SARS-CoV-2 under quarantine: A study based on probabilistic cellular automaton

Currently, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a major worldwide public-health problem. Here, its propagation is modeled by using a probabilistic cellular automaton (PCA). In this model, sick individuals can either remain asymptomatic during the infection or become symptomatic. In order to derive an analytical expression for the basic reproduction number R0, a mean-field approximation written in terms of ordinary differential equations (ODE) is proposed and analyzed. By considering time-constant and time-varying parameters in both approaches (PCA and ODE), numerical simulations are performed in order to evaluate the impact of distinct quarantine regimes on the SARS-CoV-2 pandemic.

The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm.

BACKGROUND AND OBJECTIVE: One of the main goals of epidemiological studies is to build models capable of forecasting the prevalence of a contagious disease, in order to propose public health policies for combating its propagation. Here, the aim is to evaluate the influence of immune individuals in the processes of contagion and recovery from varicella. This influence is usually neglected. METHODS: An epidemic model based on probabilistic cellular automaton is introduced. By using a genetic algorithm, the values of three parameters of this model are determined from data of prevalence of varicella in Belgium and Italy, in a pre-vaccination period. RESULTS: This methodology can predict the varicella prevalence (with average relative error of 2%-4%) in these two European countries. Belgium data can be explained by ignoring the role of immune individuals in the infection propagation; however, Italy data can be explained by considering contagion exclusively mediated by immune individuals. CONCLUSIONS: The role of immune individuals should be accurately delineated in investigations on the dynamics of disease propagation. In addition, the proposed methodology can be adapted for evaluating, for instance, the role of asymptomatic carriers in the novel coronavirus spread.

An epidemiological model for SARS-CoV-2

The spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is here investigated from an epidemic model considering four pathways of person-to-person transmission. These pathways represent the propagation of this novel coronavirus by asymptomatic and symptomatic infected individuals. In this work, analytical expressions for the disease-free and endemic steady-states are derived. Also, the conditions for eradication of this contagious disease are determined. By taking into account realistic parameter values, the proposed model shows an oscillatory convergence to the endemic steady-state, which means the occurrence of a sequence of peaks in the number of sick individuals as time passes. These results are discussed from a public health standpoint. © 2020 Elsevier B.V.