How adherence to public health measures shapes epidemic spreading: A temporal network model

Abstract

The COVID-19 pandemic has laid bare the importance of non-pharmaceutical interventions in the containment of airborne infectious diseases. Social distancing and mask-wearing have been found to contain COVID-19 spreading across a number of observational studies, but a precise understanding of their combined effectiveness is lacking. An underdeveloped area of research entails the quantification of the specific role of each of these measures when they are differentially adopted by the population. Pursuing this research allows for answering several pressing questions like: how many people should follow public health measures for them to be effective for everybody? Is it sufficient to practice social distancing only or just wear a mask? Here, we make a first step in this direction, by establishing a susceptible–exposed–infected–removed epidemic model on a temporal network, evolving according to the activity-driven paradigm. Through analytical and numerical efforts, we study epidemic spreading as a function of the proportion of the population following public health measures, the extent of social distancing, and the efficacy of masks in protecting the wearer and others. Our model demonstrates that social distancing and mask-wearing can be effective in preventing COVID-19 outbreaks if adherence to both measures involves a substantial fraction of the population.

The advantage of self-protecting interventions in mitigating epidemic circulation at the community level

“…From this expression, we see that the threshold is symmetric in the parameters α i and α o , in agreement with the results in Ref. [20], and that the maximum system-wide protection is obtained, for a given value of adoption probability f , when α i α o = 0, which can be attained either in the perfect SELF scenario (α i = 0) or in the perfect OTHER case (α o = 0). In the same theoretical framework, it is also possible to calculate the total prevalence R ∞ , i.e., the overall fraction of individuals infected by the disease throughout the outbreak, obtaining, slightly above the threshold (see Appendix A 2)…”

“…The effect of protecting interventions on the propagation of a disease can be modeled by considering the modification of the probability that the disease is transmitted between an infected and a susceptible individual [20,32]. Consider a population of N individuals, whose pattern of contacts is determined by a complex network [33], in which nodes represent individuals and edges the presence of social contacts between pairs of individuals.…”

“…An intervention affecting the susceptible individual decreases, by a factor α i , the infection rate of any edge pointing to him/her. For example, in the case of airborne transmission, a mask may reduce the number of viral particles exhaled by an infected individual and also reduce the number of viral particles that a susceptible can inhale from the environment [20]. As the example of masks shows, the factors α o and α i are not necessarily equal.…”

“…As one of the most widely adopted means to fight the current pandemic, mask wearing is the paradigmatic example of these types of interventions. Given the evidence for the airborne transmission of SARS-CoV-2 by means of droplets and aerosols [18], recent research has shown that face masks are a very effective way to reduce the spread of COVID-19 [19][20][21][22][23][24][25][26]. Different types of masks exist, characterized by very different protecting performance, both qualitatively and quantitatively.…”

The basic reproduction number of COVID-19 across Africa

“…To understand the transmission dynamics of COVID-19, several mathematical models have been developed and analyzed [11,[26][27][28][29][30][31][32][33][34][35][36][37][38]. Some of these models have also been used to study the effectiveness of different intervention strategies aimed at controlling the spread of the disease and to inform public health policies [11,32,36,39,40]. Understanding the early dynamics of a disease is essential to model its transmission dynamics adequately.…”