Incorporating social opinion in the evolution of an epidemic spread.
Ontology highlight
ABSTRACT:
SUBMITTER: Carballosa A
PROVIDER: S-EPMC7815732 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Incorporating social opinion in the evolution of an epidemic spread.
Carballosa Alejandro A Mussa-Juane Mariamo M Muñuzuri Alberto P AP
Scientific reports 20210119 1
The evolution of the COVID19 pandemic worldwide has shown that the most common and effective strategy to control it used worldwide involve imposing mobility constrains to the population. A determinant factor in the success of such policies is the cooperation of the population involved but this is something, at least, difficult to measure. In this manuscript, we propose a method to incorporate in epidemic models empirical data accounting for the society predisposition to cooperate with the mobili ...[more]
Similar Datasets
Project description:Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions.
| S-EPMC11925286 | biostudies-literature
Project description:Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.
| S-EPMC7402630 | biostudies-literature
Project description:Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases-possibly stratified by region and/or age group. We investigate how an age-structured social contact matrix can be incorporated into a spatio-temporal endemic-epidemic model for infectious disease counts. To illustrate the approach, we analyze the spread of norovirus gastroenteritis over six age groups within the 12 districts of Berlin, 2011-2015, using contact data from the POLYMOD study. The proposed age-structured model outperforms alternative scenarios with homogeneous or no mixing between age groups. An extended contact model suggests a power transformation of the survey-based contact matrix toward more within-group transmission.
| S-EPMC5379927 | biostudies-literature
Project description:Evolution and epidemic spread of SARS-CoV-2 in Brazil
| PRJEB39487 | ENA
Project description:The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion (r0) and the basic reproductive ratio (R0), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases.
| S-EPMC3861041 | biostudies-literature
Project description:Current efforts to understand the epidemiology, transmission dynamics and emergence of novel SARS-CoV-2 variants worldwide has enabled the scientific community to generate critical information aimed at implementing disease surveillance and control measures, as well as to reduce the social, economic and health impact of the pandemic. Herein, we applied an epidemic model coupled with genomic analysis to assess the SARS-CoV-2 transmission dynamics in Colombia. This epidemic model allowed to identify the geographical distribution, Rt dynamics and predict the course of the pandemic considering current implementation of countermeasures. The analysis of the incidence rate per 100,000 inhabitants carried out across different regions of Colombia allowed visualizing the changes in the geographic distribution of cases. The cumulative incidence during the timeframe March 2020 to March 2021 revealed that Bogotá (8063.0), Quindío (5482.71), Amazonas (5055.68), Antioquia (4922.35) and Tolima (4724.41) were the departments with the highest incidence rate. The highest median Rt during the first period evaluated was 2.13 and 1.09 in the second period; with this model, we identified improving opportunities in health decision making related to controlling the pandemic, diagnostic testing capacity, case registration and reporting, among others. Genomic analysis revealed 52 circulating SARS-CoV-2 lineages in Colombia detected from 774 genomes sequenced throughout the first year of the pandemic. The genomes grouped into four main clusters and exhibited 19 polymorphisms. Our results provide essential information on the spread of the pandemic countrywide despite implementation of early containment measures. In addition, we aim to provide deeper phylogenetic insights to better understand the evolution of SARS-CoV-2 in light of the latent emergence of novel variants and how these may potentially influence transmissibility and infectivity.
| S-EPMC8402472 | biostudies-literature
Project description:Infectious bronchitis virus (IBV) is a pathogen affecting poultry flocks worldwide. GI-23 is an IBV lineage with a rapid spread into different continents of the world, and it was reported for the first time in South American/Brazilian broiler farms last year. This study aimed to investigate the recent introduction and epidemic spread of IBV GI-23 in Brazil. Ninety-four broiler flocks infected with this lineage were evaluated from October 2021 to January 2023. IBV GI-23 was detected using real-time RT-qPCR, and the S1 gene hypervariable regions 1 and 2 (HVR1/2) were sequenced. S1 complete and HVR1/2 nucleotide sequence datasets were used to carry out phylogenetic and phylodynamic analyses. Brazilian IBV GI-23 strains clustered into two specific subclades (SA.1 and SA.2), both in tree branches with IBV GI-23 from Eastern European poultry-producing countries, suggesting two independent and recent introductions (around 2018). Viral phylodynamic analysis showed that the IBV GI-23 population increased from 2020 to 2021, remaining constant for one year and declining in 2022. S1 amino acid sequences from Brazilian IBV GI-23 presented specific and characteristic substitutions in the HVR1/2 for subclades IBV GI-23 SA.1 and SA.2. This study brings new insights into the introduction and recent epidemiology of IBV GI-23 in Brazil.
| S-EPMC10301962 | biostudies-literature
Project description:Opinion competition is a common phenomenon in real life, such as with opinions on controversial issues or political candidates; however, modelling this competition remains largely unexplored. To bridge this gap, we propose a model of competing opinion diffusion on social networks taking into account degree-dependent fitness or persuasiveness. We study the combined influence of social networks, individual fitnesses and attributes, as well as mass media on people's opinions, and find that both social networks and mass media act as amplifiers in opinion diffusion, the amplifying effect of which can be quantitatively characterized. We analytically obtain the probability that each opinion will ultimately pervade the whole society when there are no committed people in networks, and the final proportion of each opinion at the steady state when there are committed people in networks. The results of numerical simulations show good agreement with those obtained through an analytical approach. This study provides insight into the collective influence of individual attributes, local social networks and global media on opinion diffusion, and contributes to a comprehensive understanding of competing diffusion behaviours in the real world.
| S-EPMC5717675 | biostudies-literature
Project description:An overlooked aspect of disease ecology is considering how and why animals come into contact with one and other resulting in disease transmission. Mathematical models of disease spread frequently assume mass-action transmission, justified by stating that susceptible and infectious hosts mix readily, and foregoing any detailed description of host movement. Numerous recent studies have recorded, analysed and modelled animal movement. These movement models describe how animals move with respect to resources, conspecifics and previous movement directions and have been used to understand the conditions for the occurrence and the spread of infectious diseases when hosts perform a type of movement. Here, we summarize the effect of the different types of movement on the threshold conditions for disease spread. We identify gaps in the literature and suggest several promising directions for future research. The mechanistic inclusion of movement in epidemic models may be beneficial for the following two reasons. Firstly, the estimation of the transmission coefficient in an epidemic model is possible because animal movement data can be used to estimate the rate of contacts between conspecifics. Secondly, unsuccessful transmission events, where a susceptible host contacts an infectious host but does not become infected can be quantified. Following an outbreak, this enables disease ecologists to identify 'near misses' and to explore possible alternative epidemic outcomes given shifts in ecological or immunological parameters.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.
| S-EPMC5352813 | biostudies-literature
Project description:Human to human transmissible infectious diseases spread in a population using human interactions as its transmission vector. The early stages of such an outbreak can be modeled by a graph whose edges encode these interactions between individuals, the vertices. This article attempts to account for the case when each individual entails in different kinds of interactions which have therefore different probabilities of transmitting the disease. The majority of these results can be also stated in the language of percolation theory. The main contributions of the article are: (1) Extend to this setting some results which were previously known in the case when each individual has only one kind of interactions. (2) Find an explicit formula for the basic reproduction number [Formula: see text] which depends only on the probabilities of transmitting the disease along the different edges and the first two moments of the degree distributions of the associated graphs. (3) Motivated by the recent Covid-19 pandemic, we use the framework developed to compute the [Formula: see text] of a model disease spreading in populations whose trees and degree distributions are adjusted to several different countries. In this setting, we shall also compute the probability that the outbreak will not lead to an epidemic. In all cases we find such probability to be very low if no interventions are put in place.
| S-EPMC7499022 | biostudies-literature