COVID-19 vaccination strategies depend on the underlying network of social interactions

Assortative mixing of opinions about COVID-19 vaccination in personal networks

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution.

Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories

BACKGROUND

The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories.

METHODS

Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and >1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤1 000 000 inhabitants).

RESULTS

Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community.

CONCLUSIONS

Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks

We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies by up to 80%.

Disease spreading modeling and analysis: a survey

MOTIVATION

The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization.

RESULTS

Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.

Public Policy Measures to Increase Anti-SARS-CoV-2 Vaccination Rate in Russia

The total vaccination rate remains relatively low in Russia as of March 2022 (around 55%, with around 20% in some regions). In the paper, we study the reasons for it. We communicate the results of our survey aimed at detecting reasons for the relatively low anti-SARS-CoV-2 vaccination rate in Russia (47.1% as of mid-January 2022) and suggest potential measures to increase the level of confidence in the Russian vaccination campaign. A total of 14,310 users exhibited interest to participate in the research (16.84% of the total number of invitations sent in the Russian social network VKontakte). After the sample set repair, only 5822 (40.68% of those who agreed to participate) responses were suitable for the research, and they composed the final set. The age range of the respondents was 16-51 years old (y.o.) with a mean of 29.1 ± 10.6 y.o. The proportion of the female gender in responses was 44.23%. A total of 2454 persons (42.15%) expressed their hesitant, cautious, or negative attitude towards vaccine uptake. Of the 2454 persons with cautious attitude towards vaccination, only 928 (37.82%) were concerned about the quality of the Russian vaccines. A total of 1323 individuals (53.91%) supported one or more conspiracy beliefs. A total of 5064 (86.98% of the whole set) showed cautious or negative attitude towards the planned introduction of a nationwide system of vaccination certification/verification based on QR codes. The main social factors that hinder the Russian vaccination campaign are: vexation over the lack of desire of officials to receive feedback from the general population regarding vaccination, wide support for conspiracy beliefs, and controversy over the QR code-based digital system. To elevate the vaccination rate in Russia, the following steps may be taken: social encouragement of those who support vaccination, increase in transparency of the vaccination campaign, acceptance of both digital and paper vaccination certificates, increase in participation of society in vaccination-related discussions, public disclosure of vaccine composition, and avoidance of excessive digitalization of data in the vaccination campaign.