Dataset Information

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

ABSTRACT: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.

SUBMITTER: Saunders HA

PROVIDER: S-EPMC8674282 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Saunders Helena A HA Schwartz Jean-Marc JM

Scientific reports 20211215 1

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmis ...[more]

PMID: 34912001

Similar Datasets

Project description:Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

Project description:BackgroundUnderreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests' implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death.MethodsWe present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model.ResultsFor the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization.ConclusionspBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets