ABSTRACT
The integral model with finite memory is employed to analyze the time-line of COVID-19 epidemic in the United Kingdom and government actions to miti-gate it. The model uses a realistic infection distribution. The time-varying transmission rate is determined from Volterra integral equation of the first kind. The authors construct and justify an efficient regularization algorithm for finding the transmission rate. The model and algorithm are approbated on the UK data with several waves of COVID-19 and demonstrate a remarkable resemblance between real and simulated dynamics. The timing of government preventive measures and their impact on the epidemic dynamics are discussed. © The Author(s).
ABSTRACT
The distribution of coronavirus disease 2019 (COVID-19) infection across the historically marginalized populations in the United States (US) has consistently been inequitable. In addition, systemic racism and prejudice, which have existed for decades, have caused a lack of faith in public health and medical experts and have resulted in the epidemic of misinformation. To counteract the COVID-19 pandemic and widespread misinformation, the political establishment and public health experts must work collaboratively. And because they are closely associated, there had been a significant increase in the prevalence of the disease as well as a spike in the number of hospitalizations and fatalities. Public health professionals have investigated a number of epidemiological strategies to stop the spread of the virus and mitigate its effects, but false information released via various media sources has caused serious harm to a number of people. To create the framework and guidelines for protecting audiences from lies and deceit, and eradicating false information before taking root in society, it is essential to understand the types of misinformation that are being spread since the disadvantaged and uneducated communities suffer disproportionately as a result. According to studies, spreading false information could have a negative impact on a country's health outcomes, as well as its economic and social well-being, if not immediately refuted. Public health themes, such as evidence-based programs, health communication, and health policy, among others need to be evaluated and put into action in order to prevent the dissemination of incorrect information. This review examines a number of public health themes, such as policy and evidence-based strategies that might help in the fight against misinformation that has wreaked havoc on families and communities, particularly the underserved and uninformed populations.
ABSTRACT
BACKGROUND: In Italy, the administration of COVID-19 vaccines began in late 2020. In the early stages, the number of available doses was limited. To maximize the effectiveness of the vaccine campaign, the national health agency assigned priority access to at-risk individuals, such as health care workers and the elderly. Current vaccination campaign strategies do not take full advantage of the latest mathematical models, which capture many subtle nuances, allowing different territorial situations to be analyzed aiming to make context-specific decisions. OBJECTIVES: The main objective is the definition of an agent-based model using open data and scientific literature to assess and optimize the impact of vaccine campaigns for an Italian region. Specifically, the aim is twofold: (i) estimate the reduction in the number of infections and deaths attributable to vaccines, and (ii) assess the performances of alternative vaccine allocation strategies. METHODS: The COVID-19 Agent-based simulator Covasim has been employed to build an agent-based model by considering the Lombardy region as case study. The model has been tailored by leveraging open data and knowledge from the scientific literature. Dynamic mobility restrictions and the presence of Variant of Concern have been explicitly represented. Free parameters have been calibrated using the grid search methodology. RESULTS: The model mimics the COVID-19 wave that hit Lombardy from September 2020 to April 2021. It suggests that 168,492 cumulative infections 2,990 cumulative deaths have been avoided due to the vaccination campaign in Lombardy from January 1 to April 30, 2021. Without vaccines, the number of deaths would have been 66% greater in the 80-89 age group and 114% greater for those over 90. The best vaccine allocation strategy depends on the goal. To minimize infections, the best policy is related to dose availability. If at least 1/3 of the population can be covered in 4 months, targeting at-risk individuals and the elderly first is recommended; otherwise, the youngest people should be vaccinated first. To minimize overall deaths, priority is best given to at-risk groups and the elderly in all scenarios. CONCLUSIONS: This work proposes a methodological approach that leverages open data and scientific literature to build a model of COVID-19 capable of assessing and optimizing the impact of vaccine campaigns. This methodology can help national institutions to design regional mathematical models that can support pandemic-related decision-making processes.