Estimated global public health and economic impact of COVID-19 vaccines in the pre-omicron era using real-world empirical data

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

Modularized viromimetic polymer nanoparticle vaccines (VPNVaxs) to elicit durable and effective humoral immune responses

Virus-like particle (VLP) vaccines had shown great potential during the COVID-19 pandemic, and was thought to be the next generation of antiviral vaccine technology due to viromimetic structures. However, the time-consuming and complicated processes in establishing a current recombinant-protein-based VLP vaccine has limited its quick launch to the out-bursting pandemic. To simplify and optimize VLP vaccine design, we herein report a kind of viromimetic polymer nanoparticle vaccine (VPNVax), with subunit receptor-binding domain (RBD) proteins conjugated to the surface of polyethylene glycol-b-polylactic acid (PEG-b-PLA) nanoparticles for vaccination against SARS-CoV-2. The preparation of VPNVax based on synthetic polymer particle and chemical post-conjugation makes it possible to rapidly replace the antigens and construct matched vaccines at the emergence of different viruses. Using this modular preparation system, we identified that VPNVax with surface protein coverage of 20%-25% had the best immunostimulatory activity, which could keep high levels of specific antibody titers over 5 months and induce virus neutralizing activity when combined with an aluminum adjuvant. Moreover, the polymer nano-vectors could be armed with more immune-adjuvant functions by loading immunostimulant agents or chemical chirality design. This VPNVax platform provides a novel kind of rapidly producing and efficient vaccine against different variants of SARS-CoV-2 as well as other viral pandemics.

COVID-19 vaccines should be evaluated from the societal perspective

The COVID-19 pandemic demonstrates the importance of valuing vaccines from a broad societal perspective (SP), as opposed to a narrower health-payer perspective (HPP). COVID-19's catastrophic global impacts extend not only to its health-related effects, but also to the profound macroeconomic losses caused by lockdowns required for disease control, leading to the worst global economic crisis in a century. COVID-19 vaccination (CV) has been the central policy tool for resolving this economic crisis, and it has been hypothesized that this macroeconomic benefit alone justifies the cost of CV many times over. Yet HPP-based vaccine valuations are wholly insensitive to this enormous benefit, not allowing it to influence the allocation of given health budgets nor the determination of the magnitudes of such budgets, thereby risking inadequate societal spending on CV. HPP allocates given health budgets to maximize only health, giving no weight to macroeconomic outcomes, causing allocative inefficiency by not allowing welfare-improving trade-offs of health for wealth. HPP assumes health budgets are optimal, not scrutinizing whether their scale adequately reflects the macroeconomic benefits of health spending, thereby risking productive inefficiency by foregoing health spending increases such as on CV that could raise both population-level health and wealth. These allocative and productive inefficiencies in turn distort for-profit R&D incentives, risking dynamic inefficiency. And since the socio-economic and health burdens of COVID-19 are disproportionately borne by the worse off, HPP's failure to promote optimal levels of societal investment in CV may disproportionately burden the worse off as well, exacerbating inequality. Vaccine valuations from the societal perspective allow the allocation and determination of health budgets to reflect macroeconomic and distributional values, thereby promoting allocative, productive, and dynamic efficiency, as well as equity. These considerations of efficiency and equity support evaluating CV, and to ensure a level playing field, all vaccines, from a societal perspective.

Looking beyond vaccines: Cultural tightness-looseness moderates the relationship between immunization coverage and disease prevention vigilance

Advancements in vaccination technologies mitigate disease transmission risks but may inadvertently suppress the behavioral immune system, an evolved disease avoidance mechanism. Applying behavioral immune system theory and utilizing robust big data analytics, we examined associations between rising vaccination coverage and government policies, public mobility, and online information seeking regarding disease precautions. We tested whether cultural tightness-looseness moderates the relationship between mass immunization and disease prevention vigilance. Comprehensive time series analyses were conducted using American data (Study 1) and international data (Study 2), employing transfer function modeling, cross-correlation function analysis, and meta-regression analysis. Across both the US and global analyses, as vaccination rates rose over time, government COVID-19 restrictions significantly relaxed, community mobility increased, and online searches for prevention information declined. The relationship between higher vaccination rates and lower disease prevention vigilance was stronger in culturally looser contexts. Results provide initial evidence that mass immunization may be associated with attenuated sensitivity and enhanced flexibility of disease avoidance psychology and actions. However, cultural tightness-looseness significantly moderates this relationship, with tighter cultures displaying sustained vigilance amidst immunization upticks. These findings offer valuable perspectives to inform nuanced policymaking and public health strategies that balance prudent precautions against undue alarm when expanding vaccine coverage worldwide.

COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy.

The Future of Epidemic and Pandemic Vaccines to Serve Global Public Health Needs

This Review initiates a wide-ranging discussion over 2023 by selecting and exploring core themes to be investigated more deeply in papers submitted to the Vaccines Special Issue on the "Future of Epidemic and Pandemic Vaccines to Serve Global Public Health Needs". To tackle the SARS-CoV-2 pandemic, an acceleration of vaccine development across different technology platforms resulted in the emergency use authorization of multiple vaccines in less than a year. Despite this record speed, many limitations surfaced including unequal access to products and technologies, regulatory hurdles, restrictions on the flow of intellectual property needed to develop and manufacture vaccines, clinical trials challenges, development of vaccines that did not curtail or prevent transmission, unsustainable strategies for dealing with variants, and the distorted allocation of funding to favour dominant companies in affluent countries. Key to future epidemic and pandemic responses will be sustainable, global-public-health-driven vaccine development and manufacturing based on equitable access to platform technologies, decentralised and localised innovation, and multiple developers and manufacturers, especially in low- and middle-income countries (LMICs). There is talk of flexible, modular pandemic preparedness, of technology access pools based on non-exclusive global licensing agreements in exchange for fair compensation, of WHO-supported vaccine technology transfer hubs and spokes, and of the creation of vaccine prototypes ready for phase I/II trials, etc. However, all these concepts face extraordinary challenges shaped by current commercial incentives, the unwillingness of pharmaceutical companies and governments to share intellectual property and know-how, the precariousness of building capacity based solely on COVID-19 vaccines, the focus on large-scale manufacturing capacity rather than small-scale rapid-response innovation to stop outbreaks when and where they occur, and the inability of many resource-limited countries to afford next-generation vaccines for their national vaccine programmes. Once the current high subsidies are gone and interest has waned, sustaining vaccine innovation and manufacturing capability in interpandemic periods will require equitable access to vaccine innovation and manufacturing capabilities in all regions of the world based on many vaccines, not just "pandemic vaccines". Public and philanthropic investments will need to leverage enforceable commitments to share vaccines and critical technology so that countries everywhere can establish and scale up vaccine development and manufacturing capability. This will only happen if we question all prior assumptions and learn the lessons offered by the current pandemic. We invite submissions to the special issue, which we hope will help guide the world towards a global vaccine research, development, and manufacturing ecosystem that better balances and integrates scientific, clinical trial, regulatory, and commercial interests and puts global public health needs first.