This paper studies the social acceptability and feasibility of a focused protection strategy against coronavirus disease 2019 (COVID-19). We propose a control scheme to develop herd immunity while satisfying the following two basic requirements for a viable policy option. The first requirement is social acceptability: the overall deaths should be minimized for social acceptance. The second is feasibility: the healthcare system should not be overwhelmed to avoid various adverse effects. To exploit the fact that the disease severity increases considerably with age and comorbidities, we assume that some focused protection measures for those high-risk individuals are implemented and the disease does not spread within the high-risk population. Because the protected population has higher severity ratios than the unprotected population by definition, the protective measure can substantially reduce mortality in the whole population and also avoid the collapse of the healthcare system. Based on a simple susceptible-infected-recovered model, social acceptability and feasibility of the proposed strategy are summarized into two easily computable conditions. The proposed framework can be applied to various populations for studying the viability of herd immunity strategies against COVID-19. For Japan, herd immunity may be developed by the proposed scheme if $${\mathcal {R}}_0 \le 2.0$$ R 0 ≤ 2.0 and the severity rates of the disease are 1/10 times smaller than the previously reported value, although as high mortality as seasonal influenza is expected.

Safety and reliability of large critical infrastructure systems such as long-span bridges, high-rise buildings, nuclear power plants, high-voltage transmission towers, rolling-element bearing and so on are important for a modern society. Research on reliability and safety analysis started with a `small data' business dealing with relative scarce lifetime or failure data. Later, degradation modelling that uses performance deterioration or condition data collected from inservice inspections or online health monitoring became an important analytical tool for reliability prediction and maintenance planning of highly reliable engineering systems. Over the past decades, a large number of degradation models have been developed to characterize and quantify the underlying degradation mechanism using direct and indirect measurements. Recent advancements in artificial intelligence, remote sensing, big data analytics, and Internet of Things are making far-reaching impacts on almost every aspect of our life. How these changes will affect the degradation modelling, health prognosis, and safety management is an interesting questions to explore. This paper presents a comprehensive, forward-looking review of the various degradation models and their practical applications to damage prognosis and management of critical infrastructure. The degradation models were classified into four categories: physics-based, knowledge-based, data-driven, and hybrid approaches.

No abstract

In many current state-of-the-art bridge management systems, Markov models are used for both the prediction of deterioration and the determination of optimal intervention strategies. Although transition probabilities of Markov models are generally estimated using inspection data, it is not uncommon that there are situations where there are inadequate data available to estimate the transition probabilities. In this article, a methodology is proposed to estimate the transition probabilities from mechanistic-empirical models for reinforced concrete elements. The proposed methodology includes the estimation of the transition probabilities analytically when possible and when not through the use of Bayesian statistics, which requires the formulation of a likelihood function and the use of Markov Chain Monte Carlo simulations. In an example, the difference between the average condition predicted over a 100-year time period with a Markov model developed using the proposed methodology and the condition predicted using mechanistic-empirical models were found to be 54% of that when the state-of-the-art methodology, i.e., a methodology that estimates the transition probabilities using best fit curves based on yearly condition distributions, was used. The variation in accuracy of the Markov model as a function of the number of deterioration paths generated using the mechanistic-empirical models is also shown.

No abstract