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AbstractThis study analyzed global data on epidemic control measures and economic conditions in different countries during different mutant strain epidemic periods, including the Alpha, Delta, and Omicron strains. The study estimated the elasticity coefficient through a log‐log model, which represents the percent change of the confirmed case number with respect to a percent change in the total number of screening tests in a country for epidemic control. The 7‐day rolling data of screening tests and confirmed cases from the Our World in Data database for the pandemic periods of Alpha strain in 2020, Delta strain in 2021, and Omicron strain in 2022, suggest that the magnitude of the elasticity was associated with the economic condition of a country. Compared with the results during either Alpha or Delta pandemic period, the Omicron pandemic has a much higher estimated elasticity coefficient of 1.317 (Alpha: 0.827 and Delta: 0.885). Further examining economic conditions categorized by quartile ranges, the results indicate that the elasticity is statistically significantly lower in countries with gross domestic product (GDP) per capita between $11,354 and $26,651, and in countries with GDP per capita above $26,651 than in countries with GDP per capita below $3,335. These results suggest that countries should consider not only epidemiological measures but also economic conditions when formulating epidemic control strategies. This study highlights the importance of assessing the appropriateness of epidemic control strategies within a country and provides valuable insights into the effectiveness of such strategies, particularly in the context of community screening.

PurposeConfronted with the transformation of industrial economies, the Taiwanese information technology (IT) industry has to upgrade from production to innovation orientation. The paper seeks to explore what is the core competence of the Taiwanese IT industry. In brief, what kind of intellectual capital (IC) is embedded in the Taiwanese IT industry and how this IC is managed effectively. The point is to discover how the Taiwanese IT industry should start to accumulate or enhance the core resource and strategic capability for future competitive advantage.Design/methodology/approachThe study selected a relatively representative number of IT firms in Taiwan covering six industries. This study conducted a two‐stage survey to construct a measurement model and explored the IC profile of the Taiwanese IT industries.FindingsThis study was able to identify eight IC factors as a measuring model in exploring the IC profiles of four Taiwanese IT industries. The findings indicate the stronger IC in the Taiwanese IT industry as well as the weaker side.Research limitations/implicationsThe research was limited by the sample within the Taiwan information communications technology industries. It needs to be extended across further industries in the future. It should also be compared with other countries' industrial IC under similar assessment and measurement. The significant differences between industries could be explored by case study methodology.Practical implicationsInnovation capability plays an important role in confronting the knowledge‐based economy in Taiwanese IT industries. However, there is no compelling evidence to show that investment in research and development will help to achieve the goal of establishing Taiwan as an "Asia Pacific Electronic Information Industry Resource Integration Center".Originality/valueThis research indicates that the IC System Model developed would help us to identify the interactions among IC elements. It could help to explore whether it is true that, the higher the IC management, the higher is the influence from the input of IC to the output.

A fast production scheduling system, the very fast scheduler (VFS), has
been developed by the authors. It creates a capacity constrained
production schedule within one minute of elapsed time for problems of a
size encountered in industry. The quality of the schedules is comparable
with the best alternative heuristic scheduling techniques. The speed of
the scheduler is such that it can be used on a real‐time basis to plan
capacity, adjust priorities and other parameters and derive new
schedules.

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells‐Riley mathematical model to estimate (1) the CO2exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number,R0(i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range ofR0. We also employ a standard susceptible‐infectious‐recovered (SIR) structure to relate Wells‐Riley model derivedR0to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.