Legionnaires' disease became widely known following an outbreak of pneumonia in the United States in 1976. It is often caused by infection from artificial water sources such as cooling towers, water supply and heating systems, and recirculating hot tubs. To effectively implement infection prevention measures for Legionnaires' disease, collaboration among healthcare workers, water supply and heating system managers, building hygiene personnel, and other relevant parties is essential. It is important to note that outbreaks of Legionnaires' disease continue to occur frequently both domestically and internationally. While the number of reported cases of Legionnaires' disease in Japan has increased, the mortality rate has decreased but has stabilized at a lower level. Caution is also required as reports have been made in association with disasters and travel, in addition to artificial environmental water.

With the rise of virtual currencies, Bitcoin has gradually become one of the safe-haven tools in the financial market. During situations of worldwide outbreaks of an infectious disease, investors pay special attention to asset allocation. Therefore, this study discusses the outbreak of COVID-19 in China, which has affected financial markets and has led investors to avoid risks through investing in traditional financial products or Bitcoin. We found that during the time of the COVID-19 pneumonia, Bitcoin and gold futures were used for hedging transactions in the face of unstable Chinese market conditions and under the pursuit of investors' maximization of return on investment. Furthermore, there was also no difference between hedging through Bitcoin or gold futures; however, investors had a preference to invest in gold futures for hedging under the assumption that an investor was absolutely risk averse.

Mathematical modeling plays a central role in infectious disease epidemiology, shaping outbreak response strategies and informing public health policy. The COVID-19 pandemic demonstrated the value of these models but also exposed persistent limitations related to data fragility, lack of transparency, limited stakeholder engagement, and insufficient consideration of social and political contexts. Rather than critiquing modeling as a discipline, this perspective argues for a reorientation of infectious disease modeling toward a more responsive, equity-centered, and participatory paradigm. We propose a conceptual framework built on three interrelated principles: adaptability through real-time data integration, transparency via open-source and reproducible practices, and relevance through interdisciplinary and co-produced model design. Drawing on illustrative examples from COVID-19 and dengue control efforts, we highlight how integrating behavioral dynamics, local knowledge, and policy feedback can improve model usefulness and public trust. Reconceptualizing models as dynamic systems of inquiry rather than static forecasting tools can enhance decision-making and promote more equitable and effective responses to future public health emergencies.

This research is conducted with main aim of assessing the economic consequences of Covid-19 pandemic in Western Ethiopia. Primary data is collected through questionnaire and interview from 320 respondents living in three zones of Western Ethiopia. The study areas (zones) are selected purposively from Oromia region; however, the respondents are sampled by employing random sampling technique. The respondents were stratified as community members, daily laborer, business owners, government sector and NGOs employees. Exploratory research design was adopted to achieve the research objectives. Simple descriptive statistics and ordinary least square regression model are used to analyze and interpret the collected data. The study results disclose that majority of community have good awareness about the pandemic and social interaction is reduced due to social distancing. Majority of respondents realize the negative impact of Covid-19 on their economy; reduction of office services; and reduced access to market; and absence of strong support from the government. The great severity of Covid-19 impacts is failed on daily laborers. The regression result shows that sales, experience in business, education level in years, employment status of the respondent, number of workers in the business and work hours per week are positively and significantly influencing daily income of business owner before and after the pandemic outbreak. It is advised the stakeholders to give frequent follow-up and support particularly for daily laborers and small business holders to reduce the future socio-economic impacts of Covid-19 pandemic.