A cross-sectional study on public health nurses' disaster competencies and influencing factors during the COVID-19 pandemic in Korea.

Public health nurses are performing various roles during the COVID-19 pandemic: counseling, surveillance, specimen collection, epidemiological investigation, education, and vaccination. This study investigated their disaster competencies in the context of emerging infectious diseases, and identified their influencing factors based on Deci and Ryan's self-determination theory. A convenience sample of 242 was selected from public health nurses working in a metropolitan city of South Korea. Data were collected using a structured questionnaire and analyzed using descriptive statistics, t-test, one-way ANOVA, Pearson's correlation, and multiple regression analysis using the SPSS Statistics ver. 23.0. Results showed that the significant factors influencing disaster competencies included "willingness to respond to a disaster," "preventive behavior," "experience of receiving education on emerging infectious diseases response," "public health center experience," "job satisfaction," and "education." This regression model explained 33.2% of the variance in disaster competencies. "Willingness to respond to a disaster" was the strongest factor affecting disaster competencies. Based on these results, it is concluded that interventions to improve disaster competencies and psychological well-being of public health nurses are needed. Additionally, strategies such as creating a supportive work environment, deploying experienced nurses primarily on the front line, and reducing the tasks of permanent public health nurses should be implemented.

Strategies to Develop a Mucosa-Targeting Vaccine against Emerging Infectious Diseases.

Numerous pathogenic microbes, including viruses, bacteria, and fungi, usually infect the host through the mucosal surfaces of the respiratory tract, gastrointestinal tract, and reproductive tract. The mucosa is well known to provide the first line of host defense against pathogen entry by physical, chemical, biological, and immunological barriers, and therefore, mucosa-targeting vaccination is emerging as a promising strategy for conferring superior protection. However, there are still many challenges to be solved to develop an effective mucosal vaccine, such as poor adhesion to the mucosal surface, insufficient uptake to break through the mucus, and the difficulty in avoiding strong degradation through the gastrointestinal tract. Recently, increasing efforts to overcome these issues have been made, and we herein summarize the latest findings on these strategies to develop mucosa-targeting vaccines, including a novel needle-free mucosa-targeting route, the development of mucosa-targeting vectors, the administration of mucosal adjuvants, encapsulating vaccines into nanoparticle formulations, and antigen design to conjugate with mucosa-targeting ligands. Our work will highlight the importance of further developing mucosal vaccine technology to combat the frequent outbreaks of infectious diseases.

Emerging tropical neurological infections.

A large majority of neurological infections remain undiagnosed worldwide. Emerging and re-emerging infections are likely to be responsible for a significant proportion of these. Over the last two decades, several new organisms producing neurological infection and the neurotropic potential of many other known pathogens have been identified. Large outbreaks caused by re-emerging pathogens such as Chikungunya virus, Zika virus and Ebola virus have led to better delineation of their neurological manifestations. Recognition of the pandemic potential of emerging pathogens and an improved understanding of their host-vector-environment interactions would help us be better prepared to meet these emerging threats.

COVID-19-lessons for zoonotic disease.

Disease emergence is driven by human-animal contact in a global viral ecosystem.

Impact of Refutational Two-Sided Messages on Attitudes Toward Novel Vaccines Against Emerging Infectious Diseases During the COVID-19 Pandemic.

Two-sided messages that include two perspectives (i.e., risks and benefits) are more effective than one-sided messages that convey only one perspective (usually only the benefits). Refutational two-sided messages are effective for communicating risks regarding vaccines. To examine the effectiveness of refutational two-sided messages in risk communication regarding novel vaccines against emerging infectious diseases, we conducted the randomized controlled study based on a 3 × 3 × 2 mixed design (Intervention 1: vaccines against subcutaneous influenza, "novel severe infectious disease," or intranasal influenza; intervention 2: one-sided, non-refutational two-sided, or refutational two-sided messages; two questionnaires) using a Japanese online panel. Participants completed questionnaires before and after receiving an attack message (negative information). We evaluated the impact of attack messages on the willingness to be vaccinated, and the anticipated regret of inaction (ARI). Among 1,184 participants, there was a significant difference in the willingness to be vaccinated among the message groups (p < 0.01). After receiving the attack message, willingness to be vaccinated decreased in the one-sided message group and increased in the non-refutational two-sided and refutational two-sided message groups. Additionally, ARI in the refutational two-sided message groups was significantly higher than in the one-sided groups (p = 0.03). In conclusion, two-sided messages are more effective than one-sided messages in terms of willingness to be vaccinated. Furthermore, the high ARI in the refutational two-sided message group indicated that refutational two-sided messages were more effective than one-sided messages for communicating the risks of vaccines, especially those against emerging infectious diseases.

Special Issue "Emerging Virus Infections in Adverse Pregnancy Outcomes".

Dear contributors and readers, [...].

mRNA Vaccine Development for Emerging Animal and Zoonotic Diseases.

In the prevention and treatment of infectious diseases, mRNA vaccines hold great promise because of their low risk of insertional mutagenesis, high potency, accelerated development cycles, and potential for low-cost manufacture. In past years, several mRNA vaccines have entered clinical trials and have shown promise for offering solutions to combat emerging and re-emerging infectious diseases such as rabies, Zika, and influenza. Recently, the successful application of mRNA vaccines against COVID-19 has further validated the platform and opened the floodgates to mRNA vaccine's potential in infectious disease prevention, especially in the veterinary field. In this review, we describe our current understanding of the mRNA vaccines and the technologies used for mRNA vaccine development. We also provide an overview of mRNA vaccines developed for animal infectious diseases and discuss directions and challenges for the future applications of this promising vaccine platform in the veterinary field.

Effect of travel restrictions, contact tracing and vaccination on control of emerging infectious diseases: transmission of COVID-19 as a case study.

Patch models can better reflect the impact of spatial heterogeneity and population mobility on disease transmission. While, there is relatively little work on using patch models to study the role of travel restrictions, contact tracing and vaccination in COVID-19 epidemic. In this paper, based on COVID-19 epidemic propagation and diffusion mechanism, we establish a dynamic model of disease spread among two patches in which Wuhan is regarded as one patch and the rest of Mainland China (outside Wuhan) as the other patch. The existence of the final size is proved theoretically and some model parameters are estimated by using the reported confirmed cases. The results show that travel restrictions greatly reduce the number of confirmed cases in Mainland China, and the earlier enforced, the fewer confirmed cases. However, it is impossible to bring the COVID-19 epidemic under control and lift travel restrictions on April 8, 2020 by imposing travel restrictions alone, the same is true for contact tracing. While, the disease can always be controlled if the protection rate of herd immunity is high enough and the corresponding critical threshold is given. Therefore, in order to quickly control the spread of the emerging infectious disease (such as COVID-19), it is necessary to combine a variety of control measures and develop vaccines and therapeutic drugs as soon as possible.

Pandemic inequalities: emerging infectious diseases and health equity.

The frequency and scale of Emerging Infectious Diseases (EIDs) with pandemic potential has been increasing over the last two decades and, as COVID-19 has shown, such zoonotic spill-over events are an increasing threat to public health globally. There has been considerable research into EIDs - especially in the case of COVID-19. However, most of this has focused on disease emergence, symptom identification, chains of transmission, case prevalence and mortality as well as prevention and treatment. Much less attention has been paid to health equity concerns and the relationship between socio-economic inequalities and the spread, scale and resolution of EID pandemics. This commentary article therefore explores socio-economic inequalities in the nature of EID pandemics. Drawing on three diverse case studies (Zika, Ebola, COVID-19), it hypothesises the four main pathways linking inequality and infectious disease (unequal exposure, unequal transmission, unequal susceptibility, unequal treatment) - setting out a new model for understanding EIDs and health inequalities. It concludes by considering the research directions and policy actions needed to reduce inequalities in future EID outbreaks.

Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

The Incubation Period Distribution of Coronavirus Disease 2019: A Systematic Review and Meta-analysis.

Incubation period is an important parameter to inform quarantine period and to study transmission dynamics of infectious diseases. We conducted a systematic review and meta-analysis on published estimates of the incubation period distribution of coronavirus disease 2019, and showed that the pooled median of the point estimates of the mean, median and 95th percentile for incubation period are 6.3 days (range, 1.8-11.9 days), 5.4 days (range, 2.0-17.9 days), and 13.1 days (range, 3.2-17.8 days), respectively. Estimates of the mean and 95th percentile of the incubation period distribution were considerably shorter before the epidemic peak in China compared to after the peak, and variation was also noticed for different choices of methodological approach in estimation. Our findings implied that corrections may be needed before directly applying estimates of incubation period into control of or further studies on emerging infectious diseases.

Emerging Fungal Infections: New Species, New Names, and Antifungal Resistance.

BACKGROUND: Infections caused by fungi can be important causes of morbidity and mortality in certain patient populations, including those who are highly immunocompromised or critically ill. Invasive mycoses can be caused by well-known species, as well as emerging pathogens, including those that are resistant to clinically available antifungals. CONTENT: This review highlights emerging fungal infections, including newly described species, such as Candida auris, and those that having undergone taxonomic classification and were previously known by other names, including Blastomyces and Emergomyces species, members of the Rasamsonia argillacea species complex, Sporothrix brasiliensis, and Trichophyton indotinae. Antifungal resistance also is highlighted in several of these emerging species, as well as in the well-known opportunistic pathogen Aspergillus fumigatus. Finally, the increased recognition and importance of fungal co-infections with respiratory pathogens, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is discussed. SUMMARY: Both clinicians and clinical microbiology laboratories should remain vigilant regarding emerging fungal infections. These may be difficult both to diagnose and treat due to the lack of experience of clinicians and laboratory personnel with these organisms and the infections they may cause. Many of these fungal infections have been associated with poor clinical outcomes, either due to inappropriate therapy or the development of antifungal resistance.