Virtual Objective Structured Clinical Examination (OSCE) Training in the Pandemic Era: Feasibility, Satisfaction, and the Road Ahead.

INTRODUCTION: Objective Structured Clinical Examinations (OSCEs) are essential assessments for evaluating the clinical competencies of medical students. The COVID-19 pandemic caused a significant disruption in medical education, prompting institutions to adopt virtual formats for academic activities. This study analyzes the feasibility, satisfaction, and experiences of pediatric board candidates and faculty during virtual or electronic OSCE (e-OSCE) training sessions using Zoom video communication (Zoom Video Communications, Inc., San Jose, USA). METHODS: This is a post-event survey assessing the perceptions of faculty and candidates and the perceived advantages and obstacles of e-OSCE. RESULTS: A total of 142 participants were invited to complete a post-event survey, and 105 (73.9%) completed the survey. There was equal gender representation. More than half of the participants were examiners. The overall satisfaction with the virtual e-OSCE was high, with a mean score of 4.7±0.67 out of 5. Most participants were likely to recommend e-OSCE to a friend or colleague (mean score 8.84±1.51/10). More faculty (66.1%) than candidates (40.8%) preferred e-OSCE (P=0.006). CONCLUSION: Transitioning to virtual OSCE training during the pandemic proved feasible, with high satisfaction rates. Further research on virtual training for OSCE in medical education is recommended to optimize its implementation and outcomes.

Non-typhoidal Salmonella in Nigeria: do outcomes of 'multisectoral' surveillance, treatment and control justify the intervention costs?

Non-typhoidal salmonellosis (NTS) is significant and an economic burden in Nigeria. To determine whether investment in NTS control is economically justifiable, Outbreak Costing Tool (OCT) was used to estimate the robust funding of public and animal health systems for epidemio-surveillance and control of multisectoral NTS outbreaks in Nigeria. Health, production, and economic data were collected and used to populate the tool for evaluation. The multisectoral NTS burden for the year 2020 in Nigeria was US$ 930,887,379.00. Approximately 4,835 technical officers, and 3,700 non-technical staff (n = 8,535) were needed with an investment of >2.2 million work hours. The investment cost for NTS control was US$ 53,854,660.87. The non-labour-related cost was 89.21% of the total intervention costs. The overall intervention's investment was 374.15% of the estimated national and subnational systems' annual budget for diarrhoeal diseases, and the outbreak response period attracted the highest costs (53%) of the total intervention. In conclusion, intervention against NTS was beneficial (benefit - cost ratio: 17.29), hence justifying the need for multisectoral surveillance-response against NTS in Nigeria. Complex sectoral silos must give way to coordinated collaborations to optimize benefits; and over-centralization of health interventions' associated delays must be removed through decentralized sub-national-focused framework that empowers rapid investigation, response, control, data collection, and analyses. It should assist anticipatory planning, and outbreak investigation and reduce critical response time. Anticipatory planning tools, when applied pre-emptively, can benefit budgeting, identify gaps, and assist in the delivery of cost-saving and effective measures against infectious disease.

Health Care Workers' Motivations for Enrolling in Massive Open Online Courses During a Public Health Emergency: Descriptive Analysis.

Background: Massive open online courses (MOOCs) are increasingly used to educate health care workers during public health emergencies. In early 2020, the World Health Organization (WHO) developed a series of MOOCs for COVID-19, introducing the disease and strategies to control its outbreak, with 6 courses specifically targeting health care workers as learners. In 2020, Stanford University also launched a MOOC designed to deliver accurate and timely education on COVID-19, equipping health care workers across the globe to provide health care safely and effectively to patients with the novel infectious disease. Although the use of MOOCs for just-in-time training has expanded during the pandemic, evidence is limited regarding the factors motivating health care workers to enroll in and complete courses, particularly in low-income countries (LICs) and lower-middle-income countries (LMICs). Objective: This study seeks to gain insights on the characteristics and motivations of learners turning to MOOCs for just-in-time training, to provide evidence that can better inform MOOC design to meet the needs of health care workers. We examine data from learners in 1 Stanford University and 6 WHO COVID-19 courses to identify (1) the characteristics of health care workers completing the courses and (2) the factors motivating them to enroll. Methods: We analyze (1) course registration data of the 49,098 health care workers who completed the 7 focal courses and (2) survey responses from 6272 course completers. The survey asked respondents to rank their motivations for enrollment and share feedback about their learning experience. We use descriptive statistics to compare responses by health care profession and by World Bank country income classification. Results: Health care workers completed the focal courses from all regions of the world, with nearly one-third (14,159/49,098, 28.84%) practicing in LICs and LMICs. Survey data revealed a diverse range of professional roles among the learners, including physicians (2171/6272, 34.61%); nurses (1599/6272, 25.49%); and other health care professionals such as allied health professionals, community health workers, paramedics, and pharmacists (2502/6272, 39.89%). Across all health care professions, the primary motivation to enroll was for personal learning to improve clinical practice. Continuing education credit was also an important motivator, particularly for nonphysicians and learners in LICs and LMICs. Course cost (3423/6272, 54.58%) and certification (4238/6272, 67.57%) were also important to a majority of learners. Conclusions: Our results demonstrate that a diverse range of health care professionals accessed MOOCs for just-in-time training during a public health emergency. Although all health care workers were motivated to improve their clinical practice, different factors were influential across professions and locations. These factors should be considered in MOOC design to meet the needs of health care workers, particularly those in lower-resource settings where alternative avenues for training may be limited.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

If Long-Term Suppression is not Possible, how do we Minimize Mortality for Infectious Disease Outbreaks?

OBJECTIVE: For any emerging pathogen, the preferred approach is to drive it to extinction with non-pharmaceutical interventions (NPI) or suppress its spread until effective drugs or vaccines are available. However, this might not always be possible. If containment is infeasible, the best people can hope for is pathogen transmission until population level immunity is achieved, with as little morbidity and mortality as possible. METHODS: A simple computational model was used to explore how people should choose NPI in a non-containment scenario to minimize mortality if mortality risk differs by age. RESULTS: Results show that strong NPI might be worse overall if they cannot be sustained compared to weaker NPI of the same duration. It was also shown that targeting NPI at different age groups can lead to similar reductions in the total number of infected, but can have strong differences regarding the reduction in mortality. CONCLUSIONS: Strong NPI that can be sustained until drugs or vaccines become available are always preferred for preventing infection and mortality. However, if people encounter a worst-case scenario where interventions cannot be sustained, allowing some infections to occur in lower-risk groups might lead to an overall greater reduction in mortality than trying to protect everyone equally.

Evaluating the Added Value of Digital Contact Tracing Support Tools for Citizens: Framework Development.

BACKGROUND: The COVID-19 pandemic revealed that with high infection rates, health services conducting contact tracing (CT) could become overburdened, leading to limited or incomplete CT. Digital CT support (DCTS) tools are designed to mimic traditional CT, by transferring a part of or all the tasks of CT into the hands of citizens. Besides saving time for health services, these tools may help to increase the number of contacts retrieved during the contact identification process, quantity and quality of contact details, and speed of the contact notification process. The added value of DCTS tools for CT is currently unknown. OBJECTIVE: To help determine whether DCTS tools could improve the effectiveness of CT, this study aims to develop a framework for the comprehensive assessment of these tools. METHODS: A framework containing evaluation topics, research questions, accompanying study designs, and methods was developed based on consultations with CT experts from municipal public health services and national public health authorities, complemented with scientific literature. RESULTS: These efforts resulted in a framework aiming to assist with the assessment of the following aspects of CT: speed; comprehensiveness; effectiveness with regard to contact notification; positive case detection; potential workload reduction of public health professionals; demographics related to adoption and reach; and user experiences of public health professionals, index cases, and contacts. CONCLUSIONS: This framework provides guidance for researchers and policy makers in designing their own evaluation studies, the findings of which can help determine how and the extent to which DCTS tools should be implemented as a CT strategy for future infectious disease outbreaks.

Trends and Opportunities: Hepatitis A Virus Infection, Seroprevalence, and Vaccination Coverage-United States, 1976-2020.

OBJECTIVES: The incidence of hepatitis A declined in the United States following the introduction of hepatitis A vaccines, before increasing in the setting of recent widespread outbreaks associated with person-to-person transmission. We describe the hepatitis A epidemiology in the United States, identify susceptible populations over time, and demonstrate the need for improved hepatitis A vaccination coverage, especially among adults at increased risk for hepatitis A. METHODS: We calculated the hepatitis A incidence rates for sociodemographic characteristics and percentages for risk factors and clinical outcomes for hepatitis A cases reported to the National Notifiable Diseases Surveillance System during 1990-2020. We generated nationally representative estimates and 95% CIs of hepatitis A seroprevalence during 1976-March 2020 and self-reported hepatitis A vaccination coverage during 1999-March 2020 for the noninstitutionalized civilian US population using data from the National Health and Nutrition Examination Survey. RESULTS: Overall, the rate per 100 000 population of reported cases of hepatitis A virus infection in the United States declined 17.3-fold, from 10.4 during 1990-1998 to 0.6 during 2007-2015, and then increased to 2.8 during 2016-2020. The overall hepatitis A seroprevalence in the United States increased from 38.2% (95% CI, 36.2%-40.1%) during 1976-1980 to 47.3% (95% CI, 45.4%-49.2%) during 2015-March 2020. The prevalence of self-reported hepatitis A vaccination coverage in the United States increased more than 2.5-fold, from 16.3% (95% CI, 15.0%-17.7%) during 1999-2006 to 41.9% (95% CI, 40.2%-43.7%) during 2015-March 2020. CONCLUSIONS: Hepatitis A epidemiology in the United States changed substantially during 1976-2020. Improved vaccination coverage, especially among adults recommended for vaccination by the Advisory Committee on Immunization Practices, is vital to stop current hepatitis A outbreaks associated with person-to-person transmission in the United States and prevent similar future recurrences.

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.

BACKGROUND: COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. METHODS: We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. RESULTS: The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. CONCLUSION: We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches.

BACKGROUND: Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources. OBJECTIVE: We aimed to validate and compare extreme gradient boosting (XGBoost) and logistic regression, 2 supervised learning approaches, to predict the US counties most likely to experience measles cases. We also aimed to assess the performance of hybrid versions of these models that incorporated additional predictors generated by 2 clustering algorithms, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and unsupervised random forest (uRF). METHODS: We constructed a supervised machine learning model based on XGBoost and unsupervised models based on HDBSCAN and uRF. The unsupervised models were used to investigate clustering patterns among counties with measles outbreaks; these clustering data were also incorporated into hybrid XGBoost models as additional input variables. The machine learning models were then compared to logistic regression models with and without input from the unsupervised models. RESULTS: Both HDBSCAN and uRF identified clusters that included a high percentage of counties with measles outbreaks. XGBoost and XGBoost hybrid models outperformed logistic regression and logistic regression hybrid models, with the area under the receiver operating curve values of 0.920-0.926 versus 0.900-0.908, the area under the precision-recall curve values of 0.522-0.532 versus 0.485-0.513, and F2 scores of 0.595-0.601 versus 0.385-0.426. Logistic regression or logistic regression hybrid models had higher sensitivity than XGBoost or XGBoost hybrid models (0.837-0.857 vs 0.704-0.735) but a lower positive predictive value (0.122-0.141 vs 0.340-0.367) and specificity (0.793-0.821 vs 0.952-0.958). The hybrid versions of the logistic regression and XGBoost models had slightly higher areas under the precision-recall curve, specificity, and positive predictive values than the respective models that did not include any unsupervised features. CONCLUSIONS: XGBoost provided more accurate predictions of measles cases at the county level compared with logistic regression. The threshold of prediction in this model can be adjusted to align with each county's resources, priorities, and risk for measles. While clustering pattern data from unsupervised machine learning approaches improved some aspects of model performance in this imbalanced data set, the optimal approach for the integration of such approaches with supervised machine learning models requires further investigation.

Detection of pathogenic viruses in the urban wastewater in Kuwait-implications for monitoring viral disease outbreaks.

Effective surveillance for epidemic-prone viral diseases is essential for emergency preparedness to respond to threats and occurrences of pandemics. While it is difficult and expensive to conduct health facility-based surveillance, there is a growing interest in conducting sewage-based epidemiological studies to monitor the health of the urban population because of the relative ease of sample collection and the availability of advanced molecular techniques for the detection of pathogens in the sewage. Sewage samples offer unique means to study the aggregate health of the population as opposed to the monitoring of the health of any individual by traditional methods. We worked together with the Ministry of Public Works in Kuwait and developed a platform for the collection and testing of sewage samples from different regions of Kuwait for studying population health. In this report, we describe the results of a cross-sectional study conducted between 16 and 23 September 2019 in an attempt to detect influenza, Noro, Rota, hepatitis A, and hepatitis E viruses in urban sewage samples collected in Kuwait. All five targeted viruses were detected in the samples collected from urban wastewater in Kuwait using reverse-transcriptase quantitative PCR (RT-qPCR). We recently checked for the presence of SARS-CoV-2 in the stored cDNA samples and confirmed the absence of SARS-CoV-2 in them. This is the first report that demonstrates the preparedness in Kuwait for using sewage samples for the detection and monitoring of many pathogenic viruses which may greatly increase the capacity of the country to deal with a viral disease outbreak in the future.

Revisiting the Term "Compassionate Use" and Leadership of the World Health Organization in Resolving Confusion in the Age of COVID-19 and Beyond.

We discuss the term "compassionate use" (CU) as an example of terminology having a huge impact on drug regulation. CU is used in many confusing situations, and its meaning varies significantly. We ethically affirm the necessity of CU. We insist that CU should be properly placed in exceptional status. The regulation of CUs is much more lenient than that of clinical trials because of the difference in the purpose. Whether consciously or unconsciously, abuse results in confusion and is never acceptable. The World Health Organization (WHO) proposed not to use the previous term CU but to replace it with another one. WHO also proposed the term MEURI (monitored emergency use of unregistered and experimental interventions). However, this was extremely incomplete, and WHO used the term CU subsequently. The main purpose of the proposal needs to be thoroughly implemented. In the context of the COVID-19 pandemic and beyond, expectations regarding WHO's role and leadership in global health issues are rising. We hope that WHO will play a major role in promoting research ethics preparedness while discontinuing the use of confusing terms such as CU and will develop alternative terms and their content. We discuss the evaluation of MEURI, the Japanese version of CU, and appropriate and inappropriate terminology related to the therapeutic use of unapproved drugs. We also discuss the expected appearance of CU including its name. It is appropriate to target group/cohort patients and unapproved drugs in the late stage of development. It is also important to solve the problem of incentives for CUs of pharmaceutical companies that are rushing to obtain marketing approval. The UK's Early Access to Medicine Scheme has provided many suggestions. We believe that our opinion can contribute to WHO's efforts to resolve the confusion and promote research ethics preparedness in health emergencies.

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.

BACKGROUND: Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. OBJECTIVE: The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. METHODS: We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. RESULTS: Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. CONCLUSIONS: We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings.

Psychological stress and influencing factors of hospital workers in different periods under the public health background of infectious disease outbreak: A cross-sectional study.

Background and Aims: It is well known that public health emergencies can affect the mental health of medical personnel, and many studies have focused on cross-sectional studies with short-term benefits. The present study aimed to investigate the long-term influence of infectious disease outbreak about the mental health of hospital staff. Methods: The demographic characteristics and mental health status of staff in Fuzhou, China, were analyzed by using the Generalized Anxiety Disorder (GAD-7) Scale and Depression Screening Scale (9-item Patient Health Questionnaire [PHQ-9]) in February and December 2020. Results: There were no significant differences in anxiety levels during different time periods (p > 0.05), but there were significant differences among anxiety level and total score of GAD-7 scale (p < 0.001). There were significant differences among the number of people with depression, depression level, and total score on the PHQ-9 scale (p < 0.001). As the pandemic progressed, total scores of anxiety in medical staff with different titles decreased (p < 0.05), but depression scores in professionals with intermediate and senior titles increased significantly (p < 0.05). changes in anxiety and depression scores during different time periods also changed according to hospital worker specialty. Total scores of anxiety in doctors, nurses, medical technicians, and other staff members all decreased (p < 0.05), while total scores of depression in doctors, nurses, and other staff members significantly increased (p < 0.05). There were no significant differences in total depression score among medical technicians (p > 0.05). Conclusions: Since the outbreak of an infectious disease public health emergency, the anxiety of hospital staff has decreased over time, but the depression has increased. The management and psychological support personnel in medical institutions should continue to pay attention to the mental health of medical staff, and it is necessary to take different intervention measures in different periods when implementing the psychological crisis prevention mechanism.

Knowledge about the COVID-19 and ways of its prevention among Medical University of Warsaw students.

BACKGROUND: The aim of the study was to assess the state of knowledge of Medical University of Warsaw (MUW) students on COVID-19, modes of transmission and preventive measures. MATERIAL AND METHODS: The study was conducted in October 2020. The participants were all the students attending classes at MUW - 8922 persons. All had completed the online training "Work safety and hygiene during COVID-19." To assess their state of knowledge an online questionnaire was made available on the MUW e-learning platform. The questionnaire comprised 4 parts: (1) awareness of rules of hand hygiene, (2) medical aspects of COVID-19, (3) preventing SARS-CoV-2 infection transmission in health care facilities, and (4) preventing infection transmissions in the society. RESULTS: The majority of students (93.9%) demonstrated a sufficient level of knowledge. The highest passing threshold was found on the medical programme (96.7% of students with satisfactory level of knowledge), dentistry (96.2%) and pharmacy (95.5%). The statistically significant factors that differentiated student results proved to be faculty (p < 0.001), study programme (p < 0.001), year of studies (p = 0.001), form of studies (p < 0.001). The participants most often showed full knowledge (100% correct answers in sub-area) of preventing infection transmissions in the society (93.3%) and medical aspects of COVID-19 (91.8%), less complete in terms of ways of preventing infection transmission in health care facilities (85.4%), and in particular hand hygiene rules (78.3%). All the variables characterizing academic status (study programme, faculty, year and form of studies) were statistically significant differentiating factors for students' full knowledge in all of the 4 analyzed sub-areas, while students' sex only in the sub-area of COVID-19 medical aspects. CONCLUSIONS: There is a clear need for conducting systematic educational activities among students of all medical study programmes and assessing their level of knowledge in those areas that were identified as least frequently controlled, namely, hand hygiene and infection transmission in health care facilities. Med Pr. 2022;73(5):369-81.

Monkeypox 2022: A Primer and Identify-Isolate-Inform (3I) Tool for Emergency Medical Services Professionals.

Monkeypox 2022 exhibits unprecedented human-to-human transmission and presents with different clinical features than those observed in prior outbreaks. Previously endemic only to West and Central Africa, the monkeypox virus spread rapidly world-wide following confirmation of a case in the United Kingdom on May 7, 2022 of an individual that had traveled to Nigeria. Detection of cases with no travel history confirms on-going community spread. Emergency Medical Services (EMS) professionals will likely encounter patients suspected or confirmed to have monkeypox, previously a rare disease and therefore unfamiliar to most clinicians. Consequently, it is critical for EMS medical directors to immediately implement policies and procedures for EMS teams - including emergency medical dispatchers - to identify potential monkeypox cases. These must include direction on actions EMS professionals should take to protect themselves and others from virus transmission. Monkeypox 2022 may manifest more subtly than it has historically. Presentations include a subclinical prodrome and less dramatic skin lesions - potentially limited to genital or anal body regions - which can be easily confused with dermatologic manifestations of common sexually transmitted infections (STIs). While most readily spread by close contact with infectious skin lesions on a patient, it is also transmissible from fomites, such as bed sheets. Additionally, droplet transmission can occur, and the virus can be spread by aerosolization under certain conditions. The long incubation period could have profound negative consequences on EMS staffing if clinicians are exposed to monkeypox. This report summarizes crucial information needed for EMS professionals to understand and manage the monkeypox 2022 outbreak. It presents an innovative Identify-Isolate-Inform (3I) Tool for use by EMS policymakers, educators, and clinicians on the frontlines who may encounter monkeypox patients. Patients are identified as potentially exposed or infected after an initial assessment of risk factors with associated signs and symptoms. Prehospital workers must immediately don personal protective equipment (PPE) and isolate infectious patients. Also, EMS professionals must report exposures to their agency infection control officer and alert health authorities for non-transported patients. Prehospital professionals play a crucial role in emerging and re-emerging infectious disease mitigation. The monkeypox 2022 3I Tool includes knowledge essential for all clinicians, plus specific information to guide critical actions in the prehospital environment.

Deep similarity analysis and forecasting of actual outbreak of major infectious diseases using Internet-Sourced data.

Perhaps no other generation in the span of recorded human history has endured the risks of infectious diseases as has the current generation. The prevalence of infectious diseases is caused mainly by unlimited contact between people in a highly globalized world. Disease control and prevention (CDC) promptly collect and produce disease outbreak statistics, but CDCs rely on a curated, centralized collection system, and requires up to two weeks of lead time. Consequently, the quick release of disease outbreak information has become a great challenge. Infectious disease outbreak information is recorded and spread somewhere on the Internet much faster than CDC announcements, and Internet-sourced data have shown non-substitutable potential to watch and predict infectious disease outbreaks in advance. In this study, we performed a thorough analysis to show the similarity between the Korean Center of Disease Control (KCDC) infectious disease datasets and three Internet-sourced data for nine major infectious diseases in terms of time-series volume. The results show that many of infectious disease outbreak have strongly related to Internet-sourced data. We analyzed several factors that affect the similarity. Our analysis shows that the increase in the number of Internet-sourced data correlates with the increase in the number of infected people and thus, show the positive similarity. We also found that the greater the number of infectious disease outbreaks corresponds to having a wider spread of outbreak regions, in which it also proves to have higher similarity. We presented the prediction result of infectious disease outbreak using various Internet-sourced data and an effective deep learning algorithm. It showed that there are positive correlations between the number of infected people or the number of related web data and the prediction accuracy. We developed and currently operate a web-based system to show the similarity between KCDC and related Internet-sourced data for infectious diseases. This paper helps people to identify what kind of Internet-sourced data they need to use to predict and track a specific infectious disease outbreak. We considered as much as nine major diseases and three kinds of Internet-sourced data together, and we can say that our finding did not depend on specific infectious disease nor specific Internet-sourced data.

Getting Critical Information During the COVID-19 Pandemic: Experiences of Spanish and Chinese Speakers With Limited English Proficiency.

People with limited English proficiency in the United States have suffered disproportionate negative health outcomes during the COVID-19 pandemic. Effective communications are critical tools in addressing inequities insofar as they can motivate adoption of protective behaviors and reduce incidence of disease; however, little is known about experiences of communities with limited English proficiency receiving relevant information during COVID-19 or other outbreaks. To address this gap and provide inputs for communication strategies, we completed a study based on 2 novel and nationally representative surveys conducted between June and August 2020 among Spanish and Chinese speakers with limited English proficiency (n = 764 and n = 355, respectively). Results first showed that Spanish and Chinese speakers did not consistently receive information about protective behaviors from key public health and government institutions early in the pandemic. Second, for such information, Spanish and Chinese speakers used a diverse set of information resources that included family and friends, social media, and traditional media from both inside and outside the United States. Third, Spanish and Chinese speakers faced challenges getting COVID-19 information, including receiving media messages that felt discriminatory toward Latinx or Chinese people. Together, these findings suggest gaps in effectively reaching Spanish and Chinese speakers. Data highlight the important role of bilingual materials to support sharing of information between Spanish or Chinese speakers and English speakers within their social networks, and the need for digital news content for traditional and social media. Finally, efforts are needed to address discriminatory messaging in media and to actively counter it in public health communications.

Impact of infectious disease epidemics on xenophobia: A systematic review.

Background Globally, xenophobia towards out-groups is frequently increased in times of economic and political instability, such as in infectious disease outbreaks. This systematic review aims to: (1) assess the xenophobic attitudes and behaviors towards migrants during disease outbreaks; and (2) identify adverse health outcomes linked to xenophobia. Methods We searched nine scientific databases to identify studies measuring xenophobic tendencies towards international migrants during disease outbreaks and evaluated the resulting adverse health effects. Results Eighteen articles were included in the review. The findings were grouped into: (1) xenophobia-related outcomes, including social exclusion, out-group avoidance, support for exclusionary health policies, othering, and germ aversion; and (2) mental health problems, such as anxiety and fear. Depending on the disease outbreak, different migrant populations were negatively affected, particularly Asians, Africans, and Latino people. Factors such as perceived vulnerability to disease, disgust sensitivity, medical mistrust individualism, collectivism, disease salience, social representation of disease and beliefs in different origins of disease were associated with xenophobia. Conclusions Overall, migrants can be a vulnerable population frequently blamed for spreading disease, promoting irrational fear, worry and stigma in various forms, thus leading to health inequities worldwide. It is urgent that societies adopt effective support strategies to combat xenophobia and structural forms of discrimination against migrants.

Holding a stigmatizing attitude at the start of the COVID-19 outbreak: A cross-sectional survey.

OBJECTIVES: To identify the prevalence of a stigmatizing attitude towards people of Chinese origin at the start of the COVID-19 outbreak in the UK population and investigate factors associated with holding the stigmatizing attitude. DESIGN: Online cross-sectional survey conducted 10-13 February 2020 (n = 2006, people aged 16 years or over and living in the UK). METHODS: We asked participants to what extent they agreed it was best to avoid areas heavily populated by Chinese people because of the COVID-19 outbreak. Survey materials also asked about: worry, perceived risk, knowledge, information receipt, perception of government response to COVID-19, and personal characteristics. We ran binary logistic regressions to investigate associations between holding a stigmatizing attitude, personal characteristics, and psychological and contextual factors. RESULTS: 26.1% people (95% CI 24.2-28.0%, n = 524/2006) agreed it was best to avoid areas heavily populated by Chinese people. Holding a stigmatizing attitude was associated with greater worry about COVID-19, greater perceived risk of COVID-19, and poorer knowledge about COVID-19. CONCLUSIONS: At the start of the COVID-19 pandemic, a large percentage of the UK public endorsed avoiding areas in the UK heavily populated by people of Chinese origin. This attitude was associated with greater worry about, and perceived risk of, the COVID-19 outbreak as well as poorer knowledge about COVID-19. At the start of future novel infectious disease outbreaks, proactive communications from official sources should provide context and facts to reduce uncertainty and challenge stigmatizing attitudes, to minimize harms to affected communities.