Candida auris screening practices at healthcare facilities in the United States: An Emerging Infections Network survey.

We surveyed members of the Emerging Infections Network about Candida auris screening practices at US healthcare facilities. Only 37% of respondents reported conducting screening; among these, 75% reported detection of at least 1 C. auris case in the last year. Increased screening could improve C. auris detection and prevent spread.

Approach towards surveillance-based diagnosis of acute respiratory illness in India: Expert recommendations.

BACKGROUND: Emerging infectious diseases, often zoonotic, demand a collaborative "One-Health" surveillance approach due to human activities. The need for standardized diagnostic and surveillance algorithms is emphasized to address the difficulty in clinical differentiation and curb antimicrobial resistance. OBJECTIVE: The present recommendations are comprehensive diagnostic and surveillance algorithm for ARIs, developed by the Indian Council of Medical Research (ICMR), which aims to enhance early detection and treatment with improved surveillance. This algorithm shall be serving as a blueprint for respiratory infections landscape in the country and early detection of surge of respiratory infections in the country. CONTENT: The ICMR has risen up to the threat of emerging and re-emerging infections. Here, we seek to recommend a structured approach for diagnosing respiratory illnesses. The recommendations emphasize the significance of prioritizing respiratory pathogens based on factors such as the frequency of occurrence (seasonal or geographical), disease severity, ease of diagnosis and public health importance. The proposed surveillance-based diagnostic algorithm for ARI relies on a combination of gold-standard conventional methods, innovative serological and molecular techniques, as well as radiological approaches, which collectively contribute to the detection of various causative agents. The diagnostic part of the integrated algorithm can be dealt at the local microbiology laboratory of the healthcare facility with the few positive and negative specimens shipped to linked viral disease research laboratories (VRDLs) and other ICMR designated laboratories for genome characterisation, cluster identification and identification of novel agents.

Diagnostic stewardship and the coronavirus disease 2019 (COVID-19) pandemic: Lessons learned for prevention of emerging infectious diseases in acute-care settings.

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the importance of stewardship of viral diagnostic tests to aid infection prevention efforts in healthcare facilities. We highlight diagnostic stewardship lessons learned during the COVID-19 pandemic and discuss how diagnostic stewardship principles can inform management and mitigation of future emerging pathogens in acute-care settings. Diagnostic stewardship during the COVID-19 pandemic evolved as information regarding transmission (eg, routes, timing, and efficiency of transmission) became available. Diagnostic testing approaches varied depending on the availability of tests and when supplies and resources became available. Diagnostic stewardship lessons learned from the COVID-19 pandemic include the importance of prioritizing robust infection prevention mitigation controls above universal admission testing and considering preprocedure testing, contact tracing, and surveillance in the healthcare facility in certain scenarios. In the future, optimal diagnostic stewardship approaches should be tailored to specific pathogen virulence, transmissibility, and transmission routes, as well as disease severity, availability of effective treatments and vaccines, and timing of infectiousness relative to symptoms. This document is part of a series of papers developed by the Society of Healthcare Epidemiology of America on diagnostic stewardship in infection prevention and antibiotic stewardship.1.

Role of Laboratory in Emerging Infectious Disease Control in Iran, Pasteur Institute of Iran, and national laboratory network.

Strengthening surveillance systems is a key aspect of outbreak response and was particularly important during the COVID-19 pandemic. Respiratory pathogens spread rapidly, and laboratory capacity is key to monitoring the spread. Prior to the pandemic, Iran had established a rapid response team and laboratory network to provide identification, monitoring, and detection of emerging infectious diseases, but did not have the laboratory capacity to respond to COVID-19. Following the announcement of the COVID-19 pandemic, the rapid response team diverted all attention to supporting COVID-19 surveillance. Iran built on the existing national laboratory infrastructure to incorporate SARS-CoV-2 surveillance into the response network. Based on existing international protocols, in-house molecular diagnosis capacity was operationalized, and commercial controls and assays were acquired and validated to national standards. The first COVID-19 laboratory was operational by January 25, less than 4 weeks before the initial detection of SARS-CoV-2 was announced. Assays and support were expanded and rolled out to form the COVID-19 National Laboratory Network, which consists of 560 multi-sectoral laboratories covering all provinces of Iran. The national laboratory network supports a wide range of operational capacities, including assay validation and protocol development, quality assurance, respiratory pathogen diagnosis and surveillance, and variant identification and assessment using multiple sequencing platforms. This network has supported the testing of over 55 million samples over the past 36 months using RT-qPCR and has sequenced approximately 2200 samples across the country, contributing the data to international databases, including GISAID.

Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm.

Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.

One Health: The Role of Pathology as it Pertains to Diagnosis of Zoonoses and Discovery of Emerging Infections.

Pathologists are an integral part of One Health as they are a critical component of the multidisciplinary team that diagnoses zoonotic diseases and discovers emerging pathogens. Both human and veterinary pathologists are uniquely positioned to identify clusters or trends in patient populations that can be caused by an infectious agent and preface emerging outbreaks. The repository of tissue samples available to pathologists is an invaluable resource that can be used to investigate a variety of pathogens. One Health is an encompassing approach that focuses on optimizing the health of humans, animals (domesticated and sylvatic), and the ecosystem, including plants, water, and vectors. In this integrated and balanced approach, multiple disciplines and sectors from local and global communities work together to promote overall well-being of the 3 components and address threats such as emerging infectious diseases and zoonoses. Zoonoses are defined as infectious diseases that are spread between animals and humans through different mechanisms, including direct contact, food, water, vectors, or fomites. This review highlights examples in which human and veterinary pathologists were an integral part of the multisectoral team that identified uncommon etiologic agents or pathologies that had not been elucidated clinically. As the team discovers an emerging infectious disease, pathologists develop and validate tests for epidemiologic and clinical use and provide surveillance data on these diseases. They define the pathogenesis and pathology that these new diseases cause. This review also presents examples that demonstrate the crucial role pathologists play in diagnosing zoonoses that have an impact on the food supply and the economy.

Surveillance and risk assessment for early detection of emerging infectious diseases in livestock.

Those who work in the area of surveillance and prevention of emerging infectious diseases (EIDs) face a challenge in accurately predicting where infection will occur and who (or what) it will affect. Establishing surveillance and control programmes for EIDs requires substantial and long-term commitment of resources that are limited in nature. This contrasts with the unquantifiable number of possible zoonotic and non-zoonotic infectious diseases that may emerge, even when the focus is restricted to diseases involving livestock. Such diseases may emerge from many combinations of, and changes in, host species, production systems, environments/habitats and pathogen types. Given these multiple elements, risk prioritisation frameworks should be used more widely to support decision-making and resource allocation for surveillance. In this paper, the authors use recent examples of EID events in livestock to review surveillance approaches for the early detection of EIDs, and highlight the need for surveillance programmes to be informed and prioritised by regularly updated risk assessment frameworks. They conclude by discussing some unmet needs in risk assessment practices for EIDs, and the need for improved coordination in global infectious disease surveillance.

Les personnes travaillant dans le domaine de la surveillance et de la prévention des maladies infectieuses émergentes (MIE) sont confrontées à la difficulté de prédire avec exactitude le lieu d'émergence d'une maladie, ainsi que l'espèce, le système ou le site affectés. La mise en place de programmes de surveillance et de lutte contre les MIE exige une mobilisation conséquente et durable de ressources nécessairement limitées. Par contraste, le nombre des maladies infectieuses zoonotiques et non zoonotiques pouvant se déclarer est impossible à quantifier, même si l'on s'en tient aux seules maladies affectant les animaux d'élevage. Ces maladies surviennent à la faveur des nombreuses et diverses configurations, associations ou modifications qui peuvent se produire parmi les espèces hôtes, les systèmes de production, les environnements ou habitats et les types d'agents pathogènes. Compte tenu de la multiplicité de ces éléments, il devrait être fait plus largement appel à des cadres de priorisation du risque afin de soutenir les processus de prise de décision et d'allocation des ressources en matière de surveillance. Les auteurs s'appuient sur des exemples récents d'événements liés à des MIE pour faire le point sur les méthodes de surveillance appliquées pour la détection précoce de ces maladies et soulignent l'importance de documenter et de prioriser les programmes de surveillance en procédant à des mises à jour régulières des cadres utilisés pour l'évaluation du risque. Ils concluent en évoquant certains aspects importants que les pratiques actuelles d'évaluation du risque ne permettent pas de couvrir lorsqu'il s'agit de MIE, ainsi que l'importance d'améliorer la coordination de la surveillance des maladies infectieuses au niveau mondial.

Cuantos trabajan en el ámbito de la vigilancia y la prevención de enfermedades infecciosas emergentes (EIE) tienen dificultades para predecir con precisión dónde va a surgir y a quién (o qué) afectará una infección. La instauración de programas de vigilancia y control de EIE exige una inversión sustancial y duradera de recursos que por definición son escasos, sobre todo teniendo en cuenta el número incalculable de enfermedades infecciosas zoonóticas y no zoonóticas que pueden aparecer, aun considerando solo aquellas que afectan al ganado. Este tipo de enfermedades pueden surgir como resultado de muchas combinaciones distintas de especie hospedadora, sistema productivo, medio/hábitat y tipo de patógeno o por efecto de cambios que se den en cualquiera de estos elementos. En vista de la multiplicidad de factores que concurren, convendría emplear de modo más generalizado un sistema de jerarquización de los riesgos en el cual fundamentar las decisiones de vigilancia y la distribución de los recursos destinados a ella. Los autores, valiéndose de ejemplos recientes de episodios infecciosos emergentes que afectaron al ganado, pasan revista a distintos métodos de vigilancia para la detección temprana de EIE y recalcan que los programas de vigilancia deben reposar en procedimientos de determinación del riesgo periódicamente actualizados y en las prioridades fijadas a partir de estos procedimientos. Por último, los autores se detienen en algunas necesidades desatendidas en la praxis de la determinación del riesgo de EIE y en la necesidad de una mejor coordinación de la vigilancia mundial de las enfermedades infecciosas.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Screening for Zika virus in US armed services blood program donors: An opportunity to compare emerging infectious disease risk between the general US population and military donors.

BACKGROUND: The U.S. Department of Defense (DoD) collects blood from volunteer DoD donors in U.S. Food and Drug Administration (FDA)-regulated centers, and from emergency donor panels in overseas operations. Emerging infectious diseases could reduce DoD access to blood products. In August 2016, FDA determined that Zika virus was transfusion-transmitted and advised that donated blood should be screened for Zika utilizing one of two investigational new drug (IND) applications. The Armed Services Blood Program (ASBP) tested blood using its own protocol concurrently with the IND study sponsored by Roche Molecular Systems, Inc., titled "A Prospective Study to Evaluate the Specificity of the cobas Zika test for use on the cobas 6800/8800 System for Screening of Blood Donations for the Presence of Zika virus RNA." STUDY DESIGN AND METHODS: This prospective clinical trial (September 2016-August 2017) evaluated the specificity of the cobas Zika 6800/8800 System. Consenting volunteers were screened for Zika by participating reference labs. Participants with positive screens were offered a follow-up study using alternative PCR and serology assays. RESULTS: 92,618 DoD donors enrolled; four tested positive on screening (0.0043%; CI 0.001176896%, 0.01105894%). Three enrolled in follow-up testing and none were positive. These results were comparable to all U.S. donors: 3,858,114 enrolled (excluding Puerto Rico) with 459 positive screens (0.0119%; CI 0.01083582%, 0.01303962%). CONCLUSION: The study demonstrated the effectiveness of the cobas Zika test. DoD donors, who are included in emergency donor panels during military operations, were at no higher risk for Zika than the overall U.S. donor population.

The Impact of Emerging Plasmodium knowlesi on Accurate Diagnosis by Light Microscopy: A Systematic Review and Modeling Analysis.

The five major Plasmodium spp. that cause human malaria appear similar under light microscopy, which raises the possibility that misdiagnosis could routinely occur in clinical settings. Assessing the extent of misdiagnosis is of particular importance for monitoring P. knowlesi, which cocirculates with the other Plasmodium spp. We performed a systematic review and meta-analysis of studies comparing the performance of microscopy and polymerase chain reaction (PCR) for diagnosing malaria in settings with co-circulation of the five Plasmodium spp. We assessed the extent to which co-circulation of Plasmodium parasites affects diagnostic outcomes. We fit a Bayesian hierarchical latent class model to estimate variation in microscopy sensitivity and specificity measured against PCR as the gold standard. Mean sensitivity of microscopy was low, yet highly variable across Plasmodium spp., ranging from 65.7% (95% confidence interval: 48.1-80.3%) for P. falciparum to 0.525% (95% confidence interval 0.0210-3.11%) for P. ovale. Observed PCR prevalence was positively correlated with estimated microscopic sensitivity and negatively correlated with estimated microscopic specificity, though the strength of the associations varied by species. Our analysis suggests that cocirculation of Plasmodium spp. undermines the accuracy of microscopy. Sensitivity was considerably lower for P. knowlesi, P. malariae, and P. ovale. The negative association between specificity and prevalence imply that less frequently encountered species may be misdiagnosed as more frequently encountered species. Together, these results suggest that the burden of P. knowlesi, P. malariae, and P. ovale may be underappreciated in a clinical setting.

Monkeypox as an emerging infectious disease: the ophthalmic implications.

The 2022 outbreak of monkeypox is of worldwide significance. There has been a rapid escalation in case numbers despite efforts to contain it and the WHO has declared it a Public Health Emergency of International Concern. To date, over 51 257 laboratory-confirmed cases have been reported, the majority in non-endemic countries, with 3279 in the UK. It is vital for ophthalmologists to understand this disease and the risk it poses. Human monkeypox is a zoonotic disease caused by the monkeypox virus, a double-stranded DNA virus in the Orthopoxvirus genus of the Poxviridae family. Other orthopoxviruses include variola (smallpox), cowpox and vaccinia; all of which have significant ocular sequelae. Transmission occurs from an animal reservoir (unknown, likely rodents) to a human host, leading to secondary human-to-human spread. During the recent outbreak, a higher incidence has been found in gay, bisexual or other men who have sex with men. Clinical diagnosis may be challenging as presentation can mimic common ophthalmic diseases. A thorough history is key to identifying potential cases. Ophthalmic manifestations may include preseptal cellulitis, conjunctivitis and keratitis. The oral antiviral agent tecovirimat, which was developed to treat smallpox, is the mainstay of treatment. Trifluorothymidine (trifluridine) eye-drops can be used for ophthalmic involvement. In addition, smallpox vaccines have provided some cross-immunity. Ocular monkeypox should be managed by infectious diseases specialists, in consultation with ophthalmologists to provide the expertise needed to treat potentially vision-threatening complications. This outbreak highlights the need for healthcare providers to implement appropriate infection control measures and be familiar with the identification and treatment of both cutaneous and ocular disease.

Managing Viral Emerging Infectious Diseases via current Molecular Diagnostics in the Emergency Department: the Tricky Cases.

INTRODUCTION: Emerging infectious diseases' diagnosis has been a major problem in most hospitals and other senior care facilities, especially for the current Coronavirus Disease 2019 (COVID-19). The various clinical manifestations, and the several radiology and laboratory data combined with the misleading test results for identifying the virus, are responsible for certain misdiagnoses, especially for suspected cases that visit the emergency department and require urgent management and further treatment. AREAS COVERED: The major challenges for emerging infectious diseases' molecular diagnosis are being described here on a great scale, and, finally, strategies for a precise and on-the-spot molecular diagnosis are thoroughly discussed. Related literature was searched using the PubMed, Science Direct, and EMBASE databases published until May 2022 on the general information for viral infections and relevant false test results. EXPERT OPINION: Emerging diseases' molecular diagnosis via current common diagnostic assays seems to be extremely tricky, and front-line physicians and other senior care facilities should be able to recognize some falsely diagnosed cases or even prevent their existence. Further biotechnologic revolution concerning viral molecular diagnostics will be evident in the near future, thus new methods' limitations should be highlighted to physicians from the very beginning of their performances and wide utilization.

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.

Cutaneous diphtheria: three case-reports to discuss determinants of re-emergence in resource-rich settings.

Diphtheria is a re-emerging disease in resource-rich settings. We here report three cases of cutaneous diphtheria diagnosed and managed in our infectious disease department and discuss the determinants of its re-emergence. Migration, travel and vaccine scepticism are key factors not only for diphtheria re-emergence, but for the future of most preventable diseases.

CRISPR Tackles Emerging Viral Pathogens.

Understanding the dynamic relationship between viral pathogens and cellular host factors is critical to furthering our knowledge of viral replication, disease mechanisms and development of anti-viral therapeutics. CRISPR genome editing technology has enhanced this understanding, by allowing identification of pro-viral and anti-viral cellular host factors for a wide range of viruses, most recently the cause of the COVID-19 pandemic, SARS-CoV-2. This review will discuss how CRISPR knockout and CRISPR activation genome-wide screening methods are a robust tool to investigate the viral life cycle and how other class 2 CRISPR systems are being repurposed for diagnostics.

Early Warning Diagnostics for Emerging Infectious Diseases in Developing into Late-Stage Pandemics.

The spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaks-SARS, MERS, swine flu, and COVID-19-has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from pre-human predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.

Wa-1 Equine-Like G3P[8] Rotavirus from a Child with Diarrhea in Colombia.

Rotavirus A (RVA) has been considered the main cause of diarrheal disease in children under five years in emergency services in both developed and developing countries. RVA belongs to the Reoviridae family, which comprises 11 segments of double-stranded RNA (dsRNA) as a genomic constellation that encodes for six structural and five to six nonstructural proteins. RVA has been classified in a binary system with Gx[Px] based on the spike protein (VP4) and the major outer capsid glycoprotein (VP7), respectively. The emerging equine-like G3P[8] DS-1-like strains reported worldwide in humans have arisen an important concern. Here, we carry out the complete genome characterization of a previously reported G3P[8] strain in order to recognize the genetic diversity of RVA circulating among infants in Colombia. A near-full genome phylogenetic analysis was done, confirming the presence of the novel equine-like G3P[8] with a Wa-like backbone for the first time in Colombia. This study demonstrated the importance of surveillance of emerging viruses in the Colombian population; furthermore, additional studies must focus on the understanding of the spread and transmission dynamic of this important RVA strain in different areas of the country.