Infectious Disease Surveillance Research Articles

[Epidemiological characteristics and genotype trends of rotavirus diarrhea in China from 2009 to 2020].

Objective: To investigate the epidemiological characteristics and genotype trends of rotavirus infection among the population with diarrhea in China, from 2009 to 2020 and provide evidence for strategic surveillance and prevention. Methods: Surveillance data on diarrhea syndrome from 252 sentinel hospitals across 28 provinces (municipalities, autonomous regions) were obtained from the information management system of the Infectious Disease Surveillance Technology Platform of the National Science and Technology Major Project. Descriptive epidemiological methods were employed to analyze the distribution of rotavirus diarrhea cases in different climatic zones, populations, and times from 2009 to 2020, as well as the genotyping characteristics and changing trends of group A rotavirus diarrhea cases. Results: From 2009 to 2020, a total of 114 606 diarrhea cases were tested for rotavirus, and the positive rate was 19.1% (21 872/114 606); group A rotavirus was dominant (98.2%, 21 471/21 872). The positive rate of rotavirus was the highest in 2009 (36.9%, 2 436/6 604) and 2010 (30.6%, 5 130/16 790), fluctuated between 14.0% to 18.0% from 2011 to 2017, raised slightly in 2018 (20.3%, 2 211/10 900), and declined continuously in the following two years (15.5%, 2 262/14 611 and 9.5%, 470/4 963). The positive rate of males (20.2%, 13 660/67 471) was significantly higher than that of females (17.4%, 8 212/47 135). Children under five had the highest positive rate (28.4%, 18 261/64 300), more than four times that of adults. The positive rate peaked from December to February in the mediate temperate zone, warm temperate zone, and subtropical zone, while there were two peaks from November to January and May to June in the frigid zone of the plateau. The dominant genotype of group A rotavirus gradually changed from G3P[8] and G1P[8] to G9P[8] during 2009-2020. Conclusions: The overall rotavirus infection rate in China was on a downward trend. Meanwhile, significant variations of positive rates were observed in seasonal epidemics and different age groups from 2009 to 2020. Rotavirus diarrhea in children was still a prominent concern. Vaccination of rotavirus vaccine should be promoted, and the epidemiological characteristics and genotypes of rotavirus diarrhea should be continuously monitored.

One Health communication channels: a qualitative case study of swine influenza in Canada in 2020

BackgroundWith increased attention to the importance of integrating the One Health approach into zoonotic disease surveillance and response, a greater understanding of the mechanisms to support effective communication and information sharing across animal and human health sectors is needed. The objectives of this qualitative case study were to describe the communication channels used between human and animal health stakeholders and to identify the elements that have enabled the integration of the One Health approach.MethodsWe combined documentary research with interviews with fifteen stakeholders to map the communication channels used in human and swine influenza surveillance in Alberta, Canada, as well as in the response to a human case of H1N2v in 2020. A thematic analysis of the interviews was also used to identify the barriers and facilitators to communication among stakeholders from the animal and human health sectors.ResultsWhen a human case of swine influenza emerged, the response led by the provincial Chief Medical Officer of Health involved players at various levels of government and in the human and animal health sectors. The collaboration of public and animal health laboratories and of the swine sector, in addition to the information available through the surveillance systems in place, was swift and effective. Elements identified as enabling smooth communication between the human and animal health systems included preexisting relationships between the various stakeholders, a relationship of trust between them (e.g., the swine sector and their perception of government structures), the presence of stakeholders acting as permanent liaisons between the ministries of health and agriculture, and stakeholders' understanding of the importance of the One Health approach.ConclusionsInformation flows through formal and informal channels and both structural and relational features that can support rapid and effective communication in infectious disease surveillance and outbreak response.

Surveillance of Wastewater to Monitor the Prevalence of Gastroenteritis Viruses in Chiba Prefecture (2014-2019).

In Japan, sentinel surveillance is used to monitor the trend of infectious gastroenteritis. Another method of pathogen surveillance, wastewater-based epidemiology, has been used recently because it can help to monitor infectious disease without relying on patient data. Here, we aimed to determine the viral trends reflected in the number of reported patients and number of gastroenteritis virus-positive samples. We focused on gastroenteritis viruses present in wastewater and investigated the usefulness of wastewater surveillance for the surveillance of infectious gastroenteritis. Real-time polymerase chain reaction was used for viral gene detection in wastewater. The number of reported patients per pediatric sentinel site and number of viral genome copies were compared for correlation potential. The number of gastroenteritis virus-positive samples reported by National Epidemiological Surveillance of Infectious Disease (NESID) and the status of gastroenteritis viruses detected in wastewater were also evaluated. Genes of norovirus genotype I, norovirus genotype II, sapovirus, astrovirus, rotavirus group A, and rotavirus group C were detected in wastewater samples. Viruses were detected in wastewater during periods when no gastroenteritis virus-positive samples were reported to NESID. Norovirus genotype II and other gastroenteritis viruses were detected in wastewater even during periods when no gastroenteritis virus-positive samples were found. Therefore, surveillance using wastewater can complement sentinel surveillance and is an effective tool for the surveillance of infectious gastroenteritis.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Surveillance for Emerging and Reemerging Pathogens Using Pathogen Agnostic Metagenomic Sequencing in the United States: A Critical Role for Federal Government Agencies.

The surveillance and identification of emerging, reemerging, and unknown infectious disease pathogens is essential to national public health preparedness and relies on fluidity, coordination, and interconnectivity between public and private pathogen surveillance systems and networks. Developing a national sentinel surveillance network with existing resources and infrastructure could increase efficiency, accelerate the identification of emerging public health threats, and support coordinated intervention strategies that reduce morbidity and mortality. However, implementing and sustaining programs to detect emerging and reemerging pathogens in humans using advanced molecular methods, such as metagenomic sequencing, requires making large investments in testing equipment and developing networks of clinicians, laboratory scientists, and bioinformaticians. In this study, we sought to gain an understanding of how federal government agencies currently support such pathogen agnostic testing of human specimens in the United States. We conducted a landscape analysis of federal agency websites for publicly accessible information on the availability and type of pathogen agnostic testing and details on flow of clinical specimens and data. The website analysis was supplemented by an expert review of results with representatives from the federal agencies. Operating divisions within the US Department of Health and Human Services and the US Department of Veterans Affairs have developed and sustained extensive clinical and research networks to obtain patient specimens and perform metagenomic sequencing. Metagenomic facilities supported by US agencies were not equally geographically distributed across the United States. Although many entities have work dedicated to metagenomics and/or support emerging infectious disease surveillance specimen collection, there was minimal formal collaboration across agencies.

West Nile Virus Emergence in Germany 2019: Looking for Hidden Human West Nile Virus Infections.

Background: Autochthonous human West Nile virus (WNV) infections were notified in the infectious disease surveillance system in Germany in 2018 for the first time and every year since then. Since clinically apparent infections are infrequent, we conducted two studies to investigate subclinical infections of this emerging disease in Germany in 2019 to detect infections not visible to surveillance based on symptomatic infections: limited-scope blood donor testing and a serosurvey among employees at two Berlin zoos with a history of demonstrated WNV infections in animals. Methods: For the zoo study, employees of the two zoos in Berlin were invited to participate in the study in late 2019. Blood samples were drawn and tested for the presence of antibodies (immunoglobulin M [IgM] and immunoglobulin G [IgG]) against WNV, and two other flaviviruses present in Germany: Usutu virus and Tick-borne encephalitis virus (TBEV). For the study in blood donors, four blood establishments with collection sites in regions with documented WNV-infected animals in 2018 and 2019 participated in the study. All donations in these regions were tested for WNV genome from July to November 2019. Results: In the enzyme-linked immunosorbent assay, none of the 70 tested zoo employees were WNV IgM-positive, 8 were WNV IgG-positive, additional 2 participants had equivocal results. All 10 were negative in the virus neutralization test (VNT) for WNV, but positive in the VNT for TBEV. None of the 4273 samples from blood donors tested in areas with WNV-infected animals was positive for WNV-RNA. Conclusion: Our results indicate that WNV circulation in Germany, though clearly documented in animals in 2019, apparently affected very few humans. Still areas with WNV-positive animals remain risk areas for human infection as well.

Changing trends in Japanese spotted fever epidemiology in Shimane, Japan: A two-decade retrospective study with emphasis on the COVID-19 pandemic era

IntroductionThe objective of this study is to evaluate the epidemiological characteristics of Japanese Spotted Fever (JSF) cases reported through the National Epidemiological Surveillance of Infectious Disease (NESID) system in Shimane Prefecture, Japan, from 2000 to 2022, encompassing the periods before and during the COVID-19 pandemic period. MethodsA retrospective analysis was conducted on JSF cases reported to the NESID system in Shimane Prefecture between 2000 and 2022. Population data were obtained from the Statistical Information of Shimane. Poisson regression analysis was employed to examine the relationship between the annual prevalence rate of JSF and the COVID-19 pandemic. ResultsA total of 301 JSF cases were analyzed during the study period. Even the pre-COVID-19 pandemic period, a gradual and significant increase in the prevalence rate was observed (prevalence rate = 1.03, 95% CI: 1.01–1.05; p = 0.01), and a rapid increase was noted since 2020 (prevalence rate = 1.57, 95% CI: 1.39–1.78; p < 0.01). The slope during the pre-COVID-19 pandemic period was estimated to have significantly increased (p < 0.01). ConclusionsOur findings suggest that while the prevalence rate of JSF had exhibited a slight increase in the years preceding the COVID-19 pandemic, it demonstrated a more significant increase following the pandemic in Shimane Prefecture. JSF has increased since 2020 and may be linked to COVID-19 pandemic. The COVID-19 pandemic has had a substantial impact on global health, underscoring the importance of investigating the prevalence of specific infectious diseases and other health-related indicators to accurately assess the pandemic's impact.

Trends in temporal and spatial changes of Japanese encephalitis in Chinese mainland, 2004–2019: A population-based surveillance study

BackgroundJapanese encephalitis (JE) is a serious health concern in China, with approximately 80 % of global infections occurring in China. To develop effective prevention and control strategies, this study explored the epidemiological characteristics of JE in China based on spatiotemporal data, to understand the patterns and trends of JE incidence in different regions and time periods. MethodThe incidence and mortality rates of JE were extracted from the Public Health Data Center, the official website of the National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System from 2004 to 2019. Joinpoint regression was applied to examine the spatiotemporal patterns and annual percentage change in incidence and mortality of the JE. ResultsFrom 2004 to 2019, a total of 43,569 cases of JE were diagnosed, including 2081 deaths. The annual incidence rate of JE decreased from 0.4171/100,000 in 2004 to 0.0298/100,000 in 2019, with an annual percentage change (APC) of −13.5 % (P < 0.001). The annual mortality rate of JE showed three stages of change, with inflection points in 2006 and 2014. The incidence and mortality rates of JE have declined in all provinces of China, and more cases were reported in 0–14 years of age, accounting for nearly 80 % of all patients. ConclusionsThe morbidity and mortality rates of JE in China are generally on a downward trend, and emphasis should be placed on strengthening disease surveillance in special areas and populations, popularizing vaccination, and increasing publicity.

Factors that support public health infrastructure recovery in Puerto Rico and US Virgin Islands after Hurricanes Irma and Maria.

This paper describes the factors that support recovery of public health infrastructure (PHI), including conditions that facilitated or hindered recovery in United States (US) territories impacted by hurricanes Irma and Maria. A deductive approach was used to categorize data from five organizations that received crisis hurricane recovery (CHR) funds from the Centers for Disease Control and Prevention.* Spending was grouped into five infrastructure gaps: (1) human resources, (2) informatic upgrades, (3) equipment, (4) minor repairs, and (5) preventive maintenance. Unanticipated PHI costs, facilitators, and hinderances to PHI recovery were identified. Most (72 percent) of the $53,529,823 CHR funding was used to address infrastructure gaps in (1) human resources (56 percent), (2) informatics (16 percent), (3) equipment (13 percent), (4) minor repairs (10 percent), and (5) preventive maintenance (5 percent). Most of the requests (56 percent) to redirect funds were associated with unanticipated costs in initial work plans and budgets. The use of administrative partners, planning tools, dedicated staff, streamlined procedures, eg, contracts, and cost sharing facilitated PHI recovery. The most common hindrance to PHI recovery were delays in procurement and shipping. In summary, investments in dedicated funding to upgrade, repair, or replace critical structures and systems for infectious disease surveillance, laboratory capacity, vector control, environmental health inspections, and vaccine storage and administration in Puerto Rico and the US Virgin Islands after Hurricanes Irma and Maria contributed to their recovery capacity. These findings may inform funding and resource allocation considerations for PHI recovery in the US territories.

Spatial-temporal analysis of hepatitis B in Fujian Province, China in 2012–2021

BackgroundFujian Province has one of the highest reported incidences of hepatitis B virus infection in China. This study aimed to provide a theoretical framework for preventing and controlling hepatitis B in Fujian Province, and to assess the trends and the spatial-temporal distribution patterns of hepatitis B in this region. MethodsData on hepatitis B cases were extracted from the National Notifiable Infectious Disease Surveillance System. Spatial autocorrelation analysis, trend surface analysis, and spatial-temporal scanning statistics were used to identify the spatial and aggregation patterns at the county level. The Joinpoint was used to assess the reported incidence trends. ResultsThe average reported incidence of hepatitis B in Fujian from 2012 to 2021 was 14.46/10,000 population, with 583,262 notified cases. The age-adjusted reported incidence of hepatitis B decreased from 17.44/10,000 population in 2012 to 11.88/10,000 population in 2021, with an average reduction in the annual percentage change of 4.5%. There were obvious spatial-temporal aggregation characteristics in hepatitis B cases, and a high-incidence area was located in eastern Fujian. Spatio-temporal scanning statistics revealed four levels of aggregation of hepatitis B reporting rates. The first level of aggregation area included Minhou, Gulou, Jin'an, Taijiang, and nine other districts and counties. ConclusionThe incidence of hepatitis B is declining in Fujian Province. Spatial clusters of hepatitis B cases in Fujian Province were identified, and high-risk areas in eastern Fujian still exist. Closely monitoring the general patterns in the occurrence of hepatitis B and implementing focused control and preventative strategies are important.

Seasonal syndromic surveillance by sentinel system of ILI, ARI, SARI and pneumonia in Romania – season 2023/2024

Syndromic surveillance was initially developed for early detection compared to traditional methods of an epidemiological event with an impact on public health, but as it was applied by more countries it was observed that it can provide information on the size, dynamics of the spread with an emphasis on the potential for national, regional and global evolution, the severity and the most affected population groups, but also to the development of a quick, specific response. Globalization, which involves the intensification of the movement of people (including sick or incubating infectious diseases), live animals, or products of animal origin in and from any part of the planet, but also climate change and pollution, amplifies the danger of the spread of communicable infectious diseases from areas so-called “specific” diseases in new areas, unknown to populations and healthcare systems. In this context, the One Health initiative needs efficient surveillance methods to help promote the health of people, animals, the environment, and ultimately the planet Earth. Recently, there have been substantial changes in the surveillance and control of infectious diseases with an impact on public health aimed at assessing the risks of the emergence of infectious agents with epidemic and pandemic potential by identifying and analyzing favorable factors related to the infectious agent (virulence, variability, transmissibility, etc.), the host organism (immunity, physiological factors, vaccination status, associated chronic diseases, nutritional status, living conditions, etc.), demographic factors (birth rate, mortality, population agglomerations), climatic factors and insect populations vectors, rodents and wild animals (including birds) and last but not least domestic animal populations (including birds). Among these methods, syndromic surveillance stands out, which in addition has the quality of using automatic data acquisition and generating statistical alerts, monitors disease indicators in real-time or near real-time to detect disease outbreaks earlier than would be possible with conventional methods traditional public health. In this context, we will present the Romanian experience regarding the syndromic surveillance of ILI, ARI and SARI in Romania through the national sentinel system season 2023/2024.

Usefulness of a pluralistic approach in sentinel surveillance: seasonal influenza activity based on case counts per sentinel site in the National Epidemiological Surveillance of Infectious Diseases Program and test counts, case counts, and test positivity from the National Hospital Organization.

In Japan, based on the National Epidemiological Surveillance of Infectious Diseases (NESID) Program, influenza cases from ~5,000 sentinel sites are monitored weekly as part of influenza surveillance (as number of influenza cases/sentinel site). One limitation is that the number of influenza tests conducted is not reported. Separately, the National Hospital Organization (NHO), with ~140 hospitals, routinely publishes three indicators: number of influenza tests, influenza-positive case counts, and test positivity. We used NESID and NHO data from April 2011 to June 2022 to assess the usefulness of multiple indicators to monitor influenza activity. Temporal trends of the NHO and NESID indicators were similar, and NHO indicator levels well-correlated with those of the NESID indicator. Influenza positivity in the NHO data, however, showed an earlier rise and peak time compared to the NESID indicator. Importantly, through the non-epidemic summer periods and the coronavirus disease 2019 pandemic, a sizable number of influenza tests continued to be done at NHO hospitals, with results showing considerably low case counts and test positivity. These data show that a relatively small number of sentinel sites is sufficient to monitor influenza activity nationally, and, that utilizing multiple indicators can increase our confidence in situational awareness and data interpretations.

A review of wastewater-based epidemiology for antimicrobial resistance surveillance

Antimicrobial resistance (AMR) is recognized as one of the most serious threats to public health. Unparalleled population growth and accelerated rates of AMR emergence and dissemination have resulted in both novel resistance in pathogenic organisms and the re-appearance of infections that were formerly under control. Consequently, this has led to an increased quantity of infectious diseases. One of the main drivers of antimicrobial overuse is inappropriate prescribing in human and veterinary medicine. The ability to rapidly survey the spread of antimicrobial resistance within human populations is key for its prevention, intervention, and control. However, many constraints are present for current clinical surveillance systems and their capacity to determine AMR dynamics in the microbiome of healthy individuals as well as in clinical pathogens causing infections. Wastewater-based epidemiology (WBE) is an emergent technique that has the capacity to act as a supplementary measure for current infectious disease surveillance systems and as an early warning system for infectious disease outbreaks. The development of disease outbreaks to the community level can be monitored in real time through the analysis of population pooled wastewater. This review provides an introduction to using wastewater-based epidemiology to monitor AMR bacteria, as well as an overview of wastewater-based epidemiology and its components.

Advancing kidney xenotransplantation with anesthesia and surgery - bridging preclinical and clinical frontiers challenges and prospects.

Xenotransplantation is emerging as a vital solution to the critical shortage of organs available for transplantation, significantly propelled by advancements in genetic engineering and the development of sophisticated immunosuppressive treatments. Specifically, the transplantation of kidneys from genetically engineered pigs into human patients has made significant progress, offering a potential clinical solution to the shortage of human kidney supply. Recent trials involving the transplantation of these modified porcine kidneys into deceased human bodies have underscored the practicality of this approach, advancing the field towards potential clinical applications. However, numerous challenges remain, especially in the domains of identifying suitable donor-recipient matches and formulating effective immunosuppressive protocols crucial for transplant success. Critical to advancing xenotransplantation into clinical settings are the nuanced considerations of anesthesia and surgical practices required for these complex procedures. The precise genetic modification of porcine kidneys marks a significant leap in addressing the biological and immunological hurdles that have traditionally challenged xenotransplantation. Yet, the success of these transplants hinges on the process of meticulously matching these organs with human recipients, which demands thorough understanding of immunological compatibility, the risk of organ rejection, and the prevention of zoonotic disease transmission. In parallel, the development and optimization of immunosuppressive protocols are imperative to mitigate rejection risks while minimizing side effects, necessitating innovative approaches in both pharmacology and clinical practices. Furthermore, the post-operative care of recipients, encompassing vigilant monitoring for signs of organ rejection, infectious disease surveillance, and psychological support, is crucial for ensuring post-transplant life quality. This comprehensive care highlights the importance of a multidisciplinary approach involving transplant surgeons, anesthesiologists, immunologists, infectiologists and psychiatrists. The integration of anesthesia and surgical expertise is particularly vital, ensuring the best possible outcomes of those patients undergoing these novel transplants, through safe procedural practices. As xenotransplantation moving closer to clinical reality, establishing consensus guidelines on various aspects, including donor-recipient selection, immunosuppression, as well as surgical and anesthetic management of these transplants, is essential. Addressing these challenges through rigorous research and collective collaboration will be the key, not only to navigate the ethical, medical, and logistical complexities of introducing kidney xenotransplantation into mainstream clinical practice, but also itself marks a new era in organ transplantation.

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data.

Molecular surveillance of infectious diseases allows the monitoring of pathogens beyond the granularity of traditional epidemiological approaches and is well-established for some of the most relevant infectious diseases such as malaria. The presence of genetically distinct pathogenic variants within an infection, referred to as multiplicity of infection (MOI) or complexity of infection (COI) is common in malaria and similar infectious diseases. It is an important metric that scales with transmission intensities, potentially affects the clinical pathogenesis, and a confounding factor when monitoring the frequency and prevalence of pathogenic variants. Several statistical methods exist to estimate MOI and the frequency distribution of pathogen variants. However, a common problem is the quality of the underlying molecular data. If molecular assays fail not randomly, it is likely to underestimate MOI and the prevalence of pathogen variants. A statistical model is introduced, which explicitly addresses data quality, by assuming a probability by which a pathogen variant remains undetected in a molecular assay. This is different from the assumption of missing at random, for which a molecular assay either performs perfectly or fails completely. The method is applicable to a single molecular marker and allows to estimate allele-frequency spectra, the distribution of MOI, and the probability of variants to remain undetected (incomplete information). Based on the statistical model, expressions for the prevalence of pathogen variants are derived and differences between frequency and prevalence are discussed. The usual desirable asymptotic properties of the maximum-likelihood estimator (MLE) are established by rewriting the model into an exponential family. The MLE has promising finite sample properties in terms of bias and variance. The covariance matrix of the estimator is close to the Cramér-Rao lower bound (inverse Fisher information). Importantly, the estimator's variance is larger than that of a similar method which disregards incomplete information, but its bias is smaller. Although the model introduced here has convenient properties, in terms of the mean squared error it does not outperform a simple standard method that neglects missing information. Thus, the new method is recommendable only for data sets in which the molecular assays produced poor-quality results. This will be particularly true if the model is extended to accommodate information from multiple molecular markers at the same time, and incomplete information at one or more markers leads to a strong depletion of sample size.

Association between work sick-leave absenteeism and SARS-CoV-2 notifications in the Netherlands during the COVID-19 epidemic.

Alternative data sources for surveillance have gained importance in maintaining coronavirus disease 2019 (COVID-19) situational awareness as nationwide testing has drastically decreased. Therefore, we explored whether rates of sick-leave from work are associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) notification trends and at which lag, to indicate the usefulness of sick-leave data for COVID-19 surveillance. We explored trends during the COVID-19 epidemic of weekly sick-leave rates and SARS-CoV-2 notification rates from 1 June 2020 to 10 April 2022. Separate time series were inspected visually. Then, Spearman correlation coefficients were calculated at different lag and lead times of zero to four weeks between sick-leave and SARS-CoV-2 notification rates. We distinguished between four SARS-CoV-2 variant periods, two labour sectors and overall, and all-cause sick-leave versus COVID-19-specific sick-leave. The correlation coefficients between weekly all-cause sick-leave and SARS-CoV-2 notification rate at optimal lags were between 0.58 and 0.93, varying by the variant period and sector (overall: 0.83, lag -1; 95% CI [0.76, 0.88]). COVID-19-specific sick-leave correlations were higher than all-cause sick-leave correlations. Correlations were slightly lower in healthcare and education than overall. The highest correlations were mostly at lag -2 and -1 for all-cause sick-leave, meaning that sick-leave preceded SARS-CoV-2 notifications. Correlations were highest mostly at lag zero for COVID-19-specific sick-leave (coinciding with SARS-CoV-2 notifications). All-cause sick-leave might offer an earlier indication and evolution of trends in SARS-CoV-2 rates, especially when testing is less available. Sick-leave data may complement COVID-19 and other infectious disease surveillance systems as a syndromic data source.

PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.

PANDEM-Source (PS) is a tool to collect and integrate openly available public health-related data from heterogeneous data sources to support the surveillance of infectious diseases for pandemic management. The tool may also be used for pandemic preparedness by generating surveillance data for training purposes. It was developed as part of the EU-funded Horizon 2020 PANDEM-2 project during the COVID-19 pandemic as a result of close collaboration in a consortium of 19 partners, including six European public health agencies, one hospital, and three first responder organizations. This manuscript describes PS's features and design to disseminate its characteristics and capabilities to strengthen pandemic preparedness and response. A requirement-gathering process with EU pandemic managers in the consortium was performed to identify and prioritize a list of variables and indicators useful for surveillance and pandemic management. Using the COVID-19 pandemic as a use case, we developed PS with the purpose of feeding all necessary data to be displayed in the PANDEM-2 dashboard. PS routinely monitors, collects, and standardizes data from open or restricted heterogeneous data sources (users can upload their own data). It supports indicators and health resources related data from traditional data sources reported by national and international agencies, and indicators from non-traditional data sources such as those captured in social and mass media, participatory surveillance, and seroprevalence studies. The tool can also calculate indicators and be used to produce data for training purposes by generating synthetic data from a minimal set of indicators to simulate pandemic scenarios. PS is currently set up for COVID-19 surveillance at the European level but can be adapted to other diseases or threats and regions. With the lessons learnt during the COVID-19 pandemic, it is important to keep building capacity to monitor potential threats and develop tools that can facilitate training in all the necessary aspects to manage future pandemics. PS is open source and its design provides flexibility to collect heterogeneous data from open data sources or to upload end users's own data and customize surveillance indicators. PS is easily adaptable to future threats or different training scenarios. All these features make PS a unique and valuable tool for pandemic management.

Application of genomic studies in epidemiological surveillance: A mini-overview

The 21st century has already seen the emergence of several pandemics both in developed and developing nations, including the recent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. The recent upsurge in certain diseases clearly indicates the need for continuous epidemiological surveillance and real-time monitoring of infectious diseases. Interestingly, genomics has found extensive application in monitoring infectious diseases through the sequencing of bacteria, parasites, and virus genomes. The continual scrutiny of pathogens and the examination of their genetic similarities and distinctions constitute genomic surveillance. The potential of genomic sequencing lies in its ability to offer a significantly more detailed depiction of the evolution and transmission of antimicrobial resistance (AMR), thus initiating a transformative impact on how public health surveillance networks address bacterial AMR. The utilization of whole-genome sequencing (WGS) stands as a prevalent technique for identifying and tracking pathogens, establishing transmission routes, and managing outbreaks. However, despite several decades of advancements, the seamless integration of genomics into surveillance pipelines faces substantial barriers that necessitate overcoming. Recent advances in genomics and next-generation sequencing approaches provide new paradigms for monitoring transmission pipelines and reducing overall morbidity and mortality. In this mini-review, we highlight the advances in genomics, how they have been critical in epidemiological surveillance and monitoring outbreaks, and how they can help predict and monitor possible future outbreaks.

Molecular detection of herpesvirus in a skin lesion of a humpback whale (Megaptera novaeangliae) from the Western Mediterranean Sea

Herpesvirus has the potential to infect a wide variety of animal species. In cetaceans, Alpha- and/or Gammaherpesvirinae have been identified in eight families of odontocetes, and one family of mysticetes. In May 2022, an adult humpback whale (Megaptera novaeangliae) was found stranded in Valencia, Spain. The whale was emaciated, in poor body condition, with multiple lacerations on the dorsal fin and a high number of epibionts of the Cyamidae family, known as whale lice. The individual had been previously released from a ghost net entanglement 5 days before becoming stranded. In a closer examination, various skin lesions were observed, including chronic, proliferative, and erosive dermatitis and a large ulcer extending to the deep dermis. As part of the infectious disease surveillance programme, molecular testing was performed on skin samples for herpesvirus, cetacean morbillivirus, and poxvirus. A positive result for herpesvirus was obtained from one of the skin lesions. The sequence was found to belong to the Alphaherpesvirinae subfamily, and it was closely related to alphaherpesvirus sequences from a fin whale (Balaenoptera physalus) and a humpback whale. Cetacean morbillivirus and poxvirus testing was negative. To the authors’ knowledge, this is the first report of herpesvirus in a humpback whale from the Mediterranean Sea. Reports on herpesvirus detection or infection in humpback whales (only species within the genus Megaptera) are scarce. In consequence, future virological assessments of humpback whales should include testing for herpesvirus.

Molecular genetic surveillance of important infectious disease and calculation for mutation rate: a simple but not-to-be-forgotten error regarding accumulation effect

No abstract available