Monitoring Of Infectious Diseases Research Articles

Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey

Infectious diseases usually have the characteristics of rapid spread with a large impact range. Once they break out, they will cause a large area of infection, which creates tremendous health and security risks. Thus, early infectious disease monitoring and prevention are critical. Current surveillance systems can predict the incidence of infectious diseases to a certain extent. However, the diversity, inaccuracy and incompleteness of the data collected by sensors make it difficult to obtain accurate monitoring results. Moreover, the limited local resources of a monitoring system cannot process the increasing volume of data in a timely manner. To address these challenges, fuzzy logic and edge computing have been applied to infectious disease monitoring in recent years. This paper presents a comprehensive review of infectious disease monitoring technologies based on fuzzy logic and edge computing. Fuzzy neural networks in infectious disease surveillance are introduced in detail, followed by a brief study of applications of fuzzy systems in infectious disease surveillance. Finally, improvements in existing disease detection systems based on the combination of edge computing and fuzzy logic are described. The review shows that edge computing and fuzzy logic are complementary and that their combination greatly improves the processing efficiency and the storage space of the data. At the same time, with edge computing as the carrier, the combination of fuzzy logic, neural networks, expert systems and other technologies can effectively carry out disease prediction and diagnosis.

Advance on research and application of laboratory pathogen monitoring and early warning technology of bacterial infectious diseases

Human beings are still facing the public health challenges from bacterial infectious diseases. Carrying out systematic infectious disease monitoring and early warning is the most direct solution to prevent and control infectious diseases. Etiology is an important part of infectious disease monitoring and early warning. Effective pathogen monitoring can identify pathogens, outbreaks and sources at the first time. In this study, we have reviewed the research and application of etiology monitoring and early warning technology of bacterial infectious diseases and summarized the importance and application scenarios of etiology in infectious disease monitoring and early warning, as well as the research progress of etiology monitoring and early warning technology. Based on the work of existing laboratory monitoring networks, such as Chinese Pathogen Identification Network, the development trend and prospect of infectious disease laboratory network monitoring are put forward to provide a reference for establishing and perfecting the infectious disease monitoring and early warning system.

Application and reflection of laboratory-based monitoring in early warning of infectious diseases

Despite the fact that our cognition towards infectious disease prevention, the advanced technology and the economic status of the whole society has made a great progress in the last decade, the outbreak of COVID-19 pneumonia has again enabled the public to acquire more about super-challenges of infectious diseases, epidemics and the relevant preventive measurements. In order to identify the epidemic signals in early stage or even before the onset of epidemic, the data research and utilization of a series of factors related to the occurrence and transmission of infectious diseases have played a significant role in research of prevention and control during the whole period of surveillance and early warning. Laboratory-based monitoring for the etiology has always been an important part of infectious disease warning system due to pathogens as the direct cause of such diseases. China has initially established a laboratory-based monitoring and early warning system for bacterial infectious diseases based on the Chinese Pathogen Identification Network with an aim to identify pathogens, outbreaks and sources. This network has played an essential role in early detection, tracking and precise prevention and control of bacterial infectious diseases, such as plague, cholera, and epidemic cerebrospinal meningitis. This issue focuses on the function of laboratory-based monitoring during the period of early warning, prevention, and control of bacterial infectious diseases, and conducted a wide range of researches based on the analysis of the epidemic and outbreak isolates, together with field epidemiological studies and normal monitoring systems. All of these could illustrate the effect of laboratory surveillance in the infectious disease risk assessment and epidemic investigation. At the same time, we have put forward our review and expectation of scenarios about laboratory-based monitoring and early warning technologies to provide innovative thoughts for promoting a leapfrog development of infectious disease monitoring and early warning system in China.

Rescuing low frequency variants within intra-host viral populations directly from Oxford Nanopore sequencing data

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides important insights with respect to elucidating within-host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

Визначення показників ризику виникнення дифтерії на підставі дослідження антидифтерійного імунітету у населення Дніпропетровщини

Актуальність. Вакцинація є найбільш ефективною ланкою первинної профілактики. Серологічний моніторинг за інфекційними захворюваннями, що охоплюються національними програмами імунізації, дуже важливий, оскільки він надає актуальну інформацію щодо тягаря інфекції та імунологічного статусу населення. Мета дослідження: навести аналіз епідеміологічного моніторингу протидифтерійного захисту населення, показати узагальнюючу епідеміологічну ситуацію щодо дифтерії та визначити показники ризику виникнення дифтерії серед населення Дніпропетровщини. Матеріали та методи. Епідеміологічний аналіз протидифтерійного імунітету (2017–2019 рр.) проводився на підставі результатів імуноферментного аналізу рівнів антитіл IgG проти дифтерійного токсину у 271 мешканця Дніпропетровщини. Результати. Аналіз результатів виявив, що лише 30,6 % (n = 83) населення мають рівні антитоксичних антитіл 1,0 МО/мл і більше, що обумовлює цим особам достатній захист проти дифтерії в найближчі 5–7 років життя. Разом з тим більшість населення (69,4 %) потребує негайної одноразової бустерної вакцинації (n = 134; 49,5 %) або негайної базової вакцинації (n = 54; 19,9 %) внаслідок низьких показників антитоксичних протидифтерійних антитіл. У групі віком 8–15 років 65,9 % (n = 62) потребують негайної базової або бустерної вакцинації, це свідчить про те, що у дітей цього віку не сформований базисний імунологічний захист внаслідок порушень графіку вакцинації або її відсутності. У групі віком 27 років і старше 79,1 % (n = 72) досліджених не мають протективних рівнів антидифтерійних антитіл, що свідчить про відсутність фактичного захисту проти дифтерії. Висновки. Результати роботи свідчать про недостатній популяційний захист населення проти дифтерії. У зв’язку з цим є актуальними питання розробки стратегічних заходів масової імунопрофілактики дифтерії як дітей, так і дорослих. У країні повинні проводитися регулярні епідеміологічні моніторинги, які вивчали б популяційний імунітет проти дифтерії та інших керованих інфекцій і складали перспективний стратегічний і тактичний план з ліквідації недоліків в роботі щодо проведення масової імунопрофілактики населення.

Serological testing for SARS-CoV-2 antibodies in clinical practice: A comparative diagnostic accuracy study.

BackgroundSerological tests are a powerful tool in the monitoring of infectious diseases and the detection of host immunity. However, manufacturers often provide diagnostic accuracy data generated through biased studies, and the performance in clinical practice is essentially unclear.ObjectivesWe aimed to determine the diagnostic accuracy of various serological testing strategies for (a) identification of patients with previous coronavirus disease‐2019 (COVID‐19) and (b) prediction of neutralizing antibodies against SARS‐CoV‐2 in real‐life clinical settings.MethodsWe prospectively included 2573 consecutive health‐care workers and 1085 inpatients with suspected or possible previous COVID‐19 at a Swiss University Hospital. Various serological immunoassays based on different analytical techniques (enzyme‐linked immunosorbent assays, ELISA; chemiluminescence immunoassay, CLIA; electrochemiluminescence immunoassay, ECLIA; and lateral flow immunoassay, LFI), epitopes of SARS‐CoV‐2 (nucleocapsid, N; receptor‐binding domain, RBD; extended RBD, RBD+; S1 or S2 domain of the spike [S] protein, S1/S2), and antibody subtypes (IgG, pan‐Ig) were conducted. A positive real‐time PCR test from a nasopharyngeal swab was defined as previous COVID‐19. Neutralization assays with live SARS‐CoV‐2 were performed in a subgroup of patients to assess neutralization activity (n = 201).ResultsThe sensitivity to detect patients with previous COVID‐19 was ≥85% in anti‐N ECLIA (86.8%) and anti‐S1 ELISA (86.2%). Sensitivity was 84.7% in anti‐S1/S2 CLIA, 84.0% in anti‐RBD+LFI, 81.0% in anti‐N CLIA, 79.2% in anti‐RBD ELISA, and 65.6% in anti‐N ELISA. The specificity was 98.4% in anti‐N ECLIA, 98.3% in anti‐N CLIA, 98.2% in anti‐S1 ELISA, 97.7% in anti‐N ELISA, 97.6% in anti‐S1/S2 CLIA, 97.2% in anti‐RBD ELISA, and 96.1% in anti‐RBD+LFI.The sensitivity to detect neutralizing antibodies was ≥85% in anti‐S1 ELISA (92.7%), anti‐N ECLIA (91.7%), anti‐S1/S2 CLIA (90.3%), anti‐RBD+LFI (87.9%), and anti‐RBD ELISA (85.8%). Sensitivity was 84.1% in anti‐N CLIA and 66.2% in anti‐N ELISA. The specificity was ≥97% in anti‐N CLIA (100%), anti‐S1/S2 CLIA (97.7%), and anti‐RBD+LFI (97.9%). Specificity was 95.9% in anti‐RBD ELISA, 93.0% in anti‐N ECLIA, 92% in anti‐S1 ELISA, and 65.3% in anti‐N ELISA. Diagnostic accuracy measures were consistent among subgroups.ConclusionsThe diagnostic accuracy of serological tests for SARS‐CoV‐2 antibodies varied remarkably in clinical practice, and the sensitivity to identify patients with previous COVID‐19 deviated substantially from the manufacturer's specifications. The data presented here should be considered when using such tests to estimate the infection burden within a specific population and determine the likelihood of protection against re‐infection.

School Health and Nutrition Monitoring: What Practitioners and Policy Makers Can Learn from China

School Health and Nutrition Monitoring: What Practitioners and Policy Makers Can Learn from China

Advisory teams on the streets: A nurse's experience report.

To report a nurse's work experience with the street medical consultation teams in the city of São Paulo/SP Brazil. Descriptive, experience report study, which describes the care for homeless people, from a nurse's perspective and experience. Among the attributions of the nurses working with the street medical consultation teams, there are the accurate knowledge of the territory, the ability to build bonds, the performance of diagnoses of health and epidemiological conditions, the planning of the team's actions, the establishment of integration flows with the Health Care Network, the knowledge and understanding about the people ending up on the streets, the supervision of the actions of nursing assistants and community health agents. Acting as a nurse on the street medical consultation team is a new and challenging experience that requires dynamic, strategic, creative, and empathic actions. The presence of nurses in the Street Medical Consultation teams contributes to ensuring access to health services and comprehensive care, expanding the possibilities of early detection, treatment, monitoring, and healing of chronic and infectious diseases.

Количественная оценка концентрации антител к вирусу инфекционной бурсальной болезни (ИББ) в сыворотках крови цыплят и родительского поголовья методом ELISA

The use of ELISA serology data in the bird health management system has been universally accepted for many diseases, including infectious bursal disease (IBD). Serological testing using enzyme-linked immunosorbent assay (ELISA) is an important tool for monitoring the immune response after vaccination, as well as for diagnosing and ensuring bird health. Even in vaccinated birds, foci of IBB are recorded. The flow system of poultry rearing, the concentration of large livestock in small areas, the artificial microclimate, the violation of the technology of keeping, feeding and the influence of other stress factors lead to an increase in the virulence of pathogenic microflora. Timely monitoring of infectious diseases of birds is an integral part of a set of measures aimed at the prevention of these diseases, which involves the introduction into practice of diagnostic studies that meet the current level of scientific development. The article presents data on determining the timing of vaccination of chickens using the Deventer formula. The calculation of the timing of vaccination of infectious bursal disease is based on the regularities of lowering the titer of maternal antibodies (the effect of the 4th day).

Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model

Twitter has been an important platform for people to discuss and share health-related information. It provides a massive amount of data for real-time monitoring of infectious diseases (such as COVID-19) and freeing disease-prevention organizations from the tedious labor involved in public health surveillance. Personal health mention (PHM) detection is one of the critical methods to keep up-to-date on an epidemic’s condition; it attempts to identify a person’s health condition based on online text information. This paper explores PHM identification for COVID-19 through Twitter. We built a COVID-19 PHM data set containing tweets annotated with four types of COVID-19-related health conditions. A masked attention model was devised to classify the tweets as self-mention, other-mention, awareness, and non-health. We obtained promising results on the PHM identification task. The classification results facilitate timely health monitoring and surveillance for digital epidemiology. We also evaluate how the attention mechanism and training method affect the model’s predictive performance.

Managing emerging pathogen risks in recycled water

The COVID-19 pandemic raised the public profile of wastewater-based infectious disease monitoring. General media coverage about wastewater detection of SARS-CoV-2 (the COVID-19 coronavirus) increased community awareness of the potential use of wastewater for the detection and surveillance of emerging diseases and also heightened recognition of the potential for wastewater to harbour and convey a variety of pathogens. This has also generated questions about the potential public health impacts of emerging pathogens, such as SARS-CoV-2 and mpox, in sewage and recycled water. To ensure water security in an era of climate change, water recycling is increasingly important in Australia and other water-stressed nations and managing disease risks in integrated water management is thus of critical importance. This paper demonstrates the existing risk management provisions for recycled water and explores potential issues posed by novel and emerging pathogens. First, a synopsis of some key emerging and re-emerging human pathogens is presented and the risks associated with these pathogens in the context of recycled water provision is considered. Then, an overview of the engineered treatment systems and regulatory framework used to manage these emerging risks in Australia is presented, together with a discusion of how emerging pathogen risks can be managed to ensure safe recycled water supply now and into the future.

Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

GroupTesting: an R package for group testing estimation

Group (pooled) testing has long been used for the monitoring and detection of infectious diseases. In group testing, pools comprised of individual biospecimens are amalgamated and tested initially, and then the individuals belonging to positive pools are retested to complete case identification. This procedure and its variants can offer substantial savings with regard to time and testing cost. Unfortunately, these savings come at the expense of a complex data structure; e.g., ambiguities due to imperfect testing and complex dependence caused by individuals potentially being tested in multiple pools. To account for these complex features, several advanced statistical methods have been proposed. Regretfully, these methods are non-trivial to implement, especially for non-statistician. Recognizing this as an important issue in surveillance programs, we have developed a user-friendly R package called groupTesting that can be used to analyze group testing data. In particular, our package consists of R functions which possess a great deal of versatility and generality, and can be used in estimating both proportions and binary regression functions. The computing efficiency of our package is greatly enhanced through the strategic implementation of compiled Fortran subroutines. The primary features of the R package are illustrated using HIV and chlamydia data.

Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review.

BackgroundThe COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a “double-edge sword,” as the ethical governance of such approaches often lags behind technological achievements.ObjectiveThe aim was to investigate ethical issues identified from utilizing artificial intelligence–augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks.MethodsIn a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework.ResultsThis systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients’ highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights.ConclusionsOur findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure.Trial RegistrationPROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180

EU-OSHÁs perspective on occupational safety and health in the Covid-19 pandemic

In July 2020, EU-OSHA published the results of a comprehensive research project on the exposure of workers to biological agents at work and the related health problems. Although the research was initiated long before the pandemic, researchers had highlighted pandemic preparedness as an issue, as well as the general lack of monitoring of serious infectious diseases. The research also made evident a lack of awareness among workplace actors of the risks from exposure to biological agents at work, although a detailed legislative framework has been in place for many years, and consequently a lack of prevention measures at work, which became evident after the pandemic broke out. It highlighted the need for public health and occupational safety and health organisations to cooperate to mitigate the impact of the pandemic. The presentation will focus on the links between the research findings and the developments during the pandemic. It will furthermore present guidance produced by EU-OSHA and other relevant actions, with a view on the legislation that is in place. It will cover biological agents exposure as well as telework and ergonomic issues arising from it.

Health monitoring of wild bears in the Nature Park Skakavac, Canton Sarajevo

Many wild animal populations are considered endangered due to anthropogenic activities. Wildlife and nature habitat preservation requires holistic and science based approaches supported by adequate regulations. One of the means for wildlife preservation is undoubtedly heath monitoring and investigation of infectious diseases of the wild animal populations, particularly if spillover effects are considered. Even though the theoretical background is well researched, implementation of disease prevention and control measures in wildlife populations entails more challenges than in domestic animal populations. Immediate signs of health disorders in wildlife often become evident when the infectious agent is well established in an area. Additionally, due to unrestricted and often long-range movement of wildlife, diseases are easily spread across borders. Brown bears, indigenous in Europe, are classified by EU regulations as endangered, almost extinct and rare. The wild bear population in Bosnia and Herzegovina shares a genetic lineage with bear populations of neighbouring Croatia, Serbia and Montenegro and is one of the few remaining fragments of bear populations in Europe. The aim of this paper is to describe implemented activities for health and telemetric monitoring of wild bears in the Nature Park Skakavac, Canton Sarajevo, Bosnia and Herzegovina.

Graphene-Based Technologies for Tackling COVID-19 and Future Pandemics.

The COVID‐19 pandemic highlighted the need for rapid tools and technologies to combat highly infectious viruses. The excellent electrical, mechanical and other functional properties of graphene and graphene‐like 2D materials (2DM) can be utilized to develop novel and innovative devices to tackle COVID‐19 and future pandemics. Here, the authors outline how graphene and other 2DM‐based technologies can be used for the detection, protection, and continuous monitoring of infectious diseases including COVID‐19. The authors highlight the potential of 2DM‐based biosensors in rapid testing and tracing of viruses to enable isolation of infected patients, and stop the spread of viruses. The possibilities of graphene‐based wearable devices are discussed for continuous monitoring of COVID‐19 symptoms. The authors also provide an overview of the personal protective equipment, and potential filtration mechanisms to separate, destroy or degrade highly infectious viruses, and the potential of graphene and other 2DM to increase their efficiency, and enhance functional and mechanical properties. Graphene and other 2DM could not only play a vital role for tackling the ongoing COVID‐19 pandemic but also provide technology platforms and tools for the protection, detection and monitoring of future viral diseases.

Deployment of a Mobile Laboratory for the Control and Monitoring of High-Consequence Infectious Diseases: An Illustration With the Ebola Virus, the Biowarfare Agents, and the COVID-19

The novel coronavirus (COVID-19) pandemic has caused societal issues, economic and political tensions worldwide. This shows, once more, that dissemination of correct information based on scientific evidence together with a quick and concerted action is the key to build a sound capability for the management of biological emergencies. Here, we summarize the lessons learned from our preparedness and intervention during (i) our deployment during the 2014-2016 Ebola outbreak in West Africa; (ii) our large-scale exercises from Horizon 2020 Security program where the focus is on handling intentional dispersion of infectious agents; and (iii) our fight against COVID-19: by the deployment of Biological Light Fieldable Laboratory for Emergencies (B￾LiFE mobile laboratory) in Turin and Novara, Piedmont Region, Italy. At the latter deployment, the ultimate goal was a large screening for COVID-19 prevalence in primo intervention individuals. It cannot be ignored that the COVID-19 pandemic is an ideal situation to whet our preparedness, coordination of response and risks monitoring in case of future biological threats or attacks.

Epizootological monitoring of infectious diseases in sheep and goats

Relevance . A significant increase in the proportion of factorial infectious diseases was established, the epizootic index in which reaches 1,0, the focal coefficient — 18,97, mortality — 100,0%, the insufficient effectiveness of diagnostic and antiepizootic measures determine the presence of conjugate foci and the threat of the formation of an enzootic zone. The widespread occurrence of these pathologies is facilitated by a decrease in the natural resistance of the body of animals when kept in limited areas, unconventional feeding and the use of chemotherapeutic and disinfectants. The aim of the work was to conduct epizootological monitoring of the infectious pathology of sheep, based on the optimization of the scheme of bacteriological and mycological diagnostics. Methods . The object of the study was sheep of the “Romanovskaya” breed, goats of the “Zaanenskaya” breed, and lambs of the “Aginskaya” breed. In a retrospective analysis of the epizootic situation for infectious diseases, statistical data were taken into account, assessing the extensive and intensive indicators of the epizootic process. For the intravital diagnosis of infectious diseases, washes from the mucous membranes of the oral and nasal cavity ( n = 10), feces ( n = 35) of sick lambs were studied. In addition, we studied the washings of objects in the reproductive rooms ( n = 5). The results of the experimental data were processed by the method of statistical analysis using the Student’s t-test, the results were considered reliable at р ≤ 0,05. Results. Analyzing the data of veterinary reporting, systematization and statistical processing of epizootic indicators, it was found that out of the total number of sheep and goats (144,274), the number of sick animals with bacterial diseases was 35, of which pasteurellosis — 5,72%, escherichiosis — 60,0%, staphylococcosis — 2,86%, streptococcosis — 22,86%. In the structure of infectious pathology of sheep and goats for the period 2014–2018 there is a dynamics of changes in the nosological profile with a predominance of the share of escherichiosis — 22,86% and 20,0% in years 2014 and 2015, respectively. The etiological structure of the infectious pathology of lambs is represented by bacteria: E. coli ; K. pneumoniae — 4 (10,5%); P. vulgaris — 3 (7,9%); E. cloacae — 2 (5,3%); P. aeruginosa — 1 (2,7%); S. aureus — 1 (2,7%); S. epidermidis — 1 (2,7%); C. albicans — 2 (5,3%); C. parapsilosis — 1 (2,7%).