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What should the best practices be for modeling zoonotic disease risks, e.g. to anticipate the next pandemic, when background assumptions are unsettled or evolving rapidly? This challenge runs deeper than one might expect, all the way into how we model the robustness of contemporary phylogenetic inference and taxonomic classifications. Different and legitimate taxonomic assumptions can destabilize the putative objectivity of zoonotic risk assessments, thus potentially supporting inconsistent and overconfident policy decisions.

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a respiratory disease, which can evolve into multi-organ failure (MOF), leading to death. Several biochemical alterations have been described in COVID-19 patients. To date, many biomarkers reflecting the main pathophysiological characteristics of the disease have been identified and associated with the risk of developing severe disease. Lymphopenia represents the hallmark of the disease, and it can be detected since the early stage of infection. Increased levels of several inflammatory biomarkers, including c-reactive protein, have been found in COVID-19 patients and associated with an increased risk of severe disease, which is characterised by the so-called "cytokine storm". Also, the increase of cardiac and liver dysfunction biomarkers has been associated with poor outcome. In this review, we provide an overview of the main biochemical characteristics of COVID-19 and the associated biomarkers alterations.

The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19., Competing Interests: Declaration of Interests Wuhan Institute of Virology and Wuhan Jinyintan Hospital on behalf of the authors X. Zhou., Y.S., D.-Y.Z., Y.X., Y.Q., T.S., D.W., and M.H. have filed three Chinese patent applications (202010478392.8, 202010476095.X, and 202010476805.9) related to the biomarkers for predicting the different outcomes of COVID-19 patients., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The aim of this study is to analyze the concentrations of cytokines in tear of hospitalized COVID-19 patients compared to healthy controls. Tear samples were obtained from 41 healthy controls and 62 COVID-19 patients. Twenty-seven cytokines were assessed: interleukin (IL)-1b, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL9, IL-10, IL-12, IL-13, IL-15, IL-17, eotaxin, fibroblast growth factor basic, granulocyte colony-stimulating factor (G-CSF), granulocyte-monocyte colony-stimulating factor (GM-CSF), interferon (IFN)-γ, interferon gamma-induced protein, monocyte chemo-attractant protein-1, macrophage inflammatory protein (MIP)-1a, MIP-1b, platelet-derived growth factor (PDGF), regulated on activation normal T cell expressed and secreted, tumor necrosis factor-α and vascular endothelial growth factor (VEGF).
In tear samples of COVID-19 patients, an increase in IL-9, IL-15, G-CSF, GM-CSF, IFN-γ, PDGF and VEGF was observed, along with a decrease in eotaxin compared to the control group (p < 0.05). A poor correlation between IL-6 levels in tear and blood was found. IL-1RA and GM-CSF were significantly lower in severe patients and those who needed treatment targeting the immune system (p < 0.05). Tear cytokine levels corroborate the inflammatory nature of SARS-CoV-2., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Background: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests., Methods: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone., Results: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients., Conclusions: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: COVID-19 is an ongoing global pandemic. Changes in haematological characteristics in patients with COVID-19 are emerging as important features of the disease. We aimed to explore the haematological characteristics and related risk factors in patients with COVID-19., Methods: This retrospective cohort study included patients with COVID-19 admitted to three designated sites of Wuhan Union Hospital (Wuhan, China). Demographic, clinical, laboratory, treatment, and outcome data were extracted from electronic medical records and compared between patients with moderate, severe, and critical disease (defined according to the diagnosis and treatment protocol for novel coronavirus pneumonia, trial version 7, published by the National Health Commission of China). We assessed the risk factors associated with critical illness and poor prognosis. Dynamic haematological and coagulation parameters were investigated with a linear mixed model, and coagulopathy screening with sepsis-induced coagulopathy and International Society of Thrombosis and Hemostasis overt disseminated intravascular coagulation scoring systems was applied., Findings: Of 466 patients admitted to hospital from Jan 23 to Feb 23, 2020, 380 patients with COVID-19 were included in our study. The incidence of thrombocytopenia (platelet count <100 × 10 9 cells per L) in patients with critical disease (42 [49%] of 86) was significantly higher than in those with severe (20 [14%] of 145) or moderate (nine [6%] of 149) disease (p<0·0001). The numbers of lymphocytes and eosinophils were significantly lower in patients with critical disease than those with severe or moderate disease (p<0·0001), and prothrombin time, D-dimer, and fibrin degradation products significantly increased with increasing disease severity (p<0·0001). In multivariate analyses, death was associated with increased neutrophil to lymphocyte ratio (≥9·13; odds ratio [OR] 5·39 [95% CI 1·70-17·13], p=0·0042), thrombocytopenia (platelet count <100 × 10 9 per L; OR 8·33 [2·56-27·15], p=0·00045), prolonged prothrombin time (>16 s; OR 4·94 [1·50-16·25], p=0·0094), and increased D-dimer (>2 mg/L; OR 4·41 [1·06-18·30], p=0·041). Thrombotic and haemorrhagic events were common complications in patients who died (19 [35%] of 55). Sepsis-induced coagulopathy and International Society of Thrombosis and Hemostasis overt disseminated intravascular coagulation scores (assessed in 12 patients who survived and eight patients who died) increased over time in patients who died. The onset of sepsis-induced coagulopathy was typically before overt disseminated intravascular coagulation., Interpretation: Rapid blood tests, including platelet count, prothrombin time, D-dimer, and neutrophil to lymphocyte ratio can help clinicians to assess severity and prognosis of patients with COVID-19. The sepsis-induced coagulopathy scoring system can be used for early assessment and management of patients with critical disease., Funding: National Key Research and Development Program of China., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The coronavirus disease (COVID-19) pandemic continues to be a devastating chapter in history. The consequences of the pandemic unfold daily and they extend beyond physical health. Current research suggests that it is a public mental health crisis. With regard to the physical effects of COVID-19, policy-makers have drawn from past experiences, such as the severe acute respiratory syndrome (SARS) outbreak of 2003, to craft unique responses. A similar approach must be taken to address the mental health effects of the pandemic. Because COVID-19 can fit the definitions of a mental health disaster, it can be addressed using the principles of disaster mental health management. This letter to the editor presents arguments for defining COVID-19 as a mental health disaster, the challenges facing policy-makers in addressing it as such, and calls upon researchers to fill this gap in the literature.

Objective: To explore the CT imaging features/signs of patients with different clinical types of Coronavirus Disease 2019 (COVID-19) via the application of artificial intelligence (AI), thus improving the understanding of COVID-19., Pantients and Methods: Clinical data and chest CT imaging features of 58 patients confirmed with COVID-19 in the Fifth Medical Center of PLA General Hospital were retrospectively analyzed. According to the Guidelines on Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment (Provisional 6th Edition), COVID-19 patients were divided into mild type (7), common type (34), severe type (7) and critical type (10 patients). The CT imaging features of the patients with different clinical types of COVID-19 types were analyzed, and the volume percentage of pneumonia lesions with respect to the lung lobes (where the lesion was located) and to the whole lung was calculated with the use of AI software. SPSS 21.0 software was used for statistical analysis., Results: Common clinical manifestations of COVID-19 patients: fever was found in 47 patients (81.0%), cough in 31 (53.4%) and weakness in 10 (17.2%). Laboratory examinations: normal or decreased white blood cell (WBC) counts were observed in 52 patients (89.7%), decreased lymphocyte counts (LCs) in 14 (24.1%) and increased C-reactive protein (CRP) levels in 18 (31.0%). CT imaging features: there were 48 patients (94.1%) with lesions distributed in both lungs and 46 patients (90.2%) had lesions most visible in the lower lungs; the primary manifestations in patients with common type COVID-19 were ground-glass opacities (GGOs) (23/34, 67.6%) or mixed type (17/34, 50.0%), with lesions mainly distributed in the periphery of the lungs (28/34, 82.4%); the primary manifestations of patients with severe/critical type COVID-19 were consolidations (13/17, 76.5%) or mixed type (14/17, 82.4%), with lesions distributed in both the peripheral and central areas of lungs (14/17,82.4%); other common signs, including pleural parallel signs, halo signs, vascular thickening signs, crazy-paving signs and air bronchogram signs, were visible in patients with different clinical types, and pleural effusion was found in 5 patients with severe/critical COVID-19. AI software was used to calculate the volume percentages of pneumonia lesions with respect to the lung lobes (where the lesion was located) and to the whole lung. There were significant differences in the volume percentages of pneumonia lesions for the superior lobe of the left lung, the inferior lobe of the left lung, the superior lobe of the right lung, the inferior lobe of the right lung and the whole lung among patients with different clinical types (p<0.05). The area under the ROC curve (AUC) of the volume percentage of pneumonia lesions for the whole lung for the diagnosis of severe/critical type COVID-19 was 0.740, with sensitivity and specificity of 91.2% and 58.8%, respectively., Conclusions: The clinical and CT imaging features of COVID-19 patients were characteristic to a certain degree; thus, the clinical course and severity of COVID-19 could be evaluated with a combination of an analysis of clinical features and CT imaging features and assistant diagnosis by AI software.

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Current pandemic caused by SARS-CoV-2 inducing viral COVID-19 pneumonia, is categorized in 3 stages. Some biomarkers could be assigned to one of these stages, showing a correlation to mortality in COVID-19 patients. Laboratory findings in COVID-19, especially when serially evaluated, may represent individual disease severity and prognosis. These may help planning and controlling therapeutic interventions. Biomarkers for myocardial injury (high sensitive cardiac troponin, hsTn) or hemodynamic stress (NTproBNP) may occur in COVID-19 pneumonia such as in other pneumonias, correlating with severity and prognosis of the underlying disease. In hospitalized COVID-19 patients' mild increases of hsTn or NTproBNP may be explained by cardiovascular comorbidities and direct or indirect cardiac damage or stress caused by or during COVID-19 pneumonia. In case of suspected NSTE-ACS and COVID-19, indications for echocardiography or reperfusion strategy should be carefully considered against the risk of contamination., Competing Interests: E. Giannitsis: Honorare für Vorträge von Astra Zeneca, Bayer Vital, Boehringer Ingelheim, Roche Diagnostics, Brahms Deutschland und Daiichi Sankyo. Forschungsmittel von Daiichi Sankyo, Roche Diagnostics und durch die Deutsche Herzstiftung e. V. Beraterhonorare von Roche Diagnostics, Brahms Deutschland, Boehringer Ingelheim.C. Salbach: keine., (© Georg Thieme Verlag KG Stuttgart · New York.)

- Case numbers in China are clearly declining, case numbers in many European regions are no longer increasing exponentially.- Data on mortality from SARS-CoV-2 infection are contradictory; mortality is certainly lower than for SARS and MERS, but probably higher than for most seasonal flu outbreaks in recent years- The main complication of SARS-CoV-2 infection is pneumonia with development of acute respiratory distress syndrome (ARDS)- Asymptomatic and oligosymptomatic courses with virus shedding are not uncommon; they may be more frequent in children than in adults. Virus excretion in asymptomatic people and in the pre-symptomatic phase of an infection is relevant for transmission- An effective antiviral therapy has not yet been established. Steroids for anti-inflammatory therapy are not recommended- It is very important to prepare all actors in the health care system for a longer-term burden of inpatients and complications and to create the necessary capacities. Low-threshold diagnostic testing and rapid detection of infection chains remain essential for better control of the pandemic. An effective vaccine is urgent., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (© Georg Thieme Verlag KG Stuttgart · New York.)

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: To investigate the CT findings of patients with different clinical types of coronavirus disease 2019 (COVID-19)., Methods: A total of 67 patients diagnosed as COVID-19 by nucleic acid testing were collected and divided into 4 groups according to the clinical stages based on Diagnosis and treatment of novel coronavirus pneumonia (trial version 6) . The CT imaging characteristics were analyzed among patients with different clinical types., Results: Among 67 patients, 3(4.5%) were mild, 35 (52.2%) were moderate, 22 (32.8%) were severe, and 7(10.4%) were critical ill. No significant abnormality in chest CT imaging in mild patients. The 35 cases of moderate type included 3 (8.6%) single lesions, the 22 cases of severe cases included 1 (4.5%) single lesion and the rest cases were with multiple lesions. CT images of moderate patients were mainly manifested by solid plaque shadow and halo sign (18/35, 51.4%); while fibrous strip shadow with ground glass shadow was more frequent in severe cases (7/22, 31.8%). Consolidation shadow as the main lesion was observed in 7 cases, and all of them were severe or critical ill patients., Conclusions: CT images of patients with different clinical types of COVID-19 have characteristic manifestations, and solid shadow may predict severe and critical illness.

This report describes early exploratory analysis of ICD-10-CM code U07.1 (2019-nCoV acute respiratory disease [COVID-19]) to assess the use of administrative data for case ascertainment, syndromic surveillance, and future epidemiological studies. Out of the 2,950 possible COVID-19 cases identified between 1 April 2020 and 4 May 2020, 600 (20.3%) were detected in the Defense Medical Surveillance System (DMSS) and not in the Disease Reporting System internet (DRSi) or in Health Level 7 laboratory data from the Composite Health Care System. Among the 150 out of 600 cases identified exclusively in the DMSS and selected for Armed Forces Health Longitudinal Technology Application (AHLTA) review, 16 (10.7%) had a certified positive lab result in AHLTA, 17 (11.3%) met Council of State and Territorial Epidemiologists (CSTE) criteria for a probable case, 46 (30.7%) were not cases based on CSTE criteria, and 71 (47.3%) had evidence of a positive lab result from an outside source. Lack of full capture of lab results may continue to be a challenge as the variety of available tests expands. Administrative data may provide an important stopgap measure for detecting lab positive cases, pending incorporation of new COVID-19 tests and standardization of test and result nomenclature.

The COVID-19 epidemic, which is caused by the novel coronavirus SARS-CoV-2, has spread rapidly to become a world-wide pandemic. Chest radiography and chest CT are frequently used to support the diagnosis of COVID-19 infection. However, multiple cases of COVID-19 transmission in radiology department have been reported. Here we summarize the lessons we learned and provide suggestions to improve the infection control and prevention practices of healthcare workers in departments of radiology., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Assessments of influenza season severity can guide public health action. We used the moving epidemic method to develop intensity thresholds (ITs) for 3 US surveillance indicators from the 2003-2004 through 2014-2015 influenza seasons (excluding the 2009 pandemic). The indicators were: 1) outpatient visits for influenza-like illness; 2) influenza-related hospitalizations; and 3) influenza- and pneumonia-related deaths. ITs were developed for the population overall and separately for children, adults, and older adults, and they were set at the upper limit of the 50% (IT50), 90% (IT90), and 98% (IT98) 1-sided confidence intervals of the geometric mean of each season's 3 highest values. Severity was classified as low if ≥2 systems peaked below IT50, moderate if ≥2 peaked between IT50 and IT90, high if ≥2 peaked between IT90 and IT98, and very high if ≥2 peaked above IT98. We pilot-tested this method with the 2015-2016 season and the 2009 pandemic. Overall, 4 seasons were classified as low severity, 7 as moderate, 2 as high, and none as very high. Among the age groups, older adults had the most seasons (n = 3) classified as high, and children were the only group to have seasons (n = 2) classified as very high. We will apply this method to classify the severity of future seasons and inform pandemic response.

The article presents the preliminary data analysis of past "pandemic" and regional epidemics in the world caused by the influenza virus A(H1N1)sw1. Draw conclusions about the relative softness of the clinical and epidemiological manifestations of recent developments in connection with the lack of antigenic shift from a new strain and a significant protective role of acquired earlier population of cross-immunity of population to variants of influenza virus A(H1N1)sw1. In military units as representative risky groups are reflected all the laws of the epidemic process of influenza in the population. Consequently, the results of epidemiological surveillance in them can be extrapolated to the population and make a forecast of the epidemic in order to develop strategies and tactics to combat this infection in a large scale.