HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Viral Forecasting, Pathogen Cataloging, and Disease Ecosystem Mapping: Measuring Returns on Investments.

Infectious disease emergence into humans from animals or the environment occurs primarily due to genetic changes in the microbe through mutation or re-assortment making it either more transmissible or virulent or through a change in the disease "ecosystem". Research into infectious disease emergence can be grouped into different strategic approaches. One strategic approach is to study a specific or model disease system to understand the ecology of an infectious disease and how is transmitted and propagated through the environment and different hosts and then extrapolate that disease system knowledge to related pathogens. The other strategic approach follows the genomics and phylogenetics-tracking how pathogens are evolving and changing at the amino acid level. Here we argue that for understanding complex zoonotic diseases and for the purposes of preventing emergence and re-emergence into humans, that the Return on Investment be considered for the best research strategy.

Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks.

Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom-a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Validity of diagnostic codes to identify hospitalizations for infections among patients treated with oral anti-diabetic drugs.

PURPOSE: Identification of hospitalizations for infection is important for post-marketing surveillance of drugs, but the validity of using diagnosis codes to identify these events is unknown. Differentiating between hospitalization for and with infection is important, as the latter is common and less likely to arise from pre-admission exposure to drugs. We determined positive predictive values (PPVs) of diagnostic coding-based algorithms to identify hospitalization for infection among patients prescribed oral anti-diabetic drugs (OADs). METHODS: We identified patients initiating OADs within 2 United States claims databases (Medicare, HealthCore Integrated Research DatabaseSM [HIRDSM ]) and 2 United Kingdom electronic medical record databases (Clinical Practice Research Datalink [CPRD], The Health Improvement Network [THIN]) from 2009 to 2014. To identify potential hospitalizations for infection, we selected patients with a hospital diagnosis of infection and, within 7 days prior to hospitalization, either an outpatient/emergency department visit with an infection diagnosis or outpatient antimicrobial treatment. Hospital records were reviewed by infectious disease specialists to adjudicate hospital admissions for infection. PPVs for confirmed outcomes were determined for each database. RESULTS: Code-based algorithms to identify hospitalization for infection had PPVs exceeding 80% within Medicare (PPV, 83% [90/109]; 95% CI, 74-89%), HIRDSM (PPV, 89% [73/82]; 95% CI, 80-95%), and THIN (PPV, 86% [12/14]; 95% CI, 57-98%) but not within CPRD (PPV, 67% [14/21]; 95% CI, 43-85%). CONCLUSIONS: Algorithms identifying hospitalization for infection utilizing hospital diagnoses along with antecedent outpatient/emergency infection diagnoses or antimicrobial therapy had sufficiently high PPVs for confirmed events within Medicare, HIRDSM , and THIN to enable their use for pharmacoepidemiologic research.

[Infectious diseases and their ICD coding : What could be improved by the introduction of ICD-11?] / Infektionskrankheiten und ihre Codierung : Was kann sich durch die ICD-11 verbessern?

The revision of the International Classification of Diseases (ICD) could change morbidity and mortality statistics significantly, which also affects the area of infectious diseases. Infectious diseases are classified according to their etiology, affected body system or the life period during which the episode occurs. Specific challenges arise from emerging pathogens and the respective necessary adaptation. For epidemiologic analysis ICD-10 does not always offer enough additional information.ICD provides the basis for international comparison of infectious disease morbidity and mortality statistics, but it is also used to collect data for surveillance and research purposes, e. g. the notification system for infectious diseases, syndromic surveillance systems and the evaluation of data quality by using secondary data sources.ICD-11 offers the chance to better represent epidemiological concepts of infectious diseases by adding more relevant information as affected body system or manifestation. Due to the complexity of coding, ensuring continuity of morbidity and mortality statistics could be challenging.

The Australasian Society for Infectious Diseases and Refugee Health Network of Australia recommendations for health assessment for people from refugee-like backgrounds: an abridged outline.

INTRODUCTION: In 2009, the Australasian Society of Infectious Diseases published guidelines on the post-arrival health assessment of recently arrived refugees. Since then, the number of refugees and asylum seekers reaching Australia has increased substantially (17 555 refugees in 2015-16) and the countries of origin have changed. These groups are likely to have had poor access to health care pre-arrival and, consequently, are at risk of a range of chronic and infectious diseases. We established an advisory group that included infectious diseases physicians, general practitioners, public health specialists, paediatricians and refugee health nurses to update the 2009 guidelines.Main recommendations: All people from refugee-like backgrounds, including children, should be offered a tailored comprehensive health assessment and management plan, ideally within 1 month of arrival in Australia. This can be offered at any time if initial contact with a GP or clinic is delayed. Recommended screening depends on history, examination and previous investigations, and is tailored based on age, gender, countries of origin and transit and risk profile. The full version of the guidelines is available at http://www.asid.net.au/documents/item/1225.Changes in management as a result of this guideline: These guidelines apply to all people from refugee-like backgrounds, including asylum seekers. They provide more information about non-communicable diseases and consider Asia and the Middle East as regions of origin as well as Africa. Key changes include an emphasis on person-centred care; risk-based rather than universal screening for hepatitis C virus, malaria, schistosomiasis and sexually transmissible infections; updated immunisation guidelines; and new recommendations for other problems, such as nutritional deficiencies, women's health and mental health.

Best practices in ranking communicable disease threats: a literature review, 2015.

The threat of serious, cross-border communicable disease outbreaks in Europe poses a significant challenge to public health and emergency preparedness because the relative likelihood of these threats and the pathogens involved are constantly shifting in response to a range of changing disease drivers. To inform strategic planning by enabling effective resource allocation to manage the consequences of communicable disease outbreaks, it is useful to be able to rank and prioritise pathogens. This paper reports on a literature review which identifies and evaluates the range of methods used for risk ranking. Searches were performed across biomedical and grey literature databases, supplemented by reference harvesting and citation tracking. Studies were selected using transparent inclusion criteria and underwent quality appraisal using a bespoke checklist based on the AGREE II criteria. Seventeen studies were included in the review, covering five methodologies. A narrative analysis of the selected studies suggests that no single methodology was superior. However, many of the methods shared common components, around which a 'best-practice' framework was formulated. This approach is intended to help inform decision makers' choice of an appropriate risk-ranking study design.

Profile of illness in Syrian refugees: A GeoSentinel analysis, 2013 to 2015.

Screening of 488 Syrian unaccompanied minor refugees (< 18 years-old) in Berlin showed low prevalence of intestinal parasites (Giardia, 7%), positive schistosomiasis serology (1.4%) and absence of hepatitis B. Among 44 ill adult Syrian refugees examined at GeoSentinel clinics worldwide, cutaneous leishmaniasis affected one in three patients; other noteworthy infections were active tuberculosis (11%) and chronic hepatitis B or C (9%). These data can contribute to evidence-based guidelines for infectious disease screening of Syrian refugees.

Accuracy of ICD-10 Coding System for Identifying Comorbidities and Infectious Conditions Using Data from a Thai University Hospital Administrative Database.

OBJECTIVE: To determine the accuracy of International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system in identifying comorbidities and infectious conditions using data from a Thai university hospital administrative database. MATERIAL AND METHOD: A retrospective cross-sectional study was conducted among patients hospitalized in six general medicine wards at Siriraj Hospital. ICD-10 code data was identified and retrieved directly from the hospital administrative database. Patient comorbidities were captured using the ICD-10 coding algorithm for the Charlson comorbidity index. Infectious conditions were captured using the groups of ICD-10 diagnostic codes that were carefully prepared by two independent infectious disease specialists. Accuracy of ICD-10 codes combined with microbiological dataf or diagnosis of urinary tract infection (UTI) and bloodstream infection (BSI) was evaluated. Clinical data gathered from chart review was considered the gold standard in this study. RESULTS: Between February 1 and May 31, 2013, a chart review of 546 hospitalization records was conducted. The mean age of hospitalized patients was 62.8 ± 17.8 years and 65.9% of patients were female. Median length of stay [range] was 10.0 [1.0-353.0] days and hospital mortality was 21.8%. Conditions with ICD-10 codes that had good sensitivity (90% or higher) were diabetes mellitus and HIV infection. Conditions with ICD-10 codes that had good specificity (90% or higher) were cerebrovascular disease, chronic lung disease, diabetes mellitus, cancer HIV infection, and all infectious conditions. By combining ICD-10 codes with microbiological results, sensitivity increased from 49.5 to 66%for UTI and from 78.3 to 92.8%for BS. CONCLUSION: The ICD-10 coding algorithm is reliable only in some selected conditions, including underlying diabetes mellitus and HIV infection. Combining microbiological results with ICD-10 codes increased sensitivity of ICD-10 codes for identifying BSI. Future research is needed to improve the accuracy of hospital administrative coding system in Thailand.

Factors affecting the accuracy of a class prediction model in gene expression data.

BACKGROUND: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. RESULTS: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. CONCLUSIONS: We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting.

BACKGROUND: Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field. METHODS: 111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity. RESULTS: Formal source reports lagged behind informal source reports by a median of 1.26 days (p=0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting. CONCLUSION: The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.

A method for reporting and classifying acute infectious diseases in a prospective study of young children: TEDDY.

BACKGROUND: Early childhood environmental exposures, possibly infections, may be responsible for triggering islet autoimmunity and progression to type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) follows children with increased HLA-related genetic risk for future T1D. TEDDY asks parents to prospectively record the child's infections using a diary book. The present paper shows how these large amounts of partially structured data were reduced into quantitative data-sets and further categorized into system-specific infectious disease episodes. The numbers and frequencies of acute infections and infectious episodes are shown. METHODS: Study subjects (n = 3463) included children who had attended study visits every three months from age 3 months to 4 years, without missing two or more consecutive visits during the follow-up. Parents recorded illnesses prospectively in a TEDDY Book at home. The data were entered into the study database during study visits using ICD-10 codes by a research nurse. TEDDY investigators grouped ICD-10 codes and fever reports into infectious disease entities and further arranged them into four main categories of infectious episodes: respiratory, gastrointestinal, other, and unknown febrile episodes. Incidence rate of infections was modeled as function of gender, HLA-DQ genetic risk group and study center using the Poisson regression. RESULTS: A total of 113,884 ICD-10 code reports for infectious diseases recorded in the database were reduced to 71,578 infectious episodes, including 74.0% respiratory, 13.1% gastrointestinal, 5.7% other infectious episodes and 7.2% febrile episodes. Respiratory and gastrointestinal infectious episodes were more frequent during winter. Infectious episode rates peaked at 6 months and began declining after 18 months of age. The overall infectious episode rate was 5.2 episodes per person-year and varied significantly by country of residence, sex and HLA genotype. CONCLUSIONS: The data reduction and categorization process developed by TEDDY enables analysis of single infectious agents as well as larger arrays of infectious agents or clinical disease entities. The preliminary descriptive analyses of the incidence of infections among TEDDY participants younger than 4 years fits well with general knowledge of infectious disease epidemiology. This protocol can be used as a template in forthcoming time-dependent TEDDY analyses and in other epidemiological studies.

Health status of newly arrived refugees in Toronto, Ont: Part 1: infectious diseases.

OBJECTIVE: To determine the prevalence of selected infectious diseases among newly arrived refugee patients and whether there is variation by key demographic factors. DESIGN: Retrospective chart review. SETTING: Primary care clinic for refugee patients in Toronto, Ont. PARTICIPANTS: A total of 1063 refugee patients rostered at the clinic from December 2011 to June 2014. MAIN OUTCOME MEASURES: Demographic information (age, sex, and region of birth); prevalence of HIV, hepatitis B, hepatitis C, Strongyloides, Schistosoma, intestinal parasites, gonorrhea, chlamydia, and syphilis infections; and varicella immune status. RESULTS: The median age of patients was 29 years and 56% were female. Refugees were born in 87 different countries. Approximately 33% of patients were from Africa, 28% were from Europe, 14% were from the Eastern Mediterranean Region, 14% were from Asia, and 8% were from the Americas (excluding 4% born in Canada or the United States). The overall rate of HIV infection was 2%. The prevalence of hepatitis B infection was 4%, with a higher rate among refugees from Asia (12%, P < .001). Hepatitis B immunity was 39%, with higher rates among Asian refugees (64%, P < .001) and children younger than 5 years (68%, P < .001). The rate of hepatitis C infection was less than 1%. Strongyloides infection was found in 3% of tested patients, with higher rates among refugees from Africa (6%, P = .003). Schistosoma infection was identified in 15% of patients from Africa. Intestinal parasites were identified in 16% of patients who submitted stool samples. Approximately 8% of patients were varicella nonimmune, with higher rates in patients from the Americas (21%, P < .001). CONCLUSION: This study highlights the importance of screening for infectious diseases among refugee patients to provide timely preventive and curative care. Our data also point to possible policy and clinical implications, such as targeted screening approaches and improved access to vaccinations and therapeutics.

Semi-supervised learning of causal relations in biomedical scientific discourse.

BACKGROUND: The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery. RESULTS: In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before. CONCLUSION: Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks.

Orchard Sports Injury and Illness Classification System (OSIICS) Version 15.

BACKGROUND: Sports medicine (injury and illnesses) requires distinct coding systems because the International Classification of Diseases is insufficient for sports medicine coding. The Orchard Sports Injury and Illness Classification System (OSIICS) is one of two sports medicine coding systems recommended by the International Olympic Committee. Regular updates of coding systems are required. METHODS: For Version 15, updates for mental health conditions in athletes, sports cardiology, concussion sub-types, infectious diseases, and skin and eye conditions were considered particularly important. RESULTS: Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes. Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes. Rugby union protocols on head injury assessment were used to create additional concussion codes. CONCLUSION: It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility. The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS.

Evaluation of syndromic algorithms for detecting patients with potentially transmissible infectious diseases based on computerised emergency-department data.

BACKGROUND: The objective of this study was to ascertain the performance of syndromic algorithms for the early detection of patients in healthcare facilities who have potentially transmissible infectious diseases, using computerised emergency department (ED) data. METHODS: A retrospective cohort in an 810-bed University of Lyon hospital in France was analysed. Adults who were admitted to the ED and hospitalised between June 1, 2007, and March 31, 2010 were included (N=10895). Different algorithms were built to detect patients with infectious respiratory, cutaneous or gastrointestinal syndromes. The performance parameters of these algorithms were assessed with regard to the capacity of our infection-control team to investigate the detected cases. RESULTS: For respiratory syndromes, the sensitivity of the detection algorithms was 82.70%, and the specificity was 82.37%. For cutaneous syndromes, the sensitivity of the detection algorithms was 78.08%, and the specificity was 95.93%. For gastrointestinal syndromes, the sensitivity of the detection algorithms was 79.41%, and the specificity was 81.97%. CONCLUSIONS: This assessment permitted us to detect patients with potentially transmissible infectious diseases, while striking a reasonable balance between true positives and false positives, for both respiratory and cutaneous syndromes. The algorithms for gastrointestinal syndromes were not specific enough for routine use, because they generated a large number of false positives relative to the number of infected patients. Detection of patients with potentially transmissible infectious diseases will enable us to take precautions to prevent transmission as soon as these patients come in contact with healthcare facilities.

[The concept of infectious diseases pathogenesis].

In this article a concept of infectious disease pathogenesis as consisted with clinical symptoms is provided. The course of disease, immediate and long-term consequences depend on the mode of entry. If the infection comes via oropharynx, airway, gastrointestinal tract or via skin, the immune system provides adequate immune response. This leads to typical symptoms, cyclical clinic progression and usually to the recovery with the formation of full sterile immunity. In case of parenteral way of infection, which includes perinatal way, there is no full mode of entry, the disease takes chronic course involving visceral organs because of different mechanisms of affinity and new tropic organ involving. For the full sanogenesis germ or its mediators should persist in the primary focus of infection. It is suggested, that HIV, HCV, hepatitis B virus, tetanus, rabies and other infectious diseases with inner organs involvement, as well as all slow infections, should be treated as infectious diseases with the parental way of infection, proceeding with affinity changings, which lead to the appearance of new tropic sites in visceral organs. The theory of the mode of entry, affinity, appearance of tropic sites in visceral organs should form the basis of modern infectology.

Infectious diseases in Poland in 2011.

UNLABELLED: The aim of the study was assessment of the epidemiological situation of infectious and parasitic diseases in Poland in 2011 MATERIALS AND METHODS: The main source of data to develop the statistical overview was the annual bulletin "Infectious diseases in Poland in 2011," and "Vaccinations in Poland in 2011,"/NIPH-NIH, CSI, 2011 and information contained in the articles of epidemiological journal in which authors depth discussion of the epidemiological situation of 27 diseases or groups of diseases. Data on deaths are based on the statements of the Department of the Central Statistical Office of Demographic Studies. RESULTS: Upper respiratory tract infection classified as "influenza and influenza-like illness" in 2011, were reported in a total number of 1,156,357 cases, which was an 108.0% increase of incidence as compared with 2010. and in relation to the median of the years 2005 - 2009 of 205.9%. In 2011, food infections dominated among the bacterial infections caused by Salmonella, with the continuing decline of incidence and fraction of salmonellosis among other etiologies. Among the diseases that can be prevented by vaccination it was reported 30.7% increase in the incidence of pertussis. In relation to the median of the years 2005-2009 is a decrease of 16.9%. A downward trend in the incidence of mumps was maintained. As compared to 2010, the incidence decreased by 7.0%. When compared to the median of the years 2005 to 2009 the decline was 38.3%. In relation to the median of the years 2005-2009 there have been a decrease of the number of rubella cases by 67.7% and there have been no reported cases of congenital rubella. A further decline in the incidence of invasive disease caused by H. influenzae was observed. The incidence of tuberculosis in 2011 increased as compared to the previous year from 19.7 to 22/100,000 in respect to all forms of tuberculosis, and pulmonary tuberculosis from 18.3 to 20.5/100,000. The number of newly diagnosed HIV-infected persons also increased. In 2011 it was reported 1,105 cases (2.87/100,000), compared with the previous year, an increase of 14.8%. In 2011, there were reported 221 cases (0.57/100,000) of tick-borne encephalitis, i.e. by 25.5% less than in the previous year, the incidence of viral meningitis decreased by 11.8%. In 2011, there were no cases of especially dangerous infectious diseases: plague, anthrax, diphtheria, polio, rabies and viral hemorrhagic fevers besides dengue, of which 5 cases acquired in endemic areas were reported to the epidemiological surveillance. Due to infectious and parasitic diseases in 2011, died in Poland 3,408 people total. The share of deaths from these causes in the total number of deaths was 0.91%, and the mortality rate--8.8 per 100,000 population, 52.0% of all deaths were due to sepsis.