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AbstractEmpirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fractionfof links each individual shares outside its own department: residents, wanderers, and linkers. Linkers (f~ 50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotteda prioriin the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.

International audience ; Influenza surveillance systems aim to annually track influenza epidemics, detecting their start, monitoring their spatio-temporal spread, identifying populations at risk and circulating viruses, and estimating the impact on the community and healthcare structures [1]. Sentinel surveillance systems are based on primary care and report the weekly number of patients examined with influenza-related illness. Recently, a variety of non-traditional surveillance approaches have emerged [ 2-9] where large amounts of crowdsourced digital data produced by individuals enable such indi-viduals to contribute to monitoring the health of their community and to provide authorities with additional characterisations of the epidemic. In the European Union, more than one third of the countries run a participatory surveillance system to monitor influenza epidemics, under the standardised framework of the Influenzanet network established in 2011 [2,10,11]. The Influenzanet network is a syndromic surveillance system based on voluntary self-reporting of symptoms by participants residing in countries, which are part of the Influenzanet.

International audience ; Influenza surveillance systems aim to annually track influenza epidemics, detecting their start, monitoring their spatio-temporal spread, identifying populations at risk and circulating viruses, and estimating the impact on the community and healthcare structures [1]. Sentinel surveillance systems are based on primary care and report the weekly number of patients examined with influenza-related illness. Recently, a variety of non-traditional surveillance approaches have emerged [ 2-9] where large amounts of crowdsourced digital data produced by individuals enable such indi-viduals to contribute to monitoring the health of their community and to provide authorities with additional characterisations of the epidemic. In the European Union, more than one third of the countries run a participatory surveillance system to monitor influenza epidemics, under the standardised framework of the Influenzanet network established in 2011 [2,10,11]. The Influenzanet network is a syndromic surveillance system based on voluntary self-reporting of symptoms by participants residing in countries, which are part of the Influenzanet.

Background Morbidity, severity, and mortality associated with annual influenza epidemics are of public health concern. We analyzed surveillance data on hospitalized laboratory-confirmed influenza cases admitted to intensive care units to identify common determinants for fatal outcome and inform and target public health prevention strategies, including risk communication. Methods We performed a descriptive analysis and used Poisson regression models with robust variance to estimate the association of age, sex, virus (sub)type, and underlying medical condition with fatal outcome using European Union data from 2009 to 2017. Results Of 13 368 cases included in the basic dataset, 2806 (21%) were fatal. Age 40 years and infection with influenza A virus were associated with fatal outcome. Of 5886 cases with known underlying medical conditions and virus A subtype included in a more detailed analysis, 1349 (23%) were fatal. Influenza virus A(H1N1)pdm09 or A(H3N2) infection, age 60 years, cancer, human immunodeficiency virus infection and/or other immune deficiency, and heart, kidney, and liver disease were associated with fatal outcome; the risk of death was lower for patients with chronic lung disease and for pregnant women. Conclusions This study re-emphasises the importance of preventing influenza in the elderly and tailoring strategies to risk groups with underlying medical conditions. ; (VLID)6072872

International audience ; BackgroundThe Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness.MethodsIn 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators.ResultsMore than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries.ConclusionsExisting demographic and national datasets allowed the quantification ...

International audience ; BackgroundThe Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness.MethodsIn 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators.ResultsMore than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries.ConclusionsExisting demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts.

Free PMC article: https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32258201/ ; Background: Morbidity, severity, and mortality associated with annual influenza epidemics are of public health concern. We analyzed surveillance data on hospitalized laboratory-confirmed influenza cases admitted to intensive care units to identify common determinants for fatal outcome and inform and target public health prevention strategies, including risk communication. Methods: We performed a descriptive analysis and used Poisson regression models with robust variance to estimate the association of age, sex, virus (sub)type, and underlying medical condition with fatal outcome using European Union data from 2009 to 2017. Results: Of 13 368 cases included in the basic dataset, 2806 (21%) were fatal. Age ≥40 years and infection with influenza A virus were associated with fatal outcome. Of 5886 cases with known underlying medical conditions and virus A subtype included in a more detailed analysis, 1349 (23%) were fatal. Influenza virus A(H1N1)pdm09 or A(H3N2) infection, age ≥60 years, cancer, human immunodeficiency virus infection and/or other immune deficiency, and heart, kidney, and liver disease were associated with fatal outcome; the risk of death was lower for patients with chronic lung disease and for pregnant women. Conclusions: This study re-emphasises the importance of preventing influenza in the elderly and tailoring strategies to risk groups with underlying medical conditions. ; info:eu-repo/semantics/publishedVersion

Background: Morbidity, severity, and mortality associated with annual influenza epidemics are of public health concern. We analyzed surveillance data on hospitalized laboratory-confirmed influenza cases admitted to intensive care units to identify common determinants for fatal outcome and inform and target public health prevention strategies, including risk communication. Methods: We performed a descriptive analysis and used Poisson regression models with robust variance to estimate the association of age, sex, virus (sub)type, and underlying medical condition with fatal outcome using European Union data from 2009 to 2017. Results: Of 13 368 cases included in the basic dataset, 2806 (21%) were fatal. Age ≥40 years and infection with influenza A virus were associated with fatal outcome. Of 5886 cases with known underlying medical conditions and virus A subtype included in a more detailed analysis, 1349 (23%) were fatal. Influenza virus A(H1N1)pdm09 or A(H3N2) infection, age ≥60 years, cancer, human immunodeficiency virus infection and/or other immune deficiency, and heart, kidney, and liver disease were associated with fatal outcome; the risk of death was lower for patients with chronic lung disease and for pregnant women. Conclusions: This study re-emphasises the importance of preventing influenza in the elderly and tailoring strategies to risk groups with underlying medical conditions. ; This work was funded by an internal ECDC budget. ; Sí

BACKGROUND: Morbidity, severity, and mortality associated with annual influenza epidemics are of public health concern. We analyzed surveillance data on hospitalized laboratory-confirmed influenza cases admitted to intensive care units to identify common determinants for fatal outcome and inform and target public health prevention strategies, including risk communication. METHODS: We performed a descriptive analysis and used Poisson regression models with robust variance to estimate the association of age, sex, virus (sub)type, and underlying medical condition with fatal outcome using European Union data from 2009 to 2017. RESULTS: Of 13 368 cases included in the basic dataset, 2806 (21%) were fatal. Age ≥40 years and infection with influenza A virus were associated with fatal outcome. Of 5886 cases with known underlying medical conditions and virus A subtype included in a more detailed analysis, 1349 (23%) were fatal. Influenza virus A(H1N1)pdm09 or A(H3N2) infection, age ≥60 years, cancer, human immunodeficiency virus infection and/or other immune deficiency, and heart, kidney, and liver disease were associated with fatal outcome; the risk of death was lower for patients with chronic lung disease and for pregnant women. CONCLUSIONS: This study re-emphasises the importance of preventing influenza in the elderly and tailoring strategies to risk groups with underlying medical conditions.