Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

Perrotta, Daniela; Bella, Antonino; Rizzo, Caterina; Paolotti, Daniela

doi:10.1371/journal.pone.0169801

Cited by 46 publications

(37 citation statements)

References 14 publications

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“…InfluNet is a network of sentinel practitioners, representative of all Italian regions, based on the voluntary participation of an average 973 general practitioners and family pediatricians per year (range 754-1,055), providing health care to about 2% of the general population. InfluNet is dedicated to monitoring ILI incidence from week 42 to week 17 of each season, to defining the extent of the seasonal epidemics, and to collecting information on circulating strains (Perrotta et al, 2017;Gasparini et al, 2013).…”

Section: Influenza Activitymentioning

confidence: 99%

Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14–2016/17 seasons)

Rosano¹,

Bella²,

Gesualdo

et al. 2019

International Journal of Infectious Diseases

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In recent years, Italy has been registering peaks in death rates, particularly among the elderly during the winter season. Influenza epidemics have been indicated as one of the potential determinants of such an excess. The objective of our study was to estimate the influenza-attributable contribution to excess mortality during the influenza seasons from 2013/14 to 2016/17 in Italy. Methods: We used the EuroMomo and the FluMomo methods to estimate the annual trend of influenzaattributable excess death rate by age group. Population data were provided by the National Institute of Statistics, data on influenza like illness and confirmed influenza cases were provided by the National Institutes of Health. As an indicator of weekly influenza activity (IA) we adopted the Goldstein index, which is the product of the percentage of patients seen with influenza-like illness (ILI) and percentage of influenza-positive specimens, in a given week. Results: We estimated excess deaths of 7,027, 20,259, 15,801 and 24,981 attributable to influenza epidemics in the 2013/14, 2014/15, 2015/16 and 2016/17, respectively, using the Goldstein index. The average annual mortality excess rate per 100,000 ranged from 11.6 to 41.2 with most of the influenzaassociated deaths per year registered among the elderly. However children less than 5 years old also reported a relevant influenza attributable excess death rate in the 2014/15 and 2016/17 seasons (1.05/ 100,000 and 1.54/100,000 respectively). Conclusions: Over 68,000 deaths were attributable to influenza epidemics in the study period. The observed excess of deaths is not completely unexpected, given the high number of fragile very old subjects living in Italy. In conclusion, the unpredictability of the influenza virus continues to present a major challenge to health professionals and policy makers. Nonetheless, vaccination remains the most effective means for reducing the burden of influenza, and efforts to increase vaccine coverage and the introduction of new vaccine strategies (such as vaccinating healthy children) should be considered to reduce the influenza attributable excess mortality experienced in Italy and in Europe in the last seasons.

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Section: Influenza Activitymentioning

confidence: 99%

Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14–2016/17 seasons)

Rosano¹,

Bella²,

Gesualdo

et al. 2019

International Journal of Infectious Diseases

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“…Influweb is a scientific project aimed at monitoring the activity of Influenza-like Illness in Italy with the aid of volunteers via the internet [49,50]. It has been operational since 2008 and it is part of the InfluenzaNet network, active in many other European and non-European countries [61,62], such as The Netherlands, Belgium, Portugal, United Kingdom, Sweden, France, Spain, Denmark and Ireland.…”

Section: Influweb Datasetmentioning

confidence: 99%

“…In this work, we combine health and behavioral data, collected from Web users, with a machine learning pipeline to characterize self-initiated behavioral changes during the seasonal flu. In particular, we developed and deployed a questionnaire via Influweb [49,50], a digital surveillance platform that since 2008 collects data about the progression of the seasonal flu in Italy, to collect socio-demographic indicators, medical history of individuals, information regarding feelings, concerns towards the flu and to query users about changes in their behaviors induced by the disease. By studying the responses, we identify three classes of behavioral changes describing those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors.…”

Section: Introductionmentioning

confidence: 99%

Towards a data-driven characterization of behavioral changes induced by the seasonal flu

et al. 2020

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In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases. Author summary OPEN ACCESSCitation: Gozzi N, Perrotta D, Paolotti D, Perra N (2020) Towards a data-driven characterization of behavioral changes induced by the seasonal flu. PLoS Comput Biol 16(5): e1007879. https://doi. ). Nevertheless, we provide the input (numerical) data for the 23 features for each questionnaire. The data, which is accessible with the code (https:// github.com/ngozzi/behavioralchange), allows to reproduce the results presented in the paper and allows for potential extensions of the work.The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of self-initiated behavioral changes implemented by each participant. Our analysis, conducted adopting machine learning algorithms, show that both past experience of illness and personal beliefs about the disease are fundamental drivers of behavioral change. These findings are in good agreement with the constructs of the Health Belief Model and provide, to the best of our knowledge, the first data driven characterization of behavioral changes during the seasonal flu. PLOS COMPUTATIONAL BIOLOGYTowards a data-driven characterization of behavioral changes induced by the seasonal flu PLOS Computational Biology | https://doi.

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“…The list of proposed symptoms has been chosen to 45 include the various ILI definitions adopted by national surveillance systems in Europe 46 and, at the same time, to get a comprehensive list of symptoms that could be clearly 47 articulated and understood by participants and would allow the detection of various 48 circulating flu-related illnesses. Even though participatory systems generally suffer from 49 self-selection bias, causing the sample to be non-representative of the general 50 population [25], previous works have shown that web-based surveillance data can 51 provide relevant information to estimate age-specific influenza attack rates [26,27], 52 influenza vaccine effectiveness [28], risk factors for ILI [29,30], and to assess health care 53 seeking behavior [31]. Moreover, it has been largely demonstrated that weekly ILI 54 incidence rates that can be extracted from web-based surveillance data correlate well 55 with ILI incidence as reported by GPs surveillance [27,32,33] (in such approaches, the 56 number of ILI cases among the web platform participants was determined by applying 57 the ECDC case definition [13] to the set of symptoms reported by participants).…”

Section: Plosmentioning

confidence: 99%

“…24 Nevertheless, a significant fraction of European countries still adopts their own clinical 25 case definition to compile seasonal influenza surveillance weekly reports. 26 In recent years the availability of novel data streams has given rise to a variety of 27 non-traditional approaches for monitoring seasonal influenza epidemics [14][15][16]. Such 28 new digital data sources can be exploited to capture additional surveillance signals that 29 can be used to complement GPs surveillance data [17][18][19][20].…”

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confidence: 99%

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Kalimeri

Delfino

Cattuto

et al. 2018

Preprint

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Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness' temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. PLOS 1/24We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition. Author summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness' temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by ...

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Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

Cited by 46 publications

References 14 publications

Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14–2016/17 seasons)

Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14–2016/17 seasons)

Towards a data-driven characterization of behavioral changes induced by the seasonal flu

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

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