Table 2 - uploaded by Oktawia P Wójcik
Content may be subject to copyright.
Influenza-like Illness (ILI) definitions for Influenzanet, FluTracking, Reporta and Flu Near You/ SaludBoricua
Source publication
The 21(st) century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries....
Contexts in source publication
Context 1
... register, complete an intake questionnaire containing various medical, geographic and behavioral questions, and receive weekly emails for reporting symptoms (or a lack thereof ) since their last visit to the website [5] (Table 1). The incidence of ILI among participants is determined in real time using a syndromic case definition [5] (Table 2) and graphic representation of the results is dynamically updated on the system's web site. ...
Context 2
... system design and objectives vary across these systems, they all include a registration process which collects varying amounts of background data and weekly email prompts to encourage reporting of symptoms expe- rienced that week (Table 1). Symptom data is processed in real-time using syndromic definitions (Table 2) and is displayed, generally in the form of maps or timelines, on the system's website communicating the information back to the public. Many of the sites also provide public health news or information about the diseases that they target. ...
Similar publications
Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease which needs proper surveillance and control strategies. Effective detection and containment of dengue has proven to be challenging due to the complex nature of interactions among the governing epidemiological factors as well as a number of environment...
Cet article propose une description multi-échelle de la surveillance et de la lutte contre la dengue en Inde. Cette démarche permet de comprendre les modalités de fonctionnement du système de santé publique et de mettre en lumière les facteurs de blocages et de dysfonctionnements dans la lutte contre la maladie, à différentes échelles. Au-delà des...
Citations
... Brazil has one of the largest global disease registration systems, the Notifiable Diseases Information System (SINAN), which provides notifications of predominant diseases occurring in Brazil (Instrução normativa n.º 02/SVS/MS, de 22 de novembro de 2005, 2005). In addition, there are the "Observatory of Dengue" and "Dengue on the Web" applications that use websites as platforms for interaction and spatialization of information (Wójcik et al., 2014). In the healthcare sector, Brazil has seen the development of significant digital platforms, including "Saúde na Copa," created in 2014 in conjunction with the World Cup, and "Guardians of Health," established in 2016 in association with the Olympic Games. ...
O georreferenciamento pode revelar a distribuição espacial das doenças. Este estudo caracterizou a localização geográfica dos casos positivos de leishmaniose visceral canina (LVC) (n=21), por meio do aplicativo C7-LVC (App), na cidade de Santa Maria, RS, Brasil. Esta tecnologia está disponível para uso em dispositivos smartphones, sendo caracterizada como primeiro instrumento para a notificação de LVC ao serviço público, com o objetivo de auxiliar nas ações de controle da doença. Neste estudo, foi utilizado o banco de dados do Serviço Municipal de Vigilância Ambiental que continha informações de notificações sobre LVC anteriormente enviadas por médicos veterinários, entre abril e dezembro de 2017. Neste período, a prevalência de LVC foi maior na região Norte (17/21 - 80,93%) da cidade. Os casos positivos foram observados em sete bairros, com maior ocorrência (21/9 - 42,85%) no bairro Perpétuo Socorro. As regiões contendo cães positivos para LVC apresentavam como características ambientais a vegetação nativa com áreas úmidas, sombreadas e ricas em matéria orgânica. Todos os casos de LVC foram identificados em regiões geográficas próximas. As características ambientais do município favorecem a proliferação e sobrevivência do vetor no perímetro urbano e periurbano e acarretam riscos à saúde humana e animal. Os dados de georreferenciamento obtidos pelo C7-LVC podem auxiliar na formulação de medidas sanitárias para conter a propagação da doença entre humanos e animais.
... Exploring this problem, Gates et al. [16] found that the reluctance to report arose from feelings of uncertainty, fear of the consequences of reporting, distrust of authorities, and unfamiliarity with the reporting process. Participatory surveillance, i.e., including members of the population at risk in the surveillance data collection process [17][18][19][20], has improved syndromic surveillance, but its effectiveness is limited by the participants' clinical experience and the inherent diagnostic ambiguity of clinical signs. Thus, Elbers et al. [21] estimated that a diagnosis of CSFV based solely on clinical signs achieved a diagnostic sensitivity of 73% and a diagnostic specificity of 53%. ...
We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.
... This analysis is limited by various factors. A known weakness of participatory surveillance systems is the bias associated with participant self-selection into the program, as those who choose to participate are systematically different than those who do not (19). As such, participant populations in participatory syndromic surveillance systems tend to differ from the population they are intended to represent. ...
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for robust surveillance of respiratory viruses. Syndromic surveillance continues to be an important surveillance component recommended by the World Health Organization (WHO). While FluWatchers, Canada's syndromic surveillance system, has been in place since 2015, the COVID-19 pandemic provided a valuable opportunity to expand the program's scope and underlying technology infrastructure. Following some structural changes to FluWatchers syndromic questionnaire, participants are now able to contribute valuable data to the non-specific surveillance of respiratory virus activity across Canada. This article examines the performance of FluWatchers' syndromic surveillance over the three years of the COVID-19 pandemic in Canada. More specifically, this article examines FluWatchers' performance with respect to the correlation between the FluWatchers influenza-like illness (ILI) and acute respiratory infection (ARI) indicators and total respiratory virus detections (RVDs) in Canada, including influenza, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and other respiratory viruses.
... Cette analyse est limitée par différents facteurs. L'une des faiblesses connues des systèmes de surveillance participatifs est le biais associé à l'autosélection des participants au programme, car ceux qui choisissent de participer sont systématiquement différents de ceux qui ne le font pas (19 (12). Cela peut être démontré par les résultats obtenus dans l'analyse de corrélation de la première année (mars 2020 à mars 2021). ...
... Unlike traditional surveillance systems, which rely on reports from health professionals or laboratory testing based on specific case definitions, participatory surveillance involves individuals sharing information, typically about symptoms rather than diagnoses [4]. As such, they are more sensitive but less specific than traditional surveillance systems and can provide timely information about disease within a population [4,5]. Research has indicated that, in view of influence surveillance, participatory surveillance systems can act as reliable complements to current sentinel surveillance systems [6]. ...
... Participatory surveillance systems have been recognised as having the potential to play an important role in population-level disease surveillance [4,5]. Research Recommendation 1.1.2 of the World Health Organization (WHO) Public Health Research Agenda for Influenza is to identify the reliability of complementary influenza surveillance systems, such as participatory surveillance, for providing real-time estimates of influenza activity. ...
Background
Seasonal influenza causes significant morbidity and mortality, with an estimated 9.4 million hospitalisations and 290 000-650 000 respiratory related-deaths globally each year. Influenza can also cause mild illness, which is why not all symptomatic persons might necessarily be tested for influenza. To monitor influenza activity, healthcare facility-based syndromic surveillance for influenza-like illness is often implemented. Participatory surveillance systems for influenza-like illness (ILI) play an important role in influenza surveillance and can complement traditional facility-based surveillance systems to provide real-time estimates of influenza-like illness activity. However, such systems differ in designs between countries and contexts, making it necessary to identify their characteristics to better understand how they fit traditional surveillance systems. Consequently, we aimed to investigate the performance of participatory surveillance systems for ILI worldwide.
Methods
We systematically searched four databases for relevant articles on influenza participatory surveillance systems for ILI. We extracted data from the included, eligible studies and assessed their quality using the Joanna Briggs Critical Appraisal Tools. We then synthesised the findings using narrative synthesis.
Results
We included 39 out of 3797 retrieved articles for analysis. We identified 26 participatory surveillance systems, most of which sought to capture the burden and trends of influenza-like illness and acute respiratory infections among cohorts with risk factors for influenza-like illness. Of all the surveillance system attributes assessed, 52% reported on correlation with other surveillance systems, 27% on representativeness, and 21% on acceptability. Among studies that reported these attributes, all systems were rated highly in terms of simplicity, flexibility, sensitivity, utility, and timeliness. Most systems (87.5%) were also well accepted by users, though participation rates varied widely. However, despite their potential for greater reach and accessibility, most systems (90%) fared poorly in terms of representativeness of the population. Stability was a concern for some systems (60%), as was completeness (50%).
Conclusions
The analysis of participatory surveillance system attributes showed their potential in providing timely and reliable influenza data, especially in combination with traditional hospital- and laboratory led-surveillance systems. Further research is needed to design future systems with greater uptake and utility.
... These have been designed and tested in different countries, with varying success. Some programs have seen success in community engagement for vector reporting via apps or internet-based systems [16][17][18][19][20][21]. Several programs have also described the importance of community engagement in the vector-control process-a process that creates and builds relationships and allows for information exchange between public health professionals and members of the community [19,22,23]. ...
... Some programs have seen success in community engagement for vector reporting via apps or internet-based systems [16][17][18][19][20][21]. Several programs have also described the importance of community engagement in the vector-control process-a process that creates and builds relationships and allows for information exchange between public health professionals and members of the community [19,22,23]. This bilateral engagement aids in the collection of data and identification of vector foci by promoting increased education and awareness for community members [18,23]. ...
... Moreover, AlertaChirimacha was designed using fundamental principles of community health education and promotion, such as community engagement and prioritizing community concerns (by responding to and providing information about all insect pictures that were worrisome to them) [30]. Other internet based vector reporting programs have been implemented for ticks, mosquitos, and triatomine insects [16][17][18][19]21,31], and, in contrast to AlertaChirimacha, many have faced challenges for sustained community use or data integration [21]. ...
Vector-borne diseases remain a significant public health threat in many regions of the world. Traditional vector surveillance and control methods have relied on active and passive surveillance programs, which are often costly and time-consuming. New internet-based vector surveillance systems have shown promise in removing some of the cost and labor burden from health authorities. We developed and evaluated the effectiveness of a new internet-based surveillance system, “AlertaChirimacha”, for detecting Triatoma infestans (known locally by its Quechua name, Chirimacha ), the Chagas disease vector, in the city of Arequipa, Peru. In the first 26 months post-implementation, AlertaChirimacha received 206 reports of residents suspecting or fearing triatomines in their homes or neighborhoods, of which we confirmed, through pictures or inspections, 11 (5.3%) to be Triatoma infestans . After microscopic examination, none of the specimens collected were infected with Trypanosoma cruzi . AlertaChirimacha received 57% more confirmed reports than the traditional surveillance system and detected 10% more infested houses than active and passive surveillance approaches combined. Through in-depth interviews we evaluate the reach, bilateral engagement, and response promptness and efficiency of AlertaChirimacha. Our study highlights the potential of internet-based vector surveillance systems, such as AlertaChirimacha, to improve vector surveillance and control efforts in resource-limited settings. This approach could decrease the cost and time horizon for the elimination of vector-mediated Chagas disease in the region.
... One such option was participatory surveillance; when a population at risk reports on their health via technology that is independent of the health care system [7]. This active participation of community members is a promising method to supplement existing surveillance systems in the context of a pandemic and has been used in other contexts of disease outbreaks to provide real-time, lowcost data on disease spread [3,[8][9][10][11][12]. ...
Background
While laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data.
Methods
A nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman’s and Pearson’s correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data.
Results
Over course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 – 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 – 54.4) and females (45.1, 95% CI: 39.8 – 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 – 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman’s correlation coefficient = 0.49; P < 0.001) (Pearson’s correlation coefficient = 0.67; P < 0.0001).
Conclusions
The ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.
... Common platforms underlying participatory surveillance systems include social media sites, the Web, smartphone apps, and connected devices 2,10 . One of the most crucial challenges is recruitment and retention of a large cohort of participants reflecting the population of interest 3,11,12 . ...
Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6–2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4–2.5). Compared with participants aged 50–65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3–0.3). Participants in a household with both adults and children (OR = 1.6 [1.6–1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.
... KEYWORDS participatory surveillance; digital epidemiology; influenza-like illness; data transfer; surveillance; digital platform; Global Flu View; program; data sharing; public health; innovative; flu Overview Digital epidemiology has expanded significantly in recent years [1,2], encompassing not only passive digital disease detection [3], but also active crowdsourcing programs known as participatory surveillance (PS) [4][5][6][7]. PS has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population [8]. ...
Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges Influenza-like Illness (ILI) data from more than eight countries plus one region (Hong Kong) on four continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real-time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an Application Program Interface (API), providing a real-time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After two years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.
... Symptom-based participatory surveillance -such as Outbreaks Near Me (44), FluTracking (45) and InfluenzaNet (46) -in which cohorts of individuals regularly report their symptoms, is a long-established approach to monitoring seasonal influenza (47). During the COVID-19 pandemic, those systems were rapidly adapted (or established) to contribute to SARS-CoV-2 surveillance (48,49). ...
To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.
