Effectiveness of a Mobile-Based Influenza-Like Illness Surveillance System (FluMob) Among Health Care Workers: Longitudinal Study

What are the characteristics of participatory surveillance systems for influenza-like-illness?

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.

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View

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 8 countries plus 1 region (Hong Kong) on 4 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, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 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.

Serial Passaging of Seasonal H3N2 Influenza A/Singapore/G2-31.1/2014 Virus in MDCK-SIAT1 Cells and Primary Chick Embryo Cells Generates HA D457G Mutation and Other Variants in HA, NA, PB1, PB1-F2, and NS1

Influenza remains one of the most prevalent viruses circulating amongst humans and has resulted in several pandemics. The prevention and control of H3N2 influenza is complicated by its propensity for evolution, which leads to vaccine mismatch and reduced vaccine efficacies. This study employed the strategy of serial passaging to compare the evolution of the human seasonal influenza strain A/Singapore/G2-31.1/2014(H3N2) in MDCK-SIAT1 versus primary chick embryo fibroblast (CEF) cells. Genetic analysis of the HA, NS1, NA, and PB1 gene segments by Sanger sequencing revealed the presence of specific mutations and a repertoire of viral quasispecies following serial passaging. Most quasispecies were also found in PB1, which exhibited consistently high transversion-to-transition ratios in all five MDCK-SIAT1 passages. Most notably, passage 5 virus harbored the D457G substitution in the HA2 subunit, while passage 3 virus acquired K53Q and Q69H mutations in PB1-F2. An A971 variant leading to a non-synonymous R316Q substitution in PB1 was also identified in MDCK-SIAT1 passages 2 and 4. With an increasing number of passages, the proportion of D457G mutations progressively increased and was associated with larger virus plaque sizes. However, microneutralization assays revealed no significant differences in the neutralizing antibody profiles of human-influenza-immune serum samples against pre-passaged virus and passage 5 virus. In contrast, viable virus was only detected in passage 1 of CEF cells, which gave rise to multiple viral RNA quasispecies. Our findings highlight that serial passaging is able to drive differential adaptation of H3N2 influenza in different host species and may alter viral virulence. More studies are warranted to elucidate the complex relationships between H3N2 virus evolution, viral virulence changes, and low vaccine efficacy.

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

OBJECTIVE

While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high-quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza-to identify reliable complementary influenza surveillance systems which provide real-time estimates of influenza activity-we performed a scoping review to map the extent and nature of published literature on the use of non-traditional sources of syndromic surveillance data for influenza.

METHODS

We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non-traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness.

FINDINGS

We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non-traditional surveillance method.

CONCLUSION

We identified eight categories and a miscellaneous group of non-traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non-traditional surveillance systems as complements rather than replacements to traditional systems.