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

Using the near real-time effective reproduction number Rt as an early-warning tool for seasonal bronchiolitis and influenza-like illness epidemics

Yearly bronchiolitis and influenza-like illness epidemics in France often involve high morbidity and mortality, which severely impact health care. Epidemics are declared by the French National Institute of Public Health based on syndromic surveillance of primary care and emergency departments (EDs), using statistics-based alarms. Although the effective reproduction number (Rt) is used to monitor the dynamics of epidemics, it has never been used as an early-warning tool for bronchiolitis or influenza-like illness epidemics in France. We assessed whether Rt is useful for detecting seasonal epidemics by comparing it to the tool currently used (MASS) by epidemiologists to declare epidemic phases. We used anonymized ED syndromic data from the Île-de-France region in France from 2010 to 2022. We estimated Rt and compared the indication of accelerated transmission (Rt > 1) to the MASS epidemic alarm time points. We computed the difference between those 2 time points, time to epidemic peak, and the daily cases documented at first indication and peak. Rt provided alarms for influenza-like illness and bronchiolitis epidemics that were, respectively, a median of 6 days (IQR, 4, 8) and 64 days (IQR, 52, 80) earlier than the alarms provided by MASS. Rt detected earlier signals of bronchiolitis and influenza-like illness epidemics. Using this early-warning indicator in combination with others to declare an annual epidemic could provide opportunities to improve health care system readiness.

Disparities in Influenza Control and Surveillance in Latin America and the Caribbean

To identify measures that mitigate the impact of influenza in Latin America and the Caribbean, we compared the burden and detection capacity in humans and animals after the 2009 pandemic. The incidence rate in people was higher in Chile (23.72 per 100,000 people), but the impact was greater for Guatemala (503.78 disability-adjusted life years per 100,000 people). Brazil, Peru, Argentina, and Mexico built better medical testing, with typing being less frequent in Chile and Argentina, where costs for medical care were higher. The positivity rate among avian and nonhuman mammals was 5.8%, with more cases in Mexico, but constant testing in Chile. H5N1, H5N2, and H7N6 are deadly to poultry, whereas H1N1 is common in swine, and H3N8 in equines. By June 2023, H5N1 had caused severe influenza in two persons and killed millions of birds and hundreds of mammals with aquatic lifestyles. An analysis of the efforts in response to this outbreak revealed that handling of outbreaks in animals needs homogeneity and reinforcement of vaccination. Surveillance in exposed individuals requires articulation of medical and animal health authorities, and the region also demands decentralized typing and networks for genomic characterization.

Telephone calls to emergency medical service as a tool to predict influenza-like illness: A 10-year study

OBJECTIVES

Influenza-like illness (ILI) refers to the set of symptoms associated with seasonal influenza infection. In Italy, the syndromic surveillance system RespiVirNet uses both epidemiological and virological data to monitor ILI incidence with a weekly cadence. To estimate ILI incidence in real time, several countries adopted surveillance systems which include data from the emergency-urgency (E-U) system. The aim of this study was to evaluate the relationship between the number of calls for respiratory symptoms to the E-U system and the regional incidence of ILI cases identified by the Italian syndromic surveillance system.

STUDY DESIGN

Retrospective observational cohort study METHODS: We analyzed data in the Lombardy region for the flu season from 2014 to 2024, excluding the COVID-19 pandemic period (from 2020 to 2022). We performed a linear regression analysis considering ILI incidence as the dependent variable and the percentage of respiratory calls to the E-U system as the independent variable.

RESULTS

Statistical analysis showed a positive correlation (r = 0.70), with a statistically significant coefficient of 1.34 (p-value <0.001) and R2 of 0.50.

CONCLUSIONS

The observed correlation highlights the potential use of prehospital E-U system data in the surveillance systems of infectious diseases by using real-time data, encouraging future research to explore the limits and possibilities of an integrated surveillance system.

Using Passive Antibody Therapies in the Next Pandemic

The twenty-first century has witnessed seven human viral pandemics. Approximately once every three to four years over the past quarter-century, the world has experienced a new viral epidemic that expanded well beyond its original national borders to become a pandemic. The probability that another pandemic caused by a previously unknown agent will occur in the near future is thus very high and public health agencies must prioritize mechanisms for detecting their first signals. At the onset of these recent pandemics, no specific therapeutic agent was available for any of the newly emergent pathogens. However, convalescent plasma therapy can be available as soon as there are survivors and is likely to be effective if used early and in sufficient strength. But for the three forms of passive antibody-convalescent plasma, monoclonal antibodies, and hyperimmune globulins-to be available and effective in a pandemic situation, careful strategic planning will be necessary. In the pre-pandemic period, we must reinforce the capacities of blood banks and plasma fractionating companies in the production and storage of their products; ensure that outpatient settings can provide intravenous products; educate providers in the proper use of plasma; and create a research infrastructure to examine the effectiveness of passive antibody products. Once a pandemic is underway, regulatory bodies should simplify the approval of research and emergency use protocols and develop treatment registries. Incentives for the rapid production of monoclonal antibodies and hyperimmune globulins will likely be required. A national resource to link providers with passive antibody products and national databases to monitor pandemic progress and pandemic treatment will permit the most effective allocation of pandemic-fighting resources. We cannot afford to wait until the next pandemic is upon us to respond. The time to strengthen clinical, research, and manufacturing infrastructure to permit us to be ready to confront the next new virulent pathogen is now.

Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results

Background

Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey).

Objective

This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables.

Methods

LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering.

Results

The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho.

Conclusions

LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

Rapid response to a COVID-19 outbreak at a nightclub in Kagoshima prefecture, Japan, in the early phase of the COVID-19 pandemic, June and July 2020: A descriptive epidemiological study

INTRODUCTION

During COVID-19 pandemic in Japan, nightclubs were identified as high-risk locations for COVID-19 outbreaks, but an outbreak investigation in this setting is challenging because of the anonymous and opportunistic nature of interactions.

METHODS

The joint rapid response team collected epidemiological data, conducted descriptive epidemiology to determine the characteristics of cases associated with the nightclub, and implemented countermeasures. Polymerase chain reaction (PCR) tests were performed by the Local Institute of Public Health, Kagoshima University, and several commercial laboratories.

RESULTS

Between June 15 and July 20, 2020, 121 individuals tested positive for SARS-CoV-2 (59 confirmed and 62 asymptomatic) of whom 8 were nightclub staff who had no travel history of outside Kagoshima, 66 were guests, and 47 were subsequent contacts. The median age was 32 years (interquartile range: 24-43 years). One individual showed severe symptoms but there were no fatal. The epidemic curve showed one peak on June 30 and July 1 with a limited number of cases subsequently. Of the 121 cases, 116 and 5 were in individuals living in and outside Kagoshima Prefecture, respectively. Haplotype network analysis showed 5 genome-wide single-nucleotide variants between the isolates before and during this outbreak.

CONCLUSIONS

There is a possibility that unidentified guests from outside Kagoshima Prefecture could infect staff who could subsequently spread the virus to guests and other staff, who were mainly a younger population. The rapid outbreak response enabled onward transmission in the community to be minimized. This outbreak investigation could provide insights for effective responses to challenging situations in future pandemic.

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort

BACKGROUND

Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses.

METHODS

We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models.

RESULTS

Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season.

CONCLUSIONS

Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.

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.

B, Wang G, Medina RA, Van Bakel H, García-Sastre A, Nelson MI, Ghedin E, Veiga ABG. Tracking the emergence of antigenic variants in influenza A virus epidemics in Brazil

Influenza A virus (IAV) circulation patterns differ in North America and South America, with influenza seasons often characterized by different subtypes and strains. However, South America is relatively undersampled considering the size of its population. To address this gap, we sequenced the complete genomes of 220 IAVs collected between 2009 and 2016 from hospitalized patients in southern Brazil. New genetic drift variants were introduced into southern Brazil each season from a global gene pool, including four H3N2 clades (3c, 3c2, 3c3, and 3c2a) and five H1N1pdm clades (clades 6, 7, 6b, 6c, and 6b1). In 2016, H1N1pdm viruses belonging to a new 6b1 clade caused a severe influenza epidemic in southern Brazil that arrived early and spread rapidly, peaking mid-autumn. Inhibition assays showed that the A/California/07/2009(H1N1) vaccine strain did not protect well against 6b1 viruses. Phylogenetically, most 6b1 sequences that circulated in southern Brazil belong to a single transmission cluster that rapidly diffused across susceptible populations, leading to the highest levels of influenza hospitalization and mortality seen since the 2009 pandemic. Continuous genomic surveillance is needed to monitor rapidly evolving IAVs for vaccine strain selection and understand their epidemiological impact in understudied regions.