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

Exploring influenza vaccination determinants through digital participatory surveillance

BACKGROUND

Vaccination is key for mitigating the impact of recurring seasonal influenza epidemics. Despite the efficacy and safety of influenza vaccines, achieving optimal vaccination uptake remains a challenge. This study aimed to explore the determinants of influenza vaccination uptake using data from Influweb, the Italian node of the Influenzanet participatory surveillance network.

METHODS

This study utilizes a longitudinal dataset of self-reported vaccination statuses from Italian participants across the 2011-2021 flu seasons. Logistic regression models were used to identify factors associated with vaccination uptake. Post-stratification weights were applied to account for demographic differences between the Influweb sample and the general population.

RESULTS

The analysis reveals that individuals using public transport and those living with minors are less likely to receive the influenza vaccination. On the other hand, university-educated individuals, and those on medication for chronic diseases are more likely to be vaccinated. Age also plays a role: individuals aged 44 and under are less likely to vaccinate compared to those aged 45-65, while those over 65 are more likely to do so. Furthermore, higher cumulative influenza-like illness incidence rates within a macro-region are associated with increased vaccination uptake. Finally, the impact of COVID-19 pandemic was associated with an increase in influenza vaccination uptake. Comparison of the Influweb data to vaccination rates reported by the Italian Health Ministry revealed higher coverage for self-reported vaccination. This could be linked to the voluntary nature of the survey, possibly attracting a more health-conscious cohort.

CONCLUSIONS

Our study found that individuals living with minors and those relying on public transportation have lower odds of being vaccinated, despite having a higher documented risk of respiratory virus exposure. These findings highlight the importance of continued public health efforts targeting vulnerable groups and raising awareness about the risks of forgoing vaccination. The complex interplay of socioeconomic, demographic, and public health context significantly shapes vaccination decisions, emphasizing the need for tailored public health campaigns.

Data Parameters From Participatory Surveillance Systems in Human, Animal, and Environmental Health From Around the Globe: Descriptive Analysis

BACKGROUND

Emerging pathogens and zoonotic spillover highlight the need for One Health surveillance to detect outbreaks as early as possible. Participatory surveillance empowers communities to collect data at the source on the health of animals, people, and the environment. Technological advances increase the use and scope of these systems. This initiative sought to collate information from active participatory surveillance systems to better understand parameters collected across the One Health spectrum.

OBJECTIVE

This study aims to develop a compendium of One Health data parameters by examining participatory surveillance systems active in 2023. The expected outcomes of the compendium were to pinpoint specific parameters related to human, animal, and environmental health collected globally by participatory surveillance systems and to detail how each parameter is collected. The compendium was designed to help understand which parameters are currently collected and serve as a reference for future systems and for data standardization initiatives.

METHODS

Contacts associated with the 60 systems identified through the One Health Participatory Surveillance System Map were invited by email to provide specific data parameters, methodologies used for data collection, and parameter-specific considerations. Information was received from 38 (63%) active systems. Data were compiled into a searchable spreadsheet-based compendium organized into 5 sections: general, livestock, wildlife, environmental, and human parameters. An advisory group comprising experts in One Health participatory surveillance reviewed the collected parameters, refined the compendium structure, and contributed to the descriptive analysis.

RESULTS

A comprehensive compendium of data parameters from a diverse array of single-sector and multisector participatory surveillance systems was collated and reviewed. The compendium includes parameters from 38 systems used in Africa (n=3, 8%), Asia (n=9, 24%), Europe (n=12, 32%), Australia (n=3, 8%), and the Americas (n=12, 32%). Almost one-third of the systems (n=11, 29%) collect data across multiple sectors. Many (n=17, 45%) focus solely on human health. Variations in data collection techniques were observed for commonly used parameters, such as demographics and clinical signs or symptoms. Most human health systems collected parameters from a cohort of users tracking their own health over time, whereas many wildlife and environmental systems incorporated event-based parameters.

CONCLUSIONS

Several participatory surveillance systems have already adopted a One Health approach, enhancing traditional surveillance by identifying shared health threats among animals, people, and the environment. The compendium reveals substantial variation in how parameters are collected, underscoring the need for further work in system interoperability and data standards to allow for timely data sharing across systems during outbreaks. Parameters collated from across the One Health spectrum represent a valuable resource for informing the development of future systems and identifying opportunities to expand existing systems for multisector surveillance.

Monitoring Influenza A (H1N1, H3N2), RSV, and SARS-CoV-2 Using Wastewater-Based Epidemiology: A 2-Year Longitudinal Study in an Indian Megacity Covering Omicron and Post-Omicron Phases

The bourgeoning field of wastewater-based epidemiology (WBE) for the surveillance of several respiratory viruses which includes Influenza A, H1N1pdm09, H3N2, respiratory syncytial viruses (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of interest for public health concerns. However, there are few long-term monitoring studies globally. In this study, respiratory viruses were detected and quantified from 11 sewer sheds by utilizing reverse transcription-quantitative polymerase chain reaction analysis in Pune city, India, from Jan 2022 to Dec 2023. The RNA fragments of respiratory viruses were detected in sewage samples before clinical cases were reported, underscoring the potential of WBE for early detection and monitoring within the population. The Spearman correlation of wastewater viral copies was positively and significantly correlated with the clinically positive case of H1N1pdm09 (ρ = 0.55, p = 1.4 × 10-9), H3N2 (ρ = 0.25, p = 9.9 × 10-3), and SARS-CoV-2 (ρ = 0.43, p = 4.1 × 10-6). The impact of public health interventions on the circulation of infectious respiratory diseases showed a significant difference in the viral load during the period when many preventing measures were carried out against the COVID-19 pandemic (restriction phase), compared to the period when no such preventive measures are followed (no-restriction phase) for Influenza A, H1N1pdm09, H3N2, and RSV with p-value < 0.05, which indicates the influence of health policy implementation in controlling disease spread. The present study provides an effective approach to detecting multiple respiratory viruses from wastewater and provides insights into the epidemiology of respiratory illnesses. The WBE aids in providing information on the spread of pathogens (viruses) in the community, offering a proactive strategy for public health management, allowing for timely interventions and implementing targeted measures to mitigate the spread of these viruses under one health approach.

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance

BACKGROUND

European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens.

METHODS

We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software.

RESULTS

Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics.

CONCLUSIONS

The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Background

Zoonotic diseases originating in animals pose a significant threat to global public health. Recent outbreaks, such as coronavirus disease 2019 (COVID-19), have caused widespread illness, death, and socioeconomic disruptions worldwide. To cope with these diseases effectively, it is crucial to strengthen surveillance capabilities and establish rapid response systems.

Aim

The aim of this review to examine the modern technologies and solutions that have the potential to enhance zoonotic disease surveillance and outbreak responses and provide valuable insights into how cutting-edge innovations could be leveraged to prevent, detect, and control emerging zoonotic disease outbreaks. Herein, we discuss advanced tools including big data analytics, artificial intelligence, the Internet of Things, geographic information systems, remote sensing, molecular diagnostics, point-of-care testing, telemedicine, digital contact tracing, and early warning systems.

Results

These technologies enable real-time monitoring, the prediction of outbreak risks, early anomaly detection, rapid diagnosis, and targeted interventions during outbreaks. When integrated through collaborative partnerships, these strategies can significantly improve the speed and effectiveness of zoonotic disease control. However, several challenges persist, particularly in resource-limited settings, such as infrastructure limitations, costs, data integration and training requirements, and ethical implementation.

Conclusion

With strategic planning and coordinated efforts, modern technologies and solutions offer immense potential to bolster surveillance and outbreak responses, and serve as a critical resource against emerging zoonotic disease threats worldwide.