A human sentinel surveillance framework for comprehensive exposure assessment in occupational and environmental health

Existing influenza surveillance system is constantly improved to obtain comprehensive information for understanding of continuously changing situation with the influenza, which is a consequence of the highest variability of the pathogen, its ability to reassortment and the imminence of emergence a new shift-variants of the virus that could cause the next pandemic events. For this purpose, since the 2010 - 2011 epidemic season, in addition to the traditional surveillance system (TS) a new well standardized sentinel surveillance system (SS) for rapid clinical and epidemiological data obtaining was introduced in Russia. A total 7812 hospitalized patients with severe acute respiratory infection (SARI) and 9854 outpatients with influenza-like illness and acute respiratory infection (ILI/ARI) were investigated during the 6-year period in SS. Percent of SARI among all hospitalized patients ranged from 1.7 to 3.1%; about 5.3 - 7.5% SARI patients were placed in the Intensive Care Unit. Etiological monitoring using PCR showed influenza spread trends in SS similar to those registered in the TS: a clear predominance of influenza A (H1N1) pdm09 among SARI and ILI/ARI in 2010 - 2011 and 2015 - 2016 epidemic seasons, influenza A (H3N2) in the epidemic seasons 2011 - 2012 and 2014 - 2015, the co-circulation of these pathogens in 2012 - 2013, 2013 - 2014 seasons in Russia. SARI caused by influenza B virus were detected less frequently than influenza A but increased influenza B activity was registered in the epidemic of 2014 -2015, when Yamagata lineage changed suddenly for the Victorian one. The average frequency of influenza diagnosis among SARI between the seasons varied in the range 12.5 - 27.1%, at the peak of the epidemic it reached 44.8 - 73.5% and was the highest during the season with active circulation of influenza A (H1N1) pdm09 virus. The rate of influenza diagnosis among ILI/ARI has always been lower than that among SARI. Studies have also shown the importance of rhinovirus, RS-virus and parainfluenza infections in SARI development. The frequency of registration of coronaviruses, metapneumovirus and bocavirus infection was very low in SARI and ILI/ARI. It was found that in all studied seasons most of SARI patients with influenza have not been vaccinated. Among ILI/ARI outpatients with influenza, the frequency of vaccinated individuals for the entire period of the study was estimated as 10.1%, which was 4.2 times higher than that in SARI, where only 2.4% of patients were vaccinated. In addition, it was found that for all six seasons the SARI patients with influenza were treated with antivirals drugs 2 times less often compared to outpatients. Analysis of data on concomitant diseases and conditions in SARI patients with influenza confirmed the leading role of pregnancy as a risk factor for hospitalization in all influenza epidemics, irrespective of their etiology. In addition, diabetes and cardiovascular disease were recognized as risk factors for influenza associated SARI development.

Extracellular vesicles (EVs) represent small, membrane-enclosed particles that are derived from parent cells and are secreted into the extracellular space. Once secreted, EVs can then travel and communicate with nearby or distant cells. Due to their inherent stability and biocompatibility, these particles can effectively transfer RNAs, proteins, and chemicals/metabolites from parent cells to target cells, impacting cellular and pathological processes. EVs have been shown to respond to disease-causing agents and impact target cells. Given that disease-causing agents span environmental contaminants, pathogens, social stressors, drugs, and other agents, the translation of EV methods into public health is now a critical research gap. This paper reviews approaches to translate EVs into exposure science, toxicology, and public health applications, highlighting blood as an example due to its common use within clinical, epidemiological, and toxicological studies. Approaches are reviewed surrounding the isolation and characterization of EVs and molecular markers that can be used to inform EV cell-of-origin. Molecular cargo contained within EVs are then discussed, including an original analysis of blood EV data from Vesiclepedia. Methods to evaluate functional consequences and target tissues of EVs are also reviewed. Lastly, the expanded integration of these approaches into future public health applications is discussed, including the use of EVs as promising biomarkers of exposure, effect, and disease.

Human Nipah virus (NiV) infection is an epidemic-prone disease and since the first recognized outbreak in Bangladesh in 2001, human infections have been detected almost every year. Due to its high case fatality rate and public health importance, a hospital-based Nipah sentinel surveillance was established in Bangladesh to promptly detect Nipah cases and respond to outbreaks at the earliest. The surveillance has been ongoing till present. The hospital-based sentinel surveillance was conducted at ten strategically chosen tertiary care hospitals distributed throughout Bangladesh. The surveillance staff ensured that routine screening, enrollment, data, and specimen collection from suspected Nipah cases were conducted daily. The specimens were then processed and transported to the reference laboratory of Institute of Epidemiology, Disease Control and Research (IEDCR) and icddr,b for confirmation of diagnosis through serology and molecular detection. From 2006 to 2021, through this hospital-based surveillance platform, 7,150 individuals were enrolled and tested for Nipah virus. Since 2001, 322 Nipah infections were identified in Bangladesh, 75% of whom were laboratory confirmed cases. Half of the reported cases were primary cases (162/322) having an established history of consuming raw date palm sap (DPS) or tari (fermented date palm sap) and 29% were infected through person-to-person transmission. Since the initiation of surveillance, 68% (218/322) of Nipah cases from Bangladesh have been identified from various parts of the country. Fever, vomiting, headache, fatigue, and increased salivation were the most common symptoms among enrolled Nipah patients. Till 2021, the overall case fatality rate of NiV infection in Bangladesh was 71%. This article emphasizes that the overall epidemiology of Nipah virus infection in Bangladesh has remained consistent throughout the years. This is the only systematic surveillance to detect human NiV infection globally. The findings from this surveillance have contributed to early detection of NiV cases in hospital settings, understanding of Nipah disease epidemiology, and have enabled timely public health interventions for prevention and containment of NiV infection. Although we still have much to learn regarding the transmission dynamics and risk factors of human NiV infection, surveillance has played a significant role in advancing our knowledge in this regard.

This commentary presents a novel outlook for public health authorities in the affected countries to detect and respond quickly to the emerging public health threats such as Middle East respiratory syndrome coronavirus (MERS-CoV). Implementing an innovative electronic surveillance system called syndromic surveillance system is essential for global health security.

Identification of COVID-19 Outbreak Signals Prior to the Traditional Disease Surveillance System

Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels—selected according to their risk—need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches.

Shortcomings have been inherited in the traditional influenza early warning system, often expressed through the scope, accuracy on prediction and real-time performance of the monitor related programs. With the new round of scientific and technological revolution and the increasingly maturity of modern information system, related technology on influenza early warning has become the focus of research in this field, based on big data analysis technology. Using the traditional influenza surveillance and early warning system as reference, this paper summarizes the progress of influenza early warning research, based on the Internet, influencing factors, time and space trends, and risk assessment etc., to summarize the trends on the advantages, shortcomings and future development of big data, used in the early warning system on influenza.

The challenge for microbial measurements in buildings.

Investigating a single environmental exposure in isolation does not reflect the actual human exposure circumstance nor does it capture the multifactorial etiology of health and disease. The exposome, defined as the totality of environmental exposures from conception onward, may advance our understanding of environmental contributors to disease by more fully assessing the multitude of human exposures across the life course. Implementation into studies of human health has been limited, in part owing to theoretical and practical challenges including a lack of infrastructure to support comprehensive exposure assessment, difficulty in differentiating physiologic variation from environmentally induced changes, and the need for study designs and analytic methods that accommodate specific aspects of the exposome, such as high-dimensional exposure data and multiple windows of susceptibility. Recommendations for greater data sharing and coordination, methods development, and acknowledgment and minimization of multiple types of measurement error are offered to encourage researchers to embark on exposome research to promote the environmental health and well-being of all populations.

4549 Reproducible Informatics for Reproducible Translational Research

In humanitarian emergencies, traditional disease surveillance systems either do not exist to begin with or come under stress due to a huge influx of internal or external migrants. However, cramped camps with an unreliable supply of safe water and weak sanitation systems are the ideal setting for major disease outbreaks of all kinds. The Early Warning, Alert and Response Network (EWARN) has been supported by the WHO since the late 1990s to ensure health system capacity to identify and control risks early before they become major epidemics. These systems have been proven to be an excellent asset in reducing morbidity and mortality in humanitarian crises around the world. However, there is also a global challenge of transitioning them back to a regular or national monitoring system in their respective countries. This article is the result of in-country consultations arranged by the Eastern Mediterranean office of the World Health Organization. In these consultations, the unique local conditions and limitations of different countries were discussed to identify a way forward for transitioning these emergency disease surveillance systems into regular systems. After these discussions, different options were presented which could be further modified according to local needs. As there has not been any documented evidence of a successful transition of any emergency surveillance system, it is difficult to discuss or determine the gold standard for transition. As with any public health program being practiced in the field, local decision-making with some broad guidelines will be the best approach available. This article provides these guidelines and practical steps which could be further modified according to country needs.

BackgroundA dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.MethodsMilitary syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.ResultsIt was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.ConclusionMilitary syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.

BackgroundNovel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.ObjectiveThis study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.MethodsThe ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.ResultsWe designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.ConclusionsThe development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

BackgroundChina managed coronavirus disease 2019 (COVID-19) with measures against Class B infectious diseases, instead of Class A infectious diseases, in a major shift of its epidemic response policies. We aimed to generate robust information on the transmission dynamics of novel coronavirus infection in Shanxi, a province located in northern China, after the implementation of the “Class B infectious disease Class B management” policy.MethodsWe consolidated infection data in Shanxi province from December 6, 2022 to January 14, 2023 through a network questionnaire survey and sentinel surveillance. A dynamics model of the SEIQHCVR was developed to track the infection curves and effective reproduction number ().ResultsOur model was effective in estimating the trends of novel coronavirus infection, with the coefficient of determination () above 90% in infections, inpatients, and critically ill patients. The number of infections in Shanxi province as well as in urban and rural areas peaked on December 20, 2022, with the peak of inpatients and critically ill patients occurring 2 to 3 weeks after the peak of infections. By the end of January 2023, 87.72% of the Shanxi residents were predicted to be infected, and the outbreak subsequently subsided. A small wave of COVID-19 infections may re-emerge at the end of April. In less than a month, the values of positive infections, inpatients and critically ill patients were all below 1.0.ConclusionThe outbreak in Shanxi province is currently at a low prevalence level. In the face of possible future waves of infection, there is a strong need to strengthen surveillance and early warning.

Most underdeveloped countries do not meet core disease outbreak surveillance because of the lack of human resources, laboratory and infrastructural facilities. The use of cell phone technology for disease outbreak syndromic surveillance is a new phenomenon in Sierra Leone despite its successes in other developing countries like Sri Lanka. In this study we set to evaluate the effectiveness of using cell phone technology for Ebola hemorrhagic fever syndromic surveillance in a high risked community in Sierra Leone. This study evaluated the effectiveness of using cell phone messaging (text and calls) for community Ebola hemorrhagic fever syndromic surveillance in high risked community in southern Sierra Leone. All cell phone syndromic surveillance data used for this study was reported as cell phone alert messages-texts and voice calls; by the Moyamba District Health Management Team for both Ebola hemorrhagic fever suspect and mortalities. We conducted a longitudinal data analysis of the monthly cumulative confirmed Ebola hemorrhagic fever cases and mortalities collected by both the traditional sentinel and community cell phone syndromic surveillance from August 2014 to October 2014. A total of 129 and 49 Ebola hemorrhagic fever suspect and confirmed cases respectively were recorded using the community Ebola syndromic surveillance cell phone alert system by the Moyamba District Health Management Team in October 2014. The average number of Ebola hemorrhagic fever suspects and confirmed cases for October 2014 were 4.16 (Std.dev 3.76) and 1.58 (Std.dev 1.43) respectively. Thirty-four percent (n=76) of the community Ebola syndromic surveillance cell phone alerts that were followed-up within 24 hours reported Ebola hemorrhagic fever suspect cases while 65.92% (n=147) reported mortality. Our study suggests some form of underreporting by the traditional sentinel Ebola hemorrhagic fever disease surveillance system in Moyamba District southern Sierra Leone for August-September 2014. Cell phone messaging technology can be effectively use as a tool for community epidemic surveillance from peripheral health care facilities to higher levels.