Integrated Surveillance of Pathogens with Pandemic and/or Epidemic Potential

A special issue of Pathogens (ISSN 2076-0817).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1536

Special Issue Editors


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Guest Editor
Virology Unit, Institut Pasteur de Madagascar, Antananarivo 101, Madagascar
Interests: influenza; arbovirus; zoonoses; virology; public health; surveillance; epidemiology
Institut Pasteur de Dakar, Dakar, Senegal
Interests: arbovirus; zoonoses; virology; public health; surveillance

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Guest Editor
Epidemiology Unit, Institut Pasteur de Madagascar, Antananarivo 101, Madagascar
Interests: epidemiology; mortality; public health

Special Issue Information

Dear Colleagues,

In an increasingly interconnected world, the early detection and control of pandemic- and epidemic-prone pathogens require surveillance systems that extend beyond single-pathogen or siloed sectoral approaches. The recent COVID-19 pandemic has underscored the urgent need for countries to develop agile and flexible surveillance systems that can rapidly adapt to evolving public health threats and needs.

Integrated surveillance—combining data from multiple pathogens and sources (human, animal, and environmental health) in line with the One Health approach—enables a more comprehensive understanding of transmission dynamics, shared risk factors, and early signals of outbreaks. This facilitates faster, more coordinated, and more effective public health responses to mitigate the impact of emerging threats.

The development of new technologies, including high-throughput diagnostic platforms, rapid testing, genomic surveillance, machine learning, and artificial intelligence, offers unprecedented opportunities to strengthen surveillance systems and enable proactive, timely public health interventions.

A central theme of this Special Issue is the alignment of surveillance efforts with country-level priorities, ensuring that systems are context-sensitive, fit for purpose, and sustainable. In an era of constrained financial resources, integrated surveillance systems may offer a cost-beneficial alternative to fragmented, disease-specific programs. By leveraging shared infrastructure, laboratory networks, digital tools, and workforce capacities, integrated approaches not only enhance outbreak preparedness but also support more effective monitoring of both endemic and emerging health threats.

This Special Issue welcomes submissions of original research articles, reviews, case studies, and policy analyses—including, but not limited to, novel methods, practical tools, results from integrated pathogen surveillance, innovative models, lessons learned, and real-world applications of multi-pathogen surveillance systems.

Dr. Jean Michel Héraud
Dr. Gamou Fall
Dr. Rila Ratovoson
Guest Editors

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Keywords

  • integrated surveillance
  • disease surveillance
  • multi-pathogen surveillance
  • one health
  • emerging infectious diseases
  • epidemiological monitoring
  • public health
  • cost–benefit analysis
  • health systems
  • global health
  • data collection
  • disease prevention and control

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Published Papers (2 papers)

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Research

26 pages, 1957 KB  
Article
Integrated Deep Learning Surveillance of Unknown Pathogens with Pandemic Potential Using Pneumonia of Unknown Etiology
by Xiao Yang, Hui Ma, Min Zhu, Xinyu Song and Jiahao Feng
Pathogens 2026, 15(4), 413; https://doi.org/10.3390/pathogens15040413 - 10 Apr 2026
Viewed by 356
Abstract
Background: Pneumonia of unknown etiology (PUE), defined as pneumonia cases without an identified pathogen at the time of clinical presentation, represents a critical clinical warning signal for emerging infectious disease (EID) outbreaks with pandemic potential. Yet, conventional pathogen-centric surveillance systems suffer from an [...] Read more.
Background: Pneumonia of unknown etiology (PUE), defined as pneumonia cases without an identified pathogen at the time of clinical presentation, represents a critical clinical warning signal for emerging infectious disease (EID) outbreaks with pandemic potential. Yet, conventional pathogen-centric surveillance systems suffer from an inherent blind spot: they cannot detect early clustering signals before the causative agent is identified, creating a window of vulnerability during novel pathogen emergence. To address this gap, this study aims to develop a deep learning model that leverages unstructured chest imaging text—a routinely available clinical data stream—to enable real-time, automated screening of PUE cases and early warning of EID clusters, independent of prior pathogen knowledge, within an integrated multi-pathogen surveillance framework. Methods: We retrospectively collected data from 8860 patients with respiratory illnesses at a tertiary hospital in Beijing, China, including 980 PUE cases (11.1%) and 7880 known-etiology pneumonia cases. A deep learning model (RoBERTa with attention enhancement) was developed using unstructured chest imaging reports. The Matthews correlation coefficient (MCC) curve was employed to determine the optimal decision threshold. Model performance was assessed for PUE case identification and clustering signal detection on a test set. Results: The model achieved an area under the receiver operating characteristic curve of 0.986 (95% CI: 0.981–0.991). At the optimal threshold of 0.08, selected by maximizing the Matthews correlation coefficient (MCC)—a balanced metric that accounts for all four confusion matrix outcomes—sensitivity was 89.8%, and specificity was 97.0% for identifying PUE cases. In a simulated surveillance exercise, the model showed a high correlation between the predicted and actual case counts (Pearson’s r = 0.901), suggesting its potential to detect abnormal clustering signals prior to pathogen identification. Conclusions: The developed model demonstrates potential to detect clustering signals of PUE caused by unknown pathogens and can be integrated with hospital information systems, providing a feasible, low-cost tool for integrated surveillance of pathogens with pandemic potential. This approach enables earlier outbreak detection and supports public health decision-making during the critical window before pathogen identification. Full article
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14 pages, 2909 KB  
Article
Development of a Rapid and Sensitive AlphaLISA-Based Assay for Lassa Virus Glycoprotein Detection
by Hao Cai, Qingyu Lv, Wenhua Huang, Shaolong Chen, Peng Liu, Hua Jiang, Qian Li, Decong Kong, Yuhao Ren, Zhongpeng Zhao, Chengsong Wan and Yongqiang Jiang
Pathogens 2026, 15(3), 243; https://doi.org/10.3390/pathogens15030243 - 25 Feb 2026
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Abstract
Lassa virus (LASV), a member of the Arenaviridae family, is the causative agent of Lassa fever (LF), an acute zoonotic hemorrhagic disease transmitted by rodents, characterized by high infectivity and mortality rates. Due to the nonspecific nature of early clinical symptoms, the development [...] Read more.
Lassa virus (LASV), a member of the Arenaviridae family, is the causative agent of Lassa fever (LF), an acute zoonotic hemorrhagic disease transmitted by rodents, characterized by high infectivity and mortality rates. Due to the nonspecific nature of early clinical symptoms, the development of rapid, sensitive, and specific diagnostic methods is critical for effective epidemic control. In this study, the Lassa virus glycoprotein complex (LASV-G) was selected as the target antigen. High-affinity rabbit monoclonal antibodies were generated using a single B-cell cloning approach, and an AlphaLISA (Amplified Luminescent Proximity Homogeneous Assay)-based homogeneous, no-wash detection system was established. Sixteen LASV-G-specific monoclonal antibodies were isolated through flow cytometric sorting, and the optimal antibody pair (56–24) was identified by AlphaLISA pairing and performance screening. The established AlphaLISA system exhibited a limit of detection (LOD) of 0.025 ng/mL, representing approximately a 30-fold increase in sensitivity compared with conventional Enzyme Linked Immunosorbent Assay (ELISA), while reducing the total assay time to less than 30 min. The coefficient of variation (CV) was below 8%, and no cross-reactivity was observed with Ebola, dengue, yellow fever, Zika, or influenza virus antigens. These findings demonstrate that the developed AlphaLISA assay possesses high sensitivity, rapid detection, and good tolerance to matrix effects, significantly improving the efficiency of early LASV antigen detection. This work provides a potential platform for the rapid on-site screening and epidemiological surveillance of highly pathogenic viruses. Full article
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