How Scientists Look for New Pathogens Before They Spread

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 3 | 01 | Page :
    By

    V. Basil Hans,

  1. Research Professor, Department of Commerce and Management, Srinivas University, Mangalore., India

Abstract

Emerging infectious illnesses are a persistent danger to global health. They often come from unexpected places, such wildlife reservoirs, changes in the climate, or human activities. Finding new diseases before they create epidemics is one of the biggest problems in modern epidemiology. This article talks about how scientists use genetic monitoring, field sampling, and real-time data processing to find, track, and describe new infectious organisms in a way that works together. Researchers can now find viral and bacterial signatures in environmental and clinical samples long before symptoms show up in the general population. This is thanks to improvements in metagenomic sequencing, artificial intelligence, and global health networks. Case studies, ranging from the initial identification of SARS-CoV-2 variations to pathogen surveillance in isolated habitats, demonstrate how proactive monitoring can avert epidemics and inform public health strategies. In the end, early pathogen identification is not only a scientific goal, but it is also a key part of being ready for a pandemic in a world that is connected.

Keywords: Pathogen monitoring, New infectious illnesses, Metagenomic sequencing, Epidemiology, readiness for a pandemic

How to cite this article:
V. Basil Hans. How Scientists Look for New Pathogens Before They Spread. International Journal of Pathogens. 2026; 03(01):-.
How to cite this URL:
V. Basil Hans. How Scientists Look for New Pathogens Before They Spread. International Journal of Pathogens. 2026; 03(01):-. Available from: https://journals.stmjournals.com/ijpg/article=2026/view=237791


References

1. Guo C, Wu JY. Pathogen discovery in the post-COVID era. 2024. Available from: https://www.ncbi.nlm.nih.gov. 2. Mastin AJ, van den Bosch F, van den Berg F, Parnell SR. Measuring the hidden costs of inaccurate detection for early detection surveillance. Philos Trans R Soc B Biol Sci. 2019 Jul 8;374(1776):20180261. doi:10.1098/rstb.2018.0261. PMID:31104597; PMCID:PMC6558562. 3. Koopmans M. Surveillance strategy for early detection of unusual infectious disease events. 2013. Available from: https://www.ncbi.nlm.nih.gov. 4. Chiu KH, Sridhar S, Yuen KY. Getting ready for the next pandemic: problems with strengthening surveillance systems. 2023. Available from: https://www.ncbi.nlm.nih.gov. 5. Downie DL, Rao P, David-Ferdon C, Courtney S, et al. Surveillance for emerging and reemerging pathogens using pathogen-agnostic metagenomic sequencing in the United States: a critical role for federal government agencies. 2024. Available from: https://www.ncbi.nlm.nih.gov. 6. Ferguson JM, Langebrake JB, Cannataro VL, Garcia AJ, et al. Optimal sampling strategies for detecting zoonotic disease epidemics. 2014. Available from: https://www.ncbi.nlm.nih.gov. 7. Guberti V, Stancampiano L, Ferrari N. Surveillance, monitoring, and assessment of wildlife diseases: a public health and conservation perspective. Hystrix Ital J Mammal. 2014;25(1). doi:10.4404/hystrix-25.1-10114. 8. Vourc’h G, Bridges VE, Gibbens J, De Groot BD, et al. Detecting emerging diseases in farm animals through clinical observations. 2006. Available from: https://www.ncbi.nlm.nih.gov. 9. Childs JE. Pre-spillover prevention of emerging zoonotic diseases: targets and available tools. 2007. Available from: https://www.ncbi.nlm.nih.gov. 10. Elbe S, Buckland-Merrett G. Data, disease, and diplomacy: GISAID’s innovative contribution to global health. 2017. Available from: https://www.ncbi.nlm.nih.gov. 11. Morse SS. Global surveillance of infectious diseases and health intelligence. Health Aff (Millwood). 2007 Jul-Aug;26(4):1069–1077. doi:10.1377/hlthaff.26.4.1069. PMID:17630449. 12. Grubaugh ND, Ladner JT, Lemey P, Pybus OG, et al. Tracking virus outbreaks in the twenty-first century. 2018. Available from: https://www.ncbi.nlm.nih.gov. 13. Siettos CI, Russo L. Mathematical modelling of infectious disease dynamics. 2013. Available from: https://www.ncbi.nlm.nih.gov. 14. Kraemer MUG, Cummings DAT, Funk S, Reiner RC, Faria NR, Pybus OG, Cauchemez S. Reconstruction and prediction of viral disease epidemics. Epidemiol Infect. 2018 Nov 5;147:e34. doi:10.1017/S0950268818002881. PMID:30394230; PMCID:PMC6398585. 15. Marx V. The coronavirus pushes laboratories to accelerate research efforts. 2020. Available from: https://www.ncbi.nlm.nih.gov. 16. Petrone ME, Holmes EC, Harvey E. Through an ecological lens: an ecosystem-based approach to zoonotic risk assessment. 2023. Available from: https://www.ncbi.nlm.nih.gov. 17. Cassetti MC, Pierson TC, Patterson LJ, Bok K, et al. Prototype pathogen approach for vaccine and monoclonal antibody development: a critical component of the NIAID plan for pandemic preparedness. 2022. Available from: https://www.ncbi.nlm.nih.gov. 18. Keuleyan E. Liberty to decide on dual-use biomedical research: an acknowledged necessity. 2010. Available from: https://www.ncbi.nlm.nih.gov. 19. Switzer S, Morgera E, Tsioumani E, Burci GL. Biodiversity, pathogen sharing, and international law. 2019. Available from: https://www.academia.edu.

Ahead of Print Subscription Review Article
Volume 03
01
Received 10/11/2025
Accepted 11/12/2025
Published 25/01/2026
Publication Time 76 Days
70

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