Journal of Bioterrorism & Biodefense
Open Access

biosurveillance; public health; antimicrobial resistance; Artificial Intelligence; One Health; genomic surveillance; food safety; zoonotic diseases; wastewater epidemiology; digital biosurveillance

Introduction

Biosurveillance is a dynamic and evolving field critical for global public health and security. Modern approaches leverage diverse data sources and advanced technologies to detect and monitor a wide array of threats, from infectious diseases to antimicrobial resistance and foodborne pathogens. For instance, digital biosurveillance systems are increasingly utilizing unstructured digital data, such as social media, news, and search queries, to identify and monitor public health threats. These systems hold significant potential for providing early warning signals for emerging infectious diseases and even bioterrorism events, though their optimal implementation necessitates improved data integration, careful ethical considerations, and robust analytical techniques to maximize their effectiveness[1].

Beyond digital realms, environmental biosurveillance plays a crucial role in understanding and mitigating health risks. This includes tracking antimicrobial resistance (AMR) in wildlife populations, where various species act as reservoirs or sentinels for resistant bacteria. Understanding their role in the transmission dynamics of AMR within ecosystems and to humans is paramount, advocating for integrated surveillance strategies to tackle this global threat[2].

Parallelly, wastewater-based epidemiology (WBE) has emerged as a powerful biosurveillance tool. It allows for monitoring infectious disease outbreaks and tracking the prevalence of antimicrobial resistance within communities. WBE’s utility spans early warning systems, disease burden estimation, and guiding public health interventions, particularly vital for emerging pathogens and antibiotic stewardship efforts[3].

The complexity of many health threats, especially zoonotic diseases, demands a holistic perspective. The One Health approach is therefore critical for biosurveillance, emphasizing integrated surveillance across human, animal, and environmental sectors. This framework faces challenges in data sharing, interoperability, and resource allocation, yet it underscores the necessity of advanced diagnostic tools and collaborative frameworks for improving early detection and rapid response to emerging zoonotic threats[4].

Artificial Intelligence (AI) is rapidly becoming a cornerstone in infectious disease biosurveillance. AI applications range from outbreak detection and prediction modeling to sophisticated risk assessment, drawing insights from diverse data sources like social media, news, and electronic health records. While the potential of AI is significant for global health security, it also requires careful attention to ethical considerations and robust validation processes[5].

Genomic biosurveillance represents another transformative area, offering the ability to track pathogen evolution, identify transmission chains, and inform outbreak responses, notably demonstrated during the SARS-CoV-2 pandemic. Future opportunities lie in seamlessly integrating genomic data with traditional epidemiological information to enhance real-time surveillance, improve pathogen detection, and refine public health interventions[6].

In an increasingly interconnected world, event-based biosurveillance (EBS) is indispensable. It focuses on detecting infectious disease threats from unofficial sources such as news media and social media. Despite facing challenges like data overload, misinformation, and validation issues, EBS has vast potential for improving early warning systems through advanced analytics, AI applications, and strengthened international collaboration to bolster global health security[7].

The scope of biosurveillance extends beyond human health to critical areas like food safety and agriculture. Advancements in biosurveillance and rapid detection technologies are profoundly impacting foodborne pathogen detection throughout the entire food supply chain. Emerging techniques, including molecular diagnostics, biosensors, and integrated omics approaches, significantly enhance the ability to quickly identify and trace contaminants, thereby improving both food safety and public health outcomes[8].

Similarly, in agricultural biosecurity, the application of remote sensing and Artificial Intelligence (AI) for plant-pathogen biosurveillance is becoming vital. Satellite imagery, drone-based sensors, and AI algorithms can detect early signs of plant diseases, assess their spread, and support timely interventions, safeguarding crop yields and food security against phytopathogens[9].

Fundamentally, new technologies are continuously reshaping biosurveillance and biodefense strategies against both bioterrorism threats and naturally occurring outbreaks. The integration of advanced diagnostics, real-time data analytics, and global information sharing is paramount for enhancing early detection, ensuring rapid response, and building overall preparedness for biological incidents, highlighting the critical interplay between technological innovation and national security[10].

Description

The field of biosurveillance has expanded significantly, now encompassing a wide array of methods and technologies designed to detect, monitor, and respond to biological threats affecting human, animal, and plant health. One key area involves digital biosurveillance systems, which effectively leverage unstructured digital data from sources such as social media, news outlets, and search queries. These systems are instrumental in identifying and monitoring public health threats, offering potential for early warning signals regarding emerging infectious diseases and bioterrorism. However, their full potential is realized only through improved data integration, careful ethical considerations, and the application of robust analytical techniques [C001]. This digital approach is crucial for staying ahead of rapidly developing situations in a hyper-connected world.

Another critical dimension of biosurveillance involves environmental monitoring. For example, environmental biosurveillance is essential for tracking antimicrobial resistance (AMR) in wildlife populations, synthesizing findings on various species that act as reservoirs or sentinels for resistant bacteria. Understanding their role in AMR transmission within ecosystems and to human populations is vital for developing integrated surveillance strategies to mitigate this global threat [C002]. Complementing this, wastewater-based epidemiology (WBE) has proven to be a powerful tool for community-level biosurveillance. WBE monitors infectious disease outbreaks and tracks the prevalence of antimicrobial resistance, providing early warning, estimating disease burden, and guiding public health interventions, particularly relevant for emerging pathogens and antibiotic stewardship [C003].

The interconnectedness of health across species and environments underpins the One Health approach, which is crucial for addressing zoonotic diseases. This framework necessitates integrated surveillance across human, animal, and environmental sectors, though it faces challenges related to data sharing, interoperability, and resource allocation. Advanced diagnostic tools and collaborative frameworks are seen as essential for enhancing early detection and rapid response to emerging zoonotic threats [C004]. Artificial Intelligence (AI) is transforming infectious disease biosurveillance by enabling sophisticated outbreak detection, prediction modeling, and risk assessment. AI systems draw from diverse data sources, including social media, news, and electronic health records. While AI offers significant potential for global health security, its implementation requires careful ethical considerations and robust validation to ensure accuracy and fairness [C005]. Furthermore, genomic biosurveillance plays a pivotal role in public health by tracking pathogen evolution, identifying transmission chains, and informing outbreak responses, as notably demonstrated with SARS-CoV-2. Integrating genomic data with epidemiological information promises to significantly enhance real-time surveillance and public health interventions [C006].

Event-based biosurveillance (EBS) is particularly relevant in a globally interconnected world, focusing on detecting infectious disease threats from unofficial sources such as news media and social media. Despite confronting challenges like data overload, misinformation, and validation, EBS holds considerable promise for improving early warning systems through advanced analytics, AI, and international collaboration to strengthen global health security [C007]. Beyond direct human health, biosurveillance technologies are making substantial strides in food safety. Advancements in rapid detection technologies are critical for identifying foodborne pathogens across the entire food supply chain. Emerging techniques, including molecular diagnostics, biosensors, and integrated omics approaches, significantly enhance the ability to quickly identify and trace contaminants, thereby improving food safety and public health outcomes [C008]. Similarly, in agricultural contexts, plant-pathogen biosurveillance leverages remote sensing and Artificial Intelligence for agricultural biosecurity. Satellite imagery, drone-based sensors, and AI algorithms are deployed to detect early signs of plant diseases, assess their spread, and support timely interventions, which are crucial for protecting crop yields and ensuring food security against phytopathogens [C009].

Overall, the era of new technologies is fundamentally reshaping biosurveillance and biodefense strategies against both bioterrorism threats and naturally occurring outbreaks. The successful integration of advanced diagnostics, real-time data analytics, and global information sharing is paramount. These combined efforts are essential for enhancing early detection capabilities, ensuring rapid response mechanisms, and building comprehensive preparedness for biological incidents, thereby underscoring the dynamic and critical interplay between technological innovation and national security [C010].

Conclusion

Biosurveillance is rapidly advancing through the integration of cutting-edge technologies and diverse data sources to safeguard public health and security. Digital biosurveillance systems leverage unstructured data from social media and news to provide early warnings for infectious diseases and bioterrorism, emphasizing the need for robust analytics and ethical considerations. Concurrently, environmental biosurveillance tracks antimicrobial resistance in wildlife, highlighting its transmission dynamics and the necessity for integrated strategies. Wastewater-based epidemiology offers a powerful tool for monitoring community-level disease outbreaks and AMR prevalence, aiding in early warning and public health interventions. A holistic One Health approach is critical for zoonotic disease surveillance, demanding integrated efforts across human, animal, and environmental sectors despite challenges in data sharing. Artificial Intelligence plays an increasingly central role, enhancing outbreak detection, prediction, and risk assessment through varied data, while requiring careful validation and ethical oversight. Genomic biosurveillance provides crucial insights into pathogen evolution and transmission, improving outbreak response capabilities. Event-based biosurveillance, utilizing unofficial sources like social media, helps detect threats in a globally connected world, though it grapples with data overload and misinformation. Beyond human health, biosurveillance technologies are transforming food safety by enabling rapid detection of foodborne pathogens and bolstering agricultural biosecurity through remote sensing and Artificial Intelligence for plant disease monitoring. These innovations collectively enhance preparedness and response to biological incidents, underscoring the vital link between technological progress and global health security.

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