Getting Started…
The following resources comprise the core component of this week’s content, providing ‘An Introduction to Early Warning Systems and Biosurveillance’. Once you’ve completed the readthrough below, please take the time to explore the resources labelled as ‘mandatory’ - these should take approximately five to ten minutes each to either watch, listen to or read. If you have the time or the inclination, you could also have a look through the ‘Optional Resources’ provided below and further expand upon your understanding of biorisk and pandemic prevention. The ‘Optional Resources’ typically take longer to work through (e.g. the “Virology” episode of the Ologies podcast has a runtime of over an hour), so you might want to consider listening to any audio/video resources at 1.25x or 1.5x speed.
Once you’ve completed the introductory component for this week, return to the ‘Getting Started’ section and explore both of the additional sections linked below - one of which explores the possibility of ‘Disease X’ having a natural origin, whilst the other examines the potential role of malevolent actors, lab leaks and dual use research in producing an engineered pandemic. You are required to complete the introductory readthrough and explore the mandatory resources associated with each section of additional content, but are not required to work through any of the optional content. That said, you’re also very welcome to dip in and pick out any resources which feel interesting to you - even if they don’t seem directly relevant to the issues you’re considering. Having completed all required content, you can begin to work on the week’s concluding activity, as detailed below.
Week 2: Write a short piece of persuasive text (between 300 and 500 words) attempting to convince your reader that the next ‘Disease X’ will originate from (a) a natural reservoir, (b) a lab leak or industrial accident or (c) the work of malevolent actors/the military applications of dual use research. In order to ensure the rigour of your argumentation, please cite your sources and reference appropriately. You may include the resources provided throughout this programme as references for any points that you make.
Additional Content 🔬
When A Pangolin Becomes Patient Zero: Preventing ‘Natural Pandemics’ and Zoonotic Spillover
An Introduction to Early Warning Systems and Biosurveillance
It’s not difficult to understand as to why members of the general public might recoil at the suggestion of novel methods of population surveillance - with the UCLA-based AI model for the detection of pandemic potential pathogen (PPP) outbreaks collating billions of social media posts (and, therein, personal information protected by the GDPR) to synthesise data researchers could utilise to identify emergent trends. The notion of being observed and analysed by a covert organisational body might sound far from comfortable, but whilst the term ‘biosurveillance’ might sound unnerving, its incorporation into pandemic preparedness and prevention strategies is vital in ensuring the future preservation of global infrastructure in the event of ‘Disease X’. ‘Biosurveillance’ refers to the collation, synthesis and analysis of data relevant to emergent biological threats, utilised to steer the development of vaccination programmes, containment strategies and zoonotic spillover prevention. The data collected also has applications in bioterrorism monitoring, as it allows for the identification of ‘markers’ or ‘scars’ which denote the manipulation of a pathogen’s genome - therein alerting local regulatory bodies to the presence of a malevolent actor or, at the very least, an enhanced pandemic potential pathogen (EPPP) lab leak which has gone unreported. Whilst the UCLA model utilised a breadth of sociological data to identify problematic PPPs, other biosurveillance systems take a more directly biological approach.
The Nucleic Acid Observatory, a SecureBio project created with the intention of designing novel biosurveillance methods capable of pinpointing emergent pandemic-level biothreats, has pioneered the use of metagenomic sequencing of human wastewater as a means of monitoring the relative prevalence of PPPs. This approach is a form of ‘pathogen-agnostic threat detection’ - searching for atypical biological indicators (e.g. an excessive and increasing number of unknown viral particles detected across multiple countries) without adhering to the constraints of a pre-existent database of known pathogens - allowing for the identification of emergent pathogens prior to official classification and extensive analysis. In light of the continued threat of bioterrorism events, pathogen-agnostic threat detection initiatives could grow increasingly important, as pathogens specifically engineered to avoid detection may not be identified by technology adhering to the parameters of traditional classification systems.
Recently honoured with the Future Insight Prize, Professor Khalid Salaita of Emory University has developed a rolling DNA-based motor which functions as a sensor - detecting changes in mechanical force in the range of piconewtons. The presence of mechanical force, such as that exerted by a particle of SARS-Cov-2, causes the motor to ‘stall’ and trigger an alarm signal which can be detected and interpreted from a central monitoring system. More broadly, Salaita’s sensor allows for the development of a more nuanced understanding of the physical interactions between immune cells and pathogens. This function could shed light on the viral ability to avoid immune detection and help to reveal new cellular targets for antiviral drugs - but the responsiveness of the sensor to mechanical force would also allow for research into mutations in PPPs (through the monitoring of a pathogen’s mechanical functions) and the presence of identified pathogens in a given environment. Already, Salaita and his team have successfully demonstrated the capacity of what has been dubbed the ‘Rolosense’ to detect SARS-CoV-2 particles in saliva and in breath condensate.