This chapter reproduces the second horizon scan for bioengineering conducted by the Centre for the Study of Existential Risk in 2020, one of the most significant pieces of horizon scanning that has been undertaken in the field to-date. Identifying the top 20 emergent issues, the authors group them according to a likely timeline for their realisation, and discuss each throughout the chapter. This allows for the most notable issues that may impact the planet and humanity to be tracked, and the most pressing issues to be identified. The early identification of such issues is relevant for researchers, policy-makers, and the general public, providing an opportunity to consider what anticipatory or future action might need to be taken.

Background: Pandemics caused by novel pathogens produce devastating global effects. Early detection is critical, and the International Health Regulations (2005) (IHR[2005]) require countries to report outbreaks of unknown causes. However, the global extent of novel disease surveillance is unclear. Establishing baseline information is vital for guiding new global preparedness initiatives like the Pandemic Treaty. This study aimed to determine the number of countries that consider novel diseases in their national public health infrastructure.Methods: We examined 195 IHR(2005) countries for publicly available evidence of novel disease consideration. This was completed by systematically compiling country-specific information from existing datasets and official government resources. The main outcome was evidence of novel disease consideration in one or more categories: national public health laws, notifiable disease lists, health professional guidelines, and laboratory guidelines. This main outcome was compared using univariate logistic regression against existing indicators including World Health Organisation (WHO) region, World Bank income strata, health expenditure per capita, total outbreaks per country recorded by the WHO between 1996 – 2022, and national experience with at least one case of the following novel pathogens: SARS-CoV-1, MERS or Nipah virus.Findings: Overall, only 73/195 countries (37·4%) were found to have evidence of novel disease consideration. Compared with the WHO European region, novel disease consideration was significantly less likely in Africa (odds ratio [OR] 0·16 [95% CI 0·061 – 0·40]; p-value 0·00014) and the Eastern Mediterranean (0·13 [0·029 – 0·45]; p-value 0·0031). Novel disease consideration was less likely in low-income countries (0·16 [0·043 – 0·49]; vs high income; p-value 0·0025) and was positively associated with increased health expenditure per capita (1·29 [1·09 – 1·56]; per $1000 USD increase; p-value 0·0057). A country's total outbreak number was not associated with evidence of novel disease consideration (1·03 [0·99 – 1·07]; p-value 0·11), but experience with one or more cases of SARS-CoV-1, MERS or Nipah virus predicted consideration (3·89 [1·94 – 8·04]; p-value 0.00017).Interpretation: A minority of countries have public evidence of novel disease consideration, and significant geographical heterogeneity exists in where this is located. National experience with at least one of three newly emerged pathogens, but not total outbreak number, predicted novel disease consideration. Countries can act now to improve preparedness and ensure global initiatives like the Pandemic Treaty address novel disease surveillance.Funding: Open Philanthropy; Wellcome Trust.Declaration of Interest: We declare no competing interests.

A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit’s taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae and Neisseria gonorrhoeae infection, BugSplit’s taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic .

The 2021 Global Health Security (GHS) Index Report was published on 8 December 2021. With an average country score of 38.9 out of a possible 100 points, global scores are essentially unchanged from 2019. Despite experience with the COVID-19 pandemic, no country is adequately prepared for future biological threats. No country scored above 75.9 and the scores of the bottom 11 States have all fallen since 2019. Aotearoa New Zealand, however, has substantially improved its country score, rising to 13th in the world at 62.5/100. This gain is partly driven by consolidation of capabilities developed and deployed in response to COVID-19. This is promising progress, but a lot more can be done to ensure legacy benefits from the pandemic response, notably through the proposed restructuring of the health system (Pae Ora (Healthy Futures) Bill). In this viewpoint article, we discuss this recent further development of the GHS Index, highlight the global results for 2021, delve into New Zealand's progress, and discuss what more is needed.

AbstractThe COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies to detect and characterize pathogens from clinical specimens. The MinION sequencing device allows for rapid, cost-effective, high-throughput sequencing; useful features for translation to clinical laboratory settings. Metagenomic Next-Generation Sequencing (mNGS) approaches provide the opportunity to examine the entire genomic material of a sample; allowing for detection of emerging and clinically relevant pathogens that may be missed in targeted assays. Here we present a pilot study on the performance of Sequence-Independent Single Primer Amplification (SISPA) to amplify RNA randomly for the detection and characterization of SARS-CoV-2. We designed a classifier that corrects for barcode crosstalk between specimens. Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-qPCR cycle threshold value less than 30. We assembled 10 complete (>95% coverage at 20x depth), and one near-complete (>80% coverage at 20x depth) genomes from 20 specimens that were classified as positive by mNGS. We characterized these genomes through phylogenetic analysis and found that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. Of five samples that we had both assembled genomes, as well as Variant of Concern (VOC) PCR results, we found 100% concordance between these results. Additionally, our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with our VOC PCR technique. This study supports future work examining the broader feasibility of SISPA as a diagnostic strategy for the detection and characterization of viral pathogens.

Objectives The COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies. Metagenomic Next-Generation Sequencing (mNGS) may allow for the detection of pathogens that can be missed in targeted assays. The goal of this study was to assess the performance of nanopore-based Sequence-Independent Single Primer Amplification (SISPA) for the detection and characterization of SARS-CoV-2. Methods We performed mNGS on clinical samples and designed a diagnostic classifier that corrects for barcode crosstalk between specimens. Phylogenetic analysis was performed on genome assemblies. Results Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-PCR cycle threshold value less than 30. We assembled 10 complete, and one near-complete genomes from 20 specimens that were classified as positive by mNGS. Phylogenetic analysis revealed that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. We found 100% concordance between phylogenetic lineage assignment and Variant of Concern (VOC) PCR results. Our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with the current standard VOC PCR being used in British Columbia. Conclusions This study supports future work examining the broader feasibility of nanopore mNGS as a diagnostic strategy for the detection and characterization of viral pathogens.

Rapid developments in the fields of synthetic biology and biotechnology have caused shifts in the biological risk landscape and are key drivers of future threats. From a security perspective, extending our understanding beyond current risks to include emerging threats in these and related fields can play a vital role in informing risk mitigation activities. Insights that are generated can be combined with other efforts to identify vulnerabilities and prevent undesirable outcomes. Emerging risks that may occur at some point in the future are inherently difficult to assess, requiring a systematic approach to examining potential threats. Foresight is a process to consider possible future scenarios. Comprising a range of methods and techniques, foresight processes can offer novel insights into emerging synthetic biology and biotechnology threats. This chapter offers an introduction to foresight, including definitions of key terms that could support a shared lexicon across NATO partners. An overview of different foresight methodologies, their potential applications, and different strengths and limitations are presented. As a key first step, an approach to selecting appropriate questions to guide foresight activities is suggested. Example questions for synthetic biology and biotechnology are highlighted. At the end of the chapter, the authors offer recommendations for the design of a foresight process, with the intention of providing a useable resource for NATO partners investigating emerging synthetic biology and biotechnology threats.

Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.

Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.

Introduction The COVID-19 pandemic powerfully demonstrates the consequences of biothreats. Countries will want to know how to better prepare for future events. The Global Health Security Index (GHSI) is a broad, independent assessment of 195 countries’ preparedness for biothreats that may aid this endeavour. However, to be useful, the GHSI’s external validity must be demonstrated. We aimed to validate the GHSI against a range of external metrics to assess how it could be utilised by countries. Methods Global aggregate communicable disease outcomes were correlated with GHSI scores and linear regression models were examined to determine associations while controlling for a number of global macroindices. GHSI scores for countries previously exposed to severe acute respiratory syndrome (SARS), Middle East respiratory syndrome and Ebola and recipients of US Global Health Security Agenda (GHSA) investment were compared with matched control countries. Possible content omissions in light of the progressing COVID-19 pandemic were assessed. Results GHSI scores for countries had strong criterion validity against the Joint External Evaluation ReadyScore (rho=0.82, p<0.0001), and moderate external validity against deaths from communicable diseases (−0.56, p<0.0001). GHSI scores were associated with reduced deaths from communicable diseases (F(3, 172)=22.75, p<0.0001). The proportion of deaths from communicable diseases decreased 4.8% per 10-point rise in GHSI. Recipient countries of the GHSA (n=31) and SARS-affected countries (n=26), had GHSI scores 6.0 (p=0.0011) and 8.2 (p=0.0010) points higher than matched controls, respectively. Biosecurity and biosafety appear weak globally including in high-income countries, and health systems, particularly in Africa, are not prepared. Notably, the GHSI does not account for all factors important for health security. Conclusion The GHSI shows promise as a valid tool to guide action on biosafety, biosecurity and systems preparedness. However, countries need to look beyond existing metrics to other factors moderating the impact of future pandemics and other biothreats. Consideration of anthropogenic and large catastrophic scenarios is also needed.

The biological risk landscape continues to evolve as developments in synthetic biology and biotechnology offer increasingly powerful tools to a widening pool of actors, including those who may consider carrying out a deliberate biological attack. However, it remains unclear whether it is the relatively large numbers of low-resourced actors or the small handful of high-powered actors who pose a greater biosecurity risk. To answer this question, this paper introduces a simple risk chain model of biorisk, from actor intent to a biological event, where the actor can successfully pass through each of N steps. Assuming that actor success probability at each independent step is sigmoidally distributed and actor power follows a power-law distribution, if a biorisk event were to occur, this model shows that the expected perpetrator would likely be highly powered, despite lower-powered actors being far more numerous. However, as the number of necessary steps leading to a biological release scenario decreases, lower-powered actors can quickly overtake more powerful actors as the likely source of a given event. If steps in the risk chain are of unequal difficulty, this model shows that actors are primarily limited by the most difficult step. These results have implications for biosecurity risk assessment and health security strengthening initiatives and highlight the need to consider actor power and ensure that the steps leading to a biorisk event are sufficiently difficult and not easily bypassed.

Biology can be misused, and the risk of this causing widespread harm increases in step with the rapid march of technological progress. A key security challenge involves attribution: determining, in the wake of a human-caused biological event, who was responsible. Recent scientific developments have demonstrated a capability for detecting whether an organism involved in such an event has been genetically modified and, if modified, to infer from its genetic sequence its likely lab of origin. We believe this technique could be developed into powerful forensic tools to aid the attribution of outbreaks caused by genetically engineered pathogens, and thus protect against the potential misuse of synthetic biology.

Rapid developments are currently taking place in the fields of artificial intelligence (AI) and biotechnology, and applications arising from the convergence of these 2 fields are likely to offer immense opportunities that could greatly benefit human health and biosecurity. The combination of AI and biotechnology could potentially lead to breakthroughs in precision medicine, improved biosurveillance, and discovery of novel medical countermeasures as well as facilitate a more effective public health emergency response. However, as is the case with many preceding transformative technologies, new opportunities often present new risks in parallel. Understanding the current and emerging risks at the intersection of AI and biotechnology is crucial for health security specialists and unlikely to be achieved by examining either field in isolation. Uncertainties multiply as technologies merge, showcasing the need to identify robust assessment frameworks that could adequately analyze the risk landscape emerging at the convergence of these 2 domains.This paper explores the criteria needed to assess risks associated with Artificial intelligence and biotechnology and evaluates 3 previously published risk assessment frameworks. After highlighting their strengths and limitations and applying to relevant Artificial intelligence and biotechnology examples, the authors suggest a hybrid framework with recommendations for future approaches to risk assessment for convergent technologies.