From Years to Days: How Digital Twins Could Compress Pharmaceutical Response to Bio-Threats

Nicolas Waern WINNIIO AB / Life Atlas ORCID: 0000-0001-7970-2707

Corresponding author: ceo@winniio.io

License: CC-BY-4.0

Submitted: 2026-04-16

Abstract

The standard pharmaceutical response to an emergent bio-threat requires twelve to eighteen months from pathogen identification to validated countermeasure deployment at scale. During a rapid-onset event, this timeline is not a logistical inconvenience — it is a structural vulnerability. This paper analyses how city-scale digital twin infrastructure, combined with mobile biologics manufacturing units and AI-accelerated candidate screening, could compress this timeline to days rather than years for known pathogen families. Drawing on Actor-Network Theory (ANT) to trace the heterogeneous network of actants — biological agents, simulation models, regulatory inscription devices, logistics nodes, and human decision-makers — that must be enrolled to produce a credible readiness posture, the paper argues that the bottleneck is not laboratory science but the translation chain between pathogen surveillance, simulation fidelity, manufacturing flexibility, and last-mile distribution. We introduce the concept of a Bio-Threat Readiness Index (BTRI): a composite score across five dimensions (surveillance sensitivity, simulation resolution, manufacturing agility, distribution density, and regulatory pre-clearance depth) that provides a quantifiable, auditable benchmark for national and sub-national preparedness. We examine a hypothetical scenario in which a novel aerosolised pathogen enters a mid-sized European city, tracing the actant translations required to move from detection to distributed countermeasure deployment under current infrastructure versus a digital twin-enabled architecture. Three falsifiable propositions are stated. We engage the primary counter-argument — that simulation fidelity is insufficient for regulatory reliance — and argue that the relevant threshold is not perfect prediction but decision-relevant uncertainty reduction. This paper is intended as a contribution to the defence-pharma intersection and to the broader literature on pandemic preparedness infrastructure.

Keywords: digital twin, bio-threat, pharmaceutical response, city-scale simulation, mobile manufacturing, biologics-on-demand, readiness index, pandemic preparedness, edge-native, Actor-Network Theory, pathogen modelling, defence health

1. Introduction: The Twelve-Month Gap

When a novel pathogen with pandemic potential is identified, the race begins. Epidemiologists model transmission. Virologists sequence the genome. Immunologists identify candidate epitopes. Clinical trialists design protocols. Regulatory agencies convene emergency review committees. Manufacturers scale production. Distributors pre-position stockpiles. And at the end of this twelve-to-eighteen-month chain — under emergency conditions, with every actor working at maximum speed — a countermeasure reaches the public.

Twelve months. For a pathogen with an incubation period measured in days and a doubling time measured in hours, twelve months is not a response timeline. It is an admission that the response will be perpetually late.

The COVID-19 pandemic compressed this timeline for a narrow class of mRNA vaccines from eighteen months to approximately eleven. This was widely reported as a triumph of modern biotechnology. It was also a demonstration that the standard timeline, far from being irreducible, was an artefact of institutional friction rather than biological necessity — of sequential approval processes, siloed manufacturing infrastructure, fragmented surveillance systems, and distribution networks not designed for rapid deployment.

This paper asks a different question: not how to replicate the COVID emergency response, but how to design a standing infrastructure that makes the emergency response unnecessary — or at least makes the critical first-response window measurable in days rather than months. The instrument is city-scale digital twin infrastructure. The analytical frame is Actor-Network Theory [1], chosen because the bio-threat response problem is fundamentally a problem of network construction: which actants must be enrolled, in which sequence, through which translations, to produce a viable countermeasure deployment chain?

We introduce the Bio-Threat Readiness Index (BTRI) as a composite benchmark across five dimensions, and we trace — through a hypothetical European city scenario — what the translation chain looks like under current infrastructure versus a digital twin-enabled architecture. The goal is not advocacy for a specific product or platform. It is to make visible the structural assumptions embedded in current preparedness infrastructure and to argue that digital twins can dissolve some of the most consequential bottlenecks.

2. Theoretical Framework

2.1 Actor-Network Theory and Bio-Threat Response

ANT, associated with Callon, Latour, and Law [1][2][3], treats both human and non-human actors — organisms, instruments, models, regulations, databases — as equally capable of shaping a network. The bio-threat response network is a canonical ANT case: it involves pathogens (non-human actants with highly consequential agency), surveillance instruments (PCR assays, wastewater monitoring systems, syndromic surveillance dashboards), regulatory inscription devices (emergency use authorisation frameworks, Good Manufacturing Practice certificates), logistics networks (cold-chain assets, distribution algorithms), and human decision-makers whose agency is itself shaped by the actants around them.

ANT's concept of translation is particularly useful here. A translation moment is the point at which an actant's identity, interests, or trajectory is re-defined by another actant in the network. The classic case in bio-threat response is the translation from pathogen-as-environmental-agent to pathogen-as-regulatory-category: the moment a surveillance system classifies a detected agent as a Schedule 5 biological threat, triggering a predetermined chain of institutional actions. That translation moment — from environmental fact to institutional category — can take hours or weeks depending on the surveillance architecture in place.

Digital twins enter the ANT frame as obligatory passage points [1]: elements through which all information flows in the network must pass, giving the owner of the digital twin privileged position in the translation chain. A city-scale digital twin that integrates real-time environmental surveillance, population mobility data, hospital admission patterns, and atmospheric dispersion modelling can be the obligatory passage point for the critical translation from observed anomaly to actionable response — compressing the dwell time in uncertainty that currently characterises the early hours of a bio-threat event.

2.2 Design Science Research Frame

This paper follows Design Science Research (DSR) methodology [4]: we identify a class of problems (bio-threat response latency), propose a designed artefact (BTRI plus digital twin integration architecture), evaluate it against a hypothetical scenario, and contribute knowledge in the form of design principles. The artefact is explicitly proposed rather than validated in field conditions; we distinguish carefully between what the architecture would enable and what has been empirically demonstrated.

2.3 The SMILE Methodology — Impact First

Following the SMILE methodology as applied in Waern (2025a, DOI: 10.5281/zenodo.17462962), each design decision is evaluated by a single criterion: does it close a measurable gap between as-is and to-be? The gap here is the latency between pathogen detection and distributed countermeasure availability. Every architectural element proposed in this paper is justified by its contribution to closing that gap. Elements that do not contribute to this gap, however technically interesting, are outside the scope.

3. The Bio-Threat Response Network: As-Is Analysis

3.1 Current Actants and Their Translations

The current bio-threat response network contains the following primary actants, each requiring translation before the next stage can begin:

Surveillance actants. Genomic sequencing systems (translation: raw sequence to pathogen classification), syndromic surveillance dashboards (translation: emergency department patterns to epidemiological signal), wastewater monitoring arrays (translation: environmental RNA detection to population prevalence estimate). These actants are well-developed in most high-income nations but are operationally siloed. The translation between them — producing a composite risk signal — requires manual interpretation by epidemiologists who are themselves a bottleneck actant.

Regulatory inscription devices. Emergency Use Authorisation (EUA) frameworks, Good Manufacturing Practice (GMP) certifications, clinical trial protocols, import and export licensing regimes. These actants are designed to be slow — their function is to ensure quality and safety — but their sequential arrangement means that each one blocks the next. A manufacturing facility cannot begin production until EUA is granted; EUA cannot be granted until clinical trial data exists; clinical trial data cannot exist until a candidate is nominated; candidate nomination cannot happen until surveillance identifies the pathogen. The chain is the problem.

Manufacturing actants. Large-scale bioreactor facilities, viral vector production lines, fill-and-finish capacity. These actants are capital-intensive, geographically concentrated, and difficult to redirect. A manufacturing site optimised for producing a seasonal influenza vaccine cannot rapidly transition to producing a novel mRNA countermeasure without significant equipment reconfiguration, re-validation, and regulatory re-approval.

Distribution actants. Cold-chain logistics networks, primary care access points, dispensing databases. These actants work well for planned distribution (seasonal vaccination campaigns) but have demonstrated fragility under surge conditions.

3.2 The Translation Bottlenecks

Analysis of the current network identifies three critical translation bottlenecks — points at which information or material must change form, and where that change is currently slow:

T1: Anomaly to alert. Time from first environmental or clinical detection of an anomalous pathogen signal to the issuance of a formal public health alert. Currently: hours to weeks depending on surveillance architecture and institutional risk tolerance.

T2: Alert to candidate. Time from pathogen characterisation to nomination of a manufacturing-ready countermeasure candidate. Currently: weeks to months depending on whether the pathogen belongs to a known family with existing platform compatibility.

T3: Candidate to deployed dose. Time from candidate nomination to distributed countermeasure availability at point of care in an affected urban area. Currently: months to years.

The total latency (T1 + T2 + T3) under current infrastructure for an entirely novel threat is twelve to eighteen months. For threats within known families — coronavirus variants, influenza subtypes, anthrax strains — it is shorter but still measured in months.

4. The Digital Twin Architecture: Proposed To-Be State

4.1 City-Scale Simulation Infrastructure

A city-scale digital twin for bio-threat response integrates four data streams into a unified simulation environment:

Environmental surveillance layer. Real-time integration of wastewater RNA monitoring, air quality sensor networks, hospital emergency department syndromic surveillance, and genomic sequencing outputs. The twin updates continuously, maintaining a probabilistic estimate of pathogen prevalence and spatial distribution across the urban area.

Population mobility layer. Anonymised, aggregated mobility data from public transit systems, mobile network signalling (consented and aggregated), and pedestrian traffic sensors. Combined with population density data, this layer enables agent-based transmission modelling at neighbourhood resolution.

Healthcare capacity layer. Real-time bed availability, pharmacy inventory, primary care appointment capacity, and emergency services load. This layer enables the twin to model healthcare system saturation under different transmission scenarios, supporting decisions about countermeasure distribution priorities.

Manufacturing and logistics layer. Location, current production schedule, and reconfiguration capacity of mobile manufacturing units (see Section 4.2), along with distribution network topology. This layer makes the logistics actants visible within the simulation, enabling optimisation of distribution strategies before physical deployment begins.

The integration of these four layers enables the city-scale twin to serve as the obligatory passage point for bio-threat response decision-making. All four surveillance streams pass through the twin; all response decisions are grounded in the twin's probabilistic output.

4.2 Mobile Biologics Manufacturing Units

The manufacturing bottleneck identified in Section 3.1 is structural: large fixed facilities cannot be rapidly redirected, and their geographic concentration means that distribution must cover long supply chains. Mobile biologics manufacturing units — containerised GMP-compliant bioreactor systems capable of producing small batches of mRNA, viral vector, or monoclonal antibody products — address this bottleneck by decoupling production location from existing fixed infrastructure.

Several such systems exist in early-stage development contexts, including containerised vaccine manufacturing platforms trialled in low-income country contexts by international health organisations. This paper proposes that the digital twin architecture incorporates these units as first-class actants: their production schedules, GMP compliance status, and geographic position are maintained in the twin and incorporated into the response simulation.

This is a proposed capability, not a currently deployed one. The relevant design question is not whether the technology is mature — it is developing rapidly — but whether the regulatory and governance actants that would need to enrol mobile units as legitimate manufacturing nodes have been identified and their translation requirements specified.

4.3 AI-Accelerated Candidate Screening

The T2 bottleneck (alert to candidate) is most compressible for known pathogen families. Large-scale pretrained protein language models can generate candidate epitope predictions for novel variants of known pathogens within hours of receiving a complete genomic sequence. When combined with physics-based molecular dynamics simulation, the ranking of candidates by predicted immunogenicity and manufacturability can produce a shortlist suitable for regulatory review within forty-eight to seventy-two hours rather than weeks.

This is not full automation of drug discovery. It is automation of the candidate-generation phase for a specific class of threat — novel variants of known pathogen families — under conditions where speed is the primary design criterion. The regulatory actants (EUA frameworks) must be pre-positioned to accept AI-assisted candidate nomination; this is a governance design problem, not a scientific one.

4.4 The Bio-Threat Readiness Index

The BTRI is proposed as a composite index across five dimensions, each scored on a 0–100 scale:

A city or national authority with BTRI scores of SS=80, SR=70, MA=60, DD=85, RP=50 has a composite score of 69/100 and can prioritise its investment in manufacturing agility and regulatory pre-clearance. The index provides an auditable baseline and a structured direction of travel.

5. Scenario Analysis: Novel Aerosolised Pathogen, European Urban Area

5.1 Current Infrastructure Scenario

Day 1: Wastewater monitoring at three treatment facilities detects an anomalous RNA signature. The signal is within normal variation for seasonal pathogens; no alert is triggered.

Day 4: Emergency department admissions at two hospitals show a cluster of atypical respiratory presentations. An infectious disease clinician flags the cluster to the regional health authority.

Day 7: Genomic sequencing confirms a novel betacoronavirus variant with no close match in existing databases. National health authority activates emergency response protocol.

Day 12: WHO is notified; international surveillance activated. First candidate nominated based on cross-reactive epitope analysis with known betacoronavirus family.

Day 45: Pre-clinical animal model data generated. EUA application submitted to competent authority.

Day 120: Emergency use authorisation granted. Manufacturing scale-up begins at two fixed facilities.

Day 210: First doses available for distribution in affected city.

Total latency from first signal to distributed dose: 210 days.

5.2 Digital Twin-Enabled Architecture Scenario

Day 1, Hour 6: City-scale environmental surveillance twin detects anomalous RNA signature across three wastewater nodes simultaneously, cross-referenced against atmospheric dispersion model. Probability estimate: 67% likelihood of novel respiratory pathogen; alert transmitted to public health authority and defence health coordination centre.

Day 1, Hour 18: Automated genomic sequencing pipeline produces full sequence of dominant variant. AI candidate screening model generates ranked list of eighteen epitope candidates within four hours of sequence receipt.

Day 2: Top three candidates transmitted to mobile manufacturing units pre-positioned in the metropolitan area. GMP synthesis of research-grade material begins. Regulatory authority activates standing pre-clearance protocol for novel betacoronavirus mRNA candidates.

Day 5: Initial safety and immunogenicity data from accelerated in vitro assays available. Candidate list narrowed to one primary and one backup.

Day 12: Expanded safety data package submitted under standing EUA framework. Twin-generated epidemiological model incorporated into regulatory submission as evidentiary support for deployment scenario analysis.

Day 21: EUA granted for primary candidate. Mobile manufacturing units, co-located with the twin's identified high-priority distribution zones, begin production.

Day 28: First doses deployed to priority population within the metropolitan area. Distribution routing optimised by the twin in real time against changing transmission estimates.

Total latency from first signal to distributed dose: 28 days.

The compression is from 210 days to 28 days — a factor of 7.5x — for this scenario. The compression is not uniform across all threat types; it is largest for threats within known pathogen families with available platform technology and pre-positioned regulatory frameworks.

6. Counter-Argument: Simulation Fidelity and Regulatory Reliance

The primary counter-argument to digital twin-enabled bio-threat response is that simulation fidelity is insufficient to justify regulatory reliance. If the city-scale twin's transmission model is wrong — if it underestimates transmissibility, misses superspreader events, or overestimates natural immunity — then decisions made on its output may accelerate deployment of an ineffective countermeasure, consuming manufacturing capacity and public trust.

This is a serious argument. Simulation models in epidemiology have a well-documented history of confident predictions that diverged substantially from observed outcomes [5]. The COVID-19 pandemic provided extensive evidence that city-scale transmission modelling, even with access to high-quality data, produces wide uncertainty intervals that planners sometimes incorrectly treat as point estimates.

The counter-argument is, however, misframed if it requires perfect prediction as the relevant threshold for regulatory reliance. The correct threshold is decision-relevant uncertainty reduction: does the twin's output reduce uncertainty enough to support a different decision than would be made without it?

Under the current as-is scenario, the decision at day 210 is made under uncertainty. The countermeasure that is distributed may be mismatched to the evolved pathogen, the distribution routing may be suboptimal, the manufacturing volume may be wrong. The digital twin does not eliminate this uncertainty. It reduces it, earlier, with a shorter feedback loop. The appropriate regulatory posture is not "trust the twin completely" but "use the twin as a structured evidence source within a human-in-the-loop decision process" — consistent with EU AI Act Article 14 requirements for meaningful human oversight on high-stakes AI-assisted decisions.

7. The ANT Reading: Translating Actants into Readiness

The BTRI's five dimensions correspond to five sets of actant translations that must be stabilised before a credible readiness posture is achievable:

Surveillance sensitivity requires translating environmental sensors, genomic sequencers, and epidemiological dashboards from siloed data producers into a unified signal — the obligatory passage point of the city twin. This translation requires technical integration (APIs, data standards), organisational commitment (shared governance of the surveillance network), and legal alignment (data sharing agreements between health authorities, environmental agencies, and transit operators).

Simulation resolution requires translating the twin from a visualisation tool into a decision-support instrument. This is the translation that regulators are most resistant to — it requires accepting that a simulation model can contribute evidentiary weight to a regulatory decision. The governance actant (EUA framework) must be re-inscribed to accommodate model-based evidence.

Manufacturing agility requires translating mobile manufacturing units from logistical assets into regulatory actors — entities whose GMP compliance status is maintained in the network and whose production capacity is pre-committed to the response chain. This is currently the least-developed translation in most national preparedness architectures.

Distribution density requires translating the population mobility layer from an analytics output into a logistics routing input. This translation is technically straightforward but organisationally complex: it requires real-time data sharing between the twin operator and the distribution logistics actants.

Regulatory pre-clearance depth requires the most fundamental actant translation: the regulatory agency must move from reactive (reviewing emergency submissions) to proactive (pre-clearing platform technologies and maintaining standing EUA eligibility for known pathogen families). This requires the regulatory agency to enrol in the preparedness network before a threat materialises — to become an actant in the readiness infrastructure rather than only in the response infrastructure.

8. Falsifiable Propositions

1. The compression proposition: Cities with BTRI composite scores above 70 can achieve countermeasure deployment timelines of 28 days or fewer for novel variants of known pathogen families. This proposition can be falsified by a systematic analysis of historical bio-threat response timelines in cities with BTRI-equivalent preparedness characteristics, showing that the timeline compression is not achievable even with integrated surveillance-manufacturing-regulatory infrastructure.

2. The obligatory passage point proposition: City-scale digital twins that integrate environmental surveillance, population mobility, healthcare capacity, and manufacturing logistics data produce decision-relevant uncertainty reduction (measured by prediction accuracy on historical outbreak data) compared to single-stream surveillance systems. This proposition can be falsified by a controlled comparison showing no statistically significant improvement in early-warning sensitivity for integrated twins versus single-stream surveillance.

3. The regulatory translation proposition: Pre-clearance depth (BTRI-RP) is the primary bottleneck in bio-threat response compression, accounting for more timeline reduction than any other single BTRI dimension. This proposition can be falsified by a scenario analysis showing that manufacturing agility or simulation resolution, when improved alone without regulatory pre-clearance, produces equivalent timeline compression.

9. Limitations and Future Research

This analysis is based on a hypothetical scenario constructed from published data on historical pandemic response timelines. It has not been validated against real-time bio-threat response operations. The compression estimates (210 days to 28 days) are scenario-specific and should not be generalised without analysis of the specific pathogen family, regulatory jurisdiction, and existing infrastructure baseline.

The mobile biologics manufacturing unit technology is at an early stage of development; the regulatory frameworks for their integration into emergency response chains are not yet established in most jurisdictions. This paper treats them as designed artefacts in the DSR sense — proposed solutions whose feasibility is argued analytically — not as deployed capabilities.

The BTRI is a proposed index, not a validated measurement instrument. Its scoring methodology requires development through expert elicitation, historical case analysis, and iterative refinement. The relative weighting of dimensions should be empirically derived from historical bio-threat response data, not assumed equal.

Future research should: (1) validate the BTRI against historical case data from at least ten bio-threat response events across multiple countries; (2) develop and test regulatory pre-clearance frameworks for AI-assisted candidate nomination in at least one national regulatory context; (3) conduct field trials of mobile biologics units in simulated emergency deployment scenarios.

10. Conclusion

The twelve-to-eighteen-month pharmaceutical response timeline for bio-threats is not irreducible. It is an artefact of sequential institutional processes, siloed surveillance infrastructure, geographically concentrated manufacturing capacity, and regulatory frameworks designed for peacetime drug development. City-scale digital twins, integrated with mobile biologics manufacturing and AI-accelerated candidate screening, can dissolve the most consequential bottlenecks in this chain — compressing the response timeline to weeks for threats within known pathogen families.

The ANT analysis reveals that this compression requires not only technical integration but actant translation: regulatory agencies, manufacturing units, surveillance systems, and logistics networks must each be re-enrolled in the preparedness network before a threat materialises. The Bio-Threat Readiness Index provides a quantifiable framework for assessing where in this translation chain a given city or nation is most vulnerable, and where investment produces the largest reduction in response latency.

The goal is not to replace human decision-making with simulation. The goal is to give decision-makers a decision-relevant substrate — grounded in continuously updated, multi-stream, city-scale data — that reduces the uncertainty interval at every critical translation moment in the response chain. Not perfect prediction. Decision-relevant uncertainty reduction, available in hours rather than weeks.

That is a tractable engineering problem. It is time to treat it as one.

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