Health Security Preparedness For Biological Threats In Healthcare Facilities: Surveillance, Containment, And Response Capacity
Bibliographic record
Abstract
Background: The contemporary global health security landscape is characterized by an intensifying frequency and complexity of biological threats, ranging from naturally occurring high-consequence infectious diseases (HCIDs) like Ebola and SARS-CoV-2 to the persistent specter of bioterrorism. Healthcare facilities (HCFs) constitute the operational frontline of biodefense, yet their capacity to effectively detect, contain, and respond to these threats remains critically uneven across geopolitical and economic divides. The convergence of workforce attrition, "just-in-time" logistical fragility, and infrastructural obsolescence has exposed profound vulnerabilities in the hospital sector's ability to maintain continuity of care under biological stress. Objectives: This comprehensive systematic review aims to evaluate the global state of health security preparedness within healthcare facilities. The primary objectives are to: (1) assess the efficacy of existing surveillance architectures—specifically comparing syndromic surveillance in high-resource settings against Integrated Disease Surveillance and Response (IDSR) frameworks in low-resource settings—in facilitating early threat detection; (2) analyze containment capacities by contrasting the engineering and operational outcomes of High-Level Isolation Units (HLIUs) versus standard infection control wards; and (3) evaluate response capacity through the lenses of workforce resilience, personal protective equipment (PPE) compliance, and supply chain sustainability. Methods: A systematic literature review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A multi-database search (PubMed, Scopus, Web of Science, Google Scholar) was executed for peer-reviewed and grey literature published through 2023. Included studies encompassed randomized trials, observational cohorts, and qualitative assessments of hospital preparedness globally. Quality assessment was rigorously performed using the Cochrane Risk of Bias tool (RoB 2) for interventional studies and the Newcastle-Ottawa Scale (NOS) for observational research, ensuring a weighted synthesis of high-quality evidence. Results: The review synthesized data from a diverse array of global studies. In the domain of surveillance, syndromic systems in high-income nations demonstrated the capacity to predict Intensive Care Unit (ICU) surges by 11–13 days, though with significant specificity trade-offs (often ~50% detection probability for covert bioterrorism by Day 2). Conversely, IDSR implementation in sub-Saharan Africa showed marked improvements in reporting completeness (rising from 84.5% to 96% in Sierra Leone) but remained hampered by a lack of laboratory integration and feedback loops. Containment analysis revealed that HLIUs achieve aerosol containment rates exceeding 99.7% and significantly lower healthcare worker (HCW) infection rates (7% vs. 11% in general wards) yet are operationally non-scalable. Response capacity assessment identified a critical "preparedness decay," characterized by PPE compliance rates as low as 21.64% in observational audits despite high theoretical knowledge, and pervasive workforce burnout, with over 96% of staff reporting anxiety during surges. Conclusion: Health security preparedness in healthcare facilities is currently defined by a "hardware-software" dissonance. While advanced engineering solutions and theoretical frameworks exist ("hardware"), they are critically undermined by the "human factor" ("software")—specifically, behavioral non-compliance, psychological exhaustion, and the inequitable distribution of resources. Achieving genuine bioresilience requires a paradigm shift from reactive, agent-specific planning to a sustained, all-hazards approach that prioritizes workforce protection, integrates real-time diagnostics with syndromic signals, and establishes resilient, equitable supply chains independent of crisis-driven funding cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".