Supplementary appendix

Transcription

1 Supplementary appendix This appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Katz R, Graeden E, Kerr J. The complexity of biological events. Lancet Glob Health 2018; 6: e

2 The Complexity of Biological Events Rebecca Katz, Ellie Graeden, and Justin Kerr Methods Appendix Scenario Generation We developed a framework to categorize biological events based on the event characteristics that drive planning and response decision making. These 12 dimensions include: event origin (natural, accidental, or deliberate) primary spread type (animal only, human only, or zoonotic disease) spread modality (communicable or non-communicable) route of transmission (airborne, bloodborne, waterborne, foodborne, or vector-borne) diagnostics (point of care, BSL1, BSL2, BSL3, BSL4) medical countermeasures (antivirals, antibiotics, vaccine, post-exposure prophylaxis, or none available) outbreak location (state controlled with international access; state controlled without access; permissive; non-permissive; and unstable) population affected (all, pregnant women, children, elderly, or targeted) personal protective equipment - PPE (respirator, containment suit, or mask/gloves/gown) morbidity rate (5 levels from very low to very high) response level (local, intermediate, national, regional, or global) policy measures in place (national, international, or none) These parameters were joined combinatorially to demonstrate the complexity and diversity of scenarios possible for biological events. Irrational combinations were specifically excluded, including combinations of targeted attacks and natural outbreak; respirators as required personal protective equipment for foodborne events; and ensuring that vector-borne diseases are only included for scenarios with communicable spread modality (Table A1). In some cases, specific combinations were excluded or included to account for unique scenario types. For example, deliberate event scenarios can include outbreaks in which the mode of transmission of the specific agent may not be plausible for a naturally occurring event. Similarly, specific personal protective gear may be required for large scale release of a non-contagious agent that would not be required for small-scale naturally-occurring cases of the same disease. Table A1: Rules used to exclude non-rational events Combinations of event characteristics were considered in combination to develop the list of excluded combinations below. Note that additional 3-part combinations were identified as not rational, but these were already excluded by existing rules based on pairwise combinations. First Category Second Category Rationale Characteristic(s) Characteristic(s) Type: Zoonotic Zoonotic diseases of concern are Spread Modality: Non-communicable Non-communicable Vector-borne, Bloodborne and Airborne expected to be communicable Bloodborne, airborne and vector-borne diseases are communicable

3 Spread Modality: Noncommunicable Spread Modality: Noncommunicable Foodborne, Waterborne Bloodborne Event Origin: Natural Policy Measures: International Diagnostics: BSL3, BSL4 PPE: Respirator, Containment Suit Accidental origin Population Affected: Targeted Non-communicable disease agents unlikely to drive international policy development for acute response Diseases requiring BSL3 or BSL4 laboratories for diagnostics are expected to be communicable Respirators and containment suits protect from airborne and bloodborne transmission No route identified for large-scale, accidental release of bloodborne agent Natural outbreaks can differentially affect populations but are not targeted to select groups Stakeholder Tagging In addition to the 12 core parameters, each scenario was tagged by relevant stakeholders based on the origin of the event. Stakeholders in logistics and the public health and medical communities were included for all events. Law enforcement was included as a stakeholder for all accidental and deliberate events. All intentional events were tagged with security/military stakeholders as were a random subset of natural events (20%), reflecting that these groups respond to some naturally occurring outbreaks as well. Stakeholders in Trade (imports and exports) were included for a subset of all events, but concentrated in the events with primary impacts to animal health (to reflect agricultural trade). Visualization The BioComplexity Visualization tool can be accessed online at The user interface is built using HTML5 and d3.js-based interactive graphics coded in JavaScript and is supported by a static database drawing from the combinatorial results of the analysis as previously described. To provide visual clarity, a randomized selection of 50 scenarios are visualized each time the site is refreshed or filters are selected. Filters allow the user to visualize subsets of the scenarios that reflect specific characteristics. Disease scenarios are sized based on the proportional number of fatalities estimated based on general characteristics, as compared to other scenarios in the dataset. Scenarios are colored from red to purple to blue, with red indicating scenarios caused by agents that infect only humans to scenarios that cause disease primarily in animals, but can also infect humans.

4 Figure 1: An overview of select event combinations for scenario types, given the 12 characteristics.

5 Figure 2: Screenshot of interactive BioScenarios model. Available at bioscenarios.talusanalytics.com