Planning and Resp Pl sponse se Reso source rces s for r Infect ctious s Dise sease se Dr. Norman L. Johnson Chief Scientist Referentia Systems Inc. (on leave from Los Alamos National Laboratory) njohnson@referentia.com Sydney, Australia 8-10 Sept 2008
Infect ctious s Dise sease se Worl rldwide ■ Infect ctious s dise sease se/outbre reaks ks are re co commo mmon and deadly, y, beca cause se of: – Increased worldwide population density, travel and transfer of goods. ■ Infect ctious s dise sease se/outbre reaks ks are re a so source rce of ma major r inst stability y in deve veloping and undeve veloped co countri ries, s, beca cause se: – Relative decline in healthcare in many countries. ■ Deve veloped co countri ries s are re at gre reat ri risk sk fro rom m new bio-t -thre reats, s, natura ral or r engineere red,beca cause se: – Developed countries operate more optimally and are therefore less robust. – Responses to new biothreats, unlike nuclear threats, are complicated by background of common threats and by advances in dual-use medical research.
Cause se Rank k ~N ~Numb mber r of Deaths s Resp spira ratory ry infect ctions 1 3,871,000 3,871,000 1 � HIV/ V/AI AIDS 2 2,866,000 2,866,000 7 � Diarrh rrheal dise sease ses 3 2,001,000 2,001,000 2 � Tubercu rculosi sis 4 1,644,000 1,644,000 3 � Ma Malari ria 5 1,124,000 1,124,000 4 � Me Measl sles 6 745,000 745,000 5 � Pert Pe rtussi ssis 7 285,000 285,000 7 � Te Tetanus 8 282,000 282,000 12 � Meningitis Me 9 173,000 173,000 8 � Syphilis Syp 10 10 167,000 167,000 11 � 1993 � rank � Source: WHO, 2002
Infra rast stru ruct cture re Imp mpact ct and Dependency cy Greatest Impact � Dependency matrix - � Critical Infrastructure Protection Task Force of Canada � Greatest Dependency � KEY � • Because workers are required to support all systems, high dependency of health care is a problem. � • Not evaluated is workforce impact - as might be drastically reduced by a failure of the health care system. �
Opera rational Resp sponse se to Infect ctious s Dise sease se Appro Ap roach ch: Capture primary impact - disease progression – Capture secondary/tertiary effects - e.g, mission readiness – Goal Goal: : Avoid breakpoints - significant system transitions from relatively – small changes - particularly, in the health system Breakpoints in one system can cause breakpoints in other – systems. Game me Changer: r: Mi Mitigations s (p (pre reve ventative ve me measu sure res) s) ca can pre reve vent bre reakp kpoints. s. What re reso source rces s are re ava vailable?
Breakp Bre kpoints s in the public c health syst systems ms (f (fro rom m AU AUS S Mo MoH) Unmi mitigated Dise sease se st stre rength or r Health system su surg rge capacity se seve veri rity Se Seaso sonal flu capacity Time me Mitigated with Pu Mi Public c Health me measu sure res Expanded se Exp servi rvice ces s avo voided
Unmi mitigated Health system sh shift capacity Dise sease se seve se veri rity Health system su surg rge capacity Seaso Se sonal flu capacity Time me Mi Mitigated with Pu Public c Health me measu sure res One syst system m tra ransi sition occu ccurs, rs, but a bre reakp kpoint is s avo voided
Unmi mitigated Health system capacity ove verw rwhelme med Health system sh shift capacity Dise sease se se seve veri rity Health system su surg rge capacity Se Seaso sonal flu capacity Time me Mitigated with Pu Mi Public c Health me measu sure res Two bre reakp kpoints s past st, third rd ma manageable
Unmi mitigated Health system capacity ove verw rwhelme med Health system sh shift capacity Dise sease se se seve veri rity Health system su surg rge capacity Se Seaso sonal flu capacity Time me Mi Mitigated with Pu Public c Health me measu sure res Thre ree bre reakp kpoints, s, outco come me unce cert rtain
Disease Progression in a diverse population Required for viruses, bacteria, toxins, etc. And the different types or strains of each. 10
mple: Biological Agent Reference Tool (BART): Exa Examp a Web-based response information tool BART � 11
Ep Epidemi miologica cal Reso source rces s Needed Past Pa st data, Population rity y re reso source rces s and (averaged) planning planning ranulari Contri ribute to “a “all- Integra rated National & & sca scale” ” integra rated Planning Gra Need Regional Eme Emerg rgency cy Pl Plan re resp sponse se plans s Deve velopme ment Comp mpatible with Loca cal-G -Global new data st stre reams ms Envi En viro ronme mental & & Syn Syndro romi mic c Need Pl and mi mitigation Mo Monitori ring syst systems ms options s Individual (resolved) City State Nation World Sp Spatial Sca Scale Reso source rce needs: s: pre redict ction of dise sease se pro rogre ressi ssion in hetero rogeneous s populations, s, acro cross ss larg rge sca scales, s, re reso solve ved at indivi vidual and loca cal leve vel
A Landsca scape of Ep Epidemi miologica cal Options S-I-R differential equations Population Reso source rce Require red solution Workstation r Reso Community Stochastic Community Supercomputer y or models Agent-based Fidelity Tool EpiSims Deterministic Individual Agent-based City State Nation World Sp Spatial Sca Scale
EpiCast (Epidemiological Forecasting) � T. C. Germann, K. Kadau, I. M. Longini, and C. A. Macken, “Mitigation Strategies for Pandemic Influenza in the United States,” Proceedings of the National Academy of Sciences 103 , 5935-40 (2006). �
Influenza in the US: Planning for the next pandemic �
Baseline - Moderate Severity � Each Census tract is represented by a dot colored according to its prevalence (number of symptomatic cases at any point in time) on a logarithmic color scale, from 0.3-30 cases per 1,000 residents. �
Baseline simulated pandemics � Most of the epidemic activity is in a 2-3 month period, starting 1-2 months after introduction
Breakpoint (R 0 ~ 1) Behavior � R 0 ~ 0.9 R 0 ~ 1.2
Introduction of 40 Day 60 infecteds on day 0, either in NY or LA, with and without nationwide travel Day 80 restrictions Day 100 Day 120
Successful Mitigation Su ons � 60% TAP (182 M) Vaccination - child-first Vaccination (random) + school closure + social distancing + travel restrictions Vaccination (child-first) + school closure + social distancing + travel restrictions 80% TAP (0.7 M) + vaccination (random) + school closure + social distancing + travel restrictions 0 10 20 30 40 50 Unce cert rtain Mi Mitigations s Failed Mi Mitigations s - - Full Pa Pandemi mic c (>1 (>10%) ) • Vaccination - random • Social distancing alone • School closure alone • Travel restrictions alone • Social distancing + travel restrictions
Modeling Military Force Structure & Interactions � How “community network” (#2) is determined: � • Each individual solider belongs to a specific squad, platoon, …, army � • The squad … division levels comprise a hierarchy of “community networks: an individual’s likelihood of becoming infected from these p sqd •n sqd + p plt •n plt + … + p div •n div � interactions is: � • where the p X are contact rates from the unit interaction survey, and n X are the number of infectious soldiers in that Squad � unit. � Squad � • A survey was done to Squad � Platoon � determine p x . � Company � Battalion � Brigade � Division �
Modeling military force structure & interactions � How “irregular travel”, typically long-range travel, is modeled: � • Interactions with other divisions are captured in a manner analogous to the long-range travel in the civilian sector: � • With a specified frequency, soldiers are randomly selected and sent for a period of 1-14 days to a unit outside their own division � • The outside unit is randomly selected, but biased towards those in the same corps to approximate “upward” interaction rates � PLT � CO � PLT � BDE � BTN � CO � BDE � BTN � DIV � DIV �
Demographics and Workflow (#2) and Irregular travel (#4) for Public-military Model for South Korea � Public (#2 and #4) : � • Census for 2000 for the 9 provinces and 6 special cities, ranging from 0.5 to 10 million people each (46 million total) - used in “public” community network. � • Worker-flow data estimated by geographic proximity (no USA census-like data available). � • Random long-range travel by public � Military demographics (#2) : � • Republic of Korea forces down to battalion level � • U.S. forces in South Korea � Military-Civilian interaction (#4): � • Based upon the geographic position of each military unit; soldiers occasionally (very rarely) interact with a random community in the local province/special city. �
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