Modeling Disease Surveillance and Assessing Its Effectiveness for Detection of Acute Respiratory Effectiveness for Detection of Acute Respiratory Outbreaks in Resource-Limited Settings L. Ramac-Thomas 1 , T. Philip 1 , H. Burkom 1 , J. Coberly 1 , S. Happel Lewis 1 , J.P. Chretien 2 1 The Johns Hopkins University Applied Physics Laboratory 2 Walter Reed Army Institute of Research, Global Emerging Infections System 7th Ann al Conference of the 7th Annual Conference of the International Society for Disease Surveillance Track 1: Surveillance Innovations (2) – Evaluation of Detection Approaches Raleigh NC Raleigh, NC December 4 2008 December 4, 2008 This presentation was supported by funding from DoD Global Emerging Infections System (GEIS).
Outline • Objectives • Background • Approach and Methods A h d M h d • Validation and Study Design Validation and Study Design • Results • Conclusions Objectives Background Approach & Methods Validation & Study Design Results Conclusions 2
Acute Respiratory Infection Epidemic Simulator (ARIES) p ( ) Develop model to – Evaluate acute respiratory illness surveillance in resource- Evaluate acute respiratory illness surveillance in resource limited settings – Measure potential benefit of policy decisions and countermeasures countermeasures Required features – Focus on early outbreak stages – Restrict demographic modeling to features relevant to disease spread – Include knowledge of existing surveillance capability – Portability Objectives Background Approach & Methods Validation & Study Design Results Conclusions 3
Background A U.S. Department of Defense program is underway to assess health surveillance in resource-poor settings and to evaluate the Early Warning Outbreak Reporting System (EWORS) Warning Outbreak Reporting System (EWORS) This program has included several information- gathering trips, including a trip to Lao PDR in September 2006 Objectives Background Approach & Methods Validation & Study Design Results Conclusions 4
Challenges of Surveillance in Resource-Poor Regions Healthcare access is limited by – Available transportation – Lack of trained care providers or insurance/ability to pay for care – Preference for herbal, spiritual healers f f – Lack of modern communication technology Magnified Threat of Major Epidemics – Infectious disease outbreaks not uncommon – Many workers at human-animal interface – Vaccine, antiviral supplies scarce if available Objectives Background Approach & Methods Validation & Study Design Results Conclusions 5
Application to EWORS Systems and Lao PDR EWORS Systems – 35 sites in 4 countries in SE Asia and in 2 sites in Peru – Daily transfer of patient records from network hospitals to national hub – Popular at each installation p Initial Application in Lao PDR – Investigative trip by 6-member EWORS International Working I ti ti t i b 6 b EWORS I t ti l W ki Group – Interviews at hospitals, National Center for Laboratory and Epidemiolog Epidemiology, Ministry of Health, and other government agencies Ministr of Health and other go ernment agencies – Collection of healthcare-seeking behavior information – Discussions of both theoretical and actual surveillance practices Objectives Background Approach & Methods Validation & Study Design Results Conclusions 6
Key Features of ARIES • Model is individual based but includes only infected and • Model is individual-based, but includes only infected and exposed • Attention limited to the stages of the event before population behavior radically changes • Assumptions of near-instantaneous detection in published research are unrealistic, especially in resource-limited settings g • Goal is to implement realistic surveillance modeling Objectives Background Approach & Methods Validation & Study Design Results Conclusions 7
Components of ARIES Demographic model generates features relevant to outbreak spread. Disease model simulates progression of disease in infected agents. Travel model simulates agent travel patterns to mimic Travel model simulates agent travel patterns to mimic geographic spread. Surveillance model simulates delays in detection, data entry, y y data transmission, and epidemiologic investigation. Information basis census data, population survey reports, site-visit interviews, acquired data from EWORS, area geography, disease model Objectives Background Approach & Methods Validation & Study Design Results Conclusions 8
Household Model Accurately estimate household makeup of specified province in Lao Considerations sim total 1800 sim urban sim urban – Household size sim rural w/roads 1600 sim rural w/o roads – Age group and sex actual total 1400 ouseholds actual urban – Preserve census actual rural w/roads 1200 actual rural w/o roads dependency ratios dependency ratios Number of Ho 1000 – Sex of household head for 800 comparison to census 600 distributions 400 – P Pregnancy status t t 200 0 1 2 3 4 5 6 7 8 9 10 11 Members per Household Objectives Background Approach & Methods Validation & Study Design Results Conclusions 9
Disease Model Disease stages model [Feighner] – Stage lengths S l h – Probability of complications – Percentage of asymptomatic Disease transmission model [Glass] [ ] – Susceptibility and infectivity are functions of disease stage and age – Transmission is also dependent on type of contact (household, Transmission is also dependent on type of contact (household, peer, random) Objectives Background Approach & Methods Validation & Study Design Results Conclusions 10
Disease Stage Model STAGE 1: STAGE 2: YES Infected, Non- Infected, Non- Stage 2 Symptomatic, Symptomatic, > 2 days? Non-Contagious Non Contagious Contagious Contagious NO STAGE 3a: Infected, Symptomatic Symptomatic, Contagious Person Dies? Recovered NO YES NO YES YES Complications? Dead STAGE 3b: Infected, Symptomatic, Contagious w/Complications w/Complications YES NO Person Dies? Objectives Background Approach & Methods Validation & Study Design Results Conclusions 11
Travel Model Establish location of an infected individual throughout course of infection C Considerations id ti – Subdivide province into rural and urban districts – Update agent locations on time scale of days – Movement is district-to-district – Occupation and age influence agent itinerary – For each new agent location recompute probabilities of travel to For each new agent location, recompute probabilities of travel to other districts – Multi-day trips are allowed Objectives Background Approach & Methods Validation & Study Design Results Conclusions 12
Surveillance Model • To include surveillance in a disease model need to consider both surveillance lag and the effect of surveillance system on surveillance lag lag • Model both traditional surveillance and EWORS • Investigate advantage of proposed EWORS expansion – Additional provincial EWORS hospitals – EWORS systems in chosen district hospitals EWORS systems in chosen district hospitals – Other interventions • EWORS hospital currently in Luang Prabang District EWORS hospital currently in Luang Prabang District • Simulations will also be run with an additional EWORS system at Nam Bak Objectives Background Approach & Methods Validation & Study Design Results Conclusions 13
Surveillance Model Time for Patients to Seek Care Effect of Surveillance System Surveillance Lag Apx. Onset of Illness Time for Dr to ID 1-14 days O tbreak Outbreak Up to Components of Surveillance System Outbreak identification depends on: Without Rapid Test 1. Time from visit to entry into system 1 week Time for patient to seek care 1. Availability of rapid tests 2. Time from entry to analysis depends on: 2. No. (+) tests 3. Time from analysis to review of results 1. Health beliefs & attitudes 3. No. patients with complications 4. Time from review to investigation 2. Income 4. No. &/or rapidity of deaths 5 Time from investigation to action 5. Time from investigation to action Flu A 3. Availability of health care 5. Age patients with 1-3 days 6. Other, e.g. time variation by site & transportation to it complications/death 4. Relationship with HCP 5. Severity of illness 6. Status of patient within H5N1 0-1 days 0-1 0-1 0-2 1-3 household days days days days days days days days Time for PH response depends on: Effect of Surveillance system depends on: 1. How soon investigation is initiated 1. Design of the system 2. How long until LPH acts or contacts RPH a. Paper or electronic p 3. How long until RPH investigates b. Data pre-computerized or specially entered 4. How long until RPH acts or contacts NPH c. Data source & its reporting lag 5. How long until NPH investigates d. System validity (IT & Epi) 6. How long until NPH acts e. Integration of system into surveillance f. Nodal differences 2. PH Belief in system 3 PH U 3. PH Use of system f t Time for Public Health Response to Start Objectives Background Approach & Methods Validation & Study Design Results Conclusions 14
Recommend
More recommend
