Dynamic Simulation of an Influenza Pandemic: Planning Aid for Public Health Decision Makers M. Eichner 1, M. Schwehm 1, S.O. Brockmann 2 1 Department of Medical Biometry, University of Tübingen, Germany 2 Baden-Württemberg State Health Office, Stuttgart, Germany If the basic reproduction number is 2.5, the epidemic peak occurs about seven weeks after introduction with 1,500 outpatients per day and 190 occupied hospital beds in a population of 100,000 individuals. Roughly 75% of outpatient visits and hospitalizations occur within three weeks. After 8-10 weeks, the epidemic has lead to 25,600 outpatients, 640 hospitalizations and 170 deaths. Combining various non-pharmaceutical and pharmaceutical interventions, the user can explore reduction of transmission and the shift of the epidemic curve. Susceptible At home (untreated) Very sick Moderately sick Asymp- tomatic Hospitalized (untreated) Infected Extremely sick age risk age At home (treated) age Hospitalized (treated) age Immune and recovered Dead age doctoral visit Immune and convalescent age Attac k rate Outpatients per population Hospitalizati ons per population Deaths per population Germany15%15, USA - moderate30%*15, severe30%*15,0003, CDC35%*17, GB25%25, France25%25, Netherlands30%30, Japan25%*13, Canada35%*16, The transmission of highly patho- genic avian influenza to humans has intensified concerns over the emergence of a new pandemic. Nations all over the world plan for pandemic contingency. Infection types:  asymptomatic infections  moderately sick cases  clinically sick cases (who need medical care)  extremely sick cases (who need hospitalization and may die) Antiviral treatment of cases:  restricted antiviral resources  reduction of contagiousness  reduction of disease duration  reduction of hospitalization Social distancing:  (partial) isolation of cases  general contact reduction  school closing  canceling of mass events Other features:  three age and two risk groups  age-dependent risk of hospitalization and death  contacts by age-mixing matrix  re-distribution of contacts when schools are closed  gamma-distributed sojourn times Model output:  time courses and cumulative values: infections, outpatients, hospitalizations, deaths, costs InfluSim can be used to optimize pandemic preparedness planning on regional and local levels. This helps to reduce the peak pressure on the public health system and to win time for vaccine production. BackgroundInfluenza ModelVisualisation Results Sensitivity AnalysesMethods InfluSim is based on 530 differential equations Optimal Intervention Planning Open Source Project Conclusions InfluSim can be used to optimize pandemic preparedness planning on the regional and local level. For example the optimal starting time for a two week general reduction of contacts is different depending of the optimization goal. Optimal reduction of the total number of outpatients is achieved if interven- tions begin on day 100 while optimal outpatient peak reduction is achieved with interventions starting on day 85 to 90. Optimal delay of the outpatient peak is achieved with intervention before day 85. Fraction of contagiousness occuring during first half of the disease Contact: InfluSim is an open source project. The source code can be downloa- ded from influsim.sourceforge.net. More information is available under Contributions and feature requests are welcome. Optimal begin: About day 100 % 0 0,1 0,2 0,3 0,4 0,5 0, Start day of a two week period of reduction in general contacts Peak value of outpatient visits [%] Optimal begin: Day 85 to 90 % Optimal begin: Before day 85 %