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Richard C. Larson, PI Stan Finkelstein, co-PI Massachusetts Institute of Technology

Preparedness Plans for Pandemic Influenza, with Emphasis on Non-Pharmaceutical Interventions (NPI’s). Richard C. Larson, PI Stan Finkelstein, co-PI Massachusetts Institute of Technology Cambridge, MA 02139 September 16, 2008. Research Supported by the Sloan Foundation, IBM and now….

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Richard C. Larson, PI Stan Finkelstein, co-PI Massachusetts Institute of Technology

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  1. Preparedness Plans for Pandemic Influenza, with Emphasis on Non-Pharmaceutical Interventions (NPI’s) Richard C. Larson, PI Stan Finkelstein, co-PI Massachusetts Institute of Technology Cambridge, MA 02139 September 16, 2008

  2. Research Supported by the Sloan Foundation, IBM and now… • “Decision-Oriented Analysis of Pandemic Flu Preparedness & Response.” • Our team includes research physicians, mathematical modelers, policy analysts and MIT students. • Our work has included new forms of mathematical modeling to address various behavioral responses to the flu, especially those involved with social distancing and hygienic responses. • And we have evaluated a sample of the state pandemic flu plans, as published on the Internet during the past year.

  3. 1918-1919 Influenza Pandemic

  4. SARS, 2003

  5. Richard C. Larson, PI Stan N. Finkelstein, co-PI Karima Nigmatulina Anna Teytelman Katsunobu Sasanuma Summer UROPs (2007) Freshman Advising Seminar: Living Through a Deadly Influenza Pandemic Advisory Board: John V Barry Peggy Enders Daniel Ford Jeffrey Levi Kenneth D Mandl MD William P Pierskalla Kimberly Thompson Eric S Toner MD William C VanSchalkwyk Sanford L Weiner Irving Wladawsky-Berger Pandemic Influenza: Social Distancing Modeling of the Dynamics of an Influenza Pandemic: Seeking Non-Medical Intervention Strategies to Deter its Progression IBM, Sloan Foundation, CDC

  6. Selected Project Papers • Simple Models of Influenza Progression within a Heterogeneous Population, Operations Research, May-June 2007. (RCL) • Stopping Pandemic Flu: Government and Community Interventions in a Multi-Community Model(Karima Nigmatulina & RCL), to appear in EJOR. • Revisiting R0 , the Basic Reproductive Number for Pandemic Influenza. (RCL). • Pandemic Flu: Yes, We Can Do Something About It! To appear in special 'flu issue' of  the Cal-OSHA Reporter. (RCL) • Planning for a Flu Pandemic: Policies to Empower Individuals and Families. Draft- for comments & suggestions. (Shiva Prakash, Stan, Dick) • Pandemic Influenza: Preparations and Plans. DRAFT Report of the MIT Team, distributed to participants of the Workshop on Pandemic Influenza, MIT, April 22, 23, 2008. Distributed for Comments & Suggestions. (all) • John M. Barry, A Conversation with John M. Barry, Oct. 15, 2007. Available on MIT World. http://mitworld.mit.edu/video/499/.

  7. Goals • Improve the Pandemic Preparedness & Response system. • National • State • City • Business • Family (Educate that much control is in our hands!) • Individual levels • Develop improved procedures for analyzing and creating Pandemic Preparedness & Response plans.

  8. Modeling Flu Progression 1: R0 & Effects of Heterogeneities

  9. Framing I Framing I • Social distancing and hygienic steps analysis via mathematical modeling, historical analysis, use of medical research and social science. • Large concern with heterogeneous populations. • By social distancing & hygienic measures, see how low we collectively can make the ‘flu multiplier’ R0. Can we get it below 1.0? • Long-term goal is to design new decision-oriented Flu Preparedness & Response Plans.

  10. Framing II • Decision makers are at all levels: governments, firms, families and individuals. • Time will be of the essence. Lateral alignments, with pre-selected policies will be necessary. • Each state is on its own: We will be in a multi-player cooperative ‘game’. • Imagine 50 simultaneous Hurricane Katrina’s with no possibility of Federal Help.

  11. Universities Are Creating Flu Plans, Too!

  12. Information About Pandemic Influenza Visit this page for current Stanford-related information on the pandemic influenza. Last Updated: August 15, 2006. http://www.stanford.edu/dept/ucomm/news/avianflu.html Public health officials have informed us that one means for minimizing the impact of an infectious disease outbreak in large populations is through “social distancing” of people. This term refers to limiting close contact of individuals so that they are less likely to spread infection. Therefore, our contingency planning includes determining criteria for decisions on issues such as the suspension of classes or the closing of dormitories and asking students to return to their homes.

  13. We Can Learn from SARS http://www.smh.com.au/ffxImage/urlpicture_id_1048962760129_2003/04/01/2wld_sars,0.jpg

  14. Hong Kong Results: Other Respiratory Infections • The SARS epidemic was stopped. • “What were the beneficial effects of the population’s hygienic steps & social distancing?” • Incidence of other acute respiratory viral diseases during the key months April and May 2003 dropped 90% compared to seasonal norms.(seasonal influenza, parainfluenza, respiratory syncytial virus, & adenovirus). • This is best evidence that behavioral modifications can dramatically reduce the spread of respiratory infections. • Any modeling analysis that ignores behavioral changes removes our greatest disease-progression control strategies. http://www.seanbonner.com/power/facemask.gif

  15. Mathematical Complications • Widely different contact patterns • Biologically non-homogenous population • Susceptibility • Infection spread • Behavioral Reactions • We are attempting to create a new adaptive ‘physics model’ of flu progression.

  16. Many Previous Models: • All members of the susceptible population are identical from a modeling point of view. • There is a fundamental input constant for the model, the ‘basic reproductive ratio R0,’ that characterizes the mean number of new influenza infections created by each newly infected person. • Local groups involving at least one infected person are characterized by homogenous mixing, meaning that each susceptible member of the group is equally likely to become infected from any infected person. • Social behavior does not change during the pandemic.

  17. Flu Fundamentals: R0=lp l=frequency of daily contacts (“lambda”) p=probability of transmitting infection, given contact

  18.  Spatiotemporal Epidemiological Modeler screenshot

  19. Social Distancing & Hygienic Policiesto Reduce Prevalence of InfectionSummary of Our Approach • Research at the intersection of Engineering, Management and Social Science. • Aims to inform decision making at multiple levels, esp. preventative decisions relating to reducing prevalence of infection. • Creates a dynamic physics, in which epidemiology is merged with social behavior. • Embraces population heterogeneity. • Links to Social Science and Medical literature, leading to feasible decision policies at all levels.

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