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Dynamics Modeling as a Weapon to Defend Ourselves Against Threats from Infectious Diseases and Bioterrorist Attacks SAMSI, February 25, 2011 Hulin Wu,

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Presentation on theme: "Dynamics Modeling as a Weapon to Defend Ourselves Against Threats from Infectious Diseases and Bioterrorist Attacks SAMSI, February 25, 2011 Hulin Wu,"— Presentation transcript:

1 Dynamics Modeling as a Weapon to Defend Ourselves Against Threats from Infectious Diseases and Bioterrorist Attacks SAMSI, February 25, 2011 Hulin Wu, Ph.D., Professor Director, Center for Biodefense Immune Modeling Chief, Division of Biomedical Modeling and Informatics Department of Biostatistics & Computational Biology University of Rochester Medical Center

2 Outline Introduction: Impact of Infectious Diseases to Public Health Dynamic Modeling for HIV Dynamic Modeling for Influenza Conclusions and Discussions Acknowledgement

3 SARS Pandemic November 1, 2002-July 31, 2003 Total Cases: 8096 Death: 774 Death rate: 9.6% 29 countries/regions USA: 27 cases (no death)

4 Bird Flu (H5N1) Epidemics in Human Total Cases: 285 Death: 170 Death Rate: 59.6% 12 countries/regions


6 Flu Pandemics: History 1918 Spanish flu (H1N1) pandemic: kill million people worldwide 1957 Asian Flu (H2N2): 1-4 million infections worldwide, 69,800 deaths in the US 1968 Hong Kong Flu (H3N2): 500,000 infections worldwide, 33,000 deaths in the US

7 An Emergency Hospital for Influenza Patients

8 Two routes to a pandemic H5N1 H3N2 H5N1* H5N1^ Species barrier

9 Annual Influenza Epidemics around the World 5-15% of the population affected 3-5 million cases of severe illness 250, ,000 deaths around the world

10 Current Estimates of the Yearly Disease Burden of Influenza in the US 40, ,000 40,000,000 4,000,000,000 8,000,000,000 Deaths - Hospitalizations - Illnesses - Direct costs ($) - Indirect costs ($) -

11 Global HIV/AIDS Epidemics: 2006 Update



14 New HIV Infection Rate in infections per minute 458 infections per hour

15 Defend Ourselves: Why and How to Use Mathematics/Statistics as a Weapon? Understand pathogenesis of infection by infectious agents Identify therapeutic targets for intervention Design and evaluate the effects of treatments and other intervention/prevention strategies

16 Example: HIV/AIDS Modeling 1 st AIDS case: reported in late 1970s AIDS virus: discovered in 1983, named HTLV AIDS virus renamed as HIV in 1986 HIV dynamics models in late 1980s: Merrill 1987; Mclean 1988; Anderson and May 1989; Perelson 1989 HIV dynamics models for clinical studies: David Ho and Alan Perelson (Nature 1995; Science 1996; Nature 1997) My research in HIV dynamics modeling: 1997-

17 Ho et al, Nature 1995

18 Ho et al., Nature HIV-1 infected patients A new antiviral drug: a protease inhibitor, ABT-538 (Ritonavir) Observations: Viral load declined exponentially in 2 weeks

19 Ho et al., Nature 1995

20 Tap-Tank Model Solution with perfect treatment P=0 Fit a linear regression model c: viral clearance rate 1/c: Mean life-span of HIV virus ln(2/c): Half-life of HIV virus

21 Ho et al., Nature 1995 Estimate of c: 0.34 (range 0.21 to 0.54) Half-life of HIV virus: 2.1 (range1.3 to 3.3) days Daily production and clearance rate of HIV virus: 0.68x10^9 (range 0.05 to 2.07x10^9) virions


23 Perelson et al. and Ho, Science 1996 A more complicated model Solution Clinical data: 5 HIV patients

24 Perelson et al. and Ho, Science 1996 Estimate of c: 3.07 Estimate of δ: 0.49 Half-life of virus: 0.24 (about 6 hours). Half-life of infected cells: 1.55 days


26 Perelson et al. and Ho, Nature 1997 Short-lived infected cells: t 1/2 =1.1 days Long-lived inected cells: t 1/2 =14.1 days Latently infected cells: t 1/2 =8.5 days


28 My Research: HIV and Influenza HIV/AIDS: Use differential equation models to study antiretroviral treatment effects and treatment strategies in HIV/AIDS research Influenza: Use differential equation models to study immune response to influenza infections and vaccinations

29 Dynamic Models for AIDS Treatment HIV Viral Dynamic Model in Vivo Viral fitness is related to antiviral drug efficacy Correlate the lab data to clinical data via the proposed model




33 Influenza Project Center for Biodefense Immune Modeling: funded by NIH from with $21.9 million in total To develop mathematical models and computer simulation tools to simulate immune response to influenza virus To design and conduct experiments to validate the mathematical models and simulation tools To expect that our modeling and simulation tools can help to rapidly design drugs or vaccines to fight against new and possibly engineered viruses


35 A Complex Dynamic System for Influenza Infection: Lee et al 2009 (J. of Virology) 6/26/09 Annual Meeing 6/2/10 Annual Meeting

36 Lung Compartment Sub-Model

37 Fig 1. HKX31 EID 50 /ml titers per murine lung Collected data Fig 2. Cytokine secreting CD8+ T cells per murine lung

38 6/26/09 Annual Meeting Lung Compartment Sub-Model Fig 3. Smoothed data for IgG and IgM pg/ml murine serum Collected data

39 Model Fitting Results

40 Estimation Result Summary –The CTL effect: 6.4x10 -5 /day. Shorten the half-life of infected cells from 1.16 days to 0.59 days in average. –The death rate of infected cells due to effects other than CTL is 0.16/day which is 26% of the death rate during the first 5 days –Antibody effect: IgM dominates the clerance of viral particles with a rate about 4.4/day. Shorten the half- life from 4 hours to 1.8 minutes in average –Antibody IgG: not significant –The clearance rate of viral particles due to factors other than antibody effect: very small.

41 Immune Response Kinetics: Useful Identify antiviral drug and vaccine targets Understand virulent viruses and their properties Prepareness

42 42 DEDiscover Software tool for developing, exploring, and applying differential equation models. Key Features: ODE & DDE Models Real-time interactive simulation Data fitting (Estimation) Clean, Cross-platform GUI High Quality Plots Ver 2.5b: freely available CBIM DEDiscover Software

43 Conclusions and Discussions Efficiently fight against infectious diseases and bioterrorism: –Need global effort with efficient collaborations and communications –Need efficient collaborations and communications among inter-disciplinary scientists –Need long-term effort and huge resources Use any weapons available to defend ourselves including mathematics, computer and statistics Dynamics modeling: an important weapon Can we defend ourselves?


45 Acknowledgments NIAID/NIH grant R01 AI : AIDS Clinical Trial Modeling and Simulations NIAID/NIH grant N01 AI50020: Center for Biodefense Immune Modeling NIAID/NIH grant P30 AI078498: Developmental Center for AIDS Research NIAID/NIH grant R21 AI078842: Analysis of Differential Resistance Emergence Risk for Differential Treatment Applications NIAID/NIH grant RO1 AI087135: Estimation Methods for Nonlinear ODE Models in AIDS Research

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