Modeling infectious disease dynamics in the complex landscape of global health

Author:

Heesterbeek Hans1,Anderson Roy M.2,Andreasen Viggo3,Bansal Shweta4,De Angelis Daniela5,Dye Chris6,Eames Ken T. D.7,Edmunds W. John7,Frost Simon D. W.8,Funk Sebastian4,Hollingsworth T. Deirdre910,House Thomas11,Isham Valerie12,Klepac Petra8,Lessler Justin13,Lloyd-Smith James O.14,Metcalf C. Jessica E.15,Mollison Denis16,Pellis Lorenzo11,Pulliam Juliet R. C.1718,Roberts Mick G.19,Viboud Cecile18,

Affiliation:

1. Faculty of Veterinary Medicine, University of Utrecht, Utrecht, Netherlands.

2. School of Public Health, Imperial College, London, UK.

3. Roskilde University, Roskilde, Denmark.

4. Georgetown University, Washington, DC, USA.

5. MRC Biostatistics Unit, Cambridge, UK.

6. WHO, Geneva, Switzerland.

7. Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene Tropical Medicine, London, UK.

8. University of Cambridge, Cambridge, UK.

9. School of Life Sciences, University of Warwick, UK.

10. School of Tropical Medicine, University of Liverpool, UK.

11. Warwick Mathematics Institute, University of Warwick, Coventry, UK.

12. Department of Statistical Science, University College London, London, UK.

13. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

14. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.

15. Department of Zoology, University of Oxford, Oxford, UK, and Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.

16. Heriot-Watt University, Edinburgh, UK.

17. Department of Biology–Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.

18. Division of International Epidemiology and Population Studies, Fogarty International Center, NIH, Bethesda, MD, USA.

19. Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand.

Abstract

Mathematical modeling of infectious diseases The spread of infectious diseases can be unpredictable. With the emergence of antibiotic resistance and worrying new viruses, and with ambitious plans for global eradication of polio and the elimination of malaria, the stakes have never been higher. Anticipation and measurement of the multiple factors involved in infectious disease can be greatly assisted by mathematical methods. In particular, modeling techniques can help to compensate for imperfect knowledge, gathered from large populations and under difficult prevailing circumstances. Heesterbeek et al. review the development of mathematical models used in epidemiology and how these can be harnessed to develop successful control strategies and inform public health policy. Science , this issue 10.1126/science.aaa4339

Funder

Isaac Newton Institute for Mathematical Sciences

Research and Policy for Infectious Disease Dynamics (RAPIDD)

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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