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BMC Med. 2017 Dec 29;15(1):223. doi: 10.1186/s12916-017-0985-3.

Simulations for designing and interpreting intervention trials in infectious diseases.

Author information

1
Vaccine and Infectious Disease Division, Fred Hutchinson Research Center, 1100 Fairview Ave N, Seattle, WA, 98109, USA. betz@fhcrc.org.
2
Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA. betz@fhcrc.org.
3
Department of Mathematics and Statistics, University of Turku, Turku, Finland.
4
Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington DC, USA.
5
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
6
Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA.
7
Department of Biostatistics, The Fielding School of Public Health, UCLA, Los Angeles, CA, USA.
8
Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand.
9
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
10
Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
11
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
12
Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, USA.
13
Department of Biostatistics, University of Florida, Gainesville, FL, USA.
14
Development Research Group, The World Bank, Washington DC, USA.
15
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
16
Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, Basel, Switzerland.
17
University of Basel, Basel, Switzerland.
18
Network Science Institute, Northeastern University, Boston, MA, USA.
19
Modelling and Economics Unit, Public Health England, Colindale, UK.
20
TB Modelling Group, Centre for Mathematical Modelling of Infectious Diseases, TB Centre and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.

Abstract

BACKGROUND:

Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods.

DISCUSSION:

Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects.

CONCLUSION:

Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

KEYWORDS:

Clinical trial design; Infectious diseases; Mathematical modeling; Simulations; Vaccine

PMID:
29287587
PMCID:
PMC5747936
DOI:
10.1186/s12916-017-0985-3
[Indexed for MEDLINE]
Free PMC Article

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