Real-time modeling should be routinely integrated into outbreak response

American Journal of Tropical Medicine and Hygiene

Volumn 98, Issue 5, 2018, Pages 1214-1215

  Bausch, Daniel G ^a,b   Edmunds, John ^a,b  

^a LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^b PUBLIC HEALTH ENGLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ANALYSIS; DATA PROCESSING; DATA QUALITY; EDITORIAL; EPIDEMIC; FORECASTING; HUMAN; ILLNESS TRAJECTORY; MATHEMATICAL MODEL; MEASLES; MEASUREMENT PRECISION; MODEL; PREDICTION; QUALITY CONTROL; RAPID RESPONSE TEAM; GUINEA;

DISEASE OUTBREAKS; FORECASTING; GUINEA; HUMANS; MEASLES;

EID: 85046900632     PISSN: 00029637     EISSN: None     Source Type: Journal    
DOI: 10.4269/ajtmh.18-0150     Document Type: Editorial

References (6)

1. Bausch DG, 2013. West Africa 2013 Ebola: from virus outbreak to humanitarian crisis. Curr Top Microbiol Immunol 411: 63–92.
2. Lofgren ET et al., 2014. Opinion: mathematical models: a key tool for outbreak response. Proc Natl Acad Sci USA 111: 18095–18096.
3. Graham M, Suk JE, Takahashi S, Metcalf CJ, Jimenez AP, Prikazsky V, Ferrari MJ, Lessler J, 2018. Challenges and opportunities in disease forecasting in outbreak settings: a case study of measles in Lola Prefecture, Guinea. Am J Trop Med Hyg 98: 1489–1497.
4. Rivers C, 2014. Ebola: models do more than forecast. Nature 515: 492.
5. Van Kerkhove MD, Ferguson NM, 2012. Epidemic and intervention modelling–a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bull World Health Organ 90: 306–310.
6. Bower H et al., 2017. The UK Public Health Rapid Support Team: A Novel Program Integrating Outbreak Response, Operational Research, and Capacity Building (Abstract). 66th Annual Meeting of the American Society of Tropical Medicine and Hygiene. November 6, 2017, Baltimore, MD.