Reconstruction and prediction of viral disease epidemics

M. U. G. Kraemer (1, 2, 3) , D. A. T. Cummings (4) , S. Funk (5) , R. C. Reiner Jr. (6) , N. R. Faria (3) , O. G. Pybus (3) , S. Cauchemez (7, 8)

1 HMS - Harvard Medical School [Boston]
2 Boston Children's Hospital
3 University of Oxford
4 UF - University of Florida [Gainesville]
5 LSHTM - London School of Hygiene and Tropical Medicine
6 University of Washington [Seattle]
7 Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases
8 C3BI - Centre de Bioinformatique, Biostatistique et Biologie Intégrative

A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.

Mots clés: Geographic spread; real-time prediction; viral epidemics

Domaines: Mathématiques [math]

M. U. G. Kraemer, D. A. T. Cummings, S. Funk, R. C. Reiner Jr., N. R. Faria, et al.. Reconstruction and prediction of viral disease epidemics. Epidemiology and Infection, 2019, 147, pp.e34. ⟨10.1017/S0950268818002881⟩. ⟨pasteur-03430143⟩