A simple approach to measure transmissibility and forecast incidence

Epidemics

Volumn 22, Issue , 2018, Pages 29-35

  Nouvellet, Pierre ^a,b   Cori, Anne ^a   Garske, Tini ^a   Blake, Isobel M ^a   Dorigatti, Ilaria ^a   Hinsley, Wes ^a   Jombart, Thibaut ^a,b   Mills, Harriet L ^a   Nedjati Gilani, Gemma ^a   Van Kerkhove, Maria D ^a,c   Fraser, Christophe ^a,b   Donnelly, Christl A ^a,b   Ferguson, Neil M ^a,b   Riley, Steven ^a,b  

^a MEDICAL RESEARCH COUNCIL   (United Kingdom)

^b Institute Pasteur ^*  (United Kingdom)

^c INSTITUT PASTEUR   (France)

Author keywords

Branching process; Forecasting; MCMC; Rapid response; Renewal equation

Indexed keywords

ARTICLE; CONTACT EXAMINATION; DISEASE TRANSMISSION; EBOLA HEMORRHAGIC FEVER; EPIDEMIC; FORECASTING; HUMAN; INCIDENCE; PANDEMIC INFLUENZA; PREDICTION; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SEVERE ACUTE RESPIRATORY SYNDROME; SUSCEPTIBLE POPULATION; COMMUNICABLE DISEASE; STATISTICS AND NUMERICAL DATA; TRANSMISSION;

COMMUNICABLE DISEASES; EPIDEMICS; FORECASTING; HUMANS; INCIDENCE; RETROSPECTIVE STUDIES;

EID: 85016029141     PISSN: 17554365     EISSN: 18780067     Source Type: Journal    
DOI: 10.1016/j.epidem.2017.02.012     Document Type: Article

References (26)

1. Cauchemez, S., Temime, L., Guillemot, D., et al. Investigating heterogeneity in pneumococcal transmission. J. Am. Stat. Assoc. 101:475 (2006), 946–958.
2. Chikungunya Forecasting. 2017 http://www.darpa.mil/news-events/2015-05-27.
3. Chretien, J.-P., Riley, S., George, D.B., Mathematical modeling of the West Africa Ebola epidemic. eLife, 4, 2015, e09186.
4. Cori, A., Ferguson, N.M., Fraser, C., Cauchemez, S., A new framework and software to estimate time-Varying reproduction numbers during epidemics. Am. J. Epidemiol. 178:9 (2013), 1505–1512.
5. Cori, A., Donnelly, C.A., Dorigatti, I., et al. Key data for outbreak evaluation: building on the Ebola experience. Philos. Trans. R. Soc. B, 2017 (in press) 20160371.
6. Dengue Forecasting. 2017 http://dengueforecasting.noaa.gov/.
7. Fraser, C., Estimating individual and household reproduction numbers in an emerging epidemic. PLoS One, 2(8), 2007, e758.
8. Garske, T., Cori, A., Ariyarajah, A., Blake, I.M., Dorigatti, I., Eckmanns, T., Fraser, C., Hinsley, W., Jombart, T., Mills, H.L., Nedjati-Gilani, G., Newton, E., Nouvellet, P., Perkins, D., Riley, S., Schumacher, D., Shah, A., Van Kerkhove, M.D., Dye, C., Ferguson, N.M., Donnelly, C.A., Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013–2016. Phil. Trans. R. Soc. B, 372, 2017, 20160308, 10.1098/rstb.2016.0308 in press.
9. Goldstein, E., Cobey, S., Takahashi, S., Miller, J.C., Lipsitch, M., Predicting the epidemic sizes of influenza A/H1N1, A/H3N2, and B: a statistical method. PLoS Med., 8(7), 2011, e1001051.
10. Gomes, M.F.C., Pastore y Piontti, A., Rossi, L., et al. Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLoS Curr., 6, 2014 (ecurrents. outbreaks. cd818f63d40e24aef769dda7df9e0da5).
11. Haiden, T., Janousek, M., Bauer, P., et al., Evaluation of ECMWF forecasts, including 2013–2014 upgrades, Technical Memorandum of the European Centre for Medium-Range Weather Forecasting 2015, 1–51.
12. Influenza Forecasting. 2017 http://predict.phiresearchlab.org/flu/index.html.
13. International Ebola Response Team, Exposure patterns driving Ebola transmission in West Africa, a retrospective observational study. PLoS Med., 13(11), 2016, e1002170.
14. Meltzer, M.I., Atkins, C.Y., Santibanez, S., et al. Estimating the future number of cases in the Ebola epidemic—Liberia and Sierra Leone, 2014–2015. MMWR Suppl. 63:3 (2014), 1–14.
15. Merler, S., Ajelli, M., Fumanelli, L., et al. Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis. Lancet Infect. Dis. 15:2 (2015), 204–211.
16. Nouvellet, P., Garske, T., Mills, H.L., et al. The role of rapid diagnostics in managing Ebola epidemics. Nature 528:7580 (2015), S109–S116.
17. Nsoesie, E.O., Brownstein, J.S., Ramakrishnan, N., Marathe, M.V., A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir. Viruses 8:3 (2014), 309–316.
18. O'Neill, P.D., Roberts, G.O., Bayesian inference for partially observed stochastic epidemics. J. R. Stat. Soc.: A (Stat. Soc.) 162:1 (1999), 121–129.
19. Roebber, P.J., Bosart, L.F., The complex relationship between forecast skill and forecast value: a real-word analysis. Weather Forecast. 11:4 (1996), 544–559.
20. Van Kerkhove, M.D., Bento, A.I., Mills, H.L., Ferguson, N.M., Donnelly, C.A., A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making. Sci. Data, 2, 2015, 150019.
21. Viboud, C., Simonsen, L., Chowell, G., A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks. Epidemics 15 (2016), 27–37.
22. WHO Ebola Response Team, Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N. Engl. J. Med. 371:16 (2014), 1481–1495.
23. WHO Ebola Response Team, West African Ebola epidemic after one year—slowing but not yet under control. N. Engl. J. Med. 372:6 (2015), 584–587.
24. WHO Ebola Response Team, Ebola virus disease among children in West Africa. New Engl. J. Med. 372:13 (2015), 1274–1277.
25. WHO Ebola Response Team, Ebola virus disease among male and female persons in West Africa. N. Engl. J. Med. 374:1 (2016), 96–98.
26. Wallinga, J., Lipsitch, M., How generation intervals shape the relationship between growth rates and reproductive numbers. Proc. R. Soc. B: Biol. Sci. 274:1609 (2007), 599–604.