A simple approach to measure transmissibility and forecast incidence

Highlights • Our simple approach relies on very few parameters and minimal assumptions• Subjective choice of best training period improved forecasts• Despites its simplicity, our model forecasted well under a range scenarios.• This approach can be a natural 'null model' for comparison with methods.

[1]  W. Team.  Ebola virus disease among children in West Africa. , 2015 .

[2]  Mosoka P. Fallah,et al.  Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study , 2016, PLoS medicine.

[3]  M. Meltzer,et al.  Estimating the future number of cases in the Ebola epidemic--Liberia and Sierra Leone, 2014-2015. , 2014, MMWR supplements.

[4]  Alessandro Vespignani,et al.  Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak , 2014, PLoS currents.

[5]  Wes Hinsley,et al.  Ebola Virus Disease among Male and Female Persons in West Africa. , 2016, The New England journal of medicine.

[6]  W. Team.  Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections , 2014 .

[7]  W. Team,et al.  Ebola Virus Disease among Male and Female Persons in West Africa , 2016 .

[8]  W. Team,et al.  West African Ebola Epidemic after One Year — Slowing but Not Yet under Control , 2015 .

[9]  Christl A. Donnelly,et al.  A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making , 2015, Scientific Data.

[10]  Simon Cauchemez,et al.  Investigating Heterogeneity in Pneumococcal Transmission , 2006 .

[11]  Dylan B. George,et al.  Mathematical modeling of the West Africa Ebola epidemic , 2015, eLife.

[12]  M. Lipsitch,et al.  How generation intervals shape the relationship between growth rates and reproductive numbers , 2007, Proceedings of the Royal Society B: Biological Sciences.

[13]  L. Bosart,et al.  The Complex Relationship between Forecast Skill and Forecast Value: A Real-World Analysis , 1996, Weather and Forecasting.

[14]  Thibaut Jombart,et al.  Key data for outbreak evaluation: building on the Ebola experience , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  C. Fraser,et al.  Ebola Virus Disease among Children in West Africa , 2015 .

[16]  C. Fraser,et al.  A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics , 2013, American journal of epidemiology.

[17]  Christl A. Donnelly,et al.  The role of rapid diagnostics in managing Ebola epidemics , 2015, Nature.

[18]  Alessandro Vespignani,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. , 2015, Lancet. Infectious Diseases (Print).

[19]  G. Roberts,et al.  Bayesian inference for partially observed stochastic epidemics , 1999 .

[20]  Sarah Cobey,et al.  Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method , 2011, PLoS medicine.

[21]  C. Viboud,et al.  A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks , 2015, Epidemics.

[22]  C. Fraser,et al.  Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013–2016 , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[23]  Mikiko Senga,et al.  Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections. , 2014, The New England journal of medicine.

[24]  E. Nsoesie,et al.  A systematic review of studies on forecasting the dynamics of influenza outbreaks , 2013, Influenza and other respiratory viruses.