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. 2018 Mar:22:56-61.
doi: 10.1016/j.epidem.2016.11.003. Epub 2016 Dec 16.

Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

Sebastian Funk  1 , Anton Camacho  2 , Adam J Kucharski  2 , Rosalind M Eggo  2 , W John Edmunds  2
Affiliations

Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

Sebastian Funk et al. Epidemics. 2018 Mar.

Abstract

Real-time forecasts of infectious diseases can help public health planning, especially during outbreaks. If forecasts are generated from mechanistic models, they can be further used to target resources or to compare the impact of possible interventions. However, paremeterising such models is often difficult in real time, when information on behavioural changes, interventions and routes of transmission are not readily available. Here, we present a semi-mechanistic model of infectious disease dynamics that was used in real time during the 2013-2016 West African Ebola epidemic, and show fits to a Ebola Forecasting Challenge conducted in late 2015 with simulated data mimicking the true epidemic. We assess the performance of the model in different situations and identify strengths and shortcomings of our approach. Models such as the one presented here which combine the power of mechanistic models with the flexibility to include uncertainty about the precise outbreak dynamics may be an important tool in combating future outbreaks.

Keywords: Forecasting; Infectious disease dynamics; Outbreak; Real-time modelling.

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Figures

Fig. 1
Fig. 1
Flow between compartments of the transmission model.
Fig. 2
Fig. 2
Fitted (black) and predicted (blue) incidence at time point 4 (week 35) of scenario 1. Median lines and 50% (dark) and 95% (light) credible interval ranges are shown, calculated across all trajectories at every time point. Fitted data points are shown in red, future data points (not included in the fits) in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
Fig. 3
Fig. 3
Parameter estimates of the transmission rate volatility σ (top, blue) and reporting overdispersion ϕ (bottom, red) at time point 4 (week 35) of scenario 1. Shown are the median (vertical bar), interquartile range (box), the most extreme values within 1.5 times the interquartile range (outer lines) and outliers (dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
Fig. 4
Fig. 4
Fitted (black) and predicted (blue) trajectories of the transmission rate at time point 4 (week 35) of scenario 1, shown here rescaled with the infectious period to correspond to the reproduction number Rt. Median lines and 50% (dark) and 95% (light) credible interval ranges are shown. The horizontal dashed lines are at Rt=1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
Fig. 5
Fig. 5
Forecasting performance, shown as proportion of data points (across regions) within the 50% credible interval (left), 95% credible interval (centre) and above the median (right) as a function of the distance in weeks predicted ahead. Shown are the mean and Bayesian 95% confidence interval using a conjugate beta prior (Gelman et al., 2013) across time points and counties of scenario 1. Trend lines for the means were obtained using locally weighted smoothing.
See this image and copyright information in PMC

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