On the predictability of infectious disease outbreaks

@article{Scarpino2019OnTP,
  title={On the predictability of infectious disease outbreaks},
  author={Samuel V. Scarpino and Giovanni Petri},
  journal={Nature Communications},
  year={2019},
  volume={10}
}
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and… 
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References

SHOWING 1-10 OF 86 REFERENCES
Anticipating epidemic transitions with imperfect data
TLDR
It is demonstrated that most EWS can predict emergence even when calculated from imperfect data, and it is concluded that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases.
Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases.
Prediction of infectious disease epidemics via weighted density ensembles
TLDR
This work applied ensemble methods to predict measures of influenza season timing and severity in the United States, both at the national and regional levels, using three component models and showed average performance that was similar to the best of the component models, but offered more consistent performance across seasons.
Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
TLDR
A general stochastic meta-population model that incorporates actual travel and census data among 3 100 urban areas in 220 countries shows that the integration of long-range mobility and demographic data provides epidemic models with a predictive power that can be consistently tested and theoretically motivated.
Network theory and SARS: predicting outbreak diversity
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
TLDR
A framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting d Dengue incidence in Mexico found short-term and seasonal autocorrelation were key to improving short- term and long-term forecasts.
Flexible Modeling of Epidemics with an Empirical Bayes Framework
TLDR
A framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework was developed and applied to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data.
Forecasting infectious disease emergence subject to seasonal forcing
TLDR
Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing, and wavelet-based early warning statistics can also be used to forecast infectious disease.
...
...