We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other. Keywords: Forecasting, Epidemics, Renewal equation, Compartmental mode
<div><p>We constructed dynamic Ebola virus disease (EVD) transmission models to predict epidemic tre...
The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West A...
Public health officials are increasingly recognizing the need to develop disease-forecasting systems...
We describe a relatively simple stochastic model of Ebola transmission that was used to produce fore...
Background: The rising number of novel pathogens threatening the human population has motivated the ...
Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investme...
Infectious disease forecasting is gaining traction in the public health community; however, limited ...
AbstractBackgroundThe rising number of novel pathogens threatening the human population has motivate...
The contributions by epidemic modeling experts describe how mathematical models and statistical fore...
Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in t...
Mathematical modeling offers a powerful toolkit to improve our understanding of infectious disease t...
AbstractMathematical modeling is increasingly accepted as a tool that can inform disease control pol...
The Ebola forecasting challenge organized by the Research and Policy for Infectious Disease Dynamics...
Real-time forecasts of infectious diseases can help public health planning, especially during outbre...
We report on and evaluate the process and findings of a real-time modeling exercise in response to a...
<div><p>We constructed dynamic Ebola virus disease (EVD) transmission models to predict epidemic tre...
The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West A...
Public health officials are increasingly recognizing the need to develop disease-forecasting systems...
We describe a relatively simple stochastic model of Ebola transmission that was used to produce fore...
Background: The rising number of novel pathogens threatening the human population has motivated the ...
Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investme...
Infectious disease forecasting is gaining traction in the public health community; however, limited ...
AbstractBackgroundThe rising number of novel pathogens threatening the human population has motivate...
The contributions by epidemic modeling experts describe how mathematical models and statistical fore...
Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in t...
Mathematical modeling offers a powerful toolkit to improve our understanding of infectious disease t...
AbstractMathematical modeling is increasingly accepted as a tool that can inform disease control pol...
The Ebola forecasting challenge organized by the Research and Policy for Infectious Disease Dynamics...
Real-time forecasts of infectious diseases can help public health planning, especially during outbre...
We report on and evaluate the process and findings of a real-time modeling exercise in response to a...
<div><p>We constructed dynamic Ebola virus disease (EVD) transmission models to predict epidemic tre...
The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West A...
Public health officials are increasingly recognizing the need to develop disease-forecasting systems...