Mathematical models are often regarded as recent innovations in the description and analysis of infectious disease outbreaks and epidemics, but simple models have been in use for projection of epidemic trajectories for more than a century. We recently described a single equation model (the incidence decay with exponential adjustment, or IDEA, model) that can be used for short term forecasting. ...

Real-time forecasts based on mathematical models have become increasingly important to help guide critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and it has not been established what the best metrics for assessment are. Here, we disentangle different components of forecasting ability by defining three metrics ...

Background Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. Methods We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2...

Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health ...

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models deep learning are built this task. However, most have difficulty estimating the time/region-varying epidemiological parameters, while lack guidance of domain knowledge interpretability results. In study, we propose novel hybrid model called MepoGNN multi-step multi-region forecasting by incorp...

BACKGROUND A forecast can be defined as an endeavor to quantitatively estimate a future event or probabilities assigned to a future occurrence. Forecasting stochastic processes such as epidemics is challenging since there are several biological, behavioral, and environmental factors that influence the number of cases observed at each point during an epidemic. However, accurate forecasts of epid...

background and aims: the world is threatened by disease outbreak that reaches a public health emergency of international concern, studies to inform public and agencies for effective containment strategy are necessary. several studies provided vital information of the dynamics and control of ebola epidemic outbreak in west africa. methodologies and techniques in those researches have to be revie...

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zik...

The Epidemic Marketplace is part of a computational framework for organizing data for epidemic modeling and forecasting. It is a distributed data management platform where epidemiological data can be stored, managed and made available to the scientific community. It includes tools for the automatic interaction with other applications through web services, for the collection of epidemiological d...

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding ...