Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accura...

The availability of novel digital data streams that can be used as proxy for monitoring infectious disease incidence is ushering in a new era for real-time forecast approaches to disease spreading. Here, we propose the first seasonal influenza forecast framework based on a stochastic, spatially structured mechanistic model (individual level microsimulation) initialized with geo-localized microb...

BACKGROUND The recent emergence of hypervirulent subtypes of avian influenza has underlined the potentially devastating effects of pandemic influenza. Were such a virus to acquire the ability to spread efficiently between humans, control would almost certainly be hampered by limited vaccine supplies unless global spread could be substantially delayed. Moreover, the large increases that have occ...

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenic...

OBJECTIVE To evaluate recurrent neural networks as a predictive technique for time-series in the health field. METHODS The study was carried out during a cholera epidemic which took place in 1993 and 1994 in the state of Ceará, northeastern Brazil, and was based on excess deaths having 'poorly defined intestinal infections' as the underlying cause (ICD-9). The monthly number of deaths with du...

Early detection and modeling of a contagious epidemic can provide important guidance about quelling the contagion, controlling its spread, or the effective design of countermeasures. A topic of recent interest has been to design social network sensors, i.e., identifying a small set of people who can be monitored to provide insight into the emergence of an epidemic in a larger population. We for...

We detail our work on evaluating and selecting a network of related ontologies for caracterising information relevant to the epidemiological domain. Instead of defning a new ontology from scratc, we propose a Network of Epidemiology-Related Ontologies (NERO), whic can be combined to form the core of semantic models in epidemic forecasting infrastructures. Departing from the metadata model of Ep...

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious di...

Porcine epidemic diarrhea virus (PEDV), which is the causative agent of porcine epidemic diarrhea in China and other countries, is responsible for serious economic losses in the pork industry. Inactivated PEDV vaccine plays a key role in controlling the prevalence of PEDV. However, consistently low viral titers are obtained during the propagation of PEDV in vitro; this represents a challenge to...