Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

No abstract

No abstract

No abstract

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Background : The worldwide corona virus disease outbreak, generally known as COVID-19 pandemic outbreak resulted in a major health crisis globally. The morbidity and transmission modality of COVID-19 appear more severe and uncontrollable. The respiratory failure and following cardiovascular complications are the main pathophysiology of this deadly disease. Several therapeutic strategies are put forward for the development of safe and effective treatment against SARS-CoV-2 virus from the pharmacological view point but till date there are no specific treatment regimen developed for this viral infection. Purpose : The present review emphasizes the role of herbs and herbs-derived secondary metabolites in inhibiting SARS-CoV-2 virus and also for the management of post-COVID-19 related complications. This approach will foster and ensure the safeguards of using medicinal plant resources to support the healthcare system. Plant-derived phytochemicals have already been reported to prevent the viral infection and to overcome the post-COVID complications like parkinsonism, kidney and heart failure, liver and lungs injury and mental problems. In this review, we explored mechanistic approaches of herbal medicines and their phytocomponenets as antiviral and post-COVID complications by modulating the immunological and inflammatory states. Study design : Studies related to diagnosis and treatment guidelines issued for COVID-19 by different traditional system of medicine were included. The information was gathered from pharmacological or non-pharmacological interventions approaches. The gathered information sorted based on therapeutic application of herbs and their components against SARSCoV-2 and COVID-19 related complications. Methods : A systemic search of published literature was conducted from 2003 to 2021 using different literature database like Google Scholar, PubMed, Science Direct, Scopus and Web of Science to emphasize relevant articles on medicinal plants against SARS-CoV-2 viral infection and Post-COVID related complications. Results : Collected published literature from 2003 onwards yielded with total 625 articles, from more than 18 countries. Among these 625 articles, more than 95 medicinal plants and 25 active phytomolecules belong to 48 plant families. Reports on the therapeutic activity of the medicinal plants belong to the Lamiaceae family (11 reports), which was found to be maximum reported from 4 different countries including India, China, Australia, and Morocco. Other reports on the medicinal plant of Asteraceae (7 reports), Fabaceae (8 reports), Piperaceae (3 reports), Zingiberaceae (3 reports), Ranunculaceae (3 reports), Meliaceae (4 reports) were found, which can be explored for the development of safe and efficacious products targeting COVID-19. Conclusion : K...

Rapidly increasing volumes of news feeds from diverse data sources, such as online newspapers, Twitter and online blogs are proving to be extremely valuable resources in helping anticipate, detect, and forecast outbreaks of rare diseases. This paper presents SourceSeer, a novel algorithmic framework that combines spatio-temporal topic models with sourcebased anomaly detection techniques to effectively forecast the emergence and progression of infectious rare diseases. SourceSeer is capable of discovering the location focus of each source allowing sources to be used as experts with varying degrees of authoritativeness. To fuse the individual source predictions into a final outbreak prediction we employ a multiplicative weights algorithm taking into account the accuracy of each source. We evaluate the performance of SourceSeer using incidence data for hantavirus syndromes in multiple countries of Latin America provided by HealthMap over a timespan of fifteen months. We demonstrate that SourceSeer makes predictions of increased accuracy compared to several baselines and is capable of forecasting disease outbreaks in a timely manner even when no outbreaks were previously reported.

Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to automatically create disease taxonomies and evaluate our model against corresponding human annotated taxonomies. We compare our model accuracies against several state-of-the art word2vec methods. Our results demonstrate that Dis2Vec outperforms traditional distributed vector representations in its ability to faithfully capture taxonomical attributes across different class of diseases such as endemic, emerging and rare.