English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Dengue which was first detected mainly in South East Asia during 1940s is now a serious public health concern across the subtropical and temperate regions of Americas, Europe and China due to the change in global climate and international travel. Hence, 3.9 billion people in 128 countries are exposed to the danger of potentially fatal dengue infection. This is a review paper of various dengue forecasting methodology to identify suitable models for predicting the disease occurrence in San Juan, Puerto Rico and Iquitos, Peru. Least Absolute Shrinkage Selector Operator (LASSO) model using climatic variables and Google Trends search terms as predictors was proposed to forecast dengue cases four weeks in advance. LASSO’s flexibility in incorporating a variety of predictors and its ease of interpretation present LASSO as a compelling case against the general predictive models. Public health regulators could make use of such nowcasting model to facilitate the timing of vector control and public health campaigns along with the medical resource allocation to cope with potential dengue outbreaks.

Review on Nowcasting using Least Absolute Shrinkage Selector Operator (LASSO) to Predict Dengue Occurrence in San Juan and Iquitos as Part of Disease Surveillance System