PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction.
Chunlin YiAram VajdiTanvir FerdousiLee W CohnstaedtCaterina M ScoglioPublished in: Pathogens (Basel, Switzerland) (2023)
Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE-Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE-Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960-2012) and Aedes mosquito occurrences (1960-2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE-Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE-Aedes' risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.
Keyphrases
- aedes aegypti
- dengue virus
- zika virus
- electronic health record
- public health
- neural network
- big data
- risk assessment
- risk factors
- data analysis
- emergency department
- dna methylation
- machine learning
- genome wide
- heavy metals
- current status
- microbial community
- artificial intelligence
- wastewater treatment
- antibiotic resistance genes