Abstract

Dengue fever, dengue hemorrhagic fever and dengue stroke syndrome are caused by dengue virus (DENV) belonging to the family Flaviviridae and spread through Aedes mosquitoes. Incessant mutation resulting from the proofreading inefficiency of their RNA polymerases under increasing air temperature and CO2 concentration make it thorny to diagnose or strengthening immune system against DENV. The vector competence of Aedes is influenced by diurnal temperature range (DTR). Under these circumstances predicting the genotype of the imminent DENV and vector competence in advance is momentous for vaccine development and vector surveillance. In order to make it efficient, this research aims in designing mobile app ‘DENV SHIELD’ which uses a novel In Vitro Replication Model (IVRM) to predict the nucleotide sequence of imminent genotype of DENV in prior. This app also makes real time prediction of the Vector Competence (VC) based on prediction of DTR corresponding to the region of the user using an Adaptive Network-Based Fuzzy Inference System (ANFIS) model. A self aware artificial intelligence model (SA-AIM) is used for forecasting air temperature and CO2 in advance. DTR is predicted using random forest algorithm which uses ARIMA time series prediction model for getting forecast of wind velocity and solar radiation. Training data are collected from National Center for Biotechnology Information, Centre for Diseases Control and Prevention and National Climate Data Center. The outcome of this research is an early warning system app which will help diagnostic centers and health workers to know genotype of forthcoming variant of virus and vector competence.