Abstract:
Dengue fever is a mosquito-borne disease, which is transferred via the bite of an Aedes
mosquito. This is also known as a life threatening disease. It poses significant threats to
global public health, especially in tropical countries.Climate change leading to increased
temperature and high rainfall. The transmission of dengue is strongly influenced by climate
factors such as temperature, rainfall, humidity, precipitation etc. Dengue global
situation as per World Health Organization(WHO) 2023 report, dengue transmission
is periodic, with significant outbreaks predicted every three to four years. The number
of cases reported globally increased tenfold between 2000 and 2019, from 500,000 to
5.2 million. In 2019, dengue fever reached unparalleled peak spreading to 129 countries
worldwide. It is crucial to reduce the dengue casualty due to the lack of vaccines and
drugs.Forecasts that are anticipated can yield vital information during potentially dangerous
epidemic events, empowering medical personnel to make well-informed decisions,
efficiently allocate resources, and carry out prompt interventions to lessen the effects
of an outbreak. People have tried to solve this problem using different techniques and
models like LSTM, ARIMA, Naïve Bayes, Decision Trees, SVMs, ETS, ANNs, ARNN
and Transformers; amongst these, LSTM, moving averages and isolating seasonality
techniques to identify underlying patterns are the current state-of-the-art. Traditional
models face difficulty in accuracy prediction due to inadequate model tuning and selection
of features.The availability of high-quality data is critical to the successful training
of machine learning algorithms. Since neural networks operate in a supervised learning
manner, training becomes a very laborious task. This study introduces a novel deep
learning hybrid approach using various models like Vector autoregression(VAR), Long
Short-Term Memory(LSTM) neural network, hybrid VAR-LSTM, ARIMA and various
other classifiers for the three distinct regional locations namely SanJuan, Ahmedabad
and Iquitos. The objective is to compare their respective performances and determine
how climatic factors play a crucial role and yields the best results for the prediction of
dengue outbreak.
