Correlations Between COVID-19 and Dengue
This provides a tool for health policy makers to improve disease prediction, but it is incremental as it applies existing neural network methods to new data correlations.
The paper tackled predicting Dengue outbreaks by correlating COVID-19 and Dengue trends using neural networks, and extended this to an LSTM model that estimates Dengue infections from COVID-19 data in countries with limited Dengue data.
A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.