Correlations Between COVID-19 and Dengue

TL;DR

This paper develops neural network models, including LSTM, to analyze and predict the correlated trends of COVID-19 and Dengue, aiding health policy decisions especially in data-scarce regions.

Contribution

It introduces a neural network-based correlation model linking COVID-19 and Dengue trends and extends it with LSTM to estimate Dengue cases from COVID-19 data.

Findings

COVID-19 and Dengue case trends are highly correlated.

The LSTM model can estimate Dengue infections using COVID-19 data.

The approach aids disease monitoring in regions with limited Dengue data.

Abstract

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.