ArticleScientific reports2023
Correlations between COVID-19 and dengue obtained via the study of South America, Africa and Southeast Asia during the 2020s.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Dengue-SARS-CoV-2 interactions: immune crosstalk, variant emergence, and clinical outcomes.Frontiers in immunology · 2026Review
- Vector-borne diseases and their role in COVID-19 dynamics and death rates: focus on India.Folia microbiologica · 2025Article
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Authors and funding
3 authors.
Funding
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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.
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