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Correlations between COVID-19 and dengue obtained via the study of South America, Africa and Southeast Asia during the 2020s.
Bergero, Paula; Schaposnik, Laura P; Wang, Grace.
  • Bergero P; Universidad Nacional de La Plata, CCT, La Plata, Argentina.
  • Schaposnik LP; University of Illinois, Chicago, USA. schapos@uic.edu.
  • Wang G; All Souls College, Oxford, UK. schapos@uic.edu.
Sci Rep ; 13(1): 1525, 2023 01 27.
Article in English | MEDLINE | ID: covidwho-2221857
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Dengue / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: South America / Asia Language: English Journal: Sci Rep Year: 2023 Document Type: Article Affiliation country: S41598-023-27983-9

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Dengue / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: South America / Asia Language: English Journal: Sci Rep Year: 2023 Document Type: Article Affiliation country: S41598-023-27983-9