Predicting the Second Wave of COVID-19 Pandemic Through the Dynamic Evolving Neuro Fuzzy Inference System

Susanna Levantesi (), Andrea Nigri () and Gabriella Piscopo ()
Additional contact information
Susanna Levantesi: Sapienza University of Rome, Department of Statistics
Andrea Nigri: University of Foggia, Department of Agricultural Sciences, Food, Natural Resources and Engineering
Gabriella Piscopo: University of Naples Federico II, Department of Economic and Statistical Science

Chapter Chapter 3 in Quantitative Methods in Demography, 2022, pp 37-46 from Springer

Abstract: Abstract In this paper, we make a prediction of the second wave of COVID-19 using a dynamic evolving neuro-fuzzy inference system (DENFIS). The model choice is motivated by the fact that the spread of the pandemic must be read in its dynamism and every prediction cannot ignore the daily updating of available data and new information. We provide results of the prediction of the second wave of COVID-19 across Europe, soliciting to update the model day by day as new information occurs. The study offers to public health stakeholders and Governments a useful tool to analyze the effectiveness of the virus containment measures in the short run and for controlling the COVID-19 spread.

Keywords: Adaptive algorithm; COVID-19; Fuzzy systems; Predictive methods (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-030-93005-9_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030930059

DOI: 10.1007/978-3-030-93005-9_3

Access Statistics for this chapter

More chapters in The Springer Series on Demographic Methods and Population Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().