JOURNAL ARTICLE

TEMPORAL AND SPATIAL MODELING OF THE SPREAD OF DENGUE FEVER IN BRAZIL.

  • Published In: Journal of Biological Systems, 2025, v. 33, n. 1. P. 129 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: RIOS, ADRIANO NICOLA; AMAKU, MARCOS 3 of 3

Abstract

Dengue is a viral infection caused by an arbovirus and spread through the bites of mosquitoes of the genus Aedes which causes significant human and economic damage every year. Preventive measures have been taken to reduce the proliferation of the transmitting mosquito and efforts are being made to obtain an efficient vaccine for the disease. Parallel to these measures, several studies present a model for the spread of the disease in order to determine relevant factors in the process and thus guide preventive measures and future vaccination initiatives. Within this context, this work brings a modeling for the spread of the disease through a system of differential equations where the geographic region studied (a Brazilian city) is divided into sub-regions with human circulation between them, thus making humans the spreader of the disease. Using the model, it was possible to characterize the time series of people infected with dengue in the city of Goiânia, Brazil, between 2000 and 2003 using two and three subdivisions of the geographic region studied. Seasonal cycles of the disease have been described without exhausting the number of susceptible humans. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Biological Systems. 2025/03, Vol. 33, Issue 1, p129
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0218-3390
  • DOI:10.1142/S0218339024500463
  • Accession Number:183581829
  • Copyright Statement:Copyright of Journal of Biological Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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