Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence

Detalhes bibliográficos
Autor(a) principal: Sequeira, J.
Data de Publicação: 2022
Outros Autores: Louçã, J., Mendes, A. M., Lind, P. G.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/24726
Resumo: We analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we first derive models of the malaria incidence, the Hurst exponent and the entropy as functions of gametocytemia, measuring the infectious power of a mosquito to a human host. Second, upon estimating the values of three observables—incidence, Hurst exponent and entropy—from the data set of different malaria empirical series we predict a value of the gametocytemia for each observable. Finally, we show that the independent predictions show considerable consistency with only a few exceptions which are discussed in further detail.
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spelling Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidenceMalariaHurst exponentShannon entropyLong range dependenceAutocorrelation functionStochastic long memoryGametocytemiaWe analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we first derive models of the malaria incidence, the Hurst exponent and the entropy as functions of gametocytemia, measuring the infectious power of a mosquito to a human host. Second, upon estimating the values of three observables—incidence, Hurst exponent and entropy—from the data set of different malaria empirical series we predict a value of the gametocytemia for each observable. Finally, we show that the independent predictions show considerable consistency with only a few exceptions which are discussed in further detail.MDPI2022-03-09T18:15:52Z2022-01-01T00:00:00Z20222022-03-09T18:15:03Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/24726eng2076-341710.3390/app12010496Sequeira, J.Louçã, J.Mendes, A. M.Lind, P. G.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T18:02:17Zoai:repositorio.iscte-iul.pt:10071/24726Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:33:34.029946Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
title Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
spellingShingle Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
Sequeira, J.
Malaria
Hurst exponent
Shannon entropy
Long range dependence
Autocorrelation function
Stochastic long memory
Gametocytemia
title_short Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
title_full Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
title_fullStr Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
title_full_unstemmed Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
title_sort Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
author Sequeira, J.
author_facet Sequeira, J.
Louçã, J.
Mendes, A. M.
Lind, P. G.
author_role author
author2 Louçã, J.
Mendes, A. M.
Lind, P. G.
author2_role author
author
author
dc.contributor.author.fl_str_mv Sequeira, J.
Louçã, J.
Mendes, A. M.
Lind, P. G.
dc.subject.por.fl_str_mv Malaria
Hurst exponent
Shannon entropy
Long range dependence
Autocorrelation function
Stochastic long memory
Gametocytemia
topic Malaria
Hurst exponent
Shannon entropy
Long range dependence
Autocorrelation function
Stochastic long memory
Gametocytemia
description We analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we first derive models of the malaria incidence, the Hurst exponent and the entropy as functions of gametocytemia, measuring the infectious power of a mosquito to a human host. Second, upon estimating the values of three observables—incidence, Hurst exponent and entropy—from the data set of different malaria empirical series we predict a value of the gametocytemia for each observable. Finally, we show that the independent predictions show considerable consistency with only a few exceptions which are discussed in further detail.
publishDate 2022
dc.date.none.fl_str_mv 2022-03-09T18:15:52Z
2022-01-01T00:00:00Z
2022
2022-03-09T18:15:03Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/24726
url http://hdl.handle.net/10071/24726
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
10.3390/app12010496
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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