Using the Hurst exponent and entropy measures to predict effective transmissibility in empirical series of malaria incidence
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134897285103616 |