Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.

The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of...

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Autores principales:	Johannes Benecke, Cornelius Benecke, Marius Ciutan, Mihnea Dosius, Cristian Vladescu, Victor Olsavszky
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/196516dd0fc041bda7b6c613be4c5d40
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spelling	oai:doaj.org-article:196516dd0fc041bda7b6c613be4c5d402021-12-02T20:23:32ZRetrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.1935-27271935-273510.1371/journal.pntd.0009831https://doaj.org/article/196516dd0fc041bda7b6c613be4c5d402021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pntd.0009831https://doaj.org/toc/1935-2727https://doaj.org/toc/1935-2735The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.Johannes BeneckeCornelius BeneckeMarius CiutanMihnea DosiusCristian VladescuVictor OlsavszkyPublic Library of Science (PLoS)articleArctic medicine. Tropical medicineRC955-962Public aspects of medicineRA1-1270ENPLoS Neglected Tropical Diseases, Vol 15, Iss 11, p e0009831 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
spellingShingle	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Johannes Benecke Cornelius Benecke Marius Ciutan Mihnea Dosius Cristian Vladescu Victor Olsavszky Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
description	The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.
format	article
author	Johannes Benecke Cornelius Benecke Marius Ciutan Mihnea Dosius Cristian Vladescu Victor Olsavszky
author_facet	Johannes Benecke Cornelius Benecke Marius Ciutan Mihnea Dosius Cristian Vladescu Victor Olsavszky
author_sort	Johannes Benecke
title	Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
title_short	Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
title_full	Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
title_fullStr	Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
title_full_unstemmed	Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
title_sort	retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in romania.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/196516dd0fc041bda7b6c613be4c5d40
work_keys_str_mv	AT johannesbenecke retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania AT corneliusbenecke retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania AT mariusciutan retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania AT mihneadosius retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania AT cristianvladescu retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania AT victorolsavszky retrospectiveanalysisandtimeseriesforecastingwithautomatedmachinelearningofascariasisenterobiasisandcysticechinococcosisinromania
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Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.

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