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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ftdoajarticles:oai:doaj.org/article:10e75fa9be98410da301864137e3de34 2023-05-15T15:16:51+02:00 Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania Johannes Benecke Cornelius Benecke Marius Ciutan Mihnea Dosius Cristian Vladescu Victor Olsavszky 2021-11-01T00:00:00Z https://doaj.org/article/10e75fa9be98410da301864137e3de34 EN eng Public Library of Science (PLoS) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584970/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 https://doaj.org/article/10e75fa9be98410da301864137e3de34 PLoS Neglected Tropical Diseases, Vol 15, Iss 11 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles 2022-12-31T13:56:35Z 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. Author summary Eastern and South-Eastern Europe is known to be severely affected by parasitic neglected tropical diseases (NTD) due to its tumultuous historical events of the past decades and to its uncontrolled socio-economic fluctuations. Romania is an example of such a South-Eastern European country that was known to have a ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
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 |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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. Author summary Eastern and South-Eastern Europe is known to be severely affected by parasitic neglected tropical diseases (NTD) due to its tumultuous historical events of the past decades and to its uncontrolled socio-economic fluctuations. Romania is an example of such a South-Eastern European country that was known to have a ... |
format |
Article in Journal/Newspaper |
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/10e75fa9be98410da301864137e3de34 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 15, Iss 11 (2021) |
op_relation |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584970/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 https://doaj.org/article/10e75fa9be98410da301864137e3de34 |
_version_ |
1766347144822784000 |