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

Abstract

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.

Cystic Echinococcosis in Hospitalized Adult Patients from Western Romania: 2007–2022

“…This could be partially attributed to higher awareness of illness in the local community and among healthcare professionals [48]. Another explanation for the decreasing trend could be the implementation of stronger standards concerning safety, food, and health since the accession of Romania to the European Union (EU) in 2007 [49]. Former investigators reported similar results to our findings.…”

Cystic Echinococcosis in Hospitalized Children from Western Romania: A 25-Year Retrospective Study

“…This trend can be attributed to the socio-economic conditions in the territory (change in the way of working in the rural area, the aging of the rural population) or to higher disease awareness among community members and among medical professionals [27,56]. Stronger norms in the areas of safety, food, and health have been implemented after Romania joined the European Union (EU) in 2007, which may be another factor contributing to the downward trend [57]. Similar results were also observed by Amahmid et al [8] and Pierangeli et al [58].…”

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study

“…Given that health care systems generate extensive amounts of so-called big data sets [ 33 , 34 ], AutoML serves as an ideal tool to quickly analyze big data and build predictive models that clinics can use to reduce costs and improve patient care. We have recently performed forecasting of several diseases using AutoML on an International Classification of Diseases, Tenth Revision (ICD-10) database of an entire European country [ 35 , 36 ].…”