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. 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 high parasitic NTD burden after the fall of Communism in 1989 but has since made significant developmental improvements. However, there is scarce data regarding the incidences of parasitic NTD in Romania after its accession to the European Union in 2007. By using the ICD-10 dataset of Romania over the period 2008–2018, we performed a retrospective epidemiologic analysis of three of its most relevant parasitic diseases, ascariasis, enterobiasis and cystic echinococcosis (CE) and confirmed a downward trend strongly correlating with the country’s decreasing poverty rate. By employing a novel technology called automated time series machine learning we predicted the progress of these diseases for the ensuing two years of 2019 and 2020. Forecasted rates were observed to be constant. Such machine learning tools can help public health officials in adapting and improving targeted measures to combat parasitic NTD.