Abstract
Background: The analysis of epidemiological data at an early phase of an epidemiological situation, when the confident correlation of contributing factors to the outcome has not yet been established, may present a challenge for conventional methods of data analysis. Objective: This study aimed to develop approaches for the early analysis of epidemiological data that can be effective in the areas with less labeled data. Methods: An analysis of a combined dataset of epidemiological statistics of national and subnational jurisdictions, aligned at approximately two months after the first local exposure to COVID-19 with unsupervised machine learning methods, including principal component analysis and deep neural network dimensionality reduction, to identify the principal factors of influence was performed. Results: The approach and methods utilized in the study allow to clearly separate milder background cases from those with the most rapid and aggressive onset of the epidemics. Conclusion: The findings can be used in the evaluation of possible epidemiological scenarios and as an effective modeling approach to identify possible negative epidemiological scenarios and design corrective and preventative measures to avoid the development of epidemiological situations with potentially severe impacts.
Highlights
In the early phases of a novel and an unknown infectious disease, prompt and reliable identification of the environmental factors, including physical and social, with the strongest influence on the developing scenario can be of paramount importance in controlling the spread and minimizing the impact on the society
In the analysis of factors that can influence the development of the COVID-19 epidemiological scenario in a given jurisdiction, where epidemiological data is collected and reported, a considerable number of factors of different nature have been investigated, including demographic factors, such as gender and age [1, 2], ethnicity [3], genetic characteristics [4], social habits such as drinking and smoking [5], social factors, such as the condition of a public health care system and quality
The strength of the conclusion derived from the application of these methods is often linked to the volume and confidence of the analyzed data, so the aggregation of significant volumes of data becomes a prerequisite of a confident statement of correlation, for example, in the drug approval process. This process can be time-consuming, and in some cases, it takes a long time to detect side effects [11]. This precondition is certainly necessary for the determination of the safety of products that can be used by millions of consumers; it may present a challenge in novel and rapidly developing situations, such as infectious epidemics, where the time factor can be essential or even critical in developing effective measures
Summary
In the early phases of a novel and an unknown infectious disease, prompt and reliable identification of the environmental factors, including physical and social, with the strongest influence on the developing scenario can be of paramount importance in controlling the spread and minimizing the impact on the society. This precondition is certainly necessary for the determination of the safety of products that can be used by millions of consumers; it may present a challenge in novel and rapidly developing situations, such as infectious epidemics, where the time factor can be essential or even critical in developing effective measures Another challenge may arise due to a large number of factors that may have an influence on the outcome of the epidemics in a given jurisdiction, and identification of the principal ones with the strongest influence on the outcome can be difficult due to the number, complexity, and interaction of contributing factors. The analysis of epidemiological data at an early phase of an epidemiological situation, when the confident correlation of contributing factors to the outcome has not yet been established, may present a challenge for conventional methods of data analysis
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