Testing the identification effectiveness of an unknown outbreak of the Infectious Diseases Seeker (IDS) using and comparing the novel coronavirus disease (COVID-19) outbreak with the past SARS and MERS epidemics

Abstract

Background: The aim of this research is to assess the predictive accuracy of the Infectious Diseases Seeker (IDS) - an innovative tool for prompt identification of the causative agent of infectious diseases during outbreaks - when field epidemiological data collected from a novel outbreak of unknown origin are analysed by the tool. For this reason, it has been taken into account the novel coronavirus disease (COVID-19) outbreak, which began in China at the end of December 2019, has rapidly spread around the globe, and it has led to a public health emergency of international concern (PHEIC), declared to the 30th of January 2020 by the World Health Organization (WHO).

Methods: The IDS takes advantage of an off-line database, built before the COVID-19 pandemic, which represents a pivotal characteristic for working without an internet connection. The software has been tested using the epidemiological data available in different and progressive stages of the COVID-19 outbreak. As a comparison, the results of the tests performed using the epidemiological data from the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) epidemic in 2002 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) epidemic in 2012, are shown.

Results: The overall outcomes provided by the software are comforting, as a matter of the fact that IDS has identified with a good accuracy the SARS and MERS epidemics (over 90%), while, as expected, it has not provided erroneous and equivocal readings after the elaboration COVID-19 epidemic data.

Conclusions: Even though IDS has not recognized the COVID-19 epidemic, it has not given to the end user a false result and wrong interpretation, as expected by the developers. For this reason, IDS reveals itself as useful software to identify a possible epidemic or outbreak. Thus, the intention of developers is to plan, once the software will be released, dedicated updates and upgrades of the database (e.g., SARS-CoV-2) in order to keep this tool increasingly useful and applicable to reality.

Keywords: COVID-19; Epidemiology; Infectious Diseases Seeker (IDS); Novel coronavirus; SARS-CoV-2.

Fig. 1

IDS screenshots of “Search” tab or green tab. (A) “Inputs” subtab, (B) “Outcomes” subtab – “Word clouds” section, and (C) “Outcomes” subtab – “More details” section. The example showed refers to SARS epidemic identification.

Fig. 2

IDS outcomes screenshots of “Related disease” word clouds (“Search” tab->”Outcomes” subtab->”Word clouds” section – “Related disease” word clouds). Each word cloud plot is a visual representation of the IDS outcomes and the size and colour of each disease identified indicating its relative accuracy ratio. (A) “Related disease” word cloud for the initial COVID-19 data; (B) “Related disease” word cloud for the latest COVID-19 data; (C) “Related disease” word cloud for SARS epidemic data (China, 2002–2003); and (D) “Related disease” word cloud for MERS epidemic data (Middle East, 2012).

Fig. 3

Bar plots that show the most 10 matched diseases and related accuracy ratio for each disease that has been taken into account: (A) initial COVID-19; (B) latest COVID-19; (C) SARS epidemic (China, 2002–2003); and (D) MERS epidemic (Middle East, 2012).

Fig. 4

SARS and MERS outcomes comparison in cases of initial and latest COVID-19 simulations. In grey, the accuracy of other diseases. The two double red arrows represent the small range of ∼15% accuracy ratio.