Using Digital Humanities for Understanding COVID-19: Lessons from Digital History about Earlier Coronavirus Pandemic

(digital) history can give us about the past pandemics and dealing with them. We show that the Google Ngram Viewer (GNV) can discover hidden patterns in history (of pandemics). Our study is searching for evidence that the COVID-19 is not a unique phenomenon in human history. By using the approach of Digital Humanities, we are testing the hypothesis that the flu-like illness that caused loss of taste and smell in the late 19th century (Russian flu) was caused by a coronavirus. This approach could give hints on how the COVID-19 might develop in the following years. The objective was to calculate the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19. The primary methodological concept of our approach is to analyse the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19 with the Google NGram analytical tool. Initially, keywords were chosen that are specific and common for the Russian flu and COVID-19. We show the graphic display on the Y-axis what percentage of words in the selected corpus of books (collective memory) over the years (X-axis) make up the word. To standardise the data, we requested the data from 1800 to 2019 in English, German and Russian (to 2012) book corpora and focused on the ten years before, during and after the outbreak of the Russian flu. We compared this frequency index with “non-epidemic periods” to test the model’s analytical potential and prove the significance of the results. The limitation of this study is that it is difficult to formulate a hypothesis for a microbiological aetiology of a pandemic that occurred 133 years ago based on symptoms. There are indications that COVID-19 is not unique because the Russian flu (1889-1891) might be a coronavirus infection. The most crucial observation of similarities between the Russian flu pandemic and COVID-19 is the loss of smell and taste (anosmia and ageusia). Results show that all the three analysed book corpora (including newspapers and magazines) show the increase in the mention of the symptoms “loss of smell” and “loss of taste” during the Russian flu (1889-1891), which are today undoubtedly proven to be key symptoms of COVID-19. The mention of symptoms and the pandemic-related words fell sharply after the pandemic stopped. According to our analysis of historical records with the approach of GNV, 1) the ‘natural’ length of a pandemic is two to five years; 2) the pandemic stops on its own; 3) the viruses weaken over time; 4) the so-called “herd immunity” is not necessary to stop the pandemic; 5) Our approach has shown that a significant crisis does not need to occur after the COVID-19 pandemic. According to our study, GNV clearly shows the influence that social changes have on word frequency. The results of this study open a discussion on the usefulness of the Google Ngram insights possibilities into past socio-cultural development, i.e. epidemics and pandemics that can serve as lessons for today. However, this method has severe limitations and can be useful only under cautious handling and testing. Despite the numerous indications, we are aware that this thesis still cannot be confirmed and that it requires further historical and medical research. Keywords: Google Ngram, big data, epidemic, COVID-19, Russian flu, digital Humanities