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Socio-Psychological Consequences of the COVID-19 Pandemic in Croatia

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Socio-Psychological Consequences of the COVID-19 Pandemic in Croatia 2021-4210-AJMS – 24 APR 2021 1 Socio-psychological Consequences of the COVID-19 2 Pandemic in Croatia - Insights into Digital 3 Demographics 4 5 Understanding how people react to the COVID-19 crisis, and which are the 6 consequences of the COVID-19 pandemic on the family‟s life is key to 7 enable public health and other agencies to develop optimal intervention 8 strategies. We gained these insights by using Google Trends (GT). Because 9 the timely identification of new cases of infection has proven to be the key to 10 timely response to the spread of infection within a particular region and the 11 families themselves, we have developed a method that can detect and predict 12 the emergence of new cases of COVID-19 at an early stage. In countries like 13 Croatia which are characterized by a higher share of intergenerational co- 14 residence and contacts among generations with the early detection of the 15 spread of the virus in certain parts of the country, measures such as closing 16 cafes, clubs, switching to online classes, etc. can be introduced quickly to 17 take preventive action. Further, this method can give useful insights into a 18 family‟s life during the pandemic and give the prediction of birth rates. 19 Results: 1.) Google Trends tools are suitable for predicting the emergence 20 of new COVID-19 cases in Croatia. The data collected by this method 21 correlate with official data. In Croatia search activities using GT for terms 22 such as „„PCR +COVID”, and symptoms "cough + corona", “pneumonia + 23 corona”; “muscle pain + corona” correlate strongly with officially reported 24 cases of the disease. 2.) The method also shows effects on family life, 25 increase in stress, and domestic violence. The mental state of the Croatian 26 nation is quite burdened, which can be seen in the increase in searches that 27 indicate an increase in alcoholism, and consumption of tranquilizers. This is 28 probably not only related to the pandemic but also to the two devastating 29 earthquakes that hit Croatia in 2020. 3.) Birth rate in 2021 will be just 87% 30 of what it would be "a normal year“ in Croatia. 4.) This tool can give useful 31 insights into domestic violence. Unquestionably, there are still significant 32 open methodological issues and the questionable integrity of the data 33 obtained using this source. The fact is also a problem that GT does not 34 provide data on which population was sampled or how it was structured. 35 Although these open-ended issues pose serious challenges for making clear 36 estimates, statistics offer a range of tools available to deal with imperfect 37 data as well as to develop controls that take data quality into account. Many 38 of these limitations can be overcome also by triangulation. The benefit of 39 this method is reliable estimates that can enable public health officials to 40 prepare and better respond to the possible return of a pandemic in certain 41 parts of the country and the need for responses to protect family well-being. 42 The method also proved suitable for assessing the consequences of a 43 pandemic on family life, and insights from this method could help, among 44 other instances, the Gender Equality Ombudsman related to indications of 45 an increase in domestic violence during a pandemic, as well as in the 46 adoption of more adequate strategies considering the consequences of 47 quarantine on family life. All these insights show that GT has the potential to 48 capture attitudes in the broad spectrum of family life themes. 49 1 2021-4210-AJMS – 24 APR 2021 1 Keywords: Google Trends, COVID-19, birth rates, domestic violence, 2 Croatia, predicting demographic trends, family 3 4 5 Introduction 6 7 In the absence of medical treatment and vaccination, the mitigation and 8 containment of the ongoing COVID-19 pandemic rely on behavioural changes. 9 Timely data on attitudes and behaviours are thus necessary to develop optimal 10 intervention strategies and to assess the consequences of the pandemic (Perotta 11 et al., 2020). A key problem is a lack of data to assess people's and thus 12 family‟s behaviour and reactions to epidemics. Decision-making and the 13 evaluation of non-pharmaceutical interventions require specific, reliable, and 14 timely data not only about infections but also about human and family 15 behaviour. We seek to narrow this data gap by monitoring individual and 16 family behaviours in response to the COVID-19 pandemic in Croatia. We used 17 Croatia as a case study because the country is an extremely interesting case of 18 studying the consequences of the pandemic on family life. After all, it was the 19 first EU member state, to experience two strong earthquakes (March and Dec. 20 2020), in addition to the pandemic. This further affected the spread of the 21 pandemic and increased risk factors in families and which increased death 22 rates. Furthermore, there are no studies of this type (digital demography) in 23 Croatia and the wider region of Southeast Europe. When it comes to the use of 24 the Internet, Croatia is generally comparable to the EU average so the results of 25 this study can be compared with other EU countries. Here it is important to 26 briefly mention that Google search engine is the most popular search engine in 27 Croatia, preferred by 97.21% of users (StatCounter 2020), similar to EU level 28 (92,92%). 29 The COVID-19 outbreak and lockdown accelerated the adoption of digital 30 solutions at an unprecedented pace, creating unforeseen opportunities for 31 scaling up alternative approaches to social science (Hantrais et al, 2020). We 32 will show that GT has the potential to capture attitudes in the broad spectrum 33 of family life themes. These insights can be very useful to understand and 34 predict some behaviours in the field of public health and for monitoring 35 families but also for predicting new COVID-19 cases in a specific area. The 36 basic methodological concept of our approach is to monitor the digital trace of 37 language searches with the Google Trends analytical tool (trends.google.com). 38 After briefly showing the results of relevant studies in digital demography 39 in the next section, in section 3. we will give a brief overview of the spread of 40 coronavirus in Croatia. This overview is important for the comparison of 41 results that we received with GT with official results. The next section shows 42 how this method can predict the increase in new cases of infection in a 43 particular region promptly and enable the public offices to act accordingly with 44 additional measures. Then, we show how Google Trends can be used as a 45 method to predict the socio-psychological impact of the pandemic in Croatia 46 on families and growth indicators of domestic violence during the pandemic in 47 Croatia. We end the article with Google Trends indicators about the 2 2021-4210-AJMS – 24 APR 2021 1 consequences of the COVID-19 pandemic on the fertility rate in Croatia in the 2 year 2021. 3 Although previous research in this area has shown the feasibility of using 4 digital data for demography, it is unquestionable that there are still significant 5 open methodological issues and the questionable integrity of the data obtained 6 using this source (see Cesare et al., 2018)., which we will discuss in more 7 detail in the section methodology. 8 9 10 How Analysing Google Searches can Support Covid-19 Research 11 12 Online searching is often where people come to get answers on health and 13 wellbeing, whether it‟s to find a doctor or treatment center or understand a 14 symptom better just before a doctor's visit (Gabrilovich, 2020). The pandemic 15 accelerated the uptake of digital solutions in data collection techniques 16 (Sogomonjan, 2020). Livingstone (2020) portrayed digital technologies being 17 harnessed to support public health responses to COVID-19 worldwide. In the 18 short term, face-to-face survey interviews were replaced by online 19 interviewing, and by turning to other data sources. This direction of travel had 20 to be abruptly scaled up as it became the “new normal” for data collection and 21 dissemination (Hantrais et al, 2020). A health monitoring start-up, using 22 natural-language processing and machine learning, correctly predicted the 23 spread of COVID-19 before anybody else (Niiler, 2020). Artificial intelligence 24 (AI) was used extensively and in various forms in the context of COVID-19 25 (COE, 2020). AI applications were introduced to track the pandemic in real- 26 time, to predict accurately where the virus might appear next, and to facilitate 27 the development of an effective vaccine (Sogomonjan, 2020). AI was capable 28 of processing vast amounts of unstructured text data to predict the number of 29 potential new cases by area (Vaishya et al, 2020) and to forecast which types of 30 populations would be most at risk, while also assessing, evaluating, and 31 optimizing strategies for controlling the spread of the epidemic (Kritikos, 32 2020). 33 In the past, researchers have used Google Search data (in the USA, 34 Germany, Italy, etc.) to gauge the health impact of heatwaves, improve 35 prediction models for influenza-like illnesses, and monitor Lyme disease 36 incidence (Gabrilovich, 2020). Google Trends makes available a dataset of 37 search trends for researchers to study the link between symptom-related 38 searches and the spread of COVID-19 with the aim of a better understanding of 39 the pandemic‟s impact (see: searchingCOVID19.com/, 2020).
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