Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022

Background and Objectives: The COVID-19 pandemic has had a significant global impact, necessitating a comprehensive understanding of its spatiotemporal patterns. The objective of this study is to explore the spatial and temporal patterns of COVID-19 infections among five age groups (<1, 1–4, 5–9, 10–14, and 15–19 years) in 72 districts of Slovakia on a quarterly basis from March 2020 to July 2022. Material and Methods: During the study period, a total of 393,429 confirmed PCR cases of COVID-19 or positive antigen tests were recorded across all studied age groups. The analysis examined the spatiotemporal spread of COVID infections per quarter, from September 2021 to May 2022. Additionally, data on hospitalizations, intensive care unit (ICU) admissions, pulmonary ventilation (PV), and death cases were analyzed. Results: The highest number of COVID-19 infections occurred between September 2021 and May 2022, particularly in the 10–14-year-old group (68,695 cases), followed by the 15–19-year-old group (62,232 cases), while the lowest incidence was observed in the <1-year-old group (1235 cases). Out of the total confirmed PCR cases, 18,886 individuals required hospitalization, 456 needed ICU admission, 402 received pulmonary ventilation, and only 16 died. The analysis of total daily confirmed PCR cases for all regions showed two major peaks on 12 December 2021 (6114 cases) and 1 February 2022 (3889 cases). Spatial mapping revealed that during December 2021 to February 2022, the highest number of infections in all age groups were concentrated mainly in Bratislava. Moreover, temporal trends of infections within each age group, considering monthly and yearly variations, exhibited distinct spatial patterns, indicating localized outbreaks in specific regions. Conclusions: The spatial and temporal patterns of COVID-19 infections among different age groups in Slovakia showed a higher number of infections in the 10–14-year-old age group, mainly occurring in urban districts. The temporal pattern of the spread of the virus to neighboring urban and rural districts reflected the movement of infected individuals. Hospitalizations, ICU and PV admissions, and deaths were relatively low. The study highlights the need for more proactive measures to contain outbreaks promptly and ensure the resilience of healthcare systems against future pandemics.


Introduction
The coronavirus disease 2019 (COVID-19) has emerged as one of the most significant global health challenges of our time.First identified in Wuhan, China, in December 2019, the virus quickly spread across international borders, leading to a pandemic declaration by the World Health Organization (WHO) in March 2020 [1].The first confirmed case of COVID-19 in Europe was documented in France on 24 January [2], with Slovakia officially reporting its first confirmed case six weeks later on 6 March 2020 [3].Furthermore, as of mid-May 2023, the WHO has reported a global total of 0.766 billion confirmed COVID-19 cases, with Europe accounting for 0.276 billion cases [4].
During the period of 2020-2022, Slovakia may have been affected by several COVID-19 strains, including variants of concern such as the B.1.1.7 and B.1.351strains, which were first reported in the UK and South Africa, respectively [5].Slovakia may have also been affected by other COVID-19 strains such as a variant named B.1.258∆ (approx.59% out of 251 sequenced samples between September and December 2020); this variant has spread and gained significant prevalence in multiple countries, including the Czech Republic, Sweden, Slovakia, Poland, Denmark, and Austria [6].
As countries around the world grappled with the unprecedented impact of the disease, it became crucial to study its spatial and temporal distribution to understand its patterns and guide effective mitigation strategies.Several studies have reported the spatiotemporal spread of COVID-19 in different countries.For example, a study employing a model to examine the spatiotemporal pattern of COVID-19 transmission in the northern provinces of Italy during the initial wave in Spring 2020 revealed a significant correlation between the spread of the virus and highway connections [7].Another study used neighborhood matrices to model the impact of border restrictions between Switzerland and Italy on the spatiotemporal spread of the virus, finding that imposing restrictions decreased the number of cases by 12% compared to no border closure, while no restrictions increased the cases by a 2.7 factor [8]. Furthermore, the implementation of geographically weighted regression models demonstrated that the distribution pattern of COVID-19 exhibited strong spatial clustering.The study revealed that the highest-risk clusters of the disease were primarily concentrated in Central and Western Europe.In Indonesia, the transmission patterns of COVID-19 during first half of the outbreak (March to August of 2020) revealed that all provinces were affected by the virus during a short period of time [9].Furthermore, after considering various variables, it was determined that people living in poverty and elderly populations were most substantially impacted by COVID-19, as they exhibited a significant relationship with the disease [10].A recent study in Brazil [11] showed that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the fatality rate.The influence of age groups, income, level of education, and access to essential services on the spread of COVID-19 was also verified.The recognition of variables that influence the spatial spread of the disease are vital for pinpointing the most vulnerable areas.Therefore, understanding the spatial and temporal spread of the COVID-19 outbreak is critical to predicting local outbreaks and developing public health policies during the early stages of COVID-19.In Slovakia (a landlocked country in Central Europe), the spread of the virus has presented unique challenges.The available literature shows only one spatiotemporal investigation on COVID-19 in Slovakia [12].The study revealed that the distribution of COVID-19 cases in Slovakia is characterized by spatial heterogeneity.By examining the distribution patterns at the district level, this study aims to gain insights into the localized dynamics of the disease within the country.Analyzing the spatial and temporal distribution of COVID-19 cases can provide valuable information on spread patterns, hotspots, clusters, and the effectiveness of control measures implemented in different districts.Therefore, this manuscript aims to provide a detailed examination of the spatial and temporal distribution of COVID-19 cases among five age groups (≤19 years) within different districts of Slovakia.By shedding light on the localized patterns and dynamics of the disease, the study contributes to ongoing efforts to control and manage the impact of the pandemic, both within the country and globally.The coordinates (latitude and longitude) of the locations of PCR-positive, antigen testpositive, and hospitalized cases within different districts of the country were converted into shape files and geospatial maps of the total number of cases in different districts within the country were created using geographic information system (GIS) software (version 10.0).Each location is linked to its respective district boundary, and infection cases were assigned to their respective location within each district.The total number of cases in each district (on a three-month basis) were assigned different colors represent varying levels of infection rates.The total number of cases were separated into 6 categories: (1) no infections (0 cases), (2) <10, (3) 11-100, (4) 101-500, (5) 501-1000, and (6) >1000.

Data
The maps were then used to explore the significance of changes in tracking COVID-19 infections within different districts, focusing on their regular updates every three months (spatial and temporal).
Furthermore, PCR positive cases, positive antigen tests, and in the early stages, positive cases measured clinically by respiratory infection, hospitalized cases, admissions to ICU, PV, and deaths were summarized for each district/region.Tables in Appendix A include a summary for total cases, cases in each age group, cases in each district and region, PCR positive cases, and hospitalizations per district area and population.
The criteria and measures for admission to hospitals, ICU, and PV are as follows: For the PCR test and antigen test, admission is based on suspected COVID-19 cases or positive antigen tests.Children who have been in contact with a suspected case within 2-14 days before the illness started, even if they are asymptomatic, are also admitted if they test positive or exhibit indicators such as fever, respiratory or digestive symptoms, and fatigue.Elevated markers of inflammation, liver enzyme abnormalities, and certain immunological responses are also considered.Imaging techniques like computed tomography (CT) scans, X-rays, and ultrasound examinations are used to detect lung damage.Hospitalization criteria include tachypnea without hypoxemia, oxygen saturation above 94% on atmospheric air, and chronic health problems or young age increasing the risk.Moderate pneumonia symptoms without hypoxemia or respiratory failure, as well as asymptomatic patients with positive CT scan findings, are also considered.Pediatric intensive care unit (PICU) admission criteria include oxygen saturation below 94%, severe febrile illness, dyspnea, tachypnea with cyanosis and oxygen hyposaturation (<92%), illness progression over 7-10 days, and evidence of organ dysfunction.Artificial PV is indicated for refractory hypoxia, respiratory insufficiency, and COVID-19 associated with acute respiratory distress syndrome (ARDS).High severity COVID-19 cases with complications such as shock, coagulation disorders, and organ failures also require ventilation.Severe diarrhea and illness progression over 7-10 days are additional factors for consideration.

Data in General
The total positive daily PCR cases all over the country were drawn; as shown in Figure 1, the inside figure shows total cases in a logarithmic scale.The increase in total cases shows an exponential trend with three peaks occurring during the study period.The first peak occurred between September 2021 and January 2022, the second between January 2022 and March 2022, and the third between March 2022 and April 2022.The maximum total number of cases were observed on 2 December 2021 (3889/393,429, 1%), the second on 1 February 2022 (6114), and the third on 1 March 2022 (2648/393,429, 0.7%).Furthermore, during the early stages of the pandemic (October 2020 to May 2021), the total daily cases were below 700 all over the country.
The total PCR cases on a monthly basis for each region for 2020, 2021, and 2022 (Figure 2a-c) were also drawn.Figure 2a shows that the highest total monthly cases for 2020 were in Trenčín during December (1594); in addition, some regions show two peaks occurring during October and December in Prešov and Žilina, respectively.For 2021 (Figure 2b), the highest numbers were in Prešov (7849) and Žilina (9027) during November.Furthermore, during 2022 (Figure 2b), the highest numbers were observed during February in Bratislava (15,881), Žilina (15,696), and Prešov (15,576).

Spatiotemporal Mapping of COVID Cases
Spatial maps for five age groups were created based on three-month intervals to provide a comprehensive visual representation of the spread of COVID-19 across the country's districts over a span of two years (Figure 3a).Analyzing the spatial maps over these intervals reveals distinct patterns and trends in the spread of COVID-19.For example, during the early months of the pandemic, maps show few isolated clusters of

Spatiotemporal Mapping of COVID Cases
Spatial maps for five age groups were created based on three-month intervals to provide a comprehensive visual representation of the spread of COVID-19 across the country's districts over a span of two years (Figure 3a).Analyzing the spatial maps over these intervals reveals distinct patterns and trends in the spread of COVID-19.For example, during the early months of the pandemic, maps show few isolated clusters of cases in specific districts, mainly in Bratislava and Košice.These clusters are centered around major urban areas or regions with international travel connections, where the virus was likely introduced.As the pandemic progressed, the virus gradually spread to neighboring districts, both in urban and rural regions, reflecting the movement of infected individuals.Maps also show that the southern districts bordering Hungary have less infected cases (with time) compared to other regions, while regions bordering Czech Republic and Poland have higher cases than other regions.This trend is mainly discernable in the 10-14 and 14-19-year-old age groups.
During the early stages of pandemic, maps of the <1-year-old group show only two districts (district-region) were affected during the first three months (Bratislava IV-Bratislava, Detva-Banská Bystrica) followed by (Bytča-Žilina and Nitra-Nitra).The virus then spread to most (about 70%) of the country's districts (Sep 2020 to May 2021).Furthermore, during June to August 2021, the spread was only present in six districts, mainly in the northern parts of the country.During December 2021 to February 2022, maps show that the whole country was infected, where the eastern and northern parts of the country and major areas in the western part of the country show 11-100 cases, while the rest show <10 cases per three-month period, and only one district (Braislava IV) shows 101-500 cases (Figure 3b).
For the 1-4-year-old group, similar trends were observed with more areas having higher numbers of infections.Figure 3c shows that the virus spread over the whole country during Sep 2021 to May 2022.During December 2021 to February 2022, maps for the 1-4-year-old group show four districts with 500-1000 cases (Senec, Žilina, Prešov, and Košice) and one with >1000 (Bratislava).

Discussion
The selection of pediatric and teenage age groups was due to the comparatively milder impact of the pandemic on children, making it rare to observe fatalities.This choice aims to better grasp the spread of COVID-19 within these age groups/demographics.
The UN Convention on the Rights of the Child in the Slovak Republic defines a child as any individual under the age of 19 years, unless otherwise stipulated by the applicable law.Furthermore, a commonly used categorization for different stages of childhood and adolescence is infants (<1 year), young children (1-4 years), children (5-9 years), pre-adolescents (10-14 years), and adolescents (15-19 years).These age groupings are widely recognized and applied in various fields such as healthcare, education, and developmental psychology.
The spatiotemporal maps of Slovakia across the five age groups offer a revealing insight into of COVID-19 over the study period.The maps, segmented into three-month intervals, demonstrate evolving patterns in the virus transmission.Regular updates every three months allow for the monitoring of infection patterns and trends in different districts; in addition, spatial maps can identify districts with higher or lower cases and the presence of hot spots at specific times.
Initially, during the early stages of the pandemic, isolated clusters of cases were primarily observed in major urban hubs like Bratislava and Košice, likely due to international travel connections [13].As time progressed, the virus spread to neighboring urban and rural districts, indicating the movement of infected individuals.Notably, the southern districts bordering Hungary showed fewer infections over time compared to regions bordering Czech Republic and Poland; this was particularly evident in the 10-14 and 14-19-year-old age groups.In the <1-year-old group, the initial impacts were limited to a few districts and gradually encompassed approximately 70% of the country's districts by May 2021.However, a resurgence in infections was evident from June to August 2021, mainly in the northern part of the country.By December 2021 to February 2022, the entire country was affected, with varying intensity across regions.The 1-4-year-old age group exhibited similar trends but with broader geographical coverage and higher infection numbers, including districts with 500-1000 cases and one district with over 1000 cases.Moreover, the 5-9, 10-14, and 15-19-year-old age groups showed widespread infected areas, notably peaking during December 2021 to February 2022, with several districts surpassing 1000 cases and a significant number of districts reporting between 501 and 1000 cases.These findings underscore the varied temporal and spatial dynamics of COVID-19 transmission across different age groups in Slovakia, highlighting the need for targeted intervention strategies and public health measures to mitigate the impact of the pandemic.
The spread of COVID-19 in Slovakia, as depicted in the spatiotemporal maps across different age groups, can be attributed to several factors.Initially, the emergence of isolated clusters in major urban centers such as Bratislava and Košice suggests that the virus was likely introduced through international travel connections, a common pathway for global early infections [13,14].As the pandemic progressed, factors such as population density, mobility within and between regions, and social interactions likely facilitated the virus's spread to urban and rural neighboring districts [15].The observed patterns of infection in districts bordering Hungary, Czech Republic, and Poland could be influenced by crossborder travel and interactions, highlighting the importance of regional dynamics in the virus transmission [16].Additionally, varying levels of adherence to preventive measures, such as closing preschools, primary and secondary schools, mask-wearing, social distancing, and testing availability, could have impacted infection rates across different areas and age groups.The resurgence of infections in specific regions during certain periods, such as the northern region in mid-2021, may reflect localized outbreaks linked to events or gatherings.Moreover, age-specific behaviors and activities could contribute to the observed differences, with teenage groups possibly engaging in more social interactions and activities, leading to higher infection rates.Overall, the complex interplay of geographic, demographic, behavioral, and preventive factors shapes the spatial and temporal dynamics of COVID-19 transmission in Slovakia, emphasizing the need for tailored interventions and vigilant public health measures to curb the spread of the virus.
According to the available data and research, there are several factors that contribute to the higher COVID-19 infection rates among individuals under the age of 19.(1) Behavioral patterns: teenage individuals generally tend to engage in more social interactions than younger ones, which increases their chances of coming into contact with the virus.This age group may be more likely to disregard preventive measures (e.g., social distancing and mask-wearing) [17][18][19].(2) School settings: educational institutions, such as schools and colleges, provide an environment where the virus can easily spread among students due to close contact and shared spaces [20].(3) Asymptomatic cases: younger individuals are more likely to be asymptomatic, meaning they do not exhibit typical COVID-19 symptoms even if they are infected.This makes it harder to identify and isolate cases, leading to further spread within their communities [21].
Therefore, these maps offer valuable insights into the temporal and spatial dynamics of the pandemic, allowing policymakers, healthcare professionals, and the general public to track the progression of the virus and make data-driven decisions.Hotspots, depicted as areas with high infection rates, shift across districts as localized outbreaks occur and are brought under control through targeted measures.These maps also reveal the impact of various factors, such as population density, socioeconomic conditions (urban vs. rural areas), and healthcare infrastructure, on the spread and containment of the virus within different districts [22,23].
By examining the maps over the study period, it becomes evident that the spread of COVID-19 is not uniform throughout the country.Some districts had multiple waves of infections, while others managed to maintain a relatively lower number of cases.This highlights the importance of localized strategies and interventions tailored to specific districts, considering their unique characteristics and vulnerabilities.A detailed spatiotemporal analysis in six periods based on six-month intervals by [12] using the spatial autocorrelation of COVID-19 in Slovakia showed that the spatial distribution of COVID-19 in Slovakia is heterogeneous, and cases were concentrated in all regions depending on the period in which they were monitored.In addition, their research suggested that geographic and temporal analyses will be very important in the future.
A study conducted on eight age groups (<20, 20-29, 30-39, . .., and >80 years) revealed that neighborhood socioeconomic status was a more significant risk factor for COVID-19 incidence in children and working-age adults compared to seniors [24].Social demographics and housing conditions were important risk factors for COVID-19 incidence in older age groups.Additionally, transportation-related variables showed significant associations with COVID-19 incidences across multiple age groups.The study concluded that age played a role in modifying the relationship between neighborhood characteristics and COVID-19 incidence.
A geographic information system (GIS) modeling approach was employed to pinpoint the risk factors influencing COVID-19 incidence rates at the district level.The model outcomes indicated that urban population and proximity to the capital city were significant factors contributing to the COVID-19 incidence rates in Bangladesh.Moreover, the study revealed that urban areas exhibited a higher prevalence of COVID-19 infections compared to rural areas [25].
Demographic studies provide a good understanding of the spread and fatality rates of COVID-19.Several demographic factors contribute to the age-specific infection rates.The main factor is likely to be age itself.Older individuals tend to have a higher risk of contracting the virus due to their weakened immune system and preexisting health conditions (e.g., heart disease, diabetes, or respiratory issues).Other demographic factors, such as healthcare workers or professions that require close contact with others, may increase the risk of exposure to the virus.In addition, living conditions and socioeconomic factors can also impact the likelihood of infection, as individuals in crowded households or low-income communities may have limited access to preventive measures like social distancing or healthcare [26].
A study explored the spatial association between socio-demographic factors and COVID-19 outcomes across Europe [27].In Slovakia, this study showed moderate associations between socio-demographic variables (such as income and poverty) and COVID-19 cases and deaths.This suggests that factors like total population, poverty, and income play a significant role in regulating COVID-19 casualties.The analysis also indicated heterogeneous distributions of cases and deaths across Europe, with Western European countries showing higher actual values.The study underlined the importance of considering infection rates and recovery rates for a more accurate interpretation of COVID-19 impacts.Additionally, spatial regression models highlighted a strong positive association between income, total population, and COVID-19 cases and deaths in European countries, suggesting these as key controlling variables for pandemic outcomes.
In the context of Slovakia, exploring the spatial association between socio-demographic factors and COVID-19 outcomes can provide valuable insights into the potential drivers of transmission and the severity of the disease.By analyzing the geographic distribution of COVID-19 cases, we can identify areas with higher incidence rates and investigate the socio-demographic characteristics that may be associated with these hotspots.This information can help policymakers and health authorities tailor their response efforts to target vulnerable populations and mitigate the further spread of the virus.
Furthermore, the policy of admitting children and adolescents to the hospital in general is easy to uphold, especially during the pandemic in Slovakia; even so, all the admitted cases had symptoms of respiratory infection or gastrointestinal acute disorders like diarrhea and vomiting.Otherwise, the referral of severe COVID-19 cases from small, rural hospitals to large children's hospitals was common; that is why the number of intensive care unit admissions and patients receiving pulmonary ventilation in large teaching children's hospitals significantly increased in comparison to those in smaller hospitals (See Table A3, Appendix A), like hospitals in Nitra, Košice, and Bratislava.
The spatial maps offer a crucial retrospective tool for assessing intervention effectiveness in districts during the COVID-19 pandemic.Policymakers can correlate intervention timing with map patterns to identify successful containment measures and areas needing further attention [23].However, these maps rely on evolving data sources, which are potentially affected by testing capacity and reporting discrepancies among districts, impacting data reliability.This underscores the need for comprehensive data for decision-making.Disparities in testing access can lead to varying case reports, affecting our grasp of the virus's spread.To enhance understanding, these maps should be used alongside other data and local knowledge.Overall, these maps aid in monitoring, analyzing, and responding to COVID-19, and guiding targeted interventions and resource allocation to safeguard public health [28,29].
This study presented a comprehensive analysis of the spatiotemporal patterns of COVID-19 infections across five ≤19-year-old age groups in 72 districts of Slovakia from March 2020 to July 2022.The findings highlight the significant impact of the pandemic on the country and provide valuable insights for understanding the disease's dynamics.
One limitation of this study is the potential concern arising from the substantial loss of outpatient data, especially for patients recruited from hospital settings, which could result in selection bias and affect the validity of the study's findings; however, in this case, the available data were only analyzed for registered cases.

Conclusions
Spatial mapping showed high-risk infection clusters mainly in urban areas with gradual spread to neighboring districts.The highest infections were associated with preadolescent and adolescent groups.The study highlighted the impact of population density and socioeconomic conditions on the spread and containment of the virus.These insights are vital for targeted public health strategies and resource allocation.

Conflicts of Interest:
The authors declare no conflicts of interest.

Appendix A
were obtained from the National Health Information Centre (NCZI), a statefunded organization founded by the Ministry of Health of the Slovak Republic for the period spanning from 12 March 2020 to 20 July 2022 covering 72 districts (8 regions) across Slovakia.Collected data of related COVID-19 infections encompassed the following cases: (1) Polymerase Chain Reaction (PCR) positive tests, positive antigen tests, and in the early stages, positive cases measured clinically by respiratory infection, (2) hospitalized patients, (3) admissions to intensive care units (ICUs), (4) patients who received pulmonary ventilation (PV), and (5) deaths.The records were specifically limited to five age groups: <1, 1-4, 5-9, 10-14, and 15-19 years.

Figure 2 .
Figure 2. (a) Total positive monthly PCR cases in Slovakia during 2020.(b) Total positive monthly PCR cases in Slovakia during 2021.(c) Total positive monthly PCR cases in Slovakia during 2022.

Figure 2 .
Figure 2. (a) Total positive monthly PCR cases in Slovakia during 2020.(b) Total positive monthly PCR cases in Slovakia during 2021.(c) Total positive monthly PCR cases in Slovakia during 2022.

Figure 3 .
Figure 3. (a) Districts of Slovakia.(b) Spatiotemporal spread of positive PCR COVID-19 cases in the <1-year-old group based on three-month intervals in Slovakia.(c) Spatiotemporal spread of positive PCR COVID-19 cases in the 1-4-year-old group based on three-month intervals in Slovakia.(d) Spatiotemporal spread of positive PCR COVID-19 cases in the 5-9-year-old group based on three-month intervals in Slovakia.(e) Spatiotemporal spread of positive PCR COVID-19 cases in the 10-14-year-old group based on three-month intervals in Slovakia.(f) Spatiotemporal spread of positive PCR COVID-19 cases in the 15-19-year-old group based on three-month intervals in Slovakia.

Table 1 .
Case per population and case per area (km 2 ) of all regions of Slovakia.

Table A1 .
Total PCR cases in different age groups, case per population, and case per area (km 2 ) of all regions and districts of Slovakia.
Values in bold indicate total cases in region.

Table A2 .
Total hospitalization cases in different age groups, case per population, and case per area (km 2 ) of all regions and districts of Slovakia.
Values in bold indicate total cases in region.

Table A3 .
Total intensive care unit admissions in different age groups in all regions and districts of Slovakia.
Values in bold indicate total cases in region.

Table A4 .
Total pulmonary ventilation admissions in different age groups all regions and districts of Slovakia.

Table A4 .
Cont.Values in bold indicate total cases in region.