COVID-19 Hotspots Research Articles

Sport participation levels among New York City’s kids remain low months after the COVID-19 pandemic

The COVID-19 pandemic resulted in decreased sports participation due to social distancing policies and strict return-to-play protocols. This effect has been felt profoundly in New York City, one of the earliest COVID-19 hotspots, even as restrictions have eased. This study aimed to describe sport participation rates in New York City before and after the COVID-19 pandemic. Using a cross-sectional, online retrospective survey, this study assessed sports participation among children aged 6–17 with musculoskeletal complaints. The survey included parents and guardians of 242 children (123 males, 119 females) who had an average age of 12.5 years. Statistical analyses were performed using McNemar’s test to assess changes in sports participation. The results revealed significant decreases in participation in gym class, pick-up games, basketball, and soccer after the pandemic. Notably, female adolescents were more affected, particularly in gym class. This study underscores a substantial decline in specific sports participation among New York City’s kids post-pandemic. The findings highlight the need for targeted public health strategies to encourage physical activity.

Reviving health mediation during the COVID-19 crisis and beyond: an implementation study in deprived neighbourhoods of Marseille, France.

In 2020, during France's COVID-19 response, healthcare professionals from a hospital and an association initiated health mediation interventions in Marseille's vulnerable neighbourhoods, funded by the regional health authorities. This mixed method research evaluates the CORHESAN program that lasted until June 2022. We examined CORHESAN documents and reports, conducted interviews, and analysed activity data, comparing it to the COVID-19 hotspots identified on a weekly basis at the neighbourhood level, using generalised linear mixed models (GLMMs). CORHESAN was implemented by a team of up to nine health mediators, six private nurses hired on an ad hoc basis, supervised by a general coordinator and two part-time medical and nursing coordinators. Multiple partnerships were established with shelters, associations, social-housing landlords and local institutions. The team accompanied 6,253 people affected by COVID-19 or contact in the practical implementation of their isolation and contact tracing. Of the 5,180 nasopharyngeal samples for RT-PCR and 1,875 for antigenic testing: 12% were taken at home and 27% in partner facilities in the targeted neighbourhoods; 32% were taken from symptomatic patients and 30% in the context of contact tracing; and 40% were positive. Multiple awareness sessions on prevention methods and distributions of personal protection kits and self-diagnostic tests were conducted in the streets, in shelters, in associations or at home. A total of 5,929 doses of COVID-19 vaccine were administered in a walk-in vaccination centre, at temporary street vaccination posts, during operations at partner facilities, or during home-visits to patients with limited autonomy. GLMMs showed that the intervention significantly targeted its testing interventions in neighbourhoods with socioeconomic disadvantage and/or past under-testing (adjusted odds ratio (aOR), 2.75 [1.50-5.00]) and those with high hotspot level (aOR for level-3 versus level-0, 1.83 [1.24-2.71]). The pandemic emphasised the potential of health mediation interventions to address health disparities. Building on this, a new program began in July 2022, aiming at enhancing cancer screening and vaccinations in deprived areas of Marseille. Evaluations are ongoing to assess its activities and impact, and provide evidence to future implementation initiatives.

Addressing multicollinearity in spatial modelling: A district level spatial analysis of pandemic COVID-19 in India.

This study focuses on conducting spatial analysis of COVID-19 at the district level in India. Leveraging data from www.covidindia.org for confirmed cases and deaths, and integrating population characteristics from the National Family Health Survey 5 (2019-2021) and supplementary sources. The objective is to identify risk factors using spatial modelling techniques while addressing multicollinearity through principal component analysis (PCA). This study utilizes spatial analysis to identify COVID-19 hotspots and coldspots at the district level in India. It highlights highly affected districts such as Mumbai, Pune, Chennai, Kolkata, and Bengaluru, as well as low affected districts in central and north-eastern regions. The study utilized the spatial lag model (SLM), spatial error model (SEM), geographical weighted regression (GWR), and multiscale geographical weighted regression (MGWR) models to analyse the impact of demographic, socioeconomic, climatic, and comorbidity factors on COVID-19, accounting for spatial proximity. Among these models, MGWR exhibited superior performance. Key risk factors associated with the COVID-19 phenomenon identified, providing insights into the impact of household conditions, educational level of women, tobacco and alcohol consumption rates, number of health centres, and climatic factors. Moreover, the local coefficients estimated by MGWR model furnish detailed information regarding the strength and direction of the relationships between predictors and COVID-19 cases and deaths within each spatial unit. The findings emphasize the significance of addressing multicollinearity in spatial modelling. It is beneficial for accurate parameter estimation, proper interpretation of coefficients, improved spatial analysis, and providing reliable insights to support decision-making in spatial contexts.

COVID-19 hotspot detection in a university setting.

The onset of the COVID-19 pandemic commenced an era of widespread disruptions in the academic world, including shut downs, periodic shifts to online learning, and disengagement from students. In an effort to transition back to in-person learning, many universities and schools tried to implement policy that balanced student learning with community health. While academic administrators have little control over some aspects of COVID-19 spread, they often choose to use temporary shutdowns of in-person teaching based on perceived hotspots of COVID-19. Specifically, if administrators have substantial evidence of within-group transmission for a class or other academic unit (a "hotspot"), the activities of that class or division of the university might be temporarily moved online. In this article, we describe an approach used to make these types of decisions. Using demographic information and weekly COVID-19 testing outcomes for university students, we use an XGBoost model that produces an estimated probability of testing positive for each student. We discuss variables engineered from the demographic information that increased model fit. As part of our approach, we simulate semesters under the null hypothesis of no in-class transmission, and compare the distribution of simulated outcomes to the observed group positivity rates to find an initial p-value for each group (e.g., section, housing area, or major). Using a simulation-based modification of a standard false discovery rate procedure, we identify possible hot spots-classes or groups whose COVID-19 rates exceed the levels expected for the demographic mix of students in each group of interest. We use simulation experiments and an anonymized example from Fall 2020 to illustrate the performance of our approach. While our example is based on hotspot detection in a university setting, the approach can be used for monitoring the spread of infectious disease within any interconnected organization or population.

Makeshift activism and the afterlives of refugee welcome in Covid-19 Italy

This article captures a shift occurring at the peripheries of the Italian asylum system where, as reception infrastructures are progressively gutted, dismantled, and transformed into security apparatuses, local organizations refocus their efforts on helping refugees and asylum seekers carve out spaces of agency and autonomy in the time-space after institutional reception. I introduce the concept of “makeshift activism” to describe this relentless, creative patching together of solutions to support migrant emplacement beyond and – sometimes – against the confines of official programs. Drawing on ethnographic fieldwork in one of the world’s first Covid-19 hotspots – Italy’s Veneto region, I take the pandemic as a magnifying glass to expose the precarious nature of this activism but also its potential for prefiguring alternatives to the state’s (non-existent) paths towards long-term inclusion. More broadly, I shift the anthropological gaze towards charting the afterlives and aftermaths of refugee “welcome” in less spectacular locales, such as mid-size cities and small municipalities in peripheral mountain regions.

Vaccine Attitudes and Uptake Among Latino Residents of a Former COVID-19 Hotspot

Abstract: Early in the COVID-19 pandemic, the Centers for Disease Control and Prevention identified Prince William County (PWC), Va. as a hotspot with a high disease rate among Latinos. This study uses spatial, survey, and qualitative data to understand attitudes towards vaccine uptake among PWC Latinos. A quantitative analysis (n=266) estimates the association for vaccine acceptance among Latinos. Next, qualitative interviews with Latinos (n=37) examine vaccine attitudes. Finally, a spatial analysis identifies clusters of social vulnerability and low vaccine uptake in PWC and adjacent counties. Our findings show that a substantial proportion of PWC Latinos had low vaccination rates as of December 2022, two years after the vaccine's release. Side effects and safety and approval concerns were cited in both the quantitative and qualitative studies. Persistent vaccine disparities are concerning given the high hospitalization and mortality rates that prevailed among Latinos early in the pandemic.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India’s states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space–time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space–time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062–6.205) and deaths (RR: 2.477; 95% CI: 1.361–4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers’ rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space–time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Well-Being of Children and Families in COVID-19 Hotspots in Chicago.

Families in high-risk communities for COVID-19 transmission experienced a disproportionate burden during the pandemic. This study assessed these families' needs, changes in children's well-being, and perceptions related to the pandemic. Four online surveys were administered January 2021 to September 2021 to parents of students, enrolled in parochial, kindergarten-eighth grade schools in Chicago neighborhoods with higher COVID-19 incidence rates by ZIP code, compared to the city average, and higher resource need. The response rate was 69.1% (n = 186 of 269) in the baseline survey; and other surveys were at 1 (n = 151), 3 (n = 145), and 5 months (n = 154). Of the sample, 83% of parents identified as Hispanic/Latinx with a mean age of 38.3 years (SD: 8.5). Approximately a quarter of parents reported difficulty paying cable and internet bills (26%) and paying utilities (25%). Parents reported children as happy (94% and 95%, p = .59) and hopeful (96% and 95%, p = .74) at 1-month (February to May 2021) and 5-month surveys (June to September 2021). Parents also reported fewer children were irritable (29% vs 19%, p = .03), felt lonely (17% vs 10%, p = .03), and felt isolated (28% vs 9%, p < .001) between those survey waves. The majority (67%) of parents felt that their child had no difficulty wearing a mask in public. In this longitudinal study, Chicago parents rated children's well-being highly and reported a decrease in negative emotions over time. The areas of need identified may be particularly relevant for outreach and providing resources to Hispanic/Latino families in future emergencies or global health threats.

Health status of childcare center providers in a COVID-19 hotspot.

Childcare center providers in Miami-Dade County, Florida a COVID-19 hotspot, are made up almost entirely of ethnic minority women. This is a critical frontline staff that is now encountering the triple threat of respiratory illnesses from respiratory syncytial virus (RSV), influenza viruses (or the seasonal flu), and COVID-19. To examine sociodemographic characteristics, anthropometrics, and health behaviors that were collected from a sample of CCC teachers in Miami Dade County, a COVID-19 hotspot. Cross-sectional data were used from Healthy Caregivers, Healthy Children (HC2), a randomized controlled intervention trial (#NCT02697565) for healthy weight maintenance among children 2-to-5 years old, conducted in 24 subsidized childcare centers in MDC in 2015-2018. Prevalence was determined by frequency or mean/standard deviation of each variable. Chi-squared analyses were performed to test for differences in BMI categories. In this sample of childcare center providers (n = 255), the majority (61%) had an elevated body mass index. Positive health behaviors such as regular exercise and eating fruits and vegetables were only reported in about a third of the sample. It is vital that we encourage the uptake of regular vaccination schedules as a means to protect our community, especially the critical frontline workers that have been caring for our young children.

The impact of government pandemic policies on the vulnerability of healthcare workers to SARS-CoV-2 infection and mortality in Jakarta Province, Indonesia

Introduction Healthcare workers (HCWs) are on the frontlines of the COVID-19 pandemic, putting them at a higher risk of infection and disease than non-HCWs. We analysed the effects of government policies for the public and for HCWs on the likelihood of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and mortality among HCWs during the first 8 months of the pandemic in Jakarta province, the capital city and COVID-19 hotspot in Indonesia. Methods We conducted a retrospective cohort study using secondary data from the Jakarta provincial government from March to October 2020, which included sociodemographic characteristics, symptoms, comorbidities and COVID-19 diagnosis history for all cases. A generalized linear mixed-effect regression model was used to determine the effect of each month on the odds ratio (OR) of COVID-19 cases and deaths for HCW compared with non-HCW, assuming that monthly trends were influenced by varying government policies. Results A total of 894,487 suspected and confirmed COVID-19 cases in health facilities in Jakarta province were analysed. The OR of confirmed cases for HCW was 2.04 (95% CI 2.00–2.08; p < .001) compared to non-HCW. Despite this higher OR for infection, the case fatality rate (2.32 per 100) and OR (1.02, 95% CI 0.93–1.11; p = .65) of COVID-19 deaths for HCW were similar to those of non-HCW. We observed a trend towards a lower number of COVID-19 patients in hospitals and lower odds of COVID-19 cases among HCWs during the April-to-July 2020 phase compared to the August-to-October phase. This chronologically aligned with more extensive policies to support hospital-based, community-based and well-being-related actions to protect HCW. Conclusions HCW had higher odds of having SARS-CoV-2 infection, yet similar odds of death from COVID-19, as compared to non-HCW. Government policies with collective efforts to prevent hospital overcapacity during high transmission and burden periods of the pandemic should be prioritized.

Knowledge and sources of information on COVID-19 among children in Ghana

The few works on COVID-19 in Ghana have a strong emphasis on the knowledge, attitudes, and practices of adults regarding the disease. As a supplement to the inadequate knowledge, this study examined children’s knowledge and sources of information regarding COVID-19. It was a cross-sectional study that employed mixed research methods for data collection and analysis. The study utilized remote research methods, including a web survey and phone interviews, for data collection from a sample of 385 children in two COVID-19 hotspot cities, Accra and Tema, in Ghana. The results show that almost all children sampled were aware of COVID-19. The qualitative aspects, however, reveal that some children do not have accurate knowledge about the causes and prevention of the virus and disease. This is evident from some children’s claims that bacteria, mosquitoes, and insect bites are the causes of the transmission of the virus and disease. The study reveals the internet, social media, and television are the most common methods for children to learn about COVID-19. The findings also show differences in the information sources across the major subgroups of sex, age, and educational attainment. The study further concludes that precautions should be taken by parents and carers regarding information consumed by children on social media. Hence, there is a need for the government and its partners to increase educational drives aimed at strengthening children’s knowledge about the transmission and prevention of the virus in educational institutions and in settings where children and young people are the dominant groups.

Application of Geographic Information Systems in the Study of COVID-19 in Morocco

Introduction: The 2019 coronavirus disease (COVID-19) was first identified as a respiratory disease that originated in Wuhan, Hubei Province, China. The WHO declared the COVID-19 outbreak a public health emergency of international concern on 30 January 2020. Morocco reported its first coronavirus case on 2 March 2020. During the week of 9-15 March 2020, Morocco took steps to limit the spread of the epidemic. This article describes the use of spatial data applications in epidemiological research in Morocco, specifically its response to the COVID-19 epidemic. Methods: To conduct this study, we relied on the use and analysis of data provided by the Moroccan Ministry of Health for the study period from May to July 2021, as well as the geographical and administrative map of the Kingdom of Morocco. Spatial analysis of COVID-19 was performed using ArcGIS 10.8 and QGIS, a geographic information processing software. Health data for the 12 regions of the Moroccan territory were presented in the number of COVID-19 cases as a discrete quantitative variable and over time as a continuous time variable. Results: According to a map created using GIS, the concentration of COVID-19 cases appeared to be highest in the Casablanca Settat region. Depending on the number of documented COVID-19 cases, regions were ranked as follows: Casablanca-Settat&gt; Rabat-Sale-Kenitra&gt; Marrakech-Safi &gt; Fes-Meknes &gt; Tangier-Tetouan-Alhouceima&gt;Oriental&gt;Souss-Massa &gt; Béni Mellal-Khenifra&gt; Draa-Tafilalet&gt; Laayoune-Sakia El Hamra &gt;Guelmim-Oued Noun &gt; Dakhla-Oued Eddahab. The increase in cases in major cities was due to several factors, including demographic, social and environmental factors. This demonstrated the need to consider demographic contributions to environmental health. Demographic factors helped us understand the health of our environment empirically. Geography improved health decision-making and accountability. Incorporating the geographic context of the spread of COVID-19 helped decision-makers understand the impact of location on strategies and goals to combat this pandemic. Conclusion: This study identified areas with high and low COVID-19 clusters and hotspots. The produced maps can serve as an excellent management tool to control and effectively eliminate the COVID-19 pandemic, contributing to investments in epidemiological surveillance programs.

Vaccine efficacy of COVID-19 in Bangladesh: Does vaccination prevent the pandemic?

We adopted a commonly used epidemiologic model known as Susceptible-Infectious-Recovered (SIR) by adding a compartment vaccination to locate the COVID-19 hotspot in Bangladesh. Using data from the Directorate General of Health Services (DGHS), we utilized the Susceptible-Vaccinated-Infectious-Recovered (SVIR) model to assess vaccine efficacy. Vaccination against COVID-19 has begun in industrialized nations and will be completed by early December 2020. Before 2023, mass vaccination for impoverished or low-income nations would be extremely difficult. Bangladesh, a lower-middle-income country, started a statewide COVID- 19 immunization effort in early February 2021. This study aimed to recognize the effectiveness of vaccination on the infected population in Bangladesh. Additionally, the study aimed to create public awareness of the importance of vaccination during a pandemic.&#x0D; GANIT J. Bangladesh Math. Soc. 43.1 (2023) 045- 062

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis.

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic. Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well. The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns. Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

COVID-19 Hotspot Mapping and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis

COVID-19 Hotspot Mapping and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis

The Impact of High-Density Urban Wind Environments on the Distribution of COVID-19 Based on Machine Learning: A Case Study of Macau

The COVID-19 epidemic has become a global challenge, and the urban wind environment, as an important part of urban spaces, may play a key role in the spread of the virus. Therefore, an in-depth understanding of the impact of urban wind environments on the spread of COVID-19 is of great significance for formulating effective prevention and control strategies. This paper adopts the conditional generative confrontation network (CGAN) method, uses simulated urban wind environment data and COVID-19 distribution data for machine training, and trains a model to predict the distribution probability of COVID-19 under different wind environments. Through the application of this model, the relationship between the urban wind environment and the spread of COVID-19 can be studied in depth. This study found that: (1) there are significant differences in the different types of wind environments and COVID-19, and areas with high building density are more susceptible to COVID-19 hotspots; (2) the distribution of COVID-19 hotspots in building complexes and the characteristics of the building itself are correlated; and (3) similarly, the building area influences the spread of COVID-19. In response to long COVID-19 or residential area planning in the post-epidemic era, three principles can be considered for high-density cities such as Macau: building houses on the northeast side of the mountain; making residential building layouts of “strip” or “rectangular” design; and ensuring that the long side of the building faces southeast (the windward side). (4) It is recommended that the overall wind speed around the building be greater than 2.91 m/s, and the optimal wind speed is between 4.85 and 8.73 m/s. This finding provides valuable information for urban planning and public health departments to help formulate more effective epidemic prevention and control strategies. This study uses machine learning methods to reveal the impact of urban wind environments on the distribution of COVID-19 and provides important insights into urban planning and public health strategy development.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic.

The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

Investigating the impacts of socio-economic vulnerability indicators on COVID-19: A case study of Yazd, Iran

Socio-economic vulnerability plays a major role in affected countries by disasters and emergencies. This study aims to identify the most effective socio-economic vulnerability indicators on COVID-19 cases and severity in the Yazd city. This study was conducted in 2022. Regarding purpose of study, different methods were applied during this research. They were including reviewing scientific research, expert panel sessions, weighting the socio-economic vulnerability indicators by Analytic Hierarchy Process (AHP) and examining the spatially relationships between vulnerability indicators and COVID-19. Excel and GIS software were applied for data analysis using local correlation coefficient. AHP analysis showed that employment, population density, buildings quality and distance from hospitals were obtained the most weight in the related indicators of socio-economic vulnerability. GIS mapping overlying showed that four socio-economic vulnerability sub indicators including percentage of immigrants, age, population density and distance from health centers had spatially relationships with COVID-19 cases and severity. Western, northern and some central regions of Yazd were identified as the COVID-19 hot spots. Local officials and health authorities should pay immediate attention to the most influential socio-econimic vulnerability indicators that are dominant in the Yazd city. They incorporate measures to the regions identified as hot spots because people who located in these areas are more vulnerable to COVID-19 and the other future natual or man-made disasters.

Spatial and racial covid-19 disparities in U.S. nursing homes

In many parts of the world nursing home residents have experienced a disproportionate risk of exposure to COVID-19 and have died at much higher rates than other groups. There is a critical need to identify the factors driving COVID-19 risk in nursing homes to better understand and address the conditions contributing to their vulnerability during public health crises. This study investigates the characteristics associated with COVID-19 cases and deaths among residents in U.S. nursing homes from 2020 to 2021, with a focus on geospatial and racial inequalities. Using data from the Centers for Medicare and Medicaid Services and LTCFocus, this paper uses zero-inflated negative binomial regression models, Kruskal-Wallis tests, and Local Moran's I to generate statistical and geospatial results. Our analysis reveals that majority Hispanic facilities have alarmingly high COVID-19 cases and deaths, suggesting that these facilities have the greatest need for policy improvements in staffing and financing to reduce racial inequalities in nursing home care. At the same time we also detect COVID-19 hot spots in rural areas with predominately White residents, indicating a need to rethink public messaging strategies in these areas. The top states with COVID-19 hot spots are Kentucky, Pennsylvania, Illinois, and Oklahoma. This research provides new insights into the socio-spatial contexts and inequities that contribute to the vulnerability of nursing home residents during a pandemic.

Stroke Care Pathway ensures high-quality stroke management in the COVID-19 pandemic

The aim of our study was to assess whether a well-established federal state-wide Stroke Care Pathway delivering high quality stroke care can cope with the COVID-19 pandemic and associated measures to contain the virus spread. The retrospective analysis is based on a prospective, quality-controlled, population-based registry of all stroke patients in the Tyrol, a federal state of Austria and one of the early hot-spots of COVID-19 in Europe. Patient characteristics, pre-hospital management, intra-hospital management and post-hospital were analysed. All residents of the Tyrol suffering ischemic stroke in 2020 (n = 1160) and four pre-COVID-19 years (n = 4321) were evaluated. In 2020, the annual number of stroke patients was the highest in this population-based registry. When local hospitals were overwhelmed with SARS-CoV-2-patients, stroke subjects were temporarily allocated to the comprehensive stroke centre. Stroke severity, quality metrics of stroke management, serious complications, and post-stroke mortality did not differ between 2020 and the four comparator years. Notably, iv. thrombolysis-rate was similar (19.9% versus 17.4%, P = 0.25) and endovascular stroke treatment even better (5.9% versus 3.9%, P = 0.003) but resources for in-patient rehabilitation were limited (25.8% versus 29.8%, P = 0.009). Concluding, a well-established Stroke Care Pathway was able to maintain high-quality acute stroke care even when challenged by a global pandemic.