Coronavirus Disease 2019 Hotspot Research Articles

Identification Of Locally Transmitted COVID-19 Spatial Clusters And Hotspots

Background: The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world. We conducted this study to identify locally transmitted COVID-19 spatial clusters and hotspots in this phrase of the fourth wave in Vietnam. Data used and Methods: A total of 9,192 locally transmitted cases confirmed in this phrase in the fourth wave were used in study. Global and local Moran’s I and Getis-Ord’s G_i^* statistics were employed to identify spatial autocorrelation and hotspots of COVID-19 cases. Results: It was found that global Moran’s I statistic indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran’s I statistic successfully identified three high-high spatial clusters of COVID-19 cases in Bac Giang (5,083 cases), Bac Ninh (1,407 cases), and Hanoi (464 cases). In addition, hotspots of COVID-19 cases were mainly detected in Bac Giang (5,083 cases), Bac Ninh (1,470 cases), Hanoi (464 cases), Hai Duong (51 cases), and Thai Nguyen (7 cases). Conclusion: The results of this work offer new perspectives on the geostatistical analysis of COVID-19 clusters and hotspots, which could help policy planners anticipate the dynamics of spatiotemporal transmission and develop critical control measures for SARS-CoV-2 in Vietnam. Future pandemics and epidemics can be avoided and controlled with the help of geospatial analysis techniques.

Experiences of Health-Care Professionals Who Served the Migrant Workers During the COVID-19 Pandemic in Singapore and Its Impact on Morale: A Brief Report.

Coronavirus disease 2019 (COVID-19) has caused psychological distress among health-care professionals (HCP) worldwide, suggesting that morale could also be affected. This warrants further investigation as HCPs' morale directly impacts delivery of quality care and work productivity. This study aims to explore the experiences of HCPs who served migrant workers in a local COVID-19 hotspot in Singapore and the impact on their morale. Eleven volunteer HCPs from a regional hospital in Singapore who served migrant workers in a local COVID-19 hotspot were recruited. Semi-structured face-to-face interviews were conducted, and recordings were transcribed verbatim. Transcripts were analyzed using thematic analysis. Morale of HCPs was evaluated based on responses. Four main themes emerged: motivators, challenges, support, and leadership. Motivators or factors that drove HCPs to serve include varying personal reasons and a sense of duty to do good. Challenges faced by HCPs include a language barrier, keeping up with rapidly changing workflows, fear of contagion, and coping with emotions. Support and leadership were revealed to have boosted HCPs' morale. Peer and social support and effective leadership have potential protective effects on HCPs' morale against negative experiences faced during the COVID-19 pandemic.

Cholera and COVID-19 pandemic prevention in multiple hotspot districts of Uganda: vaccine coverage, adverse events following immunization and WASH conditions survey

BackgroundBetween March, 2020 and December, 2021 due to cholera and coronavirus disease 2019 (COVID-19) pandemics, there were 1,534 cholera cases with 14 deaths and 136,065 COVID-19 cases with 3,285 deaths reported respectively in Uganda. This study investigated mass vaccination campaigns for the prevention of the two pandemics namely: oral cholera vaccine (OCV) and COVID-19 vaccine coverage; adverse events following immunization (AEFI); barriers and enablers for the vaccine uptake and assessed water, sanitation and hygiene (WASH) conditions in the six cholera and COVID-19 hotspot districts of Uganda.MethodsA household survey was conducted between January and February, 2022 in the six cholera hotspot districts of Uganda which had recently conducted OCV mass vaccination campaigns and had ongoing COVID-19 mass vaccination campaigns. The survey randomly enrolled 900 households with 4,315 persons of whom 2,085 were above 18 years. Data were collected using a data entry application designed in KoBoToolbox and analysed using STATA version 14. Frequencies, percentages, odds ratios, means, confidence intervals and maps were generated and interpreted.ResultsThe OCV coverage for dose one and two were 85% (95% CI: 84.2—86.4) and 67% (95% CI: 65.6—68.4) respectively. Among the 4,315 OCV recipients, 2% reported mild AEFI, 0.16% reported moderate AEFI and none reported severe AEFI. The COVID-19 vaccination coverage for dose one and two were 69.8% (95% CI: 67.8–71.8) and 18.8% (95% CI: 17.1–20.5) respectively. Approximately, 23% (478/2,085) of COVID-19 vaccine recipient reported AEFI; most 94% were mild, 0.6% were moderate and 2 cases were severe. The commonest reason for missing COVID-19 vaccine was fear of the side effects. For most districts (5/6), sanitation (latrine/toilet) coverage were low at 7.4%—37.4%.ConclusionThere is high OCV coverage but low COVID-19 vaccine and sanitation coverage with high number of moderate cases of AEFI recorded due to COVID-19 vaccines. The low COVID-19 vaccine coverage could indicate vaccine hesitancy for COVID-19 vaccines. Furthermore, incorporation of WASH conditions assessment in the OCV coverage surveys is recommended for similar settings to generate data for better planning. However, more studies are required on COVID-19 vaccine hesitancy.

Does Social Interaction Spread Fear Among Institutional Investors? Evidence from Coronavirus Disease 2019

We study how social connectedness affected active mutual fund manager trading behavior in the first half of 2020. In the first quarter during which the coronavirus disease 2019 (COVID-19) outbreak occurred, fund managers located in or socially connected to COVID-19 hotspots sold more stock holdings compared with a control group of unconnected managers. The economic impact of social connectedness on stock holdings was comparable with that of COVID-19 hotspots and was elevated among “epicenter” stocks most susceptible to the pandemic shock. In the second quarter, social interaction had an overall negative effect on fund performance, but this effect depended on manager skill; unskilled managers who were connected to the hotspots underperformed, whereas skillful managers suffered no deleterious effect. Our evidence suggests that social connections can intensify salience bias for all but the most skilled institutional investors, and policy makers should be wary of the destabilizing role of social networks during market downturns. This paper was accepted by Gustavo Manso, finance. Funding: This work was supported by the Social Science and Humanities Research Council of Canada. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4814 .

A Bibliometric Visualization Analysis on Vaccine Development of Coronavirus Disease 2019 (COVID-19).

Coronavirus disease 2019 (COVID-19), beginning in December 2019, has spread worldwide, leading to the death of millions. Owing to the absence of definitive treatment, vaccination against COVID-19 emerged as an effective strategy against the spread of the pandemic. Acceptance of the COVID-19 vaccine has advanced considerably, and vaccine-related research has significantly increased over the past three years. This study aimed to evaluate the content and external characteristics of COVID-19 vaccine-related literature for tracking research trends related to the global COVID-19 vaccine with the means of bibliometrics and visualization maps. A total of 18,285 records in 3499 journals were retrieved in the Web of Science Core Collection database and included in the final analysis. China was the first to focus on COVID-19 vaccine research, while European and American countries started late but developed rapidly. The USA and the UK are the top contributors to COVID-19 vaccine development, with the largest number of publications. The University of Washington and Harvard Medical School were the leading institutions, while Krammer, F. from Icahn School of Medicine at Mount Sinai was the author most active and influential to the topic. The New England Journal of Medicine had the highest number of citations and the highest TLS, and was the most cited and influential journal in the field of COVID-19 vaccine research. COVID-19 vaccine research topics and hotspots focused on populations' attitudes towards vaccination, immunity-related information analysis of spike proteins, the effectiveness and side effects of the COVID-19 vaccine, and the public management of epidemic transmission. The findings of this study provide the global status, research hotspots and potential trends in the field of COVID-19 vaccine research, which will assist researchers in mastering the knowledge structure, and evaluating and guiding future developmental directions of COVID-19 vaccine.

Experiences of COVID-19 infection in North Carolina: A qualitative analysis.

It has been demonstrated that marginalized populations across the U.S. have suffered a disproportionate burden of the coronavirus disease 2019 (COVID-19) pandemic, illustrating the role that social determinants of health play in health outcomes. To better understand how these vulnerable and high-risk populations have experienced the pandemic, we conducted a qualitative study to better understand their experiences from diagnosis through recovery. We conducted a qualitative study of patients in a North Carolina healthcare system's registry who tested positive for COVID-19 from March 2020 through February 2021, identified from population-dense outbreaks of COVID-19 (hotspots). We conducted semi-structured phone interviews in English or Spanish, based on patient preference, with trained bilingual study personnel. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge, diagnosis, disease experience, and long-term impacts. The 10 patients interviewed from our COVID-19 hotspot clusters were of equal distribution by sex, predominantly Black (70%), aged 22-70 years (IQR 45-62 years), and more frequently publicly insured (50% Medicaid/Medicare, vs 30% uninsured, vs 20% private insurance). Major themes identified included prior knowledge of COVID-19 and patient perceptions of their personal risk, the testing process in numerous settings, the process of quarantining at home after a positive diagnosis, the experience of receiving medical care during their illness, and difficulties with long-term recovery. Our findings suggest areas for targeted interventions to reduce COVID-19 transmission in these high-risk communities, as well as improve the patient experience throughout the COVID-19 illness course.

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods.

A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support.

With 25% confirmed cases of the country's total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran's I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

Misalignment between COVID-19 hotspots and clinical trial sites.

Hundreds of interventional clinical trials have been launched in the United States to identify effective treatment strategies for combating the coronavirus disease 2019 (COVID-19) pandemic. However, to date, only a small fraction of these trials have completed enrollment, delaying the scientific investigation of COVID-19 and its treatment options. This study presents novel metrics to examine the geographic alignment between COVID-19 hotspots and interventional clinical trial sites and evaluate trial access over time during the evolving pandemic. Using temporal COVID-19 case data from USAFacts.org and trial data from ClinicalTrials.gov, U.S. counties were categorized based on their numbers of cases and trials. Our analysis suggests that alignment and access have worsened as the pandemic shifted over time. We recommend strategies and metrics to evaluate the alignment between cases and trials. Future studies are warranted to investigate the impact of the misalignment of cases and clinical trial sites on clinical trial recruitment.

Special Considerations in the Management of Autoimmune Hepatitis in COVID-19 Hotspots: A Review.

The ongoing coronavirus disease-2019 (COVID-19) pandemic has necessitated special considerations in the management of diseases. The way presence of pre-existing diseases or treatment for it predisposes to, alters course of, and changes the management of COVID-19, is of relevance and is being extensively studied. Autoimmune hepatitis (AIH) is unique in that it is an autoimmune disease mandating treatment with immunosuppressive drugs, as well as a liver disease with potential for varying degrees of underlying fibrosis. The use of immunosuppressive drugs could alter the risk of acquiring COVID-19, the clinical course and severity of COVID-19 and the degree of underlying liver fibrosis could alter the clinical outcomes of patients with COVID-19. In this review, we try to summarize key areas relevant in understanding and improving the clinical care of patients with AIH in the current pandemic. Special considerations required in the management of patients with AIH in COVID-19 hotspots have been outlined based on the current evidence.

Studying the social determinants of COVID-19 in a data vacuum.

Race‐based and other demographic information on COVID‐19 patients is not being collected consistently across provinces in Canada. Therefore, whether the burden of COVID‐19 is falling disproportionately on the shoulders of particular demographic groups is relatively unknown. In this article, we first provide an overview of the available geographic and demographic data related to COVID‐19. We then make creative use of these existing data to fill the vacuum and identify key demographic risk factors for COVID‐19 across Canada's health regions. Drawing on COVID‐19 counts and tabular census data, we examine the association between communities’ demographic composition and the number of COVID‐19 infections. COVID‐19 infections are higher in communities with larger shares of Black and low‐income residents. Our approach offers a way for researchers and policymakers to use existing data to identify communities nationwide that are vulnerable to the pandemic in the absence of more detailed demographic and more granular geographic data.

Blockchain Enabled Tracking of Physician Burnout and Stressors During the COVID-19 Pandemic

With more than 54 million cases worldwide, the novel coronavirus disease 2019 (COVID-19) has disrupted every aspect of public life and poses an ever-growing burden on an already strained healthcare system. The emergence of new COVID-19 hot-spots and super-spreader events are placing an immense amount of stress on hospital systems responding to the increased influx of critically ill patients. Healthcare workers and frontline physicians disproportionately bear the additional physical and psychological burdens associated with the appearance of a new hot-spot. However, the mental health implications of COVID-19 for physicians are not well understood. In this perspective piece, we discuss widespread effects of the ongoing pandemic on physician mental health and how blockchain infrastructure can enable digital health technologies in supporting data-driven fatigue mitigation interventions. This is particularly applicable to simplifying physician workflows in newly emerging hot-spots.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh.

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p≤0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

Pediatric Care Struggles of US Trained International Medical Graduate Pediatricians in COVID-19 Pandemic.

BackgroundPediatrician shortage and healthcare access has been a serious issue especially in medically underserved and rural areas aplenty in the USA and has further worsened during the coronavirus disease 2019 (COVID-19) pandemic. Many US trained international medical graduates (IMGs) on a visa status serve these areas to fill in the physician gap. These physicians are usually on a visa and the majority of them have approved immigration petitions. During this pandemic, the sudden changes in immigration policies in addition to the longstanding administrative backlog and processing times had posed new challenges to the pediatricians and the communities served by them. The objective of this study was to determine the demographics, level of training and practice, immigration status, the clinical role they played in the communities they served and the various professional and personal setbacks they faced during the pandemic.MethodsA survey was created and data were collected using data collection platform “Survey Monkey”. Screening questions were designed to include only IMG pediatricians on a visa status.ResultsA total of 267 IMG pediatricians qualified for the survey on a nationwide basis. Of the physicians that participated in the survey, 58.4% were working in either medically underserved or physician shortage areas, 36% of the total physicians were working in a rural setting, 10.6% of the pediatricians had to be quarantined due to exposure to COVID-19, 0.8% were infected with COVID-19 themselves, and 81.3% of the pediatricians had faced hindrance in being able to work at a COVID-19 hotspot due to work site restrictions because of their visa status.ConclusionIMG pediatricians play a valuable role in taking care of the children in medically underserved areas. The challenges surrounding the immigration backlog are contributing to significant hardships for these pediatricians and their families and are causing a hindrance to healthcare access to the children in medically underserved communities during the pandemic especially limiting the pediatricians’ scope and geographic radius of the practice, thus not allowing them to practice to the full extent of their license.

Adult Bacille Calmette-Guérin vaccination during the pandemic of COVID-19 in India

The pandemic of coronavirus disease 2019 (COVID-19) has impacted many health service systems including tuberculosis (TB) control in India. As of October 19, 2020, India has the second highest number of COVID cases globally, amounting to 7.55 million reported COVID-19 cases and 114,640 deaths. Indian Council of Medical Research's Bacille Calmette-Guérin vaccine study among elderly individuals in COVID-19 hotspots involves the following strategy such as COVID screening by antibody testing and real-time reverse-transcriptase-polymerized chain reaction, TB screening by symptom and chest X-ray, and those who are tested positive will be linked to the national tuberculosis elimination programme for the management, this could be a sustainable new strategy in combating the two pandemic diseases, especially in India with high TB and COVID-19 disease burden. To ensure no one is left behind, the paradigm shift of screening for TB and COVID should be in place to sustain the progress made toward TB elimination.

COVID-19 Hotspot Trend Prediction Using Hybrid Cellular Automata in India

The coronavirus disease 2019 (COVID-19) is an infectious disease identified at Wuhan, China, in December 2019 caused by new Coronavirus. The Indian government has taken many initiatives to mitigate the effect of COVID by encouraging the standard mechanisms of social distancing, the use of masks, and various safety parameters. COVID-19 hotspot identifies regions in India where COVID-19 severity is very high. We propose a novel hybrid cellular automata classifier for predicting the trend of various Hotspots in India, processing different parameters including infection control, virus reproduction rate, critical correlation, safety parameters, and social distancing. The proposed classifier was named Hybrid Cellular Automata-Hotspot (HCA-HS), predicts the number of hotspots in various districts of states, and also gives the status of each city marked either as Totally Safe or Marginally Safe or Unsafe. This will alert the state authorities to take necessary action to mitigate the COVID effect and help the people for possibly refraining from going to the infected areas, i.e., hotspots. The data sets were collected from Kaggle and the local Indian database for more adaptability. The accuracy of the predictions of Hotspots is reported as 91.58%, which is considerable at this moment. The developed classifier is compared with Support Vector Mechanism (SVM), K-Means, Decision Tree, and HCA-HS has reported an accuracy of 10.69% higher than the existing literature.

Impact of the COVID-19 Pandemic on Continuous EEG Utilization.

The coronavirus disease 2019 (COVID-19) has significantly impacted healthcare delivery and utilization. The aim of this article was to assess the impact of the COVID-19 pandemic on in-hospital continuous electroencephalography (cEEG) utilization and identify areas for process improvement. A 38-question web-based survey was distributed to site principal investigators of the Critical Care EEG Monitoring Research Consortium, and institutional contacts for the Neurodiagnostic Credentialing and Accreditation Board. The survey addressed the following aspects of cEEG utilization: (1) general center characteristics, (2) cEEG utilization and review, (3) staffing and workflow, and (4) health impact on EEG technologists. The survey was open from June 12, 2020 to June 30, 2020 and distributed to 174 centers with 79 responses (45.4%). Forty centers were located in COVID-19 hotspots. Fifty-seven centers (72.1%) reported cEEG volume reduction. Centers in the Northeast were most likely to report cEEG volume reduction (odds ratio [OR] 7.19 [1.53-33.83]; P = 0.012). Additionally, centers reporting decrease in outside hospital transfers reported cEEG volume reduction; OR 21.67 [4.57-102.81]; P ≤ 0.0001. Twenty-six centers (32.91%) reported reduction in EEG technologist coverage. Eighteen centers had personal protective equipment shortages for EEG technologists. Technologists at these centers were more likely to quarantine for suspected or confirmed COVID-19; OR 3.14 [1.01-9.63]; P = 0.058. There has been a widespread reduction in cEEG volume during the pandemic. Given the anticipated duration of the pandemic and the importance of cEEG in managing hospitalized patients, methods to optimize use need to be prioritized to provide optimal care. Because the survey provides a cross-sectional assessment, follow-up studies can determine the long-term impact of the pandemic on cEEG utilization.

Impact of Extreme Hot Climate on COVID-19 Outbreak in India.

Coronavirus Disease 2019 (COVID‐19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID‐19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID‐19 epicenters. Daily COVID‐19 count has strong covariability with local temperature, which accounts approximately 65–85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID‐19 cases are clustered at temperature and humidity ranging within 27–32°C and 25–45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID‐19 growth at the initial phase. The results are highly significant for predicting future COVID‐19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO2 emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID‐19 epicenters are collocated on CO2 emission hotspots. The COVID‐19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961–1990) mean, value. Approximately, 72% of the COVID‐19 cases are clustered at severe to record‐breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID‐19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.

Association Between Social Vulnerability and a County's Risk for Becoming a COVID-19 Hotspot - United States, June 1-July 25, 2020.

Poverty, crowded housing, and other community attributes associated with social vulnerability increase a community's risk for adverse health outcomes during and following a public health event (1). CDC uses standard criteria to identify U.S. counties with rapidly increasing coronavirus disease 2019 (COVID-19) incidence (hotspot counties) to support health departments in coordinating public health responses (2). County-level data on COVID-19 cases during June 1-July 25, 2020 and from the 2018 CDC social vulnerability index (SVI) were analyzed to examine associations between social vulnerability and hotspot detection and to describe incidence after hotspot detection. Areas with greater social vulnerabilities, particularly those related to higher representation of racial and ethnic minority residents (risk ratio [RR]=5.3; 95% confidence interval [CI]=4.4-6.4), density of housing units per structure (RR=3.1; 95% CI = 2.7-3.6), and crowded housing units (i.e., more persons than rooms) (RR=2.0; 95% CI = 1.8-2.3), were more likely to become hotspots, especially in less urban areas. Among hotspot counties, those with greater social vulnerability had higher COVID-19 incidence during the 14 days after detection (212-234 cases per 100,000 persons for highest SVI quartile versus 35-131 cases per 100,000 persons for other quartiles). Focused public health action at the federal, state, and local levels is needed not only to prevent communities with greater social vulnerability from becoming hotspots but also to decrease persistently high incidence among hotspot counties that are socially vulnerable.

Transmission Dynamics by Age Group in COVID-19 Hotspot Counties — United States, April–September 2020

CDC works with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to state, tribal, local, and territorial health departments to limit the spread of SARS-CoV-2, the virus that causes COVID-19 (1). Understanding whether increasing incidence in hotspot counties is predominantly occurring in specific age groups is important for identifying opportunities to prevent or reduce transmission. The percentage of positive SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) test results (percent positivity) is an important indicator of community transmission.* CDC analyzed temporal trends in percent positivity by age group in COVID-19 hotspot counties before and after their identification as hotspots. Among 767 hotspot counties identified during June and July 2020, early increases in the percent positivity among persons aged ≤24 years were followed by several weeks of increasing percent positivity in persons aged ≥25 years. Addressing transmission among young adults is an urgent public health priority.