COVID-19 in South Korea.

A novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic. While this novel coronavirus disease (COVID-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes. As of 31 March 2020, a total of 9786 confirmed cases with COVID-19 have been reported in South Korea. South Korea has the highest diagnostic rate for COVID-19, which has been the major contributor in overcoming this outbreak. We are trying to reduce the reproduction number of COVID-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure. This report aimed to describe the current situation of COVID-19 in South Korea and our response to this outbreak.

Impacts of the novel coronavirus SARS-CoV-2 on wildlife behaviour via human activities.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the pandemic of the coronavirus disease 2019 (COVID-19), resulting in a global lockdown in 2020. This stagnation in human activities ('anthropause') has been reported to affect the behaviour of wildlife in various ways. The sika deer Cervus nippon in Nara Park, central Japan, has had a unique relationship with humans, especially tourists, in which the deer bow to receive food and sometimes attack if they do not receive it. We investigated how a decrease and subsequent increase in the number of tourists visiting Nara Park affects the number of deer observed in the park and their behaviour (bows and attacks against humans). Compared with the pre-pandemic years, the number of deer in the study site decreased from an average of 167 deer in 2019 to 65 (39%) in 2020 during the pandemic period. Likewise, the number of deer bows decreased from 10.2 per deer in 2016-2017 to 6.4 (62%) in 2020-2021, whereas the proportion of deer showing aggressive behaviour did not change significantly. Moreover, the monthly numbers of deer and their bows both corresponded with the fluctuation in the number of tourists during the pandemic period of 2020 and 2021, whereas the number of attacks did not. Thus, the anthropause caused by the coronavirus altered the habitat use and behaviour of deer that have continuous interactions with humans.

Impact of the COVID-19 pandemic on levels of deep-ocean acoustic noise.

The extraordinary circumstances of the COVID-19 pandemic led to measures to mitigate the spread of the disease, with lockdowns and mobility restrictions at national and international levels. These measures led to sudden and sometimes dramatic reductions in human activity, including significant reductions in ship traffic in the maritime sector. We report on a reduction of deep-ocean acoustic noise in three ocean basins in 2020, based on data acquired by hydroacoustic stations in the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty. The noise levels measured in 2020 are compared with predicted levels obtained from modelling data from previous years using Gaussian Process regression. Comparison of the predictions with measured data for 2020 shows reductions of between 1 and 3 dB in the frequency range from 10 to 100 Hz for all but one of the stations.

Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas.

The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19-namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming-and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22-35%, 9-16% and 6-15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensity associated with commercial sites, sports facilities/gyms and tourism experienced the relatively largest contraction during the lockdown. During the relaxations of restrictions, government institutions showed a 13.89% rise in intensity at the first stage of work resuming, which was the highest rate among all the working sectors. Furthermore, the GDP and road junction density were more influenced by the change in human activity intensity for all land use categories. The bus stop density was importantly associated with mixed-use land recovery during the relaxing stages, while the coefficient of density of population in entertainment land were relatively higher at these two stages. This study aims to provide additional support to investigate the human activity changes due to the spread of COVID-19 at different stages across different sectors.

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic.

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Wetland emission and atmospheric sink changes explain methane growth in 2020.

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.

Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom.

Human activities may impact animal habitat and resource use, potentially influencing contemporary evolution in animals. In the United Kingdom, COVID-19 lockdown restrictions resulted in sudden, drastic alterations to human activity. We hypothesized that short-term daily and long-term seasonal changes in human mobility might result in changes in bird habitat use, depending on the mobility type (home, parks and grocery) and extent of change. Using Google human mobility data and 872 850 bird observations, we determined that during lockdown, human mobility changes resulted in altered habitat use in 80% (20/25) of our focal bird species. When humans spent more time at home, over half of affected species had lower counts, perhaps resulting from the disturbance of birds in garden habitats. Bird counts of some species (e.g. rooks and gulls) increased over the short term as humans spent more time at parks, possibly due to human-sourced food resources (e.g. picnic refuse), while counts of other species (e.g. tits and sparrows) decreased. All affected species increased counts when humans spent less time at grocery services. Avian species rapidly adjusted to the novel environmental conditions and demonstrated behavioural plasticity, but with diverse responses, reflecting the different interactions and pressures caused by human activity.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities.

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition.

The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and problems of working with human users, capturing adequate data for each new user is not feasible. This paper presents semi-supervised adversarial learning using the LSTM (Long-short term memory) approach for human activity recognition. This proposed method trains annotated and unannotated data (anonymous data) by adapting the semi-supervised learning paradigms on which adversarial learning capitalizes to improve the learning capabilities in dealing with errors that appear in the process. Moreover, it adapts to the change in human activity routine and new activities, i.e., it does not require prior understanding and historical information. Simultaneously, this method is designed as a temporal interactive model instantiation and shows the capacity to estimate heteroscedastic uncertainty owing to inherent data ambiguity. Our methodology also benefits from multiple parallel input sequential data predicting an output exploiting the synchronized LSTM. The proposed method proved to be the best state-of-the-art method with more than 98% accuracy in implementation utilizing the publicly available datasets collected from the smart home environment facilitated with heterogeneous sensors. This technique is a novel approach for high-level human activity recognition and is likely to be a broad application prospect for HAR.

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan.

It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan. We considered human-activity patterns and related demographic information as the COVID-19 influencing determinants, and we used spatial regression models to evaluate the relationships between COVID-19 morbidity and the related factors. Furthermore, we traced Weibo users' check-in trajectories to characterize the spatial interaction between high-morbidity residential areas and activity venues with POI (point of interest) sites, and we located a series of potential at-risk places in Wuhan. The results provide statistical evidence regarding the utility of human activity and demographic factors for the determination of COVID-19 morbidity patterns in the early pandemic stage in Wuhan. The spatial interaction revealed a general transmission pattern in Wuhan and determined the high-risk areas of COVID-19 transmission. This article explores the human-activity characteristics from social media check-in data and studies how human activities played a role in COVID-19 transmission in Wuhan. From that, we provide new insights for scientific prevention and control of COVID-19.

Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization.

The development of smartphones technologies has determined the abundant and prevalent computation. An activity recognition system using mobile sensors enables continuous monitoring of human behavior and assisted living. This paper proposes the mobile sensors-based Epidemic Watch System (EWS) leveraging the AI models to recognize a new set of activities for effective social distance monitoring, probability of infection estimation, and COVID-19 spread prevention. The research focuses on user activities recognition and behavior concerning risks and effectiveness in the COVID-19 pandemic. The proposed EWS consists of a smartphone application for COVID-19 related activities sensors data collection, features extraction, classifying the activities, and providing alerts for spread presentation. We collect the novel dataset of COVID-19 associated activities such as hand washing, hand sanitizing, nose-eyes touching, and handshaking using the proposed EWS smartphone application. We evaluate several classifiers such as random forests, decision trees, support vector machine, and Long Short-Term Memory for the collected dataset and attain the highest overall classification accuracy of 97.33%. We provide the Contact Tracing of the COVID-19 infected person using GPS sensor data. The EWS activities monitoring, identification, and classification system examine the infection risk of another person from COVID-19 infected person. It determines some everyday activities between COVID-19 infected person and normal person, such as sitting together, standing together, or walking together to minimize the spread of pandemic diseases.

A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture.

Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but only a few of them contain non-standard human behavior such as theft, harassment, etc. There are datasets such as KTH that focus on abnormal activities such as sudden behavioral changes, as well as on various changes in interpersonal interactions. The UCF-crime dataset contains categories such as fighting, abuse, explosions, robberies, etc. However, this dataset is very time consuming. The events in the videos occur in a few seconds. This may affect the overall results of the neural networks that are used to detect the incident. In this article, we create a dataset that deals with abnormal activities, containing categories such as Begging, Drunkenness, Fight, Harassment, Hijack, Knife Hazard, Normal Videos, Pollution, Property Damage, Robbery, and Terrorism. We use the created dataset for the training and testing of the ConvLSTM (convolutional long short-term memory) neural network, which we designed. However, we also test the created dataset using other architectures. We use ConvLSTM architectures and 3D Resnet50, 3D Resnet101, and 3D Resnet152. With the created dataset and the architecture we designed, we obtained an accuracy of classification of 96.19% and a precision of 96.50%.

Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity.

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape's scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in "patchy" parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city's morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.

Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China.

Record rainfall and severe flooding struck eastern China in the summer of 2020. The extreme summer rainfall occurred during the COVID-19 pandemic, which started in China in early 2020 and spread rapidly across the globe. By disrupting human activities, substantial reductions in anthropogenic emissions of greenhouse gases and aerosols might have affected regional precipitation in many ways. Here, we investigate such connections and show that the abrupt emissions reductions during the pandemic strengthened the summer atmospheric convection over eastern China, resulting in a positive sea level pressure anomaly over northwestern Pacific Ocean. The latter enhanced moisture convergence to eastern China and further intensified rainfall in that region. Modeling experiments show that the reduction in aerosols had a stronger impact on precipitation than the decrease of greenhouse gases did. We conclude that through abrupt emissions reductions, the COVID-19 pandemic contributed importantly to the 2020 extreme summer rainfall in eastern China.

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices.

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

Dominance of the residential sector in Chinese black carbon emissions as identified from downwind atmospheric observations during the COVID-19 pandemic.

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.

Societal activities associated with SARS-CoV-2 infection: a case-control study in Denmark, November 2020.

Identification of societal activities associated with SARS-CoV-2 infection may provide an evidence base for implementing preventive measures. Here, we investigated potential determinants for infection in Denmark in a situation where society was only partially open. We conducted a national matched case-control study. Cases were recent RT-PCR test-positives, while controls, individually matched on age, sex and residence, had not previously tested positive for SARS-CoV-2. Questions concerned person contact and community exposures. Telephone interviews were performed over a 7-day period in December 2020. We included 300 cases and 317 controls and determined odds ratios (ORs) and 95% confidence intervals (95% CI) by conditional logistical regression with adjustment for household size and country of origin. Contact (OR 4.9, 95% CI 2.4-10) and close contact (OR 13, 95% CI 6.7-25) with a person with a known SARS-CoV-2 infection were main determinants. Contact most often took place in the household or work place. Community determinants included events with singing (OR 2.1, 95% CI 1.1-4.1), attending fitness centres (OR 1.8, 95% CI 1.1-2.8) and consumption of alcohol in a bar (OR 10, 95% CI 1.5-65). Other community exposures appeared not to be associated with infection, these included shopping at supermarkets, travel by public transport, dining at restaurants and private social events with few participants. Overall, the restrictions in place at the time of the study appeared to be sufficient to reduce transmission of disease in the public space, which instead largely took place following direct exposures to people with known SARS-CoV-2 infections.

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition.

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants' body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

Different patterns of human activities in nature during Covid-19 pandemic and African swine fever outbreak confirm direct impact on wildlife disruption.

Implementation of various restrictions to eradicate viral diseases has globally affected human activity and subsequently nature. But how can the altered routines of human activity (restrictions, lockdowns) affect wildlife behaviour? This study compared the differences between human and wildlife occurrences in the study forest area with acreage of 5430.6 ha in 2018 (African swine fever outbreak, complete entrance ban), 2019 (standard pattern) and 2020 (COVID-19 restrictions) during the breeding season. The number of visitors was lower by 64% in 2018 (non-respecting of the entry ban by forest visitors) compared to standard 2019, while in 2020, the number of visitors increased to 151%. In the COVID-19 period, distinct peaks in the number of visitors were observed between 8-11 AM and 4-7 PM. The peaks of wildlife activity were recorded between 4-7 AM and 9-12 PM. Animals avoided the localities that were visited by humans during the people-influenced time (24 h after people visit), which confirmed the direct negative impact of human activities on wildlife.

COVID-19 lockdowns drive decline in active fires in southeastern United States.

Fire is a common ecosystem process in forests and grasslands worldwide. Increasingly, ignitions are controlled by human activities either through suppression of wildfires or intentional ignition of prescribed fires. The southeastern United States leads the nation in prescribed fire, burning ca. 80% of the country's extent annually. The COVID-19 pandemic radically changed human behavior as workplaces implemented social-distancing guidelines and provided an opportunity to evaluate relationships between humans and fire as fire management plans were postponed or cancelled. Using active fire data from satellite-based observations, we found that in the southeastern United States, COVID-19 led to a 21% reduction in fire activity compared to the 2003 to 2019 average. The reduction was more pronounced for federally managed lands, up to 41% below average compared to the past 20 y (38% below average compared to the past decade). Declines in fire activity were partly affected by an unusually wet February before the COVID-19 shutdown began in mid-March 2020. Despite the wet spring, the predicted number of active fire detections was still lower than expected, confirming a COVID-19 signal on ignitions. In addition, prescribed fire management statistics reported by US federal agencies confirmed the satellite observations and showed that, following the wet February and before the mid-March COVID-19 shutdown, cumulative burned area was approaching record highs across the region. With fire return intervals in the southeastern United States as frequent as 1 to 2 y, COVID-19 fire impacts will contribute to an increasing backlog in necessary fire management activities, affecting biodiversity and future fire danger.