Analysis on traditional Chinese medicine syndrome characteristics of patients infected with severe acute respiratory syndrome coronavirus 2 omicron variant in a shelter hospital in Shanghai

Objective: To analyze the characteristics of traditional Chinese medicine (TCM) syndromes of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant in a shelter hospital in Shanghai. Methods: A total of 621 patients infected with SARS-CoV-2 omicron variant from Apr. 4 to May 24, 2022 in a shelter hospital in Shanghai were enrolled. The data of the patients, including the general information and common clinical syndromes (such as fever, headache, stuffy nose, runny nose, cough, and sputum), were collected on admission by TCM syndrome electronic scale, and core syndrome characteristics were analyzed base on the proportion of each symptom. The syndromes were divided according to the symptom score of patients, and the differences of disease course among the syndromes were compared. Results: The proportion of patients aged 30 to 49 years old was the highest among 621 patients infected with omicron variant (49.76%, 309/621). The most prominent symptoms were cough and expectoration, accounting for 62.32% (387/621) and 68.12% (423/621), respectively. The more common symptoms included sore throat, stuffy nose, runny nose, fatigue, muscle pain, and headache. White sputum was mostly seen in the expectoration and clear mucus was mostly seen in runny nose. According to the clinical symptoms, the core syndrome characteristic of patients infected with omicron variant was cold epidemic constraint in the lung featuring dampness and toxins. The main syndrome was plague invading the defensive exterior, accounting for 40.10% (249/621). The second and third ones were heat toxin attacking the lung syndrome (29.95%, 186/621) and dampness obstructing (17.55%, 109/621), while the least common syndrome was deficiency of qi and yin (7.73%, 48/621). The course of qi and yin deficiency was longer than the other 3 syndromes (P < 0.05). Conclusion: The core TCM syndrome characteristic of patients infected with SARS-CoV-2 omicron variant is cold epidemic constraint in the lung featuring dampness and toxins. The main syndrome is plague invading the defensive exterior. The pattern tends to convert into qi and yin deficiency along the long course.

Health disparity in the spread of COVID-19: Evidence from social distancing, risk of interactions, and access to testing

Objective – To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing. Methods Analysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth. Results Results identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19. Conclusion – This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.

Rapid and Sensitive Genotyping of SARS-CoV-2 Key Mutation L452R with an RPA-PfAgo Method.

In the two years of COVID-19 pandemic, the SARS-CoV-2 variants have caused waves of infections one after another, and the pandemic is not ending. The key mutations on the S protein enable the variants with enhanced viral infectivity, immune evasion, and/or antibody neutralization resistance, bringing difficulties to epidemic prevention and control. In support of precise epidemic control and precision medicine of the virus, a fast and simple genotyping method for the key mutations of SARS-CoV-2 variants needs to be developed. By utilizing the specific recognition and cleavage property of the nuclease Argonaute from Pyrococcus furiosus (PfAgo), we developed a recombinase polymerase amplification (RPA) and PfAgo combined method for a rapid and sensitive genotyping of SARS-CoV-2 key mutation L452R. With a delicate design of the strategy, careful screening of the RPA primers and PfAgo gDNA, and optimization of the reaction, the method achieves a high sensitivity of a single copy per reaction, which is validated with the pseudovirus. This is the highest sensitivity that can be achieved theoretically and the highest sensitivity as compared to the available SARS-CoV-2 genotyping assays. Using RPA, the procedure of the method is finished within 1.5 h and only needs a minimum laboratorial support, suggesting that the method can be easily applied locally or on-site. The RPA-PfAgo method established in this study provides a strong support to the precise epidemic control and precision medicine of SARS-CoV-2 variants and can be readily developed for the simultaneous genotyping of multiple SARS-CoV-2 mutations.

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2.

BACKGROUND: The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social. The ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health and economic stability worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19. METHODS: We first established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus spike protein and its host receptor ACE2. Two compound libraries of 2,864 molecules were screened with this platform. Selected candidate compounds were validated by SARS-CoV-2_S pseudotyped lentivirus and ACE2-overexpressing cell system. Molecular docking was used to analyze the interaction between S protein and compounds. RESULTS: We identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S protein and the human cell ACE2 receptor. Additionally, tannic acid showed moderate inhibitory effect on the interaction of S protein and ACE2. CONCLUSION: This platform is a rapid, sensitive, specific, and high throughput system, and available for screening large compound libraries. TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug.

COVID-19 lockdown introduces human mobility pattern changes for both Guangdong-Hong Kong-Macao greater bay area and the San Francisco bay area.

In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.

Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference.

In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian inference with weakly informative priors and by examining how home-dwelling stages in the USA varied geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (9 days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections at U.S. state and county level reveal notable spatial disparity in home-dwelling stage-related variables. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the USA from a purely data-driven perspective and in a statistically robust manner.

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Impacts of COVID-19 lockdowns and stimulus payments on low-income population's spending in the United States.

The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population's spending along the inter-temporal, geo-spatial, and cross-categorical dimensions. Leveraging the difference-in-differences analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes, followed by a higher rebound and a net gain after the stimulus payments; also, Southwest exhibited the highest initial decline whereas Southeast had the largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as liquor and cigar. In addition, spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises.

Research Progress on the Antiviral Activity of Glycyrrhizin and its Derivatives in Liquorice.

Liquorice is a traditional medicine. Triterpenoids such as glycyrrhizin and glycyrrhetinic acid are the main active constituents of liquorice. Studies have revealed that these compounds exert inhibitory effects on several viruses, including SARS-CoV-2. The main mechanisms of action of these compounds include inhibition of virus replication, direct inactivation of viruses, inhibition of inflammation mediated by HMGB1/TLR4, inhibition of ß-chemokines, reduction in the binding of HMGB1 to DNA to weaken the activity of viruses, and inhibition of reactive oxygen species formation. We herein review the research progress on the antiviral effects of glycyrrhizin and its derivatives. In addition, we emphasise the significance of exploring unknown antiviral mechanisms, structural modifications, and drug combinations in future studies.

iTRAQ-based proteome analysis of porcine group A rotavirus-infected porcine IPEC-J2 intestinal epithelial cells.

Porcine rotavirus (PoRV), particularly group A, is one of the most important swine pathogens, causing substantial economic losses in the animal husbandry industry. To improve understanding of host responses to PoRV infection, we applied isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantitatively identify the differentially expressed proteins in PoRV-infected IPEC-J2 cells and confirmed the differentially accumulated proteins (DAPs) expression differences by performing RT-qPCR and Western blot analysis. Herein, in PoRV- and mock-infected IPEC-J2 cells, relative quantitative data were identified for 4724 proteins, 223 of which were DAPs (125 up-accumulated and 98 down-accumulated). Bioinformatics analyses further revealed that a majority of the DAPs are involved in numerous crucial biological processes and signaling pathways, such as metabolic process, immune system process, amino acid metabolism, energy metabolism, immune system, MHC class I peptide loading complex, Hippo signaling pathway, Th1 and Th2 cell differentiation, antigen processing and presentation, and tubule bicarbonate reclamation. The cellular localization prediction analysis indicated that these DAPs may be located in the Golgi apparatus, nucleus, peroxisomal, cytoplasm, mitochondria, extracellular, plasma membrane, and endoplasmic reticulum (ER). Expression levels of three up-accumulated (VAMP4, IKBKE, and TJP3) or two down-accumulated (SOD3 and DHX9) DAPs upon PoRV infection, were further validated by RT-qPCR and Western blot analysis. Collectively, this work is the first time to investigate the protein profile of PoRV-infected IPEC-J2 cells using quantitative proteomics; these findings provide valuable information to better understand the mechanisms underlying the host responses to PoRV infection in piglets. SIGNIFICANCE: The proteomics analysis of this study uncovered the target associated with PoRV-induced innate immune response or cellular damage, and provided relevant insights into the molecular functions, biological processes, and signaling pathway in these targets. Out of these 223 DAPs, the expression levels of three up-accumulated (VAMP4, IKBKE, and TJP3) and two down-accumulated (SOD3 and DHX9) DAPs upon PoRV infection, have been further validated using RT-qPCR and Western blot analysis. These outcomes could uncover how PoRV manipulated the cellular machinery, which could further our understanding of PoRV pathogenesis in piglets.

Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries.

Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts.

A Ligation/Recombinase Polymerase Amplification Assay for Rapid Detection of SARS-CoV-2.

The pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to more than 117 million reported cases and 2.6 million deaths. Accurate diagnosis technologies are vital for controlling this pandemic. Reverse transcription (RT)-based nucleic acid detection assays have been developed, but the strict sample processing requirement of RT has posed obstacles on wider applications. This study established a ligation and recombinase polymerase amplification (L/RPA) combined assay for rapid detection of SARS-CoV-2 on genes N and ORF1ab targeting the specific biomarkers recommended by the China CDC. Ligase-based strategies usually have a low-efficiency problem on RNA templates. This study has addressed this problem by using a high concentration of the T4 DNA ligase and exploiting the high sensitivity of RPA. Through selection of the ligation probes and optimization of the RPA primers, the assay achieved a satisfactory sensitivity of 101 viral RNA copies per reaction, which was comparable to RT-quantitative polymerase chain reaction (RT-qPCR) and other nucleic acid detection assays for SARS-CoV-2. The assay could be finished in less than 30 min with a simple procedure, in which the requirement for sophisticated thermocycling equipment had been avoided. In addition, it avoided the RT procedure and could potentially ease the requirement for sample processing. Once validated with clinical samples, the L/RPA assay would increase the practical testing availability of SARS-CoV-2. Moreover, the principle of L/RPA has an application potential to the identification of concerned mutations of the virus.

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race.

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Viral inhibitors derived from macroalgae, microalgae, and cyanobacteria: A review of antiviral potential throughout pathogenesis.

Viruses are abiotic obligate parasites utilizing complex mechanisms to hijack cellular machinery and reproduce, causing multiple harmful effects in the process. Viruses represent a growing global health concern; at the time of writing, COVID-19 has killed at least two million people around the world and devastated global economies. Lingering concern regarding the virus' prevalence yet hampers return to normalcy. While catastrophic in and of itself, COVID-19 further heralds in a new era of human-disease interaction characterized by the emergence of novel viruses from natural sources with heretofore unseen frequency. Due to deforestation, population growth, and climate change, we are encountering more viruses that can infect larger groups of people with greater ease and increasingly severe outcomes. The devastation of COVID-19 and forecasts of future human/disease interactions call for a creative reconsideration of global response to infectious disease. There is an urgent need for accessible, cost-effective antiviral (AV) drugs that can be mass-produced and widely distributed to large populations. Development of AV drugs should be informed by a thorough understanding of viral structure and function as well as human biology. To maximize efficacy, minimize cost, and reduce development of drug-resistance, these drugs would ideally operate through a varied set of mechanisms at multiple stages throughout the course of infection. Due to their abundance and diversity, natural compounds are ideal for such comprehensive therapeutic interventions. Promising sources of such drugs are found throughout nature; especially remarkable are the algae, a polyphyletic grouping of phototrophs that produce diverse bioactive compounds. While not much literature has been published on the subject, studies have shown that these compounds exert antiviral effects at different stages of viral pathogenesis. In this review, we follow the course of viral infection in the human body and evaluate the AV effects of algae-derived compounds at each stage. Specifically, we examine the AV activities of algae-derived compounds at the entry of viruses into the body, transport through the body via the lymph and blood, infection of target cells, and immune response. We discuss what is known about algae-derived compounds that may interfere with the infection pathways of SARS-CoV-2; and review which algae are promising sources for AV agents or AV precursors that, with further investigation, may yield life-saving drugs due to their diversity of mechanisms and exceptional pharmaceutical potential.

State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures.

Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States.

BACKGROUND: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. METHODS: We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5, and O3 and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology. RESULTS: From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%-18.2%) and 16.2% (95% CI 8.7%-24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM2.5 or O3, although per IQR increase in PM2.5 (2.6 µg/m3) was marginally associated, with a 14.9% (95% CI 0.0%-31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants. DISCUSSION: Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US.

Importance: A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective: To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. Design, Setting, and Participants: This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. Exposure: State-level stay-at-home orders during the COVID-19 pandemic. Main Outcomes and Measures: The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Results: Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. Conclusions and Relevance: These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.