Modeling particulate nitrate in China: Current findings and future directions.

Particulate nitrate (pNO3) is now becoming the principal component of PM2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO3 formation and driving its trends, we reviewed the recent pNO3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO3, model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N2O5) uptake and ammonia (NH3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N2O5 dominates nocturnal pNO3 formation, however, the contribution to total pNO3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM2.5 pNO3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH3 emissions is found to be the most effective control strategy for mitigating pNO3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N2O5 and nitrogen dioxide (NO2) uptake. Future studies are also needed to quantify the relationships of pNO3 to AOC, O3, NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH3, NOX, and VOCs is required to mitigate pNO3 pollution, especially during severe winter haze events.

The Effect of Traditional Chinese Medicine on Postviral Olfactory Dysfunction: A Systematic Review and Meta-Analysis.

Objective: The aim of this study is to evaluate the efficacy and safety of traditional Chinese medicine (TCM) for postviral olfactory dysfunction (PVOD). Methods: PubMed, EMBASE, Cochrane Central Register of Controlled Trials, China Network Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Chinese Biomedical and Medical (CBM) Database, and Wanfang Database were electronically searched from their inception to July 25, 2022. Two authors independently performed study selection, data extraction, and quality assessment to ensure systematic quality evaluation. Randomized controlled trials (RCTs) comparing TCM with olfactory training and/or drug therapy (OTDT) were included. The outcomes were the effective rate, QOD-P, TDI score, UPSIT score, and adverse effects. Cochrane RoB was the guideline used to evaluate the methodological quality of the included trials. RevMan 5.3 software was used for statistical analysis. Results: A total of 6 RCTs involving 467 patients with PVOD were selected. Compared with OTDT, TCM plus OTDT decreased QOD-P (MD = -1.73, 95% CI (-2.40, -1.06), P < 0.0001) but did not increase the effective rate (T&T) (RR = 1.28, 95% CI (0.86, 1.90), P=0.22, I 2 = 61%). Compared with no treatment, TCM seemed to increase the treatment success rate (UPSIT) (RR = 3.17, 95% CI (1.78, 5.65), P < 0.0001, I 2 = 0%), but there was no statistically significant difference in improving the UPSIT score (MD = 3.44, 95% CI (-1.36, 8.24), P=0.16). Compared with drug therapy, TCM plus drug therapy appeared to increase the effective rate (ΔVAS) (RR = 2.36, 95% CI (1.41, 3.94), I 2 = 0%), but there was no statistically significant difference in improving the TDI score (MD = 2.10, 95% CI (-1.99, 6.19), P=0.31). No significant differences in adverse reactions were reported between TCM and OTDT. Conclusion: TCM may be an effective treatment for PVOD. With a lack of high-quality RCTs, further large-scale and high-quality RCTs are still warranted.

Hypertension as a sequela in patients of SARS-CoV-2 infection.

BACKGROUND: COVID-19 is a respiratory infectious disease caused by SARS-CoV-2, and cardiovascular damage is commonly observed in affected patients. We sought to investigate the effect of SARS-CoV-2 infection on cardiac injury and hypertension during the current coronavirus pandemic. STUDY DESIGN AND METHODS: The clinical data of 366 hospitalized COVID-19-confirmed patients were analyzed. The clinical signs and laboratory findings were extracted from electronic medical records. Two independent, experienced clinicians reviewed and analyzed the data. RESULTS: Cardiac injury was found in 11.19% (30/268) of enrolled patients. 93.33% (28/30) of cardiac injury cases were in the severe group. The laboratory findings indicated that white blood cells, neutrophils, procalcitonin, C-reactive protein, lactate, and lactic dehydrogenase were positively associated with cardiac injury marker. Compared with healthy controls, the 190 patients without prior hypertension have higher AngⅡ level, of which 16 (8.42%) patients had a rise in blood pressure to the diagnostic criteria of hypertension during hospitalization, with a significantly increased level of the cTnI, procalcitonin, angiotensin-II (AngⅡ) than those normal blood pressure ones. Multivariate analysis indicated that elevated age, cTnI, the history of hypertension, and diabetes were independent predictors for illness severity. The predictive model, based on the four parameters and gender, has a good ability to identify the clinical severity of COVID-19 in hospitalized patients (area under the curve: 0.932, sensitivity: 98.67%, specificity: 75.68%). CONCLUSION: Hypertension, sometimes accompanied by elevated cTnI, may occur in COVID-19 patients and become a sequela. Enhancing Ang II signaling, driven by SARS-CoV-2 infection, might play an important role in the renin-angiotensin system, and consequently lead to the development of hypertension in COVID-19.

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China.

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

Modulated Gut Microbiota for Potential COVID-19 Prevention and Treatment.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gained global attention. SARS-CoV-2 identifies and invades human cells via angiotensin-converting enzyme 2 receptors, which is highly expressed both in lung tissues and intestinal epithelial cells. The existence of the gut-lung axis in disease could be profoundly important for both disease etiology and treatment. Furthermore, several studies reported that infected patients suffer from gastrointestinal symptoms. The gut microbiota has a noteworthy effect on the intestinal barrier and affects many aspects of human health, including immunity, metabolism, and the prevention of several diseases. This review highlights the function of the gut microbiota in the host's immune response, providing a novel potential strategy through the use of probiotics, gut microbiota metabolites, and dietary products to enhance the gut microbiota as a target for COVID-19 prevention and treatment.

Optimization of Silicon Nitride Waveguide Platform for On-Chip Virus Detection.

This work presents a rigorous and generic sensitivity analysis of silicon nitride on silicon dioxide strip waveguide for virus detection. In general, by functionalizing the waveguide surface with a specific antibodies layer, we make the optical sensor sensitive only to a particular virus. Unlike conventional virus detection methods such as polymerase chain reaction (PCR), integrated refractive index (RI) optical sensors offer cheap and mass-scale fabrication of compact devices for fast and straightforward detection with high sensitivity and selectivity. Our numerical analysis includes a wide range of wavelengths from visible to mid-infrared. We determined the strip waveguide's single-mode dimensions and the optimum dimensions that maximize the sensitivity to the virus layer attached to its surface at each wavelength using finite difference eigenmode (FDE) solver. We also compared the strip waveguide with the widely used slot waveguide. Our theoretical study shows that silicon nitride strip waveguide working at lower wavelengths is the optimum choice for virus detection as it maximizes both the waveguide sensitivity (Swg) and the figure of merit (FOM) of the sensor. The optimized waveguides are well suited for a range of viruses with different sizes and refractive indices. Balanced Mach-Zehnder interferometer (MZI) sensors were designed using FDE solver and photonic circuit simulator at different wavelengths. The designed sensors show high FOM at λ = 450 nm ranging from 500 RIU-1 up to 1231 RIU-1 with LMZI = 500 µm. Different MZI configurations were also studied and compared. Finally, edge coupling from the fiber to the sensor was designed, showing insertion loss (IL) at λ = 450 nm of 4.1 dB for the design with FOM = 500 RIU-1. The obtained coupling efficiencies are higher than recently proposed fiber couplers.

SARS-CoV-2 triggered oxidative stress and abnormal energy metabolism in gut microbiota.

Specific roles of gut microbes in COVID-19 progression are critical. However, the circumstantial mechanism remains elusive. In this study, shotgun metagenomic or metatranscriptomic sequencing was performed on fecal samples collected from 13 COVID-19 patients and controls. We analyzed the structure of gut microbiota, identified the characteristic bacteria, and selected biomarkers. Further, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were employed to correlate the taxon alterations and corresponding functions. The gut microbiota of COVID-19 patients was characterized by the enrichment of opportunistic pathogens and depletion of commensals. The abundance of Bacteroides spp. displayed an inverse relationship with COVID-19 severity, whereas Actinomyces oris, Escherichia coli, and Streptococcus parasanguini were positively correlated with disease severity. The genes encoding oxidoreductase were significantly enriched in gut microbiome of COVID-19 group. KEGG annotation indicated that the expression of ABC transporter was upregulated, while the synthesis pathway of butyrate was aberrantly reduced. Furthermore, increased metabolism of lipopolysaccharide, polyketide sugar, sphingolipids, and neutral amino acids were found. These results suggested the gut microbiome of COVID-19 patients was in a state of oxidative stress. Healthy gut microbiota may enhance antiviral defenses via butyrate metabolism, whereas the accumulation of opportunistic and inflammatory bacteria may exacerbate COVID-19 progression.

Role of professionalism in response to the COVID-19 pandemic: Does a public health or medical background help?

In response to the outbreak of coronavirus disease 2019 (COVID-19), there have been substantial variations in policy response and performance for disease control and prevention within and across nations. It remains unclear to what extent these variations may be explained by bureaucrats' professionalism, as measured by their educational background or work experience in public health or medicine. To investigate the effects of officials' professionalism on their response to and performance in fighting the COVID-19 pandemic, we collect information from the résumés of government and Party officials in 294 Chinese cities, and integrate this information with other data sources, including weather conditions, city characteristics, COVID-19-related policy measures, and health outcomes. We show that, on average, cities whose top officials had public health or medical backgrounds (PHMBGs) had a significantly lower infection rate than cities whose top officials lacked such backgrounds. We test the mechanisms of these effects and find that cities whose officials had a PHMBG implemented community closure more rapidly than those lacked such backgrounds. Our findings highlight the importance of professionalism in combating the pandemic.

Multiomics integration-based molecular characterizations of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.

The Potential of Mesenchymal Stem Cells for the Treatment of Cytokine Storm due to COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seriously affected public health and social stability. The main route of the transmission is droplet transmission, where the oral cavity is the most important entry point to the body. Due to both the direct harmful effects of SARS-CoV-2 and disordered immune responses, some COVID-19 patients may progress to acute respiratory distress syndrome or even multiple organ failure. Genetic variants of SARS-CoV-2 have been emerging and circulating around the world. Currently, there is no internationally approved precise treatment for COVID-19. Mesenchymal stem cells (MSCs) can traffic and migrate towards the affected tissue, regulate both the innate and acquired immune systems, and participate in the process of healing. Here, we will discuss and investigate the mechanisms of immune disorder in COVID-19 and the therapeutic activity of MSCs, in particular human gingiva mesenchymal stem cells.

An investigation of vitamin D nutritional status in children after outbreak of coronavirus disease 2019. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žç–«æƒ åŽå„¿ç«¥ç»´ç”Ÿç´ Dè¥å »çŠ¶å†µè°ƒæŸ¥.

OBJECTIVES: To investigate vitamin D nutritional status in children after outbreak of coronavirus disease 2019 (COVID-19), as well as the effect of strict epidemic prevention and control measures for the COVID-19 epidemic on vitamin D nutritional status in children. METHODS: A total of 7 460 children who underwent routine physical examinations from February to August, 2020 and had normal results were retrospectively enrolled as the observation group, and 10 102 children who underwent routine physical examinations from February to August, 2019 (no epidemic of COVID-19) and had normal results were enrolled as the control group. The serum level of 25-hydroxy vitamin D [25(OH)D] was compared between the two groups. The children in the observation and control groups who underwent physical examinations in March and April were selected as the epidemic prevention subgroup (n=1 710) and non-epidemic subgroup (n=2 877) respectively. The subjects were divided into five age groups (infancy, early childhood, preschool, school age and adolescence), and serum 25(OH)D levels of children of all ages were compared between the epidemic prevention and non-epidemic subgroups. RESULTS: The observation group had a lower serum level of 25(OH)D than the control group in March and April (P<0.001). The epidemic prevention subgroup had a lower serum level of 25(OH)D than the non-epidemic subgroup in all age groups (P<0.001). The vitamin D sufficiency rate in early childhood, preschool, school and adolescent children from the epidemic prevention subgroup was lower than the non-epidemic subgroup (P<0.001), with a reduction of 10.71%, 18.76%, 59.63% and 56.29% respectively. CONCLUSIONS: Strict prevention and control measures for the COVID-19 epidemic may lead to a significant reduction in vitamin D level in children, especially school-aged and adolescent children. It is recommended to timely monitor vitamin D level in children, take vitamin D supplements, and increase the time of outdoor sunshine as far as possible under the premise of adherence to epidemic prevention regulations.

Clinical characteristics of 25 death cases with COVID-19: a retrospective review of medical records in a single medical center, Wuhan, China. (Coronavirus (COVID-19) collection.)

Objectives: This study aims to summarize the clinical characteristics of death cases with COVID-19 and to identify critically ill patients of COVID-19 early and reduce their mortality. Methods The clinical records, laboratory findings and radiological assessments included chest X-ray or computed tomography were extracted from electronic medical records of 25 died patients with COVID-19 in Renmin Hospital of Wuhan University from Jan 14 to Feb 13, 2020. Two experienced clinicians reviewed and abstracted the data. Results The age and underlying diseases (hypertension, diabetes, etc.) were the most important risk factors for death of COVID-19 pneumonia. Bacterial infections may play an important role in promoting the death of patients. Malnutrition was common to severe patients. Multiple organ dysfunction can be observed, the most common organ damage was lung, followed by heart, kidney and liver. The rising of neutrophils, SAA, PCT, CRP, cTnI, D-dimer, LDH and lactate levels can be used as indicators of disease progression, as well as the decline of lymphocytes counts. Conclusions The clinical characteristics of 25 death cases with COVID-19 we summarized, which would be helpful to identify critically ill patients of COVID-19 early and reduce their mortality.

Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis.

Background: The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A. Methods: All patients in the study were diagnosed at Fuyang No. 2 People's Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models. Results: In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of <3 respiratory symptoms, then a highest temperature on the first day of admission <38°C. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of <37°C, then a complete lack of respiratory symptoms. Conclusions: The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.

Development and clinical evaluation of a rapid antibody lateral flow assay for the diagnosis of SARS-CoV-2 infection.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly spread worldwide since its outbreak in December 2019. One of the primary measures for controlling the spread of SARS-CoV-2 infection is an accurate assay for its diagnosis. SARS-CoV-2 real-time PCR kits suffer from some limitations, including false-negative results in the clinic. Therefore, there is an urgent need for the development of a rapid antibody test kit for COVID-19 diagnosis. METHODS: The nuclear capsid protein (N) and spike protein 1 (S1) fragments of SARS-CoV-2 were expressed in Escherichia coli, and rapid antibody-based tests for the diagnosis of SARS-CoV-2 infection were developed. To evaluate their clinical applications, the serum from COVID-19 patients, suspected COVID-19 patients, recovering COVID-19 patients, patients with general fever or pulmonary infection, doctors and nurses who worked at the fever clinic, and health professionals was analyzed by the rapid antibody test kits. The serum from patients infected with Mycoplasma pneumoniae and patients with respiratory tract infection was further analyzed to test its cross-reactivity with other respiratory pathogens. RESULTS: A 47 kDa N protein and 67 kDa S1 fragment of SARS-CoV-2 were successfully expressed, purified, and renatured. The rapid antibody test with recombinant N protein showed higher positive rate than the rapid IgM antibody test with recombinant S1 protein. Clinical evaluation showed that the rapid antibody test kit with recombinant N protein had 88.56 % analytical sensitivity and 97.42 % specificity for COVID-19 patients, 53.48 % positive rate for suspected COVID-19 patients, 57.14 % positive rate for recovering COVID-19 patients, and 0.5-0.8 % cross-reactivity with other respiratory pathogens. The analytical sensitivity of the kit did not significantly differ in COVID-19 patients with different disease courses (p < 0.01). CONCLUSIONS: The rapid antibody test kit with recombinant N protein has high specificity and analytical sensitivity, and can be used for the diagnosis of SARS-CoV-2 infection combined with RT-PCR.

Discussion on artemisinin and its derivatives for treatment of COVID-19

Since the initial cases of the Coronavirus Disease in 2019 (COVID-19) occurred in Wuhan in December 2019, more and more cases have been found and confirmed, not only in central China but also in other countries. Given there are still no effective drugs many approved "old drugs" are in clinical test for treating COVID-19 at present. Artemisinin, a first-line antimalarial drug recommended by WHO, has been validated to possess a variety of pharmacological effects, including, without limitation, antiinflammatory, immune regulation, anti-pulmonary fibrosis, antibacterial, antiviral and cardiovascular effects, and the like. In this paper, various pharmacological effects of Artemisinin and its derivatives have been summarized, with the emphasis on the mechanism of action, so as to explore its potential medicinal value, especially for treating COVID-19.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic.

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

Impacts of COVID-19 on tourists' destination preferences: Evidence from China.

Using data of online ticket sales for attractions in the seven provinces of South Central China, this study focuses on the impact of COVID-19 on tourists' destination preferences after the end of lockdown. Empirical results reveal that tourists' destination preferences have changed significantly, which holds under a number of robustness checks. Specifically, we find that tourists avoid traveling to destinations with more confirmed cases of COVID-19 relative to their places of origin, especially Hubei Province, and prefer destinations close to home, especially local attractions. The empirical findings have significant implications for managers and policymakers in tourism and we provide potential mechanisms for these findings based on signaling, risk perception, and prospect theory.

Changes in pediatric healthcare utilization in Hunan Province, China, during the COVID-19 pandemic: a multi-center cross-sectional study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic brought remarkable disruption to the ways in which healthcare was delivered. This study aimed to examine changes in pediatric healthcare utilization in Hunan Province, China, during the COVID-19 pandemic. METHODS: An electronic survey was conducted among 142 hospitals in Hunan Province, China. Using data from January 1 to April 30, 2019 as a reference, the changes in the number of visits for different types of pediatric healthcare between January 1 and April 30, 2020 were calculated. Changes in the number of admissions for infections and injuries were also evaluated. RESULTS: The total number of pediatric healthcare presentations decreased by 53.3% in the first four months of 2020. The most remarkable reductions were observed in the utilization of emergency room (ranging from -45.7% to -94.9% among three hospital levels) and observation room (-55.8% to -77.7%); neonatal inpatient care experienced the smallest decreases (-21.2% to -25.5%). Approximately 85% of the total reduction in the number of pediatric inpatient admissions was attributable to the reduction in admissions for infections. A 13.3% increase in the number of admissions for injuries was observed among third-level hospitals. CONCLUSIONS: The utilization of all types of pediatric healthcare services in Hunan Province declined markedly after the outbreak of COVID-19. The reasons, consequences, and responses to these changes should be addressed in future studies and actions.

Disease Burden Attributable to the First Wave of COVID-19 in China and the Effect of Timing on the Cost-Effectiveness of Movement Restriction Policies.

OBJECTIVES: Movement restriction policies (MRPs) are effective in preventing/delaying COVID-19 transmission but are associated with high societal cost. This study aims to estimate the health burden of the first wave of COVID-19 in China and the cost-effectiveness of early versus late implementation of MRPs to inform preparation for future waves. METHODS: The SEIR (susceptible, exposed, infectious, and recovered) modeling framework was adapted to simulate the health and cost outcomes of initiating MRPs at different times: rapid implementation (January 23, the real-world scenario), delayed by 1 week, delayed by 2 weeks, and delayed by 4 weeks. The end point was set as the day when newly confirmed cases reached zero. Two costing perspectives were adopted: healthcare and societal. Input data were obtained from official statistics and published literature. The primary outcomes were disability-adjusted life-years, cost, and net monetary benefit. Costs were reported in both Chinese renminbi (RMB) and US dollars (USD) at 2019 values. RESULTS: The first wave of COVID-19 in China resulted in 38 348 disability adjusted life-years lost (95% CI 19 417-64 130) and 2639 billion RMB losses (95% CI 1347-4688). The rapid implementation strategy dominated all other delayed strategies. This conclusion was robust to all scenarios tested. At a willingness-to-pay threshold of 70 892 RMB (the national annual GDP per capita) per disability-adjusted life-year saved, the probability for the rapid implementation to be the optimal strategy was 96%. CONCLUSIONS: Early implementation of MRPs in response to COVID-19 reduced both the health burden and societal cost and thus should be used for future waves of COVID-19.