Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study (preprint)

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.

Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From COVID-19 Pneumonia Using a CT-based Radiomics Nomogram (preprint)

Objectives This study aimed to develop and validate a radiomics nomogram that effectively distinguishes between immune checkpoint inhibitor-related pneumonitis (CIP) and COVID-19 pneumonia using radiographic imaging features. Methods We included 97 patients in this study, identifying 269 pneumonia lesions—159 from COVID-19 and 110 from CIP. The dataset was randomly divided into a training set (70% of the data) and a validation set (30%). We extracted radiomics features from corticomedullary and nephrographic phase-contrast computed tomography (CT) images, constructed a radiomics signature, and calculated a radiomics score (Rad-score). Using these features, we built models with three classifiers and assessed demographics and CT findings to create a clinical factors model. We then constructed a radiomics nomogram that combines the Rad-score with independent clinical factors and evaluated its performance in terms of calibration, discrimination, and clinical usefulness. Results In constructing the radiomics signature, 33 features were critical for differentiating between CIP and COVID-19 pneumonia. The support vector machine classifier was the most accurate of the three classifiers used. The Rad-score, gender, lesion location, radiological features, and lesion borders were included in the nomogram. The nomogram demonstrated superior predictive performance, significantly outperforming the clinical factors model in the training set (AUC comparison, p = 0.02638). Calibration curves indicated good fit in both training and validation sets, and the nomogram displayed greater net benefit compared to the clinical model. Conclusion The radiomics nomogram emerges as a noninvasive, quantitative tool with significant potential to differentiate between CIP and COVID-19 pneumonia. It enhances diagnostic accuracy and supports radiologists, especially in overburdened medical systems, through the use of machine learning predictions.

MOI: a comprehensive database of multi-omics upon viral infection (preprint)

Viral infections are huge threats to human health and cause many diseases. For instance, SARS-CoV-2 infection causes COVID-19, resulting in millions of deaths worldwide in the past 2 years. To develop effective strategies for controlling viral infections and curing infected patients, we must understand which molecular events happened in host cells or tissues upon infection. Multi-omics data related viral infection is growing rapidly, however, a comprehensive database for systematically documenting host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MOI (http://www.fynn-guo.cn/). We manually collected high-throughput sequencing data associated infection of different viruses, and their detailed metadata including virus species, assay type and so on, from PubMed and GEO. We have processed omics data of more than 3200 viral infected samples from viral infection in both human and mice with standard pipelines, including (sc)RNA-seq, ChIP-seq and ATAC-seq. Then, we integrated this information into useful cell marker tables, gene expression tables, and epigenetic landscape charts. In addition, we developed multiple online tools to help users analyze their own data, such as DNA sequence convertion, FPKM calculation and differentially expressed genes identification and GO/KEGG enrichment analysis. Furthermore, we have implemented powerful tools for efficiently visualizing the multi-omics data with 16 common plots. MOI aspire to provide a user-friendly interface for browsing, searching, analyzing, visualizing and downloading all virus-related data, which could facilitate the analysis and utilization of available multi-omics data and promote the understanding of virus-host interactions. • MOI provides comprehensive cell markers from scRNA-seq of human and mouse. • MOI also contain abundant expression profiles and epigenetic landscapes for human and mouse with different treatment. • MOI integrates a series of online tools for multi-omics analysis and visualization.

Effects of COVID-19 infection in patients with autoimmune pulmonary alveolar proteinosis: a single-center study (preprint)

Background:Autoimmune pulmonary alveolar proteinosis (aPAP) is a rare interstitial lung disease. COVID-19 is associated with worse prognosis in previous lung diseases patients. But the prognosis of aPAP patients after infection with COVID-19 is unclear. In December 2022, China experienced a large-scale outbreak of Omicron variant of the SARS-CoV-2. In this study, we aim to explore the clinical outcomes of aPAP patients infected with COVID-19. Results: A total of 39 aPAP patients were included in this study. 30.77% patients had a decrease in oxygen saturation after COVID-19 infection. We compared the two groups of patients with or without decreased oxygen saturation after COVID-19 infection and found that patients who had previous oxygen therapy (decreased oxygen saturation vs. non decreased oxygen saturation: 6/12 vs. 4/27, P = 0.043), with lower baseline arterial oxygen partial pressure (74.50 ± 13.61 mmHg vs. 86.49 ± 11.92 mmHg, P = 0.009), lower baseline DLCO/VA% [77.0 (74.3, 93.6) % vs. 89.5 (78.2, 97.4) %, P = 0.036], shorter baseline 6MWD [464 (406, 538) m vs. 532 (470, 575) m, P = 0.028], higher disease severity score (P = 0.017), were more likely to have decreased oxygen saturation after COVID-19 infection. Conclusion: aPAP patients with poor baseline respiration have a higher probability of hypoxia after COVID-19 infection, but fatal events were rare.

Recent developments in the immunopathology of COVID-19.

There has been an important change in the clinical characteristics and immune profile of Coronavirus disease 2019 (COVID-19) patients during the pandemic thanks to the extensive vaccination programs. Here, we highlight recent studies on COVID-19, from the clinical and immunological characteristics to the protective and risk factors for severity and mortality of COVID-19. The efficacy of the COVID-19 vaccines and potential allergic reactions after administration are also discussed. The occurrence of new variants of concerns such as Omicron BA.2, BA.4, and BA.5 and the global administration of COVID-19 vaccines have changed the clinical scenario of COVID-19. Multisystem inflammatory syndrome in children (MIS-C) may cause severe and heterogeneous disease but with a lower mortality rate. Perturbations in immunity of T cells, B cells, and mast cells, as well as autoantibodies and metabolic reprogramming may contribute to the long-term symptoms of COVID-19. There is conflicting evidence about whether atopic diseases, such as allergic asthma and rhinitis, are associated with a lower susceptibility and better outcomes of COVID-19. At the beginning of pandemic, the European Academy of Allergy and Clinical Immunology (EAACI) developed guidelines that provided timely information for the management of allergic diseases and preventive measures to reduce transmission in the allergic clinics. The global distribution of COVID-19 vaccines and emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with reduced pathogenic potential dramatically decreased the morbidity, severity, and mortality of COVID-19. Nevertheless, breakthrough infection remains a challenge for disease control. Hypersensitivity reactions (HSR) to COVID-19 vaccines are low compared to other vaccines, and these were addressed in EAACI statements that provided indications for the management of allergic reactions, including anaphylaxis to COVID-19 vaccines. We have gained a depth knowledge and experience in the over 2 years since the start of the pandemic, and yet a full eradication of SARS-CoV-2 is not on the horizon. Novel strategies are warranted to prevent severe disease in high-risk groups, the development of MIS-C and long COVID-19.

Comprehensive bioinformatics analysis reveals common potential mechanisms, progression markers, and immune cells of coronary virus disease 2019 and atrial fibrillation

Objectives Atrial fibrillation (AF) is the most common arrhythmia in coronary virus disease 2019 (COVID-19) patients, especially in severe patients. A history of AF can exacerbate COVID-19 symptoms. COVID-19 Patients with new-onset AF have prolonged hospital stays and increased death risk. However, the mechanisms and targets of the interaction between COVID-19 and AF have not been elucidated. Materials and methods We used a series of bioinformatics analyses to understand biological pathways, protein-protein interaction (PPI) networks, gene regulatory networks (GRNs), and protein-chemical interactions between COVID-19 and AF and constructed an AF-related gene signature to assess COVID-19 severity and prognosis. Results We found folate and one-carbon metabolism, calcium regulation, and TFG-β signaling pathway as potential mechanisms linking COVID-19 and AF, which may be involved in alterations in neutrophil metabolism, inflammation, and endothelial cell function. We identified hug genes and found that NF-κb, hsa-miR-1-3p, hsa-miR-124-3p, valproic acid, and quercetin may be key regulatory molecules. We constructed a 3-gene signature consisting of ARG1, GIMAP7, and RFX2 models for the assessment of COVID-19 severity and prognosis, and found that they are associated with neutrophils, T cells, and hematopoietic stem cells, respectively. Conclusion Our study reveals a dysregulation of metabolism, inflammation, and immunity between COVID-19 and AF, and identified several therapeutic targets and progression markers. We hope that the results will reveal important insights into the complex interactions between COVID-19 and AF that will drive novel drug development and help in severity assessment.

Exposure to a mixture of per-and polyfluoroalkyl substances modulates pulmonary expression of ACE2 and circulating hormones and cytokines.

Genetic and environmental factors impact on the interindividual variability of susceptibility to communicable and non-communicable diseases. A class of ubiquitous chemicals, Per- and polyfluoroalkyl substances (PFAS) have been linked in epidemiological studies to immunosuppression and increased susceptibility to viral infections, but possible mechanisms are not well elucidated. To begin to gain insight into the role of PFAS in susceptibility to one such viral infection, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), male and female C57BL/6 J mice were exposed to control water or a mixture of 5 PFAS (PFOS, PFOA, PFNA, PFHxS, Genx) for 12 weeks and lungs were isolated for examination of expression of SARS-CoV-2-related receptors Angiotensin-Converting Enzyme 2 (ACE2) and others. Secondary analyses included circulating hormones and cytokines which have been shown to directly or indirectly impact on ACE2 expression and severity of viral infections. Changes in mRNA and protein expression were analyzed by RT-qPCR and western blotting and circulating hormones and cytokines were determined by ELISA and MESO QuickPlex. The PFAS mixture decreased Ace2 mRNA 2.5-fold in male mice (p < 0.0001), with no significant change observed in females. In addition, TMPRSS2, ANPEP, ENPEP and DPP4 (other genes implicated in COVID-19 infection) were modulated due to PFAS. Plasma testosterone, but not estrogen were strikingly decreased due to PFAS which corresponded to PFAS-mediated repression of 4 representative pulmonary AR target genes; hemoglobin, beta adult major chain (Hbb-b1), Ferrochelatase (Fech), Collagen Type XIV Alpha 1 Chain (Col14a1), 5'-Aminolevulinate Synthase 2 (Alas2). Finally, PFAS modulated circulating pro and anti-inflammatory mediators including IFN-γ (downregulated 3.0-fold in females; p = 0.0301, 2.1-fold in males; p = 0.0418) and IL-6 (upregulated 5.6-fold in males; p = 0.030, no change in females). In conclusion, our data indicate long term exposure to a PFAS mixture impacts mechanisms related to expression of ACE2 in the lung. This work provides a mechanistic rationale for important future studies of PFAS exposure and subsequent viral infection.

Association between fear of COVID-19 and hoarding behavior during the outbreak of the COVID-19 pandemic: The mediating role of mental health status

Hoarding behavior can effectively improve people's ability to resist risks, so as to reduce the negative effects of risks. However, excessive hoarding behavior will seriously reduce people's quality of life. The COVID-19 pandemic can cause excessive hoarding in a large number of people in a short period of time, and also cause a series of economic problems such as social material shortage. It is unclear how hoarding levels are linked to fear and negative emotions caused by COVID-19 among people of different educational backgrounds and social status. The purpose of this study was to explore the relationship between fear of COVID-19 and hoarding behavior in different populations in school and social contexts, as well as the mediating role of negative emotions and the moderating role of subjective/objective social status and education level in this process. An online cross-sectional survey was conducted in various provinces in China in January 2022. Demographic information, the MacArthur Scale of Subjective Social Status, the Fear of COVID-19 scale, the Depression Anxiety Stress-21, and the Saving Inventory-Revised were used to evaluate the severity of individual hoarding symptoms, the frequency of hoarding, the degree of fear, and the negative emotions (depression, anxiety, stress) caused by COVID-19. Research data showed that fear of COVID-19 was significantly correlated with hoarding behavior (p < 0.05). Fear of COVID-19 was significantly lower in the student sample than in the nonstudent sample (p < 0.05). Negative emotions played a mediating role in the relationship between fear of COVID-19 and hoarding behavior (p < 0.05). Educational and economic levels moderated this process, but social status did not. Compared with the student sample, educational background and income had less of a moderating effect on the depression, anxiety, and stress caused by fear of COVID-19 in the nonstudent sample. However, these factors had a more regulative effect on the clutter and excessive acquisition behavior caused by depression, anxiety, and stress, although not on difficulty discarding. These findings suggest that reduce negative emotions in the population, improve cognitive levels, and provide financial support from governments may be effective ways to reduce hoarding symptoms.

Randomized, placebo-controlled, single-blind phase 1 studies of the safety, tolerability, and pharmacokinetics of BRII-196 and BRII-198, SARS-CoV-2 spike-targeting monoclonal antibodies with an extended half-life in healthy adults

Background: BRII-196 and BRII-198 are two anti-SARS-CoV-2 monoclonal neutralizing antibodies as a cocktail therapy for treating COVID-19 with a modified Fc region that extends half-life. Methods: Safety, tolerability, pharmacokinetics, and immunogenicity of BRII-196 and BRII-198 were investigated in first-in-human, placebo-controlled, single ascending dose phase 1 studies in healthy adults. 44 participants received a single intravenous infusion of single BRII-196 or BRII-198 up to 3,000 mg, or BRII-196 and BRII-198 combination up to 1500/1500 mg, or placebo and were followed up for 180 days. Primary endpoints were incidence of adverse events (AEs) and changes from pre-dose baseline in clinical assessments. Secondary endpoints included pharmacokinetics profiles of BRII-196/BRII-198 and detection of anti-drug antibodies (ADAs). Plasma neutralization activities against SARS-CoV-2 Delta live virus in comparison to post-vaccination plasma were evaluated as exploratory endpoints. Results: All infusions were well-tolerated without systemic or local infusion reactions, dose-limiting AEs, serious AEs, or deaths. Most treatment-emergent AEs were isolated asymptomatic laboratory abnormalities of grade 1-2 in severity. BRII-196 and BRII-198 displayed pharmacokinetics characteristic of Fc-engineered human IgG1 with mean terminal half-lives of 44.6–48.6 days and 72.2–83.0 days, respectively, with no evidence of interaction or significant anti-drug antibody development. Neutralizing activities against the live virus of the SARS-CoV-2 Delta variant were maintained in plasma samples taken on day 180 post-infusion. Conclusion: BRII-196 and BRII-198 are safe, well-tolerated, and suitable therapeutic or prophylactic options for SARS-CoV-2 infection. Clinical Trial Registration:ClinicalTrials.gov under identifiers NCT04479631, NCT04479644, and NCT04691180.

A Hybrid Model for the Impact of COVID-19 Prevention Measures on the Sustainable Development of the Aviation System

An airport is a complicated system that serves as a vital link in the aviation system. Airplanes are also a popular mode of transportation for daily travel and tourism. Now that COVID-19 is spreading over the world, many airlines are under severe financial strain. Therefore, long-term air traffic planning is critical, especially since the airport is a key region for COVID-19 to expand. To anticipate, simulate, and estimate the medical and health expenditures caused by the propagation of the epidemic under lax, mild, and strict social contact intervention, we utilize a hybrid model of the agent-based model and discrete event simulation. An agent-based model can capture individual behavior, and discrete event simulation can be used to explain the entire boarding process. Limiting the rate of wearing masks while traveling and upgrading airport facilities are the most effective strategies to manage the epidemic, given the convenience of travelers and the swift recovery of the national economy. Simulating passenger boarding will aid government agencies in improving airport equipment and facilities, facilitating passengers' reasonable travel plans, avoiding large-scale contact, preventing the spread of epidemic diseases, and promoting the long-term sustainability of densely populated public transportation systems such as airports. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Anxiety of hearing impaired students in special schools during COVID-19

Objective: To understand anxiety status among students with hearing loss under the epidemic of novel coronarirus pneumonia, and to provide evidence for promoting mental health of hearing impaired students.

Do spatiotemporal units matter for exploring the microgeographies of epidemics?

From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.

Flavonoids for Treating Viral Acute Respiratory Tract Infections: A Systematic Review and Meta-Analysis of 30 Randomized Controlled Trials.

BACKGROUND: This meta-analysis aimed to investigate the efficacy and safety of flavonoids in treating viral acute respiratory tract infections (ARTIs). METHODS: Randomized controlled trials (RCTs) were entered into meta-analyses performed separately for each indication. Efficacy analyses were based on changes in disease-specific symptom scores. Safety was analyzed based on the pooled data from all eligible trials, by comparing the incidence of adverse events between flavonoids and the control. RESULTS: In this study, thirty RCTs (n = 5,166) were included. In common cold, results showed that the flavonoids group decreased total cold intensity score (CIS), the sum of sum of symptom intensity differences (SSID) of CIS, and duration of inability to work vs. the control group. In influenza, the flavonoids group improved the visual analog scores for symptoms. In COVID-19, the flavonoids group decreased the time taken for alleviation of symptoms, time taken for SARS-CoV-2 RT-PCR clearance, the RT-PCR positive subjects at day 7, time to achievement of the normal status of symptoms, patients needed oxygen, patients hospitalized and requiring mechanical ventilation, patients in ICU, days of hospitalization, and mortality vs. the control group. In acute non-streptococcal tonsillopharyngitis, the flavonoids group decreased the tonsillitis severity score (TSS) on day 7. In acute rhinosinusitis, the flavonoids group decreased the sinusitis severity score (SSS) on day 7, days off work, and duration of illness. In acute bronchitis, the flavonoids group decreased the bronchitis severity score (BSS) on day 7, days off work, and duration of illness. In bronchial pneumonia, the flavonoids group decreased the time to symptoms disappearance, the level of interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α). In upper respiratory tract infections, the flavonoids group decreased total CIS on day 7 and increased the improvement rate of symptoms. Furthermore, the results of the incidence of adverse reactions did not differ between the flavonoids and the control group. CONCLUSION: Results from this systematic review and meta-analysis suggested that flavonoids were efficacious and safe in treating viral ARTIs including the common cold, influenza, COVID-19, acute non-streptococcal tonsillopharyngitis, acute rhinosinusitis, acute bronchitis, bronchial pneumonia, and upper respiratory tract infections. However, uncertainty remains because there were few RCTs per type of ARTI and many of the RCTs were small and of low quality with a substantial risk of bias. Given the limitations, we suggest that the conclusions need to be confirmed on a larger scale with more detailed instructions in future studies.Systematic Review Registration: inplasy.com/inplasy-2021-8-0107/, identifier: INPLASY20218010.

A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective.

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various "densities" were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the "densities" were actually an abstract reflection of the "contact" frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect "contact" frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional "densities". Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.

Air quality impacts of emissions from a typical iron and steel plant in Hebei Province during the coronavirus disease（COVID-19）

Based on the meteorological forecast data from the National Meteorological Bureau, this study developed an AERMOD-based pollution forecasting model for iron and steel plants, simulated air quality impacts of a typical iron and steel plant located in Hebei Province during the controlled period（from February to March in 2020） and the uncontrolled period（from April to October in 2020） of the COVID-19 epidemic, and validated the model with real monitoring air quality data. In case of adverse wind direction, the results showed that the average contribution of SO2, NOx and PM10 from the plant to three state-controlled monitoring stations were 20.19～33.81%, 17.49～23.46% and 2.02～2.69% respectively during the controlled period, and 13.43～21.01%, 11.09～20.92% and 1.20～2.22% during the uncontrolled period. The correlation coefficients between the forecast values of SO2, NOx and PM10 emission of the plant and the real monitoring values of the three state-controlled monitoring stations were higher in the controlled period（the highest values are 0.43,0.48 and 0.29, respectively, at individual monitoring station） compared with the uncontrolled period（the highest values are 0.42,0.39 and 0.07, respectively） due to the less interference from other anthropogenic emission sources during the controlled period.

"Distance-Driven" Versus "Density-Driven": Understanding the Role of "Source-Case" Distance and Gathering Places in the Localized Spatial Clustering of COVID-19-A Case Study of the Xinfadi Market, Beijing (China).

The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June-July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the "source-case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.

Evaluation of the "safe multidisciplinary app-assisted remote patient-self-testing (SMART) model" for warfarin home management during the COVID-19 pandemic: study protocol of a multi-center randomized controlled trial.

BACKGROUND: Warfarin treatment requires frequent monitoring of INR (international normalized ratio) to adjust dosage in a therapeutic range. In China, patients living in small towns usually go to tertiary hospitals to get warfarin monitoring and dosing, resulting in low frequencies of follow-ups and high incidence of complications. Influenced by the COVID-19 pandemic, patients on warfarin have further reduced their visits to healthcare institutions. While patient self-testing (PST) via using a point-of-care testing device for INR measuring at home has been widely used in developed countries and demonstrated improved clinical outcomes compared to usual care in clinics, it is rarely applied in developing countries, including China. This proposed study will develop and assess the "Safe Multidisciplinary App-assisted Remote patient-self-Testing (SMART) model" for warfarin home management in China during the COVID-19 pandemic. METHODS: This is a multi-center randomized controlled trial. We will carry out the study in three county hospitals, three small tertiary hospitals and three large tertiary hospitals with anticoagulation clinics in Hunan province of China. Eligible patients will be randomly assigned to the SMART model group (n = 360) or the control group (usual care clinic group, n = 360; anticoagulation clinic group, n = 120). Patients in the SMART model group do PST at home once every two to 4 weeks. Controls receive usual care in the clinics. All the patients will be followed up through outpatient clinics, phone call or online interviews at the 3rd, 6th, 9th and 12th month. The percentage of time in therapeutic range (TTR), incidence of warfarin associated major bleeding and thromboembolic events and costs will be compared between the SMART model group and control groups. DISCUSSION: Patients in the SMART model group would show improved TTR, lower incidence of complications and better quality of life compared to the control groups. Our design, implementation and usage of the SMART model will provide experience and evidence in developing a novel model for chronic disease management to solve the problem of healthcare service maldistribution, an issue particularly obvious in developing countries during the COVID-19 pandemic. TRIAL REGISTRATION: ChiCTR, ChiCTR 2000038984 . Registered 11 October, 2020.

Study on the decay characteristics and transmission risk of respiratory viruses on the surface of objects.

The complex and changeable environment is a brand-new living condition for the viruses and pathogens released by the infected people to the indoor air or deposited on the surface of objects, which is an important external condition affecting the decay and transmission risk of the viruses. Exposure to contaminated surfaces is one of the main routes of respiratory diseases transmission. Therefore, it is very important for epidemic prevention and control to study the law of virus decay and the environmental coupling effect on various surfaces. Based on the analysis of the influencing mechanism, a large amount of experimental evidence on the survival of viruses on the surface of objects were excavated in this paper, and the effects of various factors, such as surface peripheral temperature, relative humidity, virus-containing droplet volume, surface materials and virus types, on the decay rate constants of viruses were comprehensively analyzed. It was found that although the experimental methods, virus types and experimental conditions varied widely in different experiments, the virus concentrations on the surface of objects all followed the exponential decay law, and the coupling effect of various factors was reflected in the decay rate constant k. Under different experimental conditions, k values ranged from 0.001 to 100 h-1, with a difference of 5 orders of magnitude, corresponding to the characteristic time t99 between 500 and 0.1 h when the virus concentration decreased by 99%. This indicates a large variation in the risk of virus transmission in different scenarios. By revealing the common law and individuality of the virus decay on the surface of objects, the essential relationship between the experimental observation phenomenon and virus decay was analyzed. This paper points out the huge difference in virus transmission risk on the surface at different time nodes, and discusses the prevention and control strategies to grasp the main contradictions in the different situations.