Early detection of disease outbreaks and non-outbreaks using incidence data (preprint)

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accurately forecasts outbreaks and non-outbreaks. We propose a novel framework, using a feature-based time series classification method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible-Infected-Recovered model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences in time series of infectives leading to future outbreaks and non-outbreaks. These differences are reflected in 22 statistical features and 5 early warning signal indicators. Classifier performance, given by the area under the receiver-operating curve, ranged from 0.99 for large expanding windows of training data to 0.7 for small rolling windows. Real-world performances of classifiers were tested on two empirical datasets, COVID-19 data from Singapore and SARS data from Hong Kong, with two classifiers exhibiting high accuracy. In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur. We could detect these differences in synthetic and real-world data sets, well before potential outbreaks occur.

Discovery of Natural Products to SARS-CoV-2 with Comparative Molecular Docking and Moderate Cell Proliferation Inhibition in vitro (preprint)

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

An early warning indicator trained on stochastic disease-spreading models with different noises (preprint)

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modeling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise, and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreak by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Simultaneous Detection of SARS-CoV-2 and Six Other Human Coronaviruses by Multiplex PCR and MALDI-TOF MS (preprint)

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is challenging the health systems worldwide, and large population testing is a vital step to control this pandemic. Here, we developed a new method (named HCoV-MS), which combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry to simultaneously detect and differentiate seven human coronaviruses (HCoVs). The HCoV-MS method had good specificity and sensitivity, with a detection limit of 1-5 copies/reaction. To validate the HCoV-MS method, we tested 151 clinical samples, and the results showed good concordance with real-time PCR. In addition, 41 D614G variants were identified, which were consistent with the sequencing results. This method was also used in EQAE-SARS-COV in 2020, and all the samples were accurately identified. Taken together, HCoV-MS could be used as an effective method for large-scale detection. It was also capable of detecting key single nucleotide polymorphism about variants.

A Negative Feedback Model to Explain Regulation of SARS-CoV-2 Replication and Transcription (preprint)

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a preliminary understanding of the replication and transcription mechanisms of SARS-CoV-2 has recently emerged, their regulation remains unclear.Results: Based on reanalysis of public data, we propose a negative feedback model to explain the regulation of replication and transcription in—but not limited to—CoVs. The key step leading to new discoveries was the identification of the cleavage sites of nsp15—an RNA uridylate-specific endoribonuclease, encoded by CoVs. According to this model, nsp15 regulates the synthesis of subgenomic RNAs (sgRNAs) and genomic RNAs (gRNAs) by cleaving transcription regulatory sequences in the body. The expression level of nsp15 determines the relative proportions of sgRNAs and gRNAs, which in turn change the expression level of nps15 to reach equilibrium between the replication and transcription of CoVs.Conclusions: The replication and transcription of CoVs are regulated by a negative feedback mechanism that influences the persistence of CoVs in hosts. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, and provide new clues for future studies. One important clue is that nsp15 may be an important and ideal target for the development of drugs (e.g. uridine derivatives) against CoVs.

The Discovery of a Recombinant SARS2-like CoV Strain Provides Insight Into SARS and COVID-19 Pandemics (preprint)

Background: In December 2019, the world awoke to a new zoonotic strain of coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Results: In the present study, we classified betacoronavirus subgroup B into the SARS-CoV-2, SARS-CoV and SARS-like CoV clusters, and the ORF8 genes of these three clusters into types 1, 2 and 3, respectively. One important result of our study is that we reported—for the first time—a recombination event of ORF8 at the whole-gene level in a bat, which had been co-infected by two betacoronavirus strains. This result provides substantial proof for long-existing hypotheses regarding the recombination and biological functions of ORF8. Based on the analysis of recombination events in the Spike gene, we propose that the Spike protein of SARS-CoV-2 may have more than one specific receptor for its function as gp120 of HIV has CD4 and CCR5. In the present study, we also found that the ancestor of betacoronavirus had a strong first Internal Ribosome Entry Site (IRES) and at least one furin cleavage site (FCS) in the junction region between S1 and S2 subunits.Conclusions: We concluded that the junction FCS in SARS-CoV-2 may increase the efficiency of its entry into cells, while the type 2 ORF8 acquired by SARS-CoV may increase its replication efficiency. These two most critical events provide the most likely explanation for SARS and COVID-19 pandemics.

The discovery of a recombinant SARS2-like CoV strain provides insights into SARS and COVID-2019 pandemics (preprint)

In December 2019, the world awoke to a new zoonotic strain of coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the present study, we classified betacoronavirus subgroup B into the SARS-CoV-2, SARS-CoV and SARS-like CoV clusters, and the ORF8 genes of these three clusters into types 1, 2 and 3, respectively. One important result of our study is that the recently reported strain RmYN02 was identified as a recombinant SARS2-like CoV strain that belongs to the SARS-CoV-2 cluster, but has an ORF8 from a SARS-like CoV. This result provides substantial proof for long-existing hypotheses regarding the recombination and biological functions of ORF8. Based on the analysis of recombination events in the Spike gene, we propose that the Spike protein of SARS-CoV-2 may have more than one specific receptor for its function as gp120 of HIV has CD4 and CCR5. We concluded that the furin protease cleavage site acquired by SARS-CoV-2 may increase the efficiency of viral entry into cells, while the type 2 ORF8 acquired by SARS-CoV may increase its replication efficiency. These two most critical events provide the most likely explanation for SARS and COVID-2019 pandemics.

COVID-19 Infections and Deaths among Connecticut Nursing Home Residents: Facility Correlates.

BACKGROUND/OBJECTIVES: To determine the associations of nursing home registered nurse (RN) staffing, overall quality of care, and concentration of Medicaid or racial and ethnic minority residents with 2019 coronavirus disease (COVID-19) confirmed cases and deaths by April 16, 2020, among Connecticut nursing home residents. DESIGN: Cross-sectional analysis on Connecticut nursing home (n = 215) COVID-19 report, linked to other nursing home files and county counts of confirmed cases and deaths. Multivariable two-part models determined the associations of key nursing home characteristics with the likelihood of at least one confirmed case (or death) in the facility, and with the count of cases (deaths) among facilities with at least one confirmed case (death). SETTING: All Connecticut nursing homes (n = 215). PARTICIPANTS: None. INTERVENTION: None. MEASUREMENTS: Numbers of COVID-19 confirmed cases and deaths among residents. RESULTS: The average number of confirmed cases was eight per nursing home (zero in 107 facilities), and the average number of confirmed deaths was 1.7 per nursing home (zero in 131 facilities). Among facilities with at least one confirmed case, every 20-minute increase in RN staffing (per resident day) was associated with 22% fewer confirmed cases (incidence rate ratio [IRR] = .78; 95% confidence interval [CI] = .68-.89; P < .001); compared with one- to three-star facilities, four- or five-star facilities had 13% fewer confirmed cases (IRR = .87; 95% CI = .78-.97; P < .015), and facilities with high concentration of Medicaid residents (IRR = 1.16; 95% CI = 1.02-1.32; P = .025) or racial/ethnic minority residents (IRR = 1.15; 95% CI = 1.03-1.29; P = .026) had 16% and 15% more confirmed cases, respectively, than their counterparts. Among facilities with at least one death, every 20-minute increase in RN staffing significantly predicted 26% fewer COVID-19 deaths (IRR = .74; 95% CI = I .55-1.00; P = .047). Other focused characteristics did not show statistically significant associations with deaths. CONCLUSION: Nursing homes with higher RN staffing and quality ratings have the potential to better control the spread of the novel coronavirus and reduce deaths. Nursing homes caring predominantly for Medicaid or racial and ethnic minority residents tend to have more confirmed cases.

The clinical characteristics of hip fracture: during the novel coronavirus disease (COVID-19) epidemic (preprint)

Background The aim of this study was to assess the characteristics of hip fracture during the novel coronavirus (COVID-19) epidemic.Method Hip-facture patients undergoing surgery from January 26 to March 31, 2020 (group A) and from January 26 to March 31, 2019 (group B) were retrospectively included. The durations from injury onset to hospital discharge, hospitalization cost, comorbidity, and complications of patients in the two groups were collected. The daily activity and light exposure time, and medical treatment interruption of patients in group A before and during their self-quarantine were also collected. In addition, the reasons for those with hospital admission delay were inquired.Results During the COVID-19 epidemic, patients with hip fracture was increased by 9 cases (69.23%). Patients in group A underwent an over 20-hour longer duration from the injury onset to hospital, an over 3-day longer hospitalization stay, and more hospitalization cost of over 4-thousand yuan compared with those for patients in group B (P < 0.05). The self-quarantine led to reduced daily activities (P <0.001), reduced light exposure time (P <0.001) and more medical interruption for hip-fracture patients. There were also slight more comorbidity number and perioperative complications for patients in group A compared with patients in group B. For those with a pre-hospital time more than 24 h, 58.33% feared go out for medical treatment because of the COVID-19 epidemic.Conclusion During the COVID-19 epidemic period, the prevention and management of hip-fracture for the elderly require more attention for the public and medical care personnel.

Effect of Hypertension on Outcomes of Adult Inpatients with COVID-19 in Wuhan, China: A Propensity Score–matching Analysis (preprint)

Background: Previous studies have shown that Coronavirus Disease 2019 (COVID-19) patients with underlying comorbidities can have worse outcomes. However, the effect of hypertension on outcomes of COVID-19 patients remains unclear. Research Question: The aim of this study was to explore the effect of hypertension on the outcomes of patients with COVID-19 by using propensity score–matching (PSM) analysis. Study Design and Methods: Participants enrolled in this study were patients with COVID-19 who had been hospitalized at the Central Hospital of Wuhan, China. Chronic comorbidities and laboratory and radiological data were reviewed; patient outcomes and lengths of stay were obtained from discharge records. We used the Cox proportional-hazard model (CPHM) to analyze the effect of hypertension on these patients’ outcomes and PSM analysis to further validate the abovementioned effect. Results: : A total of 226 patients with COVID-19 were enrolled in this study, of whom 176 survived and 50 died. The proportion of patients with hypertension among non-survivors was higher than that among survivors (26.70% vs. 74.00%; P < 0.001). Results obtained via CPHM showed that hypertension could increase risk of mortality in COVID-19 patients (hazard ratio 3.317; 95% CI [1.709–6.440]; P < 0.001). Increased D-dimer levels and higher ratio of neutrophils to lymphocytes (N/L) were also found to increase these patients’ mortality risk. After matching on propensity score, we still came to similar conclusions. After we applied the same method in critically ill patients, we found that hypertension also increased risk of death in patients with severe COVID-19. Conclusion: Hypertension, increased D-dimer and the ratio of neutrophil to lymphocyte increased mortality in patients with COVID-19, with hypertension in particular.

A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China (preprint)

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.Methods Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared to explore risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.Results A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age, N/L, CRP and D-dimer. We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. In the training sets, the negative and positive predictive values were 93.80% and 41.32%.Conclusions A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

Scheme for Cheating Prevention in Online Exams during Social Distancing (preprint)

In the current pandemic of COVID-19, students and faculty are subject to social distancing and online learning. How to test students in this unprecedented environment is a new educational challenge with immediate and global impacts. The main contribution of this paper is to establish the feasibility that by a clever design we can control the average gain (which is referred to as the g-factor) from cheating behaviors to a degree as small as pre-specified so that accurate and reliable online exams can be administered. It is underlined that even after the pandemic the methods and systems in the spirit of our proposal are still valuable for cost-effective exams to promote open courses and internet-based education.

A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China (preprint)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2. Methods: Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation. Results: A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036-1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180-1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129-1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013-1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%. Conclusions: A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.