Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses (preprint)

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility —- a methodology we refer to as WiFi mobility models (W i M ob ). This approach enables policymakers to explore more granular policies like localized closures (LC). W i M ob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, W i M ob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from W i M ob , we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. W i M ob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Mutation and Virulence of SARS-CoV-2 Strains Circulating in Anhui Province, China (preprint)

Background: The genome of SARS-CoV-2 has shown considerable variation during its spreading. Monitoring variations in the virus genome to understand the evolution and spread of the virus is extremely important. Methods: Seven SARS-CoV-2 strains (BB127, BB183, HB030, MAS525, HF3028, FY1494, and SZ005) circulating in Anhui Province, China were isolated and sequenced for evolutionary analysis. Five strains were further cultured in vitro and were subjected to viral growth assay, TCID50 assay, and detection of spike protein expression. Next generation sequence (NGS) analysis were applied to investigate the mutation frequencies throughout the whole genome at different time gradients in vitro. Findings: Our observations revealed that in vitro cultured SARS-CoV-2 virus had much higher mutation frequency (up to ~20 times) than that in infected patients, and the mutation in nonstructural protein 14 (nsp14) might increase the genomic mutation frequency. Different strains had various amount of spike protein which may positively correlated with the virus replication capacity but may be influenced by other viral factors. Interpretation: Our study suggested that SARS-CoV-2 has the potential to diversify under favorable conditions. Monitoring viral mutations is not only helpful for better understanding of virus evolution and virulence change, but also the key to prevent virus transmission and disease progression. SARS-CoV-2 genomic variation analysis may also provide potential ideas for more efficient vaccine development and clinical treatment. Funding: This work is funded by Special Project for Emergency Scientific and Technological Research on New Coronavirus Infection (YG, No. YD9110002001), Emergency Research Project of Novel Coronavirus Infection of Anhui Province (Grant numbers 202004a07020002; 202004a07020004), Postdoctoral Research Foundation of China (2020M670084ZX) and the Fundamental Research Funds for the Central Universities (WK9110000166; WK9110000167).Declaration of Interests: We declare no competing interests.Ethics Approval Statement: The study was conformed to the principles of the Declaration ofHelsinki and approved by the Ethics Committee of the First Affiliated Hospital of USTC..

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. f

DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting (preprint)

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting. DeepCOVID works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

COVID-19 diagnosis and study of serum SARS-CoV-2 specific IgA, IgM and IgG by a quantitative and sensitive immunoassay (preprint)

Background. The pandemic of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing great loss. Detecting viral RNAs is standard approach for SARS-CoV-2 diagnosis with variable success. Currently, studies describing the serological diagnostic methods are emerging, while most of them just involve the detection of SARS-CoV-2-specific IgM and IgG by ELISA or flow immunoassay with limited accuracy. Methods. Diagnostic approach depends on chemiluminescence immunoanalysis (CLIA) for detecting IgA, IgM and IgG specific to SARS-CoV-2 nucleocapsid protein (NP) and receptor-binding domain (RBD) was developed. The approach was tested with 216 sera from 87 COVID-19 patients and 483 sera from SARS-CoV-2 negative individuals. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. Concentration kinetics of RBD-specific serum antibodies were characterized. The relationship of serum RBD-specific antibodies and disease severity was analyzed. Results. The diagnostic accuracy based on RBD outperformed those based on NP. Adding IgA to a conventional serological test containing IgM and IgG improves sensitivity of SARS-CoV-2 diagnosis at early stage. CLIA for detecting RBD-specific IgA, IgM and IgG showed diagnostic sensitivities of 98.6%, 96.8% and 96.8%, and specificities of 98.1%, 92.3% and 99.8%, respectively. Median concentration of IgA and IgM peaked during 16-20 days after illness onset at 8.84 g/mL and 7.25 g/mL, respectively, while IgG peaked during 21-25 days after illness onset at 16.47 g/mL. Furthermore, the serum IgA level positively correlates with COVID-19 severity. Conclusion. CLIA for detecting SARS-CoV-2 RBD-specific IgA, IgM and IgG in blood provides additional values for diagnosing and monitoring of COVID-19.

Clinical and Laboratory Profiles of 75 Hospitalized Patients with Novel Coronavirus Disease 2019 in Hefei, China (preprint)

The outbreak of the novel coronavirus disease 2019 (COVID-19) infection began in December 2019 in Wuhan, and rapidly spread to many provinces in China. The number of cases has increased markedly in Anhui, but information on the clinical characteristics of patients is limited. We reported 75 patients with COVID-19 in the First Affiliated Hospital of USTC from Jan 21 to Feb 16, 2020, Hefei, Anhui Province, China. COVID-19 infection was confirmed by real-time RT-PCR of respiratory nasopharyngeal swab samples. Epidemiological, clinical and laboratory data were collected and analyzed. Of the 75 patients with COVID-19, 61 (81.33%) had a direct or indirect exposure history to Wuhan. Common symptoms at onset included fever (66 [88.0%] of 75 patients) and dry cough (62 [82.67%]). Of the patients without fever, cough could be the only or primary symptom. The most prominent laboratory abnormalities were lymphopenia, decreased percentage of lymphocytes (LYM%), decreased CD4+ and CD8+ T cell counts, elevated C-reactive protein (CRP) and lactate dehydrogenase (LDH). Patients with elevated interleukin 6 (IL-6) showed significant decreases in the LYM%, CD4+ and CD8+ T cell counts. Besides, the percentage of neutrophils, CRP, LDH and Procalcitonin levels increased significantly. We concluded that COVID-19 could cause different degrees of hematological abnormalities and damage of internal organs. Hematological profiles including LYM, LDH, CRP and IL-6 could be indicators of diseases severity and evaluation of treatment effectiveness. Antiviral treatment requires a comprehensive and supportive approach. Further targeted therapy should be determined based on individual clinical manifestations and laboratory indicators.