bluuebunny/crossref_metadata_2025 huggingface dataset:
Alternative Approaches for Modelling COVID-19: High-Accuracy Low-Data Predictions
Authors: Agarwal Dewang Kumar , De Soham , Shukla Ojas , Checker Archit , Mittal Apoorvi , Borah Ankita , Gupta Debayan
Published: July 2020

Background

Numerous models have tried to predict the spread of COVID-19. Many involve myriad assumptions and parameters which cannot be reliably calculated under current conditions. We describe machine-learning and curve-fitting based models using fewer assumptions and readily available data.

Methods

Instead of relying on highly parameterized models, we design and train multiple neural networks with data on a national and state level, from 9 COVID-19 affected countries, including Indian and US states and territories. Further, we use an array of curve-fitting techniques on government-reported numbers of COVID-19 infections and deaths, separately projecting and collating curves from multiple regions across the globe, at multiple levels of granularity, combining heavily-localized extrapolations to create accurate national predictions.

Findings

We achieve an R2 of 0·999 on average through the use of curve-fits and fine-tuned statistical learning methods on historical, global data. Using neural network implementations, we consistently predict the number of reported cases in 9 geographically- and demographically-varied countries and states with an accuracy of 99·53% for 14 days of forecast and 99·1% for 24 days of forecast.

Interpretation

We have shown that curve-fitting and machine-learning methods applied on reported COVID-19 data almost perfectly reproduce the results of far more complex and data-intensive epidemiological models. Using our methods, several other parameters may be established, such as the average detection rate of COVID-19. As an example, we find that the detection rate of cases in India (even with our most lenient estimates) is 2.38% - almost a fourth of the world average of 9% [1].
The Epidemiology Workbench: a Tool for Communities to Strategize in Response to COVID-19 and other Infectious Diseases
Authors: Núñez-Corrales Santiago , Jakobsson Eric
Published: July 2020

COVID-19 poses a dramatic challenge to health, community life, and the economy of communities across the world. While the properties of the virus are similar from place to place, the impact has been dramatically different from place to place, due to such factors as population density, mobility, age distribution, etc. Thus, optimum testing and social distancing strategies may also be different from place to place. The Epidemiology Workbench provides access to an agent-based model in which a community’s demographic, geographic, and public health information together with a social distancing and testing strategy may be input, and a range of possible outcomes computed, to inform local authorities on coping strategies. The model is adaptable to other infectious diseases, and to other strains of coronavirus. The tool is illustrated by scenarios for the cities of Urbana and Champaign, Illinois, the home of the University of Illinois at Urbana-Champaign. Our calculations suggest that massive testing is the most effective strategy to combat the likely increase in local cases due to mass ingress of a student population carrying a higher viral load than that currently present in the community.
Modeling the role of clusters and diffusion in the evolution of COVID-19 infections during lock-down
Authors: Bos W. J. T. , Bertoglio J.-P. , Gostiaux L.
Published: July 2020

Epidemics such as the spreading of the SARS-CoV-2 virus are highly non linear, and therefore difficult to predict. In the present pandemic as time evolves, it appears more and more clearly that a clustered dynamics is a key element of description. This means that the disease rapidly evolves within spatially localized networks, that diffuse and eventually create new clusters. We improve upon the simplest possible compartmental model, the SIR model, by adding an additional compartment associated with the clustered individuals. The so-obtained SBIR model compares satisfactorily with results on the pandemic propagation in a number of European countries, during and immediately after lock-down. Especially, the decay exponent of the number of new cases after the first peak of the epidemic, is observed to be very similar for countries in which a strict lock-down was applied. We derive an analytical expression for the value of this exponent.
Renin-Angiotensin-Aldosterone-System inhibitor use in patients with COVID-19 infection and prevention of serious events: a cohort study in commercially insured patients in the US
Authors: Schneeweiss Maria C , Leonard Sandra , Weckstein Andrew , Schneeweiss Sebastian , Rassen Jeremy A
Published: July 2020

Objectives

There is lack of clarity regarding the role of angiotensin receptor blockers (ARB) or angiotensin converting enzyme inhibitors (ACEi) in interfering with the SARS-COV-2 binding on human cells and the resulting change in disease severity. We sought to assess the risk of hospitalization for COVID-19 and serious complications in current users of ARB or ACEi compared to users of dihydropyridine calcium channel blockers (dhpCCB).

Design

Cohort study

Setting

The analysis used de-identified, patient level data from HealthVerity, linking longitudinal data from US medical and pharmacy claims, which contain information on inpatient or outpatient diagnoses, procedures and medication dispensing.

Participants

We identified patients aged 40+ and free of chronic kidney disease (CKD) who were newly diagnosed COVID-19, between March 1, 2020 and May 30, 2020, and adherent to ACEi, ARB, or dhpCCB therapy.

Interventions

Current use of an ACEi, ARB, or dhpCCB.

Main outcome measures

We compared the 30-day risk of hospitalization for COVID-19 and serious complications.

Results

Of 24,708 patients identified, 7,571 were current users of an ARB, 8,484 of an ACEi, and 8,653 of a dhpCCB. The unadjusted 30-day risk of hospitalization for COVID-19 was 2.66% among ARB users, and 2.90% among ACEi users and 3.68% in dhpCCB users. In the PS-matched cohort, the risk of hospitalization among ARB users was 17% lower as compared to dhpCCB (RR=0.83; 0.68-1.00), and the risk among ACE users was 10% lower as compared to dhpCCB (RR=0.90; 0.76-1.07). When including patients with pre-existing CKD, the protective effect of ARB (RR= 0.74; 0.62-0.88) and ACEi (RR=0.84; 0.71-0.99) was more pronounced.

Conclusions

This cohort study showed that neither ARB nor ACEi use increase the risk of severe COVID-19 disease among those infected, and instead suggests that current use of ARB may offer a protective effect. This study found no evidence to support the discontinuation of ARB/ACEi therapy.
Sentiment Informed Timeseries Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston
Authors: Desai Prathamesh S.
Published: July 2020

Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level, as opposed to the state or country level, is needed. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the latter is normalized by the number of tests. Positivity gives a better picture of the spread of this pandemic as with time more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD lacks information about the sentiment of people with respect to coronavirus. Thirdly, models that make use of social media posts might have too much noise. News sentiment, on the other hand, can capture long term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new AI model, viz., Sentiment Informed Timeseries Analyzing AI (SITALA), that has been trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for the Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76 and that for the test dataset of 22 consecutive days is 9.6. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.

One Sentence Summary

An AI model based on news sentiment and COVID-19 test positivity is developed to predict the spread of coronavirus in Houston and to guide future local public policy.
An analysis of mortality in Ontario using cremation data: Rise in cremations during the COVID-19 pandemic
Authors: Postill Gemma , Murray Regan , Wilton Andrew S. , Wells Richard A. , Sirbu Renee , Daley Mark J. , Rosella Laura C
Published: July 2020

Background

The impact of coronavirus disease 2019 (COVID-19) on mortality in Ontario is unknown. Cremations are performed for most deaths in Ontario and require coroner certification before the cremation can take place. Our objective was to provide timely analysis of deaths during the COVID-19 pandemic using cremation data.

Methods

We analyze cremation certificate data from January 1, 2017, to June 30, 2020, in Ontario. 2020 cremation records were compared to historical records from 2017-2019 by age, month, and place of death and COVID-19 status. A time series model was fit to quantify the deviation in cremation trends during the COVID-19 period.

Results

There have been 39 760 cremations in Ontario in 2020 with the highest number of seen in April (N = 7 527 cremations) when peak COVID-19 cases were seen. Over the study period, the proportion of cremations from deaths in hospitals decreased whereas cremations from long-term care and residences increased. In April there were 1 839 more cremations compared to historical averages over 2017-2019, representing a 32% increase. Time series modelling of cremations from January 2017 demonstrated that cremations in April and May 2020 exceeded the projections based on modelled estimates.

Conclusion

We demonstrate the utility of cremation data for providing timely mortality information during a public health emergency. Cremations were higher in the pandemic months compared to previous years, and there was a shift in deaths occurring in hospitals to long-term care and residences. These timely estimates of mortality are critical for understanding the impact of COVID-19.
The IeDEA Data Exchange Standard: a common data model for global HIV cohort collaboration
Authors: Duda Stephany N. , Musick Beverly S. , Davies Mary-Ann , Sohn Annette H. , Ledergerber Bruno , Wools-Kaloustian Kara , McGowan Catherine C. , Maxwell Nicola J. , Kariminia Azar , Ostinelli Cam Ha Dao , Hogan Brenna C. , Shi Qiuhu , Malateste Karen , Goodall Ruth L. , Kristensen Dennis Karsten , Hansen Erik V. , Williams Carolyn F. M. , Lewis Judith T. , Yiannoutsos Constantin T.
Published: July 2020

Objective

To describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community.

Methods

In 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter.

Findings

The resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub.

Conclusions

The IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.

Highlights

The IeDEA Data Exchange Standard is a data model for HIV epidemiology research.

The model has expedited 95 projects using data from >400 HIV clinics worldwide.

A browsable and adaptable version and data collection templates are available online.
Mathematical model study of a pandemic: Graded lockdown approach
Authors: Chatterjee S. , Vani V.C. , Banyal Ravinder K.
Published: July 2020

A kinetic approach is developed, in a “tutorial style” to describe the evolution of an epidemic with spread taking place through contact. The “infection - rate” is calculated from the rate at which an infected person approaches an uninfected susceptible individual, i.e. a potential recipient of the disease, up to a distance p, where the value of p may lie between pmin≤ p ≤ pmax. We consider a situation with a total population of N individuals, living in an area A, x(t) amongst them being infected while xd(t) = β′x(t) is the number that have died in the course of transmission and evolution of the epidemic. The evolution is developed under the conditions (1) a faction α(t) of the [N-x(t) – xd(t)] uninfected individuals and (2) a β(t) fraction of the x(t) infected population are quarantined, while the “source events” that spread the infection are considered to occur with frequency υ0. The processes of contact and transmission are considered to be Markovian. Transmission is assumed to be inhibited by several processes like the use of “masks”, “hand washing or use of sanitizers” while “physical distancing” is described by p. The evolution equation for x(t) is a Riccati - type differential equation whose coefficients are time-dependent quantities, being determined by an interplay between the above parameters. A formal solution for x(t) is presented, for a “graded lockdown” with the parameters, 0≤ α(t), β(t)≤1 reaching their respective saturation values in time scales, τ1, τ2 respectively, from their initial values α(0)=β(0)=0. The growth is predicted for several BBMP wards in Bengaluru and in urban centers in Chikkaballapur district, as an illustrative case. Above selections serve as model cases for high, moderate and thin population densities. It is seen that the evolution of [x(t)/N] with time depends upon (a) the initial time scale of evolution, (b) the time scale of cure and (c) on the time dependence of the Lockdown function Q(t) = {[1-α(t)]·[1-β(t)]}. The formulae are amenable to simple computations and show that in order to curb the spread one must ensure that Q(∞) must be below a critical value and the vigilance has to be continued for a long time (at least 100 to 150 days) after the decay starts, to avoid all chances of the infection reappearing.
Remdesivir for COVID-19: match-population analysis with compassionate use of Remdesivir for severe COVID-19
Authors: Vasylyeva Olga , Chen Tara , Hanna John
Published: July 2020

We retrospectively compared outcomes between patients who received compassionate care Remdesivir and whose who met criteria for Remdesivir, but did not received it due to period of unavailability. We observed comparable mortality rate and significantly higher mortality rate among patients with CrCl < 30 ml per minute.
Evaluating Scenarios for School Reopening under COVID19
Authors: Baxter Arden , Oruc Buse Eylul , Keskinocak Pinar , Asplund John , Serban Nicoleta
Published: July 2020

Thousands of school systems have been struggling with the decisions about how to safely and effectively deliver education during the fall semester of 2020, amid the COVID19 pandemic. The objective of this study is to evaluate the public health impact of reopening schools on the spread of COVID19. An agent-based simulation model was adapted and used to project the number of infections and deaths under multiple school reopening dates and scenarios, including different cohorts receiving in-person instruction on alternating days, only younger children returning to in-person instruction, regular schedule (all students receiving in-person instruction), and school closure (all students receiving online instruction). The study period was February 18th-November 24th, 2020 and the state of Georgia was used as a case study. Across all scenarios, the number of COVID19-related deaths ranged from approximately 17 to 22 thousand during the study period, and on the peak day, the number of new infections ranged from 43 to 68 thousand. An alternating school day schedule performed: (i) almost as well as keeping schools closed, with the infection attack rate ranging from 38.5% to 39.8% compared to that of 37.7% under school closure; (ii) slightly better than only allowing children 10 years or younger to return to in-person instruction. Delaying the reopening of schools had a minimal impact on reducing infections and deaths under most scenarios.

Significance Statement

This study provides insights on the impact of various school reopening dates and scenarios on the spread of COVID19, incorporating differences between children and adults in terms of disease progression and community transmission. School districts are faced with these challenging decisions considering the complex tradeoffs of their impact between public health, education, and society. While the number of new COVID19 confirmed cases continue to increase in many states, so are concerns about the negative impact of school closures on the children’s education and development. The systematic analysis of school reopening scenarios provided in this study will support school systems in their decision-making regarding if, when, and how to return to in-person instruction.
Decreasing median age of COVID-19 cases in the United States – changing epidemiology or changing surveillance?
Authors: Greene Dina N. , Jackson Michael L. , Hillyard David R. , Delgado Julio C. , Schmidt Robert
Published: July 2020

Background

Understanding and monitoring the demographics of SARS-CoV-2 infection can inform strategies for prevention. Surveillance monitoring has suggested that the age distribution of people infected with SARS-CoV-2 has changed since the pandemic began, but no formal analysis has been performed.

Methods

Retrospective review of SARS-CoV-2 molecular testing results from a national reference laboratory was performed. Result distributions by age and positivity were compared between early period (March-April 2020) and late periods (June-July 2020) of the COVID-19 pandemic. Additionally, a sub-analysis compared changing age distributions between inpatients and outpatients.

Results

There were 277,601 test results of which 19320 (7.0%) were positive. The median age of infected people declined over time (p < 0.0005). In March-April, the median age of positive people was 40.8 years (Interquartile range (IQR): 29.0 – 54.1). In June-July, the median age of positive people was 35.8 years (IQR: 24.0 – 50.2). The positivity rate of patients under 50 increased from 6.0 to 10.6 percent and the positivity rate for those over 50 decreased from 6.3 to 5.0 percent between the early and late periods. The trend was only observed for outpatient populations.

Conclusions

We confirm that there is a trend toward decreasing age among persons with laboratory- confirmed SARS-CoV-2 infection, but that these trends seem to be specific to the outpatient population. Overall, this suggests that observed age-related trends are driven by changes in testing patterns rather than true changes in the epidemiology of SARS-CoV-2 infection. This calls for caution in interpretation of routine surveillance data until testing patterns stabilize.

We used national reference laboratory data to compare ages of patients tested for SARS-CoV-2 in March/April 2020 vs. June/July. Median age declined overall, but increased for inpatients, suggesting that declining age is due to changes in surveillance, not COVID-19 epidemiology.
Forecasting hospitalizations due to COVID-19 in South Dakota, USA
Authors: Wesner Jeff S. , Peursem Dan Van , Flores José D. , Lio Yuhlong , Wesner Chelsea A. ,   University of South Dakota
Published: July 2020

1

Anticipating the number of hospital beds needed for patients with COVID-19 remains a challenge. Early efforts to predict hospital bed needs focused on deriving predictions from SIR models, largely at the level of countries, provinces, or states. In the United States, these models rely on data reported by state health agencies. However, predictive disease and hospitalization dynamics at the state level are complicated by geographic variation in disease parameters. In addition it is difficult to make forecasts early in a pandemic due to minimal data. However, Bayesian approaches that allow models to be specified with informed prior information from areas that have already completed a disease curve can serve as prior estimates for areas that are beginning their curve. Here, a Bayesian non-linear regression (Weibull function) was used to forecast cumulative and active COVID-19 hospitalizations for South Dakota, USA. As expected, early forecasts were dominated by prior information, which was derived from New York City. Importantly, hospitalization trends also differed within South Dakota due to early peaks in an urban area, followed by later peaks in other rural areas of the state. Combining these trends led to altered forecasts with relevant policy implications.
Longitudinal study about low back pain, mental health, and access to healthcare system during COVID-19 pandemic: protocol of an ambispective cohort
Authors: Feter Natan , Caputo Eduardo L , Doring Igor R , Leite Jayne S , Cassuriaga Júlia , Reichert Felipe F , da Silva Marcelo C , Rombaldi Airton J
Published: July 2020

This study aims to investigate the effects of physical activity before, during, and after social distancing due to the COVID-19 pandemic on low back pain (LBP), mental health and healthcare access. The PAMPA Cohort (Prospective Study About Mental and Physical Health) is a state-level ambispective longitudinal observational study that will be conducted in Rio Grande do Sul, Brazil. An online-based questionnaire will be used to assess LBP, mental health, healthcare access and physical activity at four time points: 1) pre-COVID-19 social distancing, 2) during COVID-19 social distancing, 3) 6 months and 4) 12 months after baseline. A proportional sample size calculation was conducted, and the final sample size was estimated in 1,767 people, distributed in seven state regions. Participants will be recruited by a four-arm approach: contact with universities, social media, local media and personal contacts. Descriptive analyzes will be reported as mean or proportion and respective 95% confidence interval (CI), when appropriate. Comparison between pre- and during COVID-19 social distancing, and after baseline assessments will be performed using two-way ANOVA with repeated measures. Proportions will be compared by Chi-squared test.
Cervical Whole Slide Histology Image Analysis Toolbox
Authors: Sornapudi Sudhir , Addanki Ravitej , Stanley R. Joe , Stoecker William V. , Long Rodney , Zuna Rosemary , Frazier Shellaine R. , Antani Sameer
Published: July 2020

Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus (HPV) type, which is the primary cause of the disease, helps determine the patient’s risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter- and intra-observer variability in outcomes. We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.
The answer lies in the energy: how simple atomistic molecular dynamics simulations may hold the key to epitope prediction on the fully glycosylated SARS-CoV-2 spike protein
Authors: Serapian Stefano , Marchetti Filippo , Triveri Alice , Morra Giulia , Meli Massimiliano , Moroni Elisabetta , Sautto Giuseppe A. , Rasola Andrea , Colombo Giorgio
Published: July 2020

Betacoronavirus SARS-CoV-2 is posing a major threat to human health and its diffusion around the world is having dire socioeconomical consequences. Thanks to the scientific community’s unprecedented efforts, the atomic structure of several viral proteins has been promptly resolved. As the crucial mediator of host cell infection, the heavily glycosylated trimeric viral Spike protein (S) has been attracting the most attention and is at the center of efforts to develop antivirals, vaccines, and diagnostic solutions.

Herein, we use an energy-decomposition approach to identify antigenic domains and antibody binding sites on the fully glycosylated S protein. Crucially, all that is required by our method are unbiased atomistic molecular dynamics simulations; no prior knowledge of binding properties or ad hoc combinations of parameters/measures extracted from simulations is needed. Our method simply exploits the analysis of energy interactions between all intra-protomer aminoacid and monosaccharide residue pairs, and cross-compares them with structural information (i.e., residueresidue proximity), identifying potential immunogenic regions as those groups of spatially contiguous residues with poor energetic coupling to the rest of the protein.

Our results are validated by several experimentally confirmed structures of the S protein in complex with anti- or nanobodies. We identify poorly coupled sub-domains: on the one hand this indicates their role in hosting (several) epitopes, and on the other hand indicates their involvement in large functional conformational transitions. Finally, we detect two distinct behaviors of the glycan shield: glycans with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes; those with weaker coupling could themselves be poised for antibody recognition. Predicted Immunoreactive regions can be used to develop optimized antigens (recombinant subdomains, synthetic (glyco)peptidomimetics) for therapeutic applications.
Platinum resistance induces diverse evolutionary trajectories in high grade serous ovarian cancer
Authors: Hoare J.I. , Hockings H. , Saxena J. , Silva V.L. , Maniati E. , Mirza H.B. , Huang W. , Wood G.E. , Nicolini F. , Graham T.A. , McNeish I.A. , Lockley M.
Published: July 2020

Resistance to therapy is an enduring challenge in cancer care. Here we interrogate this critical unmet need using high grade serous ovarian cancer (HGSC) as a disease model. We have generated a unique panel of platinum-resistant HGSC models and shown that they share multiple transcriptomic features with relapsed human HGSC. Moreover, they evolve diversein vivophenotypes reflecting the human disease. We previously characterised copy number signatures in HGSC that correlate with patient survival and now provide the first evidence that these signatures undergo recurrent alterations during platinum therapy. Furthermore, specific, resistance-associated signature change is associated with functionally relevant gene expression differences. For example, reduced signature 3 (BRCA1/2-related homologous recombination deficiency) is associated with increased expression of homologous recombination repair genes (Rad51C, Rad51D, BRCA1) and DNA recombination pathway enrichment. Our mechanistic examination therefore provides new and clinically relevant insights into the genomic evolution of platinum-resistant cancers.
Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China
Authors: Hu Shixiong , Wang Wei , Wang Yan , Litvinova Maria , Luo Kaiwei , Ren Lingshuang , Sun Qianlai , Chen Xinghui , Zeng Ge , Li Jing , Liang Lu , Deng Zhihong , Zheng Wen , Li Mei , Yang Hao , Guo Jinxin , Wang Kai , Chen Xinhua , Liu Ziyan , Yan Han , Shi Huilin , Chen Zhiyuan , Zhou Yonghong , Sun Kaiyuan , Vespignani Alessandro , Viboud Cécile , Gao Lidong , Ajelli Marco , Yu Hongjie
Published: July 2020

Background

Several parameters driving the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unclear, including age-specific differences in infectivity and susceptibility, and the contribution of inapparent infections to transmission. Robust estimates of key time-to-event distributions remain scarce as well.

Methods

We collected individual records for 1,178 SARS-CoV-2 infected individuals and their 15,648 contacts identified by contact tracing and monitoring over the period from January 13 to April 02, 2020 in Hunan Province, China. We provide descriptive statistics of the characteristics of cases and their close contacts; we fitted distributions to time-to-key-events distributions and infectiousness profile over time; and we used generalized linear mixed model to estimate risk factors for susceptibility and transmissibility of SARS-CoV-2.

Results

We estimated the mean serial interval at 5.5 days (95%CI −5.0, 19.9) and the mean generation time at 5.5 days (95%CI 1.7, 11.6). The infectiousness was estimated to peak 1.8 days before symptom onset, with 95% of transmission events occurring between 7.6 days before and 7.3 days after the date of symptom onset. The proportion of pre-symptomatic transmission was estimated to be 62.5%. We estimated that at least 3.5% of cases were generated asymptomatic individuals. SARS-CoV-2 transmissibility was not significantly different between working-age adults (15-59 years old) and other age groups (0-14 years old: p-value=0.16; 60 years and over: p-value=0.33), whilst susceptibility to SARS-CoV-2 infection was estimated to increase with age (p-value=0.03). In addition, transmission risk was higher for household contacts (p-value<0.001), decreased for higher generations within a cluster (second generation: odds ratio=0.13, p-value<0.001; generations 3-4: odds ratio=0.05, p-value<0.001, relative to generation 1), and decreased for infectors with a larger number of contacts (p-value=0.04).

Interpretation

Our findings warn of the possible relevant contribution of children to SARS-CoV-2 transmission. When lockdown interventions are in place, we found that odds of transmission are highest in the household setting but, with the relaxation of interventions, other settings (including schools) could bear a higher risk of transmission. Moreover, the estimated relevant fraction of pre-symptomatic and asymptomatic transmission highlight the importance of large-scale testing, contact tracing activities, and the use of personnel protective equipment during the COVID-19 pandemic.
SARS-CoV-2 serosurvey in Health Care Workers of the Veneto Region
Authors: Mario Plebani , Andrea Padoan , Ugo Fedeli , Elena Schievano , Elena Vecchiato , Giuseppe Lippi , Giuliana Lo Cascio , Stefano Porru , Giorgio Palù
Published: July 2020

Background

The ongoing outbreak of coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses formidable challenges to all health care systems. Serological assays may improve disease management when appropriately used, for better understanding the antibody responses mounted upon SARS-CoV-2 infection and for assessing its real prevalence. Although testing the whole population is impratical, well-designed serosurveys in selected subpopulations in specific risk groups may provide valuable information.

Aim

we evaluated the prevalence of SARS-CoV-2 infection in health care workers who underwent molecular testing with reverse transcription real-time polymerase chain reaction (rRT-PCR) in the main hospitals of the Veneto Region by measuring specific antibodies (Abs).

Methods

both IgM and IgG antibodies against SARS-Cov-2 S-antigen and N-protein were measured using a validated chemiluminescent analytical system (CLIA) called Maglumi™ 2000 Plus (New Industries Biomedical EngineeringCo., Ltd [Snibe], Shenzhen, China)

Results

A total of 8285 health care workers were tested. SARS-CoV-2 specific antibodies (IgM, IgG or both) were detectable in 378 cases (4.6%, 95% CI 4.1-5.0%). Seroconversion was observed in 4.4% women and 5% men, but the difference was not significant. Although detectable antibodies were found in all severe COVID-19 patients (100%), lower seropositivity was found in mild disease (83%) and the lowest prevalence (58%) was observed in asymptomatic subjects.

Conclusion

Seroprevalence surveys are of utmost importance for understanding the rate of population that has already developed antibodies against SARS-CoV-2. The present study has the statistical power to define precisely the circulation of SARS-CoV-2 in a cohort of health workers in our region, with its prevalence (4.6%) reflecting a relatively low circulation. Symptomatic individuals or those hospitalized for medical care were 100% antibody positive, whilst Abs were only detectable in 58% of asymptomatic carriers.
An novel epidemiological model for COVID-19
Authors: Gaspari Mauro
Published: July 2020

COVID-19 is characterized by a large number of asymptomatic and mild cases that are difficult to detect; most of them remain unknown, still having an important role in the transmission of the disease, this make the pandemic difficult to control. The purpose of this research is to develop an epidemiological model that allow to estimate the number of unknown/asymptomatic cases in a given area.

The SEIAMPR system, a novel simulation based model for COVID-19 is designed and implemented in Python. The intuition of the model is simple: about 80% of COVID-19 infected people evolve as asymptomatic or with a mild clinical course, many of them remain unknown to the authorities, some of them including those in critical conditions are eventually detected and classified as positive cases. The simulator reproduces this process using an adaptive method integrated with official data.

The simulator has been used for modelling the outbreak in 21 regions in Italy. The positive effects of lockdown policies are demonstrated: unknown active cases 12 days after the lockdown (March the 21th) ranged from 284101 to 374038, e.g. many more than all the official cases in Italy, reducing to 10213/20949 the reopening day. The number of unknown active cases at the beginning of June in the Lombardia region ranged from 6813 to 13390 demanding particular attention.

SEIAMPR is simple to tune and integrate with official data, it emerges as an up-and-coming tool for reporting the effect of lockdown measures, the impact of the disease on the population, and the remaining unknown active cases for evaluating the timing of exit strategies.
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
Authors: Dandekar Raj , Rackauckas Chris , Barbastathis George
Published: July 2020

1

We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions’ respective governments. Finally, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike.

Article Summary Line

Data-driven epidemiological model to quantify and compare quarantine control policies in controlling COVID-19 spread in Europe, North America, South America and Asia.
Relative Contributions of Family History and a Polygenic Risk Score on COPD and Related Outcomes: COPDGene and ECLIPSE studies
Authors: Moll Matthew , Lutz Sharon M. , Ghosh Auyon J , Sakornsalkopat Phuwanat ,   International COPD Genetics Consortium, Hersh Craig , Beaty Terri H , Dudbridge Frank , Tobin Martin D , Mittleman Murray A , Silverman Edwin K. , Hobbs Brian D. , Cho Michael H.
Published: July 2020

Introduction

Family history is a risk factor for chronic obstructive pulmonary disease (COPD). We previously developed a COPD risk score from genome-wide genetic markers (polygenic risk score, or PRS). Whether the PRS and family history provide complementary or redundant information for predicting COPD and related outcomes is unknown.

Methods

We assessed the predictive capacity of family history and PRS on COPD and COPD-related outcomes in European ancestry subjects from the COPDGene and ECLIPSE studies. We also performed interaction and mediation analyses.

Results

In COPDGene, family history and PRS were significantly associated with COPD in a single model (PFamHx = 1.6e-12; PPRS = 5.0e-92). Similar trends were seen in ECLIPSE. Area-under-the-receiver-operator-characteristic-curves (AUCs) for family history, PRS, and the combined predictors for COPD were 0.752, 0.798, and 0.803, respectively. The AUC for a model containing both family history and the PRS was significantly higher than models with PRS (p = 0.00035) or family history (p = 6.1e-29) alone. Both family history and PRS were significantly associated with BODE, SGRQ, and multiple measures of quantitative emphysema and airway thickness. There was a weakly positive interaction between family history and the PRS under the additive, but not the multiplicative scale (RERI = 0.48, p=0.04). Mediation analysis found 16.5% of the effect of family history on risk for COPD was mediated through the PRS [95% CI: 9.4%-24.3%].

Conclusion

Family history and the PRS provide complementary information for predicting COPD and related outcomes. Future studies can address the impact of obtaining both measures in clinical practice.
COVID-19: Data-Driven Mean-Field-Type Game Perspective
Authors: Tembine Hamidou
Published: July 2020

In this article, a class of mean-field-type games with discrete-continuous state spaces is considered. We establish Bellman systems which provide sufficiency conditions for mean-field-type equilibria in state-and-mean-field-type feedback form. We then derive unnormalized master adjoint systems (MASS). The methodology is shown to be flexible enough to capture multi-class interaction in epidemic propagation in which multiple authorities are risk-aware atomic decision-makers and individuals are risk-aware non-atomic decision-makers. Based on MASS, we present a data-driven modelling and analytics for mitigating Coronavirus Disease 2019 (COVID-19). The model integrates untested cases, age-structure, decision-making, gender, pre-existing health conditions, location, testing capacity, hospital capacity, mobility map on local areas, in-city, inter-cities, and international. It shown that the data-driven model can capture most of the reported data on COVID-19 on confirmed cases, deaths, recovered, number of testing and number of active cases in 66+ countries. The model also reports non-Gaussianity and non-exponential properties in 15+ countries.
Weather, Social Distancing, and the Spread of COVID-19*
Authors: Wilson Daniel J.
Published: July 2020

Using high-frequency panel data for U.S. counties, I estimate the full dynamic response of COVID-19 cases and deaths to exogenous movements in mobility and weather. I find several important results. First, holding mobility fixed, temperature is found to have a negative and significant effect on COVID-19 cases from 1 to 8 weeks ahead and on deaths from 2 to 8 weeks ahead. Second, holding weather fixed, mobility is found to have a large positive effect on subsequent growth in COVID-19 cases and deaths. The impact on cases becomes significant 3 to 4 weeks ahead and continues through 8 to 10 weeks ahead. The impact on deaths becomes significant around 4 weeks ahead and persists for at least 10 weeks. Third, I find that the deleterious effects of mobility on COVID-19 outcomes are far greater when the local virus transmission rate is above one – evidence supportive of public health policies aiming to reduce mobility specifically in places experiencing high transmission rates while relaxing restrictions elsewhere. Fourth, I find that the dynamic effects of mobility on cases are generally similar across counties, but the effects on deaths are higher for counties with older populations and, surprisingly, counties with lower black or hispanic population shares. Lastly, I find that while the marginal impact of mobility changes has been stable over recent weeks for cases, it has come down for deaths.
Lifting mobility restrictions and the induced short-term dynamics of COVID-19
Authors: Santana-Cibrian Mario , Acuña-Zegarra Manuel A. , Velasco-Hernandez Jorge X.
Published: July 2020

SARS-CoV-2 has now infected 15 million people and produced more than six hundred thousand deaths around the world. Due to high transmission levels, many governments implemented social-distancing measures and confinement with different levels of required compliance to mitigate the COVID-19 epidemic. In several countries, these measures were effective, and it was possible to flatten the epidemic curve and control it. In others, this objective was not or has not been achieved. In far to many cities around the world rebounds of the epidemic are occurring or, in others, plateau-like states have appeared where high incidence rates remain constant for relatively long periods of time. Nonetheless, faced with the challenge of urgent social need to reactivate their economies, many countries have decided to lift mitigation measures at times of high incidence. In this paper, we use a mathematical model to characterize the impact of short duration transmission events within the confinement period previous but close to the epidemic peak. The model describes too, the possible consequences on the disease dynamics after mitigation measures are lifted. We use Mexico City as a case study. The results show that events of high mobility may produce either a later higher peak, a long plateau with relatively constant but high incidence or the same peak as in the original baseline epidemic curve, but with a post-peak interval of slower decay. Finally, we also show the importance of carefully timing the lifting of mitigation measures. If this occurs during a period of high incidence, then the disease transmission will rapidly increase, unless the effective contact rate keeps decreasing, which will be very difficult to achieve once the population is released.
barcodetrackR: an R package for the interrogation of clonal tracking data
Authors: Espinoza Diego A. , Mortlock Ryland D. , Koelle Samson J. , Wu Chuanfeng , Dunbar Cynthia E.
Published: July 2020

Clonal tracking methods provide quantitative insights into the cellular output of genetically labelled progenitor cells across time and cellular compartments. In the context of gene and cell therapies, clonal tracking methods have enabled the tracking of progenitor cell output both in humans receiving cellular therapies and in corresponding animal models, providing valuable insight into lineage reconstitution, clonal dynamics, and vector genotoxicity. However, the absence of a toolbox by which to interrogate these data has precluded the development of standardized analytical frameworks within the field. Thus, we developedbarcodetrackR, an R package that provides users with tools for the analysis and visualization of clonal dynamics across time and cellular compartments in clonal tracking experiments. Here, we demonstrate the utility ofbarcodetrackRin exploring longitudinal clonal patterns and lineage relationships in the context of a number of clonal tracking studies of hematopoietic stem and progenitor cells (HSPCs) in humans receiving HSPC gene therapy and in animals receiving lentivirally transduced HSPC transplants.
Differential Mast Cell Phenotypes in Benign versus Cancer Tissues and Prostate Cancer Oncologic Outcomes
Authors: Sullivan Heidi Hempel , Maynard Janielle P. , Heaphy Christopher M. , Lu Jiayun , De Marzo Angelo M. , Lotan Tamara L. , Joshu Corinne E. , Sfanos Karen S.
Published: July 2020

We previously reported that high numbers of mast cells in benign (extra-tumoral) regions of the prostate are associated with worse outcomes after radical prostatectomy including biochemical recurrence and the development of metastases. Herein, on a cohort of 384 men, we performed mast cell subtyping and report that higher minimum number of the tryptase-only (MCT) subset of extra-tumoral mast cells is associated with increased risk of biochemical recurrence (comparing highest to lowest tertiles: HR 2.20, 95% CI 1.32-3.65; P-trend 0.004), metastases (HR 3.60, 95% CI 1.77-7.36; P-trend 0.001), and death from prostate cancer (HR 2.96, 95% CI 1.23-7.08; P-trend 0.02). RNAsequencing of benign versus cancer tissue mast cells revealed differential expression of additional site-specific genes. We demonstrate that genes more highly expressed in tumor-infiltrating mast cells, such as CXCR4 and TFE3, represent an altered tumor microenvironment. C-kit variants were also differentially expressed in benign versus cancer tissue mast cells, with C-kit variant 1 (GNNK+) mast cells identified as more prevalent in extra-tumoral regions of the prostate. Finally, using an established mouse model, we found that mast cells do not infiltrate Hi-Myc tumors, providing a model to specifically examine the role of extra-tumoral mast cells in tumorigenesis. Hi-Myc mice crossed to mast cell knockout (Wsh) mice and aged to one year revealed a higher degree of pre-invasive lesions and invasive cancer in wildtype mice versus heterozygous and knockout mice. This suggests a dosage effect where higher numbers of extra-tumoral mast cells resulted in higher cancer invasion. Overall, our studies provide further evidence for a role of extra-tumoral mast cells in driving adverse prostate cancer outcomes.