bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Epidemic Severity Index: Estimating Relative Local Severity of Novel Disease Outbreaks
Authors: Davies Gareth 
Published: April 2020

AbstractDetermining the severity of a novel pathogen in the early stages is difficult in the absence of reliable data. The pattern of outbreaks for COVID-19 across the globe have differed markedly above and below 30°N latitudes, suggesting very different levels of severity, but countries worldwide have implemented the same lockdown strategies. Existing methods for estimating severity appear not to have been useful in informing strategic decisions, possibly due to mismatches between the data required and those available, overly sophisticated methods with undesirable biases, or perhaps confusion and uncertainly generated by the wide range of estimates these methods produced early on.The Epidemic Severity Index (ESI) is a simple, robust method for estimating the local severity of novel epidemic outbreaks using early and widely-available data and that does not depend on any estimated values. ESI allows rapid, meaningful comparisons across territories that can be tracked as the outbreaks unfold. The ESI quantifies severity relative to a parameterised baseline rather than attempting to estimate values for infection fatality rates, case fatality rates or transmission rates. The relative nature of the ESI sidesteps any problems of confidence associated with absolute rate estimation methods and offers immediate practical strategic value.
Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing
Authors: Ghosh Sabyasachi , Rajwade Ajit , Krishna Srikar , Gopalkrishnan Nikhil , Schaus Thomas E. , Chakravarthy Anirudh , Varahan Sriram , Appu Vidhya , Ramakrishnan Raunak , Ch Shashank , Jindal Mohit , Bhupathi Vadhir , Gupta Aditya , Jain Abhinav , Agarwal Rishi , Pathak Shreya , Rehan Mohammed Ali , Consul Sarthak , Gupta Yash , Gupta Nimay , Agarwal Pratyush , Goyal Ritika , Sagar Vinay , Ramakrishnan Uma , Krishna Sandeep , Yin Peng , Palakodeti Dasaradhi , Gopalkrishnan Manoj 
Published: April 2020

AbstractThe COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We presentTapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests. The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection of the SARS-CoV-2 virus. In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each individual sample with a single round of testing in fewer tests than simple two-round pooling. We also present a companion Android applicationBYOM Smart Testingwhich guides users through the pipetting steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the application to recover the status and estimated viral load for each individual sample.NOTE: This protocol has been validated with in vitro experiments that used synthetic RNA and DNA fragments and additionally, its expected behavior has been confirmed using computer simulations. Validation with clinical samples is ongoing. We are looking for clinical collaborators with access to patient samples. Please contact the corresponding author if you wish to validate this protocol on clinical samples.
Ecological processes underlying the emergence of novel enzootic cycles—arboviruses in the neotropics as a case study
Authors: Guth Sarah , Hanley Kathryn , Althouse Benjamin M. , Boots Mike 
Published: April 2020

AbstractPathogens originating from wildlife (zoonoses) pose a significant public health burden, comprising the majority of emerging infectious diseases. Efforts to control and prevent zoonotic disease have traditionally focused on animal-to-human transmission, or “spillover”. However, in the modern era, increasing international mobility and commerce facilitate the spread of infected humans, non-human animals (hereafter animals), and their products worldwide, thereby increasing the risk that zoonoses will be introduced to new geographic areas. Imported zoonoses can potentially ‘spill back’ to infect local wildlife—a danger magnified by urbanization and other anthropogenic pressures that increase contacts between human and wildlife populations. In this way, humans can function as vectors, dispersing zoonoses from their ancestral enzootic systems to establish reservoirs elsewhere in novel animal host populations. Once established, these enzootic cycles are largely unassailable by standard control measures and have the potential to feed human epidemics. Understanding when and why translocated zoonoses establish novel enzootic cycles requires disentangling ecologically complex and stochastic interactions between the zoonosis, the human population, and the natural ecosystem. We address this challenge by delineating potential ecological mechanisms affecting each stage of enzootic establishment—wildlife exposure, enzootic infection, and persistence—applying existing ecological concepts from epidemiology, invasion biology, and population ecology. We ground our study in the neotropics, where four arthropod-borne viruses (arboviruses) of zoonotic origin—yellow fever, dengue, chikungunya, and Zika viruses—have separately been introduced into the human population. This paper is a step towards developing a framework for predicting and preventing novel enzootic cycles in the face of zoonotic translocations.
Modelling the impact of control measures against the COVID-19 pandemic in Viet Nam
Authors: Nguyen Thu-Anh , Cuong Quoc Nguyen , Kim Anh Le Thi , Nguyen Huyen Nguyen , Huong Thao Nguyen Thi 
Published: April 2020

AbstractObjectivesHealth care system of many countries are facing a surging burden of COVID-19. Although Vietnam has successfully controlled the COVID-19 pandemic to date, there is a sign of initial community transmission. An estimate of possible scenarios to prepare health resources in the future is needed. We used modelling methods to estimate impacts of mitigation measures on the COVID-19 pandemic in Vietnam.MethodsSEIR model built in the COVIDSIM1.1 tool was adopted using available data for estimation. The herd immunization scenario was with no intervention implemented. Other scenarios consisted of isolation and social distancing at different levels (25%, 50%, 75% and 10%, 20%, 30%, respectively). Outcomes include epidemic apex, daily new and cumulative cases, deaths, hospitalized patients and ICU beds needed.ResultsBy April 8, 2020, there would be 465 infected cases with COVID-19 in Viet Nam, of those 50% were detected. Cumulatively, there would be 1,400 cases and 30 deaths by end of 2020, if 75% of cases was detected and isolated, and 30% of social distancing could be maintained.The most effective intervention scenario is the detection and isolation of 75% infected cases and reduction of 10% social contacts. This will require an expansion of testing capacity at health facilities and in the community, posing a challenge to identify high-risk groups to prioritized testing.ConclusionsIn a localized epidemic setting, the expansion of testing should be the key measure to control the epidemic. Social distancing plays a significant role to prevent further transmission to the community.
Reconstructed diagnostic sensitivity and specificity of the RT-PCR test for COVID-19
Authors: Padhye N. S. 
Published: April 2020

AbstractReal-time reverse transcription polymerase chain reaction (RT-PCR) targeting select genes of the SARS-CoV-2 RNA has been the main diagnostic tool in the global response to the COVID-19 pandemic. This study was aimed at the estimation of diagnostic sensitivity and specificity of the first RT-PCR test developed by China CDC in January 2020. The study design is a secondary analysis of published findings on 1014 patients in Wuhan, China, of whom 59.3% tested positive for COVID-19 in RT-PCR tests and 87.6% tested positive in chest CT exams. We utilized previously ignored expert opinions in the form of verbal probability classifications of patients with conflicting test results to estimate the informative prior distribution of the infected proportion. It was then used in a Bayesian version of a previously developed model to reconstruct the sensitivity and specificity of the diagnostic tests without the need for specifying an inaccurate test as the gold standard. The sensitivity of the RT-PCR diagnostic test was estimated to be 0.707 (95% CI: 0.668, 0.749), while the specificity was 0.851 (95% CI: 0.774, 0.941). Caution is advised in generalizing these findings to other versions of the RT-PCR test that are being used in diverse geographic regions.
Evaluating a Widely Implemented Proprietary Deterioration Index Model Among Hospitalized COVID-19 Patients
Authors: Singh Karandeep , Valley Thomas S. , Tang Shengpu , Li Benjamin Y. , Kamran Fahad , Sjoding Michael W. , Wiens Jenna , Otles Erkin , Donnelly John P. , Wei Melissa Y. , McBride Jonathon P. , Cao Jie , Penoza Carleen , Ayanian John Z. , Nallamothu Brahmajee K. 
Published: April 2020

ABSTRACTIntroductionThe Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the COVID-19 pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations.MethodsWe studied adult patients admitted with COVID-19 to non-ICU care at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of ICU-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization.ResultsAmong 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. Median age of the cohort was 64 (IQR 53-75) with 168 (43%) African Americans and 169 (43%) women. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.79 (95% CI 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically-relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%.ConclusionWe found the EDI identifies small subsets of high- and low-risk COVID-19 patients with fair discrimination. We did not find evidence of bias by race or sex. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among COVID-19 patients.
Gender-Based Disparities in COVID-19 Patient Outcomes: A Propensity-matched Analysis
Authors: Singh Shailendra , Chowdhry Monica , Chatterjee Arka , Khan Ahmad 
Published: April 2020

AbstractCOVID-19 epidemiological data show higher mortality among males as compared to females. However, it remains unclear if this disparity is due to gender differences in high-risk characteristics. Our study, including a large cohort of male and female patients, showed that males have a higher risk for mortality, hospitalization and mechanical ventilation even when compared to a matched cohort of females with similar age, high-risk behavior, and comorbidities. This gender-based risk of poor outcomes among COVID-19 patients is especially more pronounced in advanced age. High-risk characteristics only partially explain the gender disparity, and further research is needed to understand the causes of this disparity.
The values of coagulation function in COVID-19 patients
Authors: Jin Xin , Duan Yongwei , Bao Tengfei , Gu Junjuan , Chen Yawen , Li Yuanyuan , Mao Shi , Chen Yongfeng , Xie Wen 
Published: April 2020

AbstractObjectiveTo investigate the blood coagulation function in COVID-19 patients, and the correlation between coagulopathy and disease severity.MethodsWe retrospectively collected 147 clinically diagnosed COVID-19 patients at Wuhan Leishenshan Hospital of Hubei, China. We analyzed the coagulation function in COVID-19 patients through the data including thrombin-antithrombin complex (TAT), α2-plasmininhibitor-plasmin Complex (PIC), thrombomodulin (TM), t-PA/PAI-1 Complex (t-PAIC), prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), fibrinogen (FIB), thrombin time (TT), D-Dimer (DD), and platelet (PLT).ResultThe levels of TAT, PIC, TM, t-PAIC, PT, INR, FIB, and DD in COVID-19 patients were higher than health controls (p<0.05), and also higher in the patients with thrombotic disease than without thrombotic disease (p<0.05). What’s more, the patients with thrombotic disease had a higher case-fatality (p<0.05). TAT, PIC, TM, t-PAIC, PT, INR, APTT, FIB, DD, and PLT were also found correlated with disease severity. Meanwhile, we found that there were significant difference in TAT, TM, t-PAIC, PT, INR, APTT, DD, and PLT in the death and survival group. Further using univariate and multivariate logistic regression analysis also found that t-PAIC and DD were independent risk factors for death in patients and are excellent predicting the mortality risk of COVID-19.ConclusionThe coagulation systems in COVID-19 patients are inordinate, and dynamic monitoring of them, might be a key in the control of COVID-19 death.
Gut microbiota features on nursing home admission are associated with subsequent acquisition of antibiotic resistant organism colonization
Authors: Wang Joyce , Cassone Marco , Gibson Kristen , Lansing Bonnie , Mody Lona , Snitkin Evan , Rao Krishna 
Published: April 2020

ABSTRACTNursing home (NH) patients often acquire colonization with antibiotic-resistant organisms (AROs). We show that patients exposed to broad-spectrum antibiotics during previous hospitalizations have elevated enterococcal relative abundances on NH admission and higher risk of subsequent ARO acquisition. Our findings suggest that interventions preventing ARO spread should extend beyond NH doors.
Unraveling the Myths of R0 in Controlling the Dynamics of COVID-19 Outbreak: a Modelling Perspective
Authors: Mohd Mohd Hafiz , Sulayman Fatima 
Published: April 2020

ABSTRACTCOVID-19 is an emerging and rapidly evolving pandemic around the world, which causes severe acute respiratory syndrome and results in substantial morbidity and mortality. To examine the transmission dynamics of COVID19 and its interactions with some exogenous factors such as limited medical resources and false detection problems, we employ a simple epidemiological model and analyse this system using modelling and dynamical systems techniques. We discover some contrasting findings with respect to the observations of basic reproduction number, and we investigate how the issues of limited medical resources and false detection problems affect the COVID-19 pandemic outbreak.
Predicting the COVID-19 epidemic in Algeria using the SIR model
Authors: Boudrioua Mohamed Samir , Boudrioua Abderrahmane 
Published: April 2020

AbstractThe aim of this study is to predict the daily infected cases with Coronavirus (COVID-19) in Algeria. We apply the SIR model on data from 25 February 2020 to 24 April 2020 for the prediction. Following Huang et al (12), we develop two SIR models, an optimal model and a model in a worst-case scenario COVID-19. We estimate the parameters of our models by minimizing the negative log likelihood function using the Nelder-Mead method. Based on the simulation of the two models, the epidemic peak of COVID-19 is predicted to attain 24 July 2020 in a worst-case scenario, and the COVID-19 disease is expected to disappear in the period between September 2020 and November 2020 at the latest. We suggest that Algerian authorities need to implement a strict containment strategy over a long period to successfully decrease the epidemic size, as soon as possible.
Pregnancy and breastfeeding during COVID-19 pandemic: A systematic review of published pregnancy cases
Authors: Rodrigues Carina , Baía Inês , Domingues Rosa , Barros Henrique 
Published: April 2020

AbstractBackgroundThe COVID-19 pandemic is an emerging concern regarding the potential adverse effects during pregnancy. This study reviews knowledge on the impact of COVID-19 on pregnancy and describes the outcome of published cases of pregnant women diagnosed with COVID-19.MethodsSearches were conducted in PubMed® up to 8 April 2020, using PRISMA standards, to identify original published studies describing pregnant women at any gestational age diagnosed COVID-19. There were no date or language restrictions on the search. All identified studies were included irrespective of assumptions on study quality.ResultsWe identified 30 original studies reporting 212 cases of pregnant women with COVID-19 (30 discharged while pregnant), 200 from China and 12 from other countries. The 182 published deliveries resulted in one stillbirth and 185 live births. Four women with severe COVID-19 required admission to an intensive care unit but no cases of maternal death were reported. There was one neonatal death. Preterm births occurred in 28.7% of cases, but it is unclear whether this was iatrogenic. All cases with amniotic fluid, placenta, and/or cord blood analyzed for the SARS-CoV-2 virus were negative. Four newborns were positive for SARS-CoV-2 and three newborns had high levels of IgM antibodies. Breast milk samples from 13 mothers and described in seven studies showed no evidence of SARS-CoV-2.ConclusionThe evidence related to the effect of COVID-19 on pregnant women is still limited. Pregnant women and newborns should be considered particularly vulnerable populations regarding COVID-19 prevention and management strategies.
COVID-19 Outcomes in Saudi Arabia and the UK: A Tale of Two Kingdoms
Authors: Komies Saleh , Aldhahir Abdulelah M. , Almehmadi Mater , Alghamdi Saeed M. , Alqarni Ali , Oyelade Tope , Alqahtani Jaber S. 
Published: April 2020

AbstractBackgroundWhile the number of COVID-19 cases and deaths around the world is starting to peak, it is essential to point out how different countries manage the outbreak and how different measures and experience resulted in different outcomes. This study aimed to compare the effect of the measures taken by Saudi Arabia and the United Kingdom (UK) governments on the outcome of the COVID-19 pandemic as predicted by a mathematical model.MethodData on the numbers of cases, deaths and government measures were collected from Saudi’s Ministry of Health and Public Health England. A prediction of the trend of cases, deaths and days to peak was then modelled using the mathematical technique, Exponential Logistic Growth and Susceptible Infectious Recovered (SIR) model. The measures taken by the governments and the predicted outcomes were compared to assess effectiveness.ResultWe found over three months that 22 fast and extreme measures had been taken in Saudi Arabia compared to eight slow and late measures in the UK. This resulted in a decline in numbers of current infected cases per day and mortality in Saudi Arabia compared to the UK. Based on the SIR model, the predicted number of COVID-19 cases in Saudi as of 31st of March was 2,064, while the predicted number of cases was 63012 in the UK. In addition, the pandemic is predicted to peak earlier on the 27th of March in Saudi Arabia compared to the 2nd of May 2020 in the UK. The end of transition phases for Saudi and UK according to the model, were predicted to be on 18th of April and 24th of May, respectively. These numbers relate to early and decisive measures adopted by the Saudi government.ConclusionWe show that early extreme measures, informed by science and guided by experience, helped reduce the spread and related deaths from COVID-19 in Saudi. Actions were taken by Saudi under the national slogan “We are all responsible” resulted in the observed reduced number of current and predicted cases and deaths compared to the UK approach “keep calm and carry on”.
Who is at the highest risk from COVID-19 in India? Analysis of health, healthcare access, and socioeconomic indicators at the district level
Authors: Nandi Arindam , Balasubramanian Ruchita , Laxminarayan Ramanan 
Published: April 2020

AbstractIntroductionDespite measures such as travel restrictions and lockdowns, the novel coronavirus (SARS-COV-2) is projected to spread across India. Considering that a vaccine for COVID-19 is will not be available soon, it is important to identify populations with high risk from COVID-19 and take measures to prevent outbreaks and build healthcare infrastructure at the local level.MethodsWe used data from two large nationally representative household surveys, administrative sources, and published studies to estimate the risk of COVID-19 at the district level in India. We employed principal component analysis to create an index of the health risk of COVID-19 from demographic and comorbidity indicators such as the proportions of elderly population and rates of diabetes, hypertension, and respiratory illnesses. Another principal component index examined the socioeconomic and healthcare access risk from COVID-19, based on the standard of living, proportion of caste groups, and per capita access to public healthcare in each district.ResultsDistricts in northern, southern and western Indian states such as Punjab, Tamil Nadu, Kerala, and Maharashtra were at the highest health risk from COVID-19. Many of these districts have been designated as COVID-19 hotspots by the Indian government because of emergent outbreaks. Districts in eastern and central states such as Uttar Pradesh, Bihar, and Madhya Pradesh have higher socioeconomic and healthcare access risk as compared with other areas.ConclusionDistricts at high risk of COVID-19 should prioritize policy measures for preventing outbreaks, and improving critical care infrastructure and socioeconomic safety nets.