bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 pandemics modeling with SEIR(+CAQH), social distancing, and age stratification. The effect of vertical confinement and release in Brazil
Authors: Lyra Wladimir , Nascimento José-Dias do , Belkhiria Jaber , de Almeida Leandro , Chrispim Pedro Paulo M. , de Andrade Ion 
Published: April 2020

AbstractThe ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of “vertical confinement”. In this strategy, contact is reduced only to specific groups (like age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.
Acceptance of and preference for COVID-19 vaccination in healthcare workers: a comparative analysis and discrete choice experiment
Authors: Fu Chuanxi , wei Zheng , Zhu Fengchang , Pei Sen , Li Shunping , Zhang Liuren , Sun Xiaohui , Wu Yue , Liu Ping , Jit Mark 
Published: April 2020

AbstractBackgroundA major obstacle to successful coronavirus disease (COVID-19) vaccine rollout is vaccine hesitancy. Acceptance of and preferences for COVID-19 vaccination among healthcare workers (HCWs) is critical, because they are a key target group for vaccination programs, and they are also highly influential to vaccine uptake in the wider population. This study sought to comparatively determine the acceptance of and preference for COVID-19 vaccination among HCWs and the general population.MethodsAn Internet-based, region-stratified discrete-choice experiment was conducted among 352 HCWs and 189 general population respondents recruited in March 2020 from 26 Chinese provinces. We accessed knowledge of disease, attitude towards and acceptance of COVID-19 vaccination. Several attributes (related to COVID-19 disease, COVID-19 vaccination and one social acceptance) were identified as key determinants of vaccine acceptance.ResultsHCWs had a more in-depth understanding of COVID-19 and showed a higher willingness to accept COVID-19 vaccines with lower effectiveness and/or more severe adverse effects compared to the general population. 76.4% of HCWs (vs 72.5% of the general population) expressed willingness to receive vaccination (χ2=2.904, p=0.234). High levels of willingness to accept influenza (65.3%) and pneumococcal (55.7%) vaccination were also seen in HCWs. Future COVID-19 disease incidence (OR: 4.367, 95% CI 3.721–5.126), decisions about vaccination among social contacts of respondents (OR 0.398, 95% CI 0.339–0.467), and infection risk >30% (OR 2.706, 95% CI 1.776–2.425) significantly increased the probability of vaccination acceptance in HCWs.ConclusionMulti-component interventions to address the key determinants of hesitancy in both HCWs and in the general population should be considered for COVID-19 vaccination programs.
An effect assessment of Airborne particulate matter pollution on COVID-19: A multi-city Study in China
Authors: Wang Bo , Liu Jiangtao , Fu Shihua , Xu Xiaocheng , Li Lanyu , Ma Yueling , Zhou Ji , Yaoc Jinxi , Liu Xingrong , Zhang Xiuxia , He Xiaotao , Yan Jun , Shi Yanjun , Ren Xiaowei , Niu Jingping , Luo Bin , zhang Kai 
Published: April 2020

ObjectiveCoronavirus disease 2019 (COVID-19) is a serious infectious disease, which has caused great number of deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution on COVID-19 across China.MethodsIn this study, we obtained confirmed cases of COVID-19, the data of airborne ambient PM with aerodynamic diameter ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10), ambient temperature (AT), absolute humidity (AH) and migration scale index (MSI) in 72 cities of China (excluded Wuhan city) on a daily basis, each of which confirmed more than 50 cases from January 20th to March 2nd, 2020. We applied a two-stage analysis. Generalized additive models with quasi-Poisson distribution was first fitted to estimate city-specific effects of PM10 and PM2.5 on daily confirmed COVID-19 cases while controlling AT, AH and MSI. Then, we used meta-analysis to generate the pooled effect estimates from city-specific results.ResultsDuring the study period, there were a total of 24 939 COVID-19 cases, most of which were reported in Hubei Province. In our meta-analysis, we found each 10 μg/m3 increase in concentration of PM2.5 and PM10 in single day lag (from lag 0 to lag 7 and lag 14) were positively associated with confirmed cases of COVID-19, not including PM10 at lag 5, lag 6 and lag 7, and PM2.5 at lag 5, lag 6. Similar trend was also found in different cumulative lag days (from lag 01 to lag 07 and lag 014). The effects of PM2.5 and PM10 on daily COVID-19 confirmed cases are statistically significant for three cumulative lag periods over 3, 7 and 14 days with the greatest effect over 14 days. The estimated RRs of which were 1.64 (95% CIs: 1.47, 1.82) and 1.47 (95% CIs: 1.34, 1.61) with each 10 μg/m3 increase in concentrations of PM2.5 and PM10, respectively. In addition, we found that the effects of PM2.5 on daily confirmed cases were greater than PM10 in all included lag days.ConclusionsThis nationwide study suggests that airborne PM pollution likely increases the risk of getting COVID-19 in China.
Do latitude and ozone concentration predict Covid-2019 cases in 34 countries?
Authors: Alipio Mark M. 
Published: April 2020

AbstractIn this paper, I used multivariate linear regression analysis to determine if latitude and ozone concentration predict Covid-2019 cases in 34 countries worldwide. Data pertaining to Covid-2019 cases were extracted from Worldometer. Ozone concentration levels were taken from the open-access database of World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Latitude of specific area where measurement took place was also provided in the database. Preliminary Kendall rank correlation test revealed that Covid-2019 incidence was positively and significantly related to ozone concentration; however, incidence was not significantly related to latitude. Using multivariate linear regression, a statistically significant link between ozone concentration and Covid-2019 incidence in 34 countries was established; however, I found no statistical association between latitude and Covid-2019 incidence refuting previous claims. Prompt health actions should be developed for areas with high ozone concentration in the present and possibly, future outbreaks; however, extensive laboratory analysis should be conducted to further confirm the findings of the study. Nevertheless, the results of this study could serve as a basis for further clinical and large-scale studies.
A Simple Colorimetric Molecular Detection of Novel Coronavirus (COVID-19), an Essential Diagnostic Tool for Pandemic Screening
Authors: Annamalai Pazhanimuthu , Kanta Madhu , Ramu Pazhanivel , Ravi Baskar , Veerapandian Kokilavani , Srinivasan Rengarajan 
Published: April 2020

AbstractThe recent outbreak of the newly emerged novel coronavirus (SARS-CoV-2) presents a big challenge for public health laboratories as virus isolates are not available while there is an increasing evidence that the epidemic is more widespread than initially thought, as well as spreading internationally across borders through travellers does already happen warranting a methodology for the rapid detection of the infection to control SARS-CoV-2. Aim: We intended to develop and deploy a robust and rapid diagnostic methodology using LAMP assay for use in point of care settings to detect SARS-COV-2 infection. Methodology: In the present study, we have developed a validated rapid diagnostic procedure to detect SARS-CoV-2 using LAMP assay, its design relying on isothermal amplification of the nucleic acids of the SARS-CoV-2. Results: The LAMP assay developed detects SARS-CoV-2 infection rapidly with high sensitivity and reliability. The data generated by LAMP assay were comparable and at par with the data generated by real-time PCR method. Conclusion: The present study demonstrates that the LAMP assay developed was a rapid, reliable, sensitive and cost effective method to detect SARS-CoV-2 infection in a point of care as well as in laboratory settings.
Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection
Authors: Laager Mirjam , Cooper Ben S , Eyre David W 
Published: April 2020

AbstractHealthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.
Chaos, Percolation and the Coronavirus Spread: the Italian case
Authors: Bonasera Aldo , Bonasera G. , Zhang Suyalatu 
Published: April 2020

ABSTRACTA model based on chaotic maps and turbulent flows is applied to the spread of Coronavirus for each Italian region in order to obtain useful information and help to contrast it. We divide the regions into different risk categories and discuss anomalies. The worst cases are confined between the Appenine and the Alps mountain ranges but the situation seem to improve closer to the sea. The Veneto region gave the most efficient response so far and some of their resources could be diverted to other regions, in particular more tests to the Lombardia, Liguria, Piemonte, Marche and V. Aosta regions, which seem to be worst affected. We noticed worrying anomalies in the Lazio, Campania and Sicilia regions to be monitored. We stress that the number of fatalities we predicted on March 12 has been confirmed daily by the bulletins. This suggests a change of strategy in order to reduce such number maybe moving the weaker population (and negative to the virus test) to beach resorts, which should be empty presently. The ratio deceased/positives on April 4, 2020 is 5.4% worldwide, 12.3% in Italy, 1.4% in Germany, 2.7% in the USA, 10.3% in the UK and 4.1% in China. These large fluctuations should be investigated starting from the Italian regions, which show similar large fluctuations.
No evidence of clinical efficacy of hydroxychloroquine in patients hospitalised for COVID-19 infection and requiring oxygen: results of a study using routinely collected data to emulate a target trial
Authors: Mahévas Matthieu , Tran Viet-Thi , Roumier Mathilde , Chabrol Amélie , Paule Romain , Guillaud Constance , Gallien Sébastien , Lepeule Raphael , Szwebel Tali-Anne , Lescure Xavier , Schlemmer Frédéric , Matignon Marie , Khellaf Medhi , Crickx Etienne , Terrier Benjamin , Morbieu Caroline , Legendre Paul , Dang Julien , Schoindre Yoland , Pawlotski Jean-Michel , Michel Marc , Perrodeau Elodie , Carlier Nicolas , Roche Nicolas , de Lastours Victoire , Mouthon Luc , Audureau Etienne , Ravaud Philippe , Godeau Bertrand , Costedoat-Chalumeau Nathalie 
Published: April 2020

AbstractBackgroundTreatments are urgently needed to prevent respiratory failure and deaths from coronavirus disease 2019 (COVID-19). Hydroxychloroquine (HCQ) has received worldwide attention because of positive results from small studies.MethodsWe used data collected from routine care of all adults in 4 French hospitals with documented SARS-CoV-2 pneumonia and requiring oxygen ≥ 2 L/min to emulate a target trial aimed at assessing the effectiveness of HCQ at 600 mg/day. The composite primary endpoint was transfer to intensive care unit (ICU) within 7 days from inclusion and/or death from any cause. Analyses were adjusted for confounding factors by inverse probability of treatment weighting.ResultsThis study included 181 patients with SARS-CoV-2 pneumonia; 84 received HCQ within 48 hours of admission (HCQ group) and 97 did not (no-HCQ group). Initial severity was well balanced between the groups. In the weighted analysis, 20.2% patients in the HCQ group were transferred to the ICU or died within 7 days vs 22.1% in the no-HCQ group (16 vs 21 events, relative risk [RR] 0.91, 95% CI 0.47–1.80). In the HCQ group, 2.8% of the patients died within 7 days vs 4.6% in the no-HCQ group (3 vs 4 events, RR 0.61, 95% CI 0.13–2.89), and 27.4% and 24.1%, respectively, developed acute respiratory distress syndrome within 7 days (24 vs 23 events, RR 1.14, 95% CI 0.65–2.00). Eight patients receiving HCQ (9.5%) experienced electrocardiogram modifications requiring HCQ discontinuation.InterpretationThese results do not support the use of HCQ in patients hospitalised for documented SARS-CoV-2-positive hypoxic pneumonia.
Risk Assesment of nCOVID-19 Pandemic In India: A Mathematical Model And Simulation
Authors: Biswas Swarnava , Mukherjee Moumita 
Published: April 2020

AbstractThe entire world is now eventually locked down due to the outbreak of nCOVID-19 corona virus outbreak. The fast and relentless spread nCOVID-19 has basically segmented the populace only into merely into three classes, such as susceptible, infected and recovered compartments. Adapting the classical SEIR-type epidemic modelling framework, the direct person-to-person contact transmission is taken as the direct route of transmission of nCOVID-19 pandemic. In this research, the authors have developed a model of the nation-wide trends of the outburst of the nCOVID-19 infection using an SEIR Model. The SEIR dynamics are expressed using ordinary differential equations. The creators initially determined the parameters of the model from the accessible day by day information for Indian States dependent on around 35 days history of diseases, recuperations and deaths. The determined parameters have been amassed to extend future patterns for the Indian subcontinent, which is right now at a beginning time in the contamination cycle. The novelty of the study lies in the prediction of both the pessimistic and optimistic mathematical model based comprehensive analysis of nCOVID-19 infection spreading, for two different conditions: (a) if lockdown gets withdrawn and (b) if lockdown continues as a whole. If the complete lockdown in India is withdrawn on 14th April 2020, as a whole, then from the simulation, the authors have predicted that the infected population will flare-up to a large extent, suddenly, however, gradual or zone specific withdrawal would be more effective solution. This study also suggested some possible way-out to get rid of this situation by providing a trade-off between ‘ flattening of the curve” as well as “less economic turbulence. The projections are intended to provide a base/ action plan for the socio-economic counter measures to alleviate nCOVID-19.
Antibiotic stewardship in premature infants: a systematic review
Authors: Rajar Polona , Saugstad Ola D. , Berild Dag , Dutta Anirban , Greisen Gorm , Lausten-Thomsen Ulrik , Mande Sharmila S. , Nangia Sushma , Petersen Fernanda C. , Dahle Ulf R. , Haaland Kirsti 
Published: April 2020

ABSTRACTAntibiotic treatment in premature infants is often empirically prescribed, and practice varies widely among otherwise comparable neonatal intensive care units. Unnecessary and prolonged antibiotic treatment is documented in numerous studies. Recent research shows serious side effects and suggests long-term adverse health effects in prematurely born infants exposed to antibiotics in early life. One preventive measure to reduce unnecessary antibiotic exposure is implementation of antibiotic stewardship programs. We reviewed the literature on implemented antibiotic stewardship programs focusing on premature infants. Six academic databases were systematically searched and eleven articles met inclusion criteria. Articles were grouped according to common area of stewardship actions; 1) Focus on reducing initiation of antibiotic therapy, 2) Focus on shortening duration of antibiotic therapy, 3) Various infrastructural stewardship implementations. The studies differed in their cohort composition and measured outcomes. We provide an overview of the reduction in antibiotic use achieved. Antibiotic stewardship programs are effective especially when they use a multifactorial approach and are tailored to premature infants. Implementation of antibiotic stewardship programs targeting premature infants should be considered in all neonatal intensive care units. The Norwegian Research Council (project number 273833) and the Olav Thon Foundation supported the study.
Potency and timing of antiviral therapy as determinants of duration of SARS CoV-2 shedding and intensity of inflammatory response
Authors: Goyal Ashish , Cardozo-Ojeda  , Fabian E. , Schiffer Joshua T. 
Published: April 2020

AbstractTreatments are desperately needed to lower the hospitalization and case fatality rates of SARS CoV-2 infection. In order to meaningfully impact the COVID-19 pandemic, promising antiviral therapies must be identified within the next several months. However, the number of clinical trials that can be performed in this timeframe is limited. We therefore developed a mathematical model which allows projection of all possible therapeutic approaches. Our model recapitulates off-treatment viral dynamics and predicts a three-phase immune response. Addition of treatment with remdesivir, hydroxychloroquine, neutralizing antibodies or cellular immunotherapy demonstrates that ifin vivodrug potency is high, then rapid elimination of virus is possible. Potent therapies dosed soon after peak viral load when infected people typically develop symptoms, are predicted to decrease shedding duration and intensity of the effector immune response, but to have little effect on viral area under the curve, which is driven by high levels of early SARS CoV-2 replication. Potent therapy dosed prior to peak viral load, when infection is usually pre-symptomatic, is predicted to be the only option to lower viral area under the curve. We also identify that clinically meaningful drug resistance is less likely to emerge with a highly potent agent that is dosed after peak viral load. Our results support an early test and treat approach for COVID-19, but also demonstrate the need to identify early viral shedding kinetic features that are the most predictive surrogates of clinical severity and transmission risk.One Sentence SummaryWe developed a mathematical model to predict the outcomes of different possible COVID-19 treatments.
Application of COVID-19 pneumonia diffusion data to predict epidemic situation
Authors: zhenguo Wu 
Published: April 2020

AbstractObjectiveTo evaluate novel coronavirus pneumonia cases by establishing the mathematical model of the number of confirmed cases daily, and to assess the current situation and development of the epidemic situation, so as to provide a digital basis for decision-making.MethodsThe number of newly confirmed covid-19 cases per day was taken as the research object, and the seven-day average value (M) and the sequential value (R) of M were calculated to study the occurrence and development of covid-19 epidemic through the analysis of charts and data.ResultsM reflected the current situation of epidemic development; R reflected the current level of infection and the trend of epidemic development.ConclusionThe current data can be used to evaluate the number of people who have been infected, and when R < 1, the peak of epidemic can be predicted.PrefaceIn December 2019, a number of cases of pneumonia with unknown causes were found in some hospitals in Wuhan, Hubei province, China. On 11 March 2020, the director-general of the world health organization (WHO), Tedros Adhanom Ghebreyesus, announced that based on the assessment, WHO believes that the current outbreak of COVID-19 can be called a global pandemic. By early April 2020, there were more than one million confirmed cases worldwide.COVID-19 has developed from sporadic cases to pandemic in a short period of 3 months. The analysis and research of its infectious data will help to prevent and control the next stage of epidemic prevention and other infectious diseases in the future.In this paper, COVID-19 rounded average of seven days (M), and M’s ring ratio (R) are used to predict the current potential patients’ data, and the relative state of epidemic prevention and control is judged through the graphic features and characteristic data, so as to provide evidence for the prevention and control decisions.