bluuebunny/crossref_metadata_2025_split huggingface dataset:
ARIMA modelling of predicting COVID-19 infections
Authors: Anne W.Regis , Jeeva S.Carolin 
Published: April 2020

AbstractThe World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.
The involvement of male partners in care: socio determinants of health among women in Prevention of Mother to Child Transmission in Malawi
Authors: Triulzi Isotta , Keiser Olivia , Somerville Claire , Salimu Sangwani , Ciccacci Fausto , Palla Ilaria , Sagno Jean Baptiste , Gondwe Jane , Marazzi Cristina , Orlando Stefano , Palombi Leonardo , Turchetti Giuseppe 
Published: April 2020

AbstractIntroductionMale partners are rarely present during PMTCT (Prevention-Mother-To-Child-Transmission) services in Sub-Saharan Africa (SSA). The involvement of men is increasingly recognised as an important element of women’s access to care. The study aims to identify the socio-demographic characteristics, HIV-Knowledge, Attitude and Practice (KAP) among women accompanied and not accompanied by the male partners to the facilities.MethodWe included pregnant women enrolled in PMTCT programme between August 2018 and November 2019 in the Southern Region of Malawi. Eligible women were aged 18 years or older, lived with a male partner, enrolled for the first time in four DREAM facilities. We provided a KAP survey to women and their partners attending the facilities. Our primary outcome was to assess and analyse the proportion of women who were accompanied by their partner at least once. We applied descriptive statistics, and logistic regressions to study the association between being accompanied and explanatory variables.ResultsWe enrolled 128 HIV-positive women : 82 (64.1%) were accompanied by their male partners and 46 (35.9%) were alone. Women with high level of knowledge on HIV/AIDS are more likely to be accompanied by the male partners (53.7% vs 34.8%,p=0.040). Level of attitude and practice were not different between women accompanied or not. Patients owning a mean of transport were more likely to go alone to facility (OR 4.16, 95% CI 1.02-16.94). Women who travelled more than 90 minutes to get to the facilities (OR 0.10, 95% CI 0.02-0.49) with high HIV-knowledge (OR 0.38, 95% CI 0.16-0.91) are more likely to be accompanied.ConclusionOur study showed a good male partner involvement compared to other studies performed in SSA. To our knowledge this is the first study outlining the relationship between male partner involvement in care with socio determinant of health. This is crucial to design and implement effective interventions.
Decline in global transmission rates of COVID-19 through May 6 2020
Authors: Romero-Severson Ethan , Hengartner Nick , Meadors Grant David , Ke Ruian 
Published: April 2020

AbstractWe analyzed COVID-19 data through May 6th, 2020 using a partially observed Markov process. Our method uses a hybrid deterministic and stochastic formalism that allows for time variable transmission rates and detection probabilities. The model was fit using iterated particle filtering to case count and death count time series from 55 countries. We found evidence for a shrinking epidemic in 30 of the 55 examined countries. Of those 30 countries, 27 have significant evidence for subcritical transmission rates, although the decline in new cases is relatively slow compared to the initial growth rates. Generally, the transmission rates in Europe were lower than in the Americas and Asia. This suggests that global scale social distancing efforts to slow the spread of COVID-19 are effective although they need to be strengthened in many regions and maintained in others to avoid further resurgence of COVID-19. The slow decline also suggests alternative strategies to control the virus are needed before social distancing efforts are partially relaxed.
Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches
Authors: Lu Fred S. , Nguyen Andre T. , Link Nicholas B. , Davis Jessica T. , Chinazzi Matteo , Xiong Xinyue , Vespignani Alessandro , Lipsitch Marc , Santillana Mauricio 
Published: April 2020

AbstractEffectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.
An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak
Authors: Rinaldi Gianluca , Paradisi Matteo 
Published: April 2020

AbstractBackgroundThe coronavirus 2019 (COVID-19) pandemic has been spreading globally for months, yet the infection fatality ratio of the disease is still uncertain. This is partly because of inconsistencies in testing and death reporting standards across countries. We provide estimates which don’t rely on official cases and deaths data but only on population level statistics.MethodsWe collected demographic and death records data from the Italian Institute of Statistics. We focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years. We also assessed the sensitivity of our results to alternative assumptions on the proportion of population infected. Based on our estimates we finally studied the heterogeneity in overall lethality across countries.FindingsWe estimate an overall infection fatality rate of 1.31% (95% credible interval [CrI] 0.94 – 1.89), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0 – 0.17) and a substantially higher 4.16% (CrI 3.05 – 5.80) for people above 60 years of age. In our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information. For instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60. Finally, using data on demographics we show large expected heterogeneity in overall IFR across countries.InterpretationOur empirical estimates show a sharp difference in fatality rates between young and old people and rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.
Is there evidence that BCG vaccination has non-specific protective effects for COVID 19 infections or is it an illusion created by lack of testing?
Authors: Shivendu Shivendu , Chakraborty Saurav , Onuchowska Agnieszka , Patidar Ankit , Srivastava Arpit 
Published: April 2020

AbstractThe goal of this paper is to showcase that the COVID-19 disease pattern is evolving and to study the relationship between mandatory BCG policy and caseload/million or death/per million. We analyze seven recent publications on the impact of BCG vaccinations on the development of COVID19 illness and extend presented findings using the latest data from April 10, 2020. We analyze data from 98 countries and we extend existing models by adding the dimension of COVID-19-related testing conducted by the analyzed countries. Similarly to prior studies, we find that COVID-19 attributable case and death incidences across countries share a relationship with a country’s BCG vaccination inclusion in the national immunization program when testing is not taken into consideration. However, this relationship vanishes when we add the dimension of testing. We observe that case and death incidences conditional on testing do not get affected by the countries’ BCG vaccination inclusion in the national immunization program. Therefore, we show that there is no statistical evidence to support the assertion that inclusion of BCG vaccination in national immunization program (NIP) has any impact of COVID 19 infections (cases) or mortality.
Repositioned chloroquine and hydroxychloroquine as antiviral prophylaxis for COVID-19: A protocol for rapid systematic review of randomized controlled trials
Authors: Chang Raymond , Sun Wei-Zen 
Published: April 2020

AbstractSince the SARS-CoV-2 outbreak rapidly evolved into a pandemic, there is an urgent need for rapid development, identification and confirmation of efficacious antiviral prophylaxis. In this setting, the existing drugs chloroquine (CQ) and hydroxychloroquine (HCQ) which has suggestive evidence of efficacy against SARS-CoV-2 infection and COVID-19 disease has become prime candidates to be repositioned as therapeutic and preventative agents, and a growing number of clinical trials have been registered to study their preventative potential for at-risk populations using a range of dosing schemes and outcome measures. This rapid systematic review protocol aims to provide streamlined and timely synthesis on methodologies and results of randomized controlled trials assessing the efficacy of CQ and HCQ in hopes that this will constructively inform further research as well as public health policy.
Clinical Characteristics of Hospitalized Covid-19 Patients in New York City
Authors: Paranjpe Ishan , Russak Adam J , De Freitas Jessica K , Lala Anuradha , Miotto Riccardo , Vaid Akhil , Johnson Kipp W. , Danieletto Matteo , Golden Eddye , Meyer Dara , Singh Manbir , Somani Sulaiman , Manna Sayan , Nangia Udit , Kapoor Arjun , O’Hagan Ross , O’Reilly Paul F , Huckins Laura M , Glowe Patricia , Kia Arash , Timsina Prem , Freeman Robert M , Levin Matthew A , Jhang Jeffrey , Firpo Adolfo , Kovatch Patricia , Finkelstein Joseph , Aberg Judith A , Bagiella Emilia , Horowitz Carol R , Murphy Barbara , Fayad Zahi A , Narula Jagat , Nestler Eric J , Fuster Valentin , Cordon-Cardo Carlos , Charney Dennis S , Reich David L , Just Allan C , Bottinger Erwin P , Charney Alexander W , Glicksberg Benjamin S , Nadkarni Girish N 
Published: April 2020

ABSTRACTBackgroundThe coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.MethodsDemographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.ConclusionsThis is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.
Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes
Authors: Benkeser David , Díaz Iván , Luedtke Alex , Segal Jodi , Scharfstein Daniel , Rosenblum Michael 
Published: April 2020

SummaryTime is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently over 400 clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital, and a Centers for Disease Control and Prevention (CDC) preliminary description of 2449 cases. We found substantial precision gains from using covariate adjustment-equivalent to 9-21% reductions in the required sample size to achieve a desired power-for a variety of estimands (targets of inference) when the trial sample size was at least 200. We provide an R package and practical recommendations for implementing covariate adjustment. The estimators that we consider are robust to model misspecification.
Analysis of SteraMist ionized hydrogen peroxide technology in the sterilization of N95 respirators and other PPE: a quality improvement study
Authors: Cramer Avilash K. , Plana Deborah , Yang Helen , Carmack Mary M. , Tian Enze , Sinha Michael S. , Krikorian David , Turner David , Mo Jinhan , Li Ju , Gupta Rajiv , Manning Heather , Bourgeois Florence T. , Yu Sherry H. , Sorger Peter K. , LeBoeuf Nicole R. 
Published: April 2020

AbstractObjectiveThe COVID-19 pandemic has led to widespread shortages of personal protective equipment (PPE) for healthcare workers, including filtering facepiece respirators (FFRs) such as N95 masks. These masks are normally intended for single use, but their sterilization and subsequent reuse could substantially mitigate a world-wide shortage.DesignQuality assurance.SettingA sealed environment chamber installed in the animal facility of an academic medical center.InterventionsOne to five sterilization cycles using ionized hydrogen peroxide (iHP), generated by SteraMist® equipment (TOMI; Frederick, MD).Main outcome measuresPersonal protective equipment, including five N95 mask models from three manufacturers, were evaluated for efficacy of sterilization following iHP treatment (measured with bacterial spores in standard biological indicator assemblies). Additionally, N95 masks were assessed for their ability to efficiently filter particles down to 0.3µm and for their ability to form an airtight seal using a quantitative fit test. Filtration efficiency was measured using ambient particulate matter at a university lab and an aerosolized NaCl challenge at a National Institute for Occupational Safety and Health (NIOSH) pre-certification laboratory.ResultsThe data demonstrate that N95 masks sterilized using SteraMist iHP technology retain function up to five cycles, the maximum number tested to date. Some but not all PPE could also be sterilized using an iHP environmental chamber, but pre-treatment with a handheld iHP generator was required for semi-enclosed surfaces such as respirator hoses.ConclusionsA typical iHP environment chamber with a volume of ~80 m3 can treat ~7000 masks per day, as well as other items of PPE, making this an effective approach for a busy medical center.
Clinical characteristics of imported and second-generation COVID-19 cases outside Wuhan, China: A multicenter retrospective study
Authors: Shi Puyu , Ren Guoxia , Yang Jun , Li Zhiqiang , Deng Shujiao , Li Miao , Wang Shasha , Xu Xiaofeng , Chen Fuping , Li Yuanjun , Li Chunyan , Yang Xiaohua , xie Zhaofeng , Wu Zhengxia , chen Mingwei 
Published: April 2020

AbstractBackgroundThe mortality of COVID-19 differs between countries and regions. By now, reports on COVID-19 are largely focused on first-generation cases. This study aimed to clarify the clinical characteristics of imported and second-generation cases.MethodsThis retrospective, multicenter cohort study included 134 confirmed COVID-19 cases from 9 cities outside Wuhan. Epidemiological, clinical and outcome data were extracted from medical records and were compared between severe and non-severe cases. We further profiled the dynamic laboratory findings of some patients.Results34.3% of the 134 patients were severe cases, and 11.2% had complications. As of March 7, 2020, 91.8% patients were discharged and one patient (0.7%) died. The median age was 46 years. The median interval from symptom onset to hospital admission was 4.5 (IQR 3-7) days. The median lymphocyte count was 1.1×109/L. Age, lymphocyte count, CRP, ESR, DBIL, LDH, HBDH showed difference between severe and no-severe cases (all P<0.05). Baseline lymphocyte count was higher in the survived patients than in the non-survivor case, and it increased as the condition improved, but declined sharply when death occurred. The IL-6 level displayed a downtrend in survivors, but rose very high in the death case. Pulmonary fibrosis was found on later chest CT images in 51.5% of the pneumonia cases.ConclusionImported and second-generation cases outside Wuhan had a better prognosis than initial cases in Wuhan. Lymphocyte count and IL-6 level could be used for evaluating prognosis. Pulmonary fibrosis as the sequelae of COVID-19 should be taken into account.SummaryImported and second-generation cases manifested less complications, lower fatality, and higher discharge rate than initial cases, which may be related to the shorter interval from symptom onset to hospital admission, younger age, and higher lymphocyte count of the imported and second-generation patients. Lymphocyte count and IL-6 level could be used as indicators for evaluating prognosis. Pulmonary fibrosis was found in later chest CT images in more than half of the pneumonia cases and should be taken into account.
Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level
Authors: Ramírez-Aldana Ricardo , Gomez-Verjan Juan Carlos , Bello-Chavolla Omar Yaxmehen 
Published: April 2020

ABSTRACTThe Islamic Republic of Iran reported its first COVID-19 cases by 19thFebruary 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths at the date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. In the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how the socioeconomic features of Iranian provinces might predict the number of cases. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran, which indicated that the spread of COVID-19 within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases. Interestingly, literacy is a protective factor that might be directly related to health literacy and compliance with public health measures. These features indicate that policies related to social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be targeted to reduce SARS-CoV2 spread in Iran. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.
A study on control of novel corona-virus (2019- nCoV) disease process by using PID controller
Authors: rad Habibolah Arasteh , Badi Arshia 
Published: April 2020

AbstractBackgroundIn this paper, the SEIR dynamic model will be used to model the epidemic of coronvirus (2019-nCoV)disease. The SEIR model has been used to model infectious diseases in Malaysia.Then, the spread and control of the disease is simulated applying a PID controller. The results of this study show that the implementation of strict restrictions such as quarantine, social distancing and closure of gathering centers is effective in controlling the disease. Using the results and analyzing them, it was found that early and strict implementation of strict restrictions such as quarantine, social distance and closure of centers with a high percentage of community is very important to control this disease and prevent irreparable economic losses and depreciation of medical staff.ObjectiveModeling the prevalence and control of corona-virus (2019-nCoV)and the impact of government actions using control engineering methods.MethodIn this study, the SEIR dynamic model was used and the common data on the prevalence of the virus in Wuhan, China and Malaysia were used. As an example, the use of control target schemes is simulated in this paper.ResultsThe findings of this study use control methods and forecasting in control engineering to provide a clear picture of macro-decisions for different governments in the field of infectious diseases.ConclusionManagement and control schemes such as travel restrictions, quarantine, social distance and closure of offices, higher education institutions must be implemented immediately to prevent major economic and social losses. The implementation of these restrictions should not be delayed during the outbreak of corona-virus(2019-nCoV) infectious diseases.