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1.
Front Immunol ; 12: 681636, 2021.
Article in English | MEDLINE | ID: covidwho-1714997

ABSTRACT

The emergence of COVID-19 has emphasised that biological assay data must be analysed quickly to develop safe, effective and timely vaccines/therapeutics. For viruses such as SARS-CoV-2, the primary way of measuring immune correlates of protection is through assays such as the pseudotype microneutralisation (pMN) assay, thanks to its safety and versatility. However, despite the presence of existing tools for data analysis such as PRISM and R the analysis of these assays remains cumbersome and time-consuming. We introduce an open-source R Shiny web application and R library (AutoPlate) to accelerate data analysis of dose-response curve immunoassays. Using example data from influenza studies, we show that AutoPlate improves on available analysis software in terms of ease of use, flexibility and speed. AutoPlate (https://philpalmer.shinyapps.io/AutoPlate/) is a tool for the use of laboratories and wider scientific community to accelerate the analysis of biological assays in the development of viral vaccines and therapeutics.


Subject(s)
COVID-19/diagnosis , Immunoassay/statistics & numerical data , Influenza A virus/physiology , Influenza, Human/diagnosis , SARS-CoV-2/physiology , Antibodies, Neutralizing/metabolism , Antibodies, Viral/metabolism , Data Interpretation, Statistical , Dose-Response Relationship, Drug , Humans , Immunoassay/standards , Quality Control , Software
2.
Anesth Analg ; 134(3): 524-531, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1709740

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) cases continue to surge in the United States with the emergence of new variants. Statewide variability and inconsistency in implementing risk mitigation strategies are widespread, particularly in regards to enforcing mask mandates and encouraging the public to become fully vaccinated. METHODS: This is a cross-sectional study conducted on July 31, 2021, utilizing publicly available data from the Wisconsin Department of Health Services. The authors abstracted data on total COVID-19-related cases, hospitalizations, and deaths in the state of Wisconsin. The primary objective was comparison of total COVID-19-related cases, hospitalizations, and deaths in vaccinated versus unvaccinated people in the state of Wisconsin over a 31-day period (July 2021). Furthermore, we also performed a narrative review of the literature on COVID-19-related outcomes based on mask use and vaccination status. RESULTS: In the state of Wisconsin during July 2021, total COVID-19 cases was 125.4 per 100,000 fully vaccinated people versus 369.2 per 100,000 not fully vaccinated people (odds ratio [OR] = 0.34, 95% confidence interval [CI], 0.33-0.35; P < .001). Total COVID-19 hospitalizations was 4.9 per 100,000 fully vaccinated people versus 18.2 per 100,000 not fully vaccinated people (OR = 0.27, 98% CI, 0.22-0.32; P < .001). Total COVID-19 deaths was 0.1 per 100,000 fully vaccinated people versus 1.1 per 100,000 not fully vaccinated people (OR = 0.09, 95% CI, 0.03-0.29; P < .001). Narrative review of the literature demonstrated high vaccine effectiveness against COVID-19 infection prevention (79%-100% among fully vaccinated people), COVID-19-related hospitalization (87%-98% among fully vaccinated people), and COVID-19-related death (96.7%-98% among fully vaccinated people). Studies have also generally reported that mask use was associated with increased effectiveness in preventing COVID-19 infection ≤70%. CONCLUSIONS: Strict adherence to public mask use and fully vaccinated status are associated with improved COVID-19-related outcomes and can mitigate the spread, morbidity, and mortality of COVID-19. Anesthesiologists and intensivists should adhere to evidence-based guidelines in their approach and management of patients to help mitigate spread.


Subject(s)
COVID-19/mortality , Cost of Illness , Hospitalization/trends , Mandatory Programs/trends , Masks/trends , Vaccination/trends , COVID-19/prevention & control , Cross-Sectional Studies , Data Interpretation, Statistical , Hospitalization/statistics & numerical data , Humans , Mandatory Programs/statistics & numerical data , Masks/statistics & numerical data , Mortality/trends , Vaccination/statistics & numerical data , Wisconsin/epidemiology
4.
Pesqui. bras. odontopediatria clín. integr ; 21: e210018, 2021. tab
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1703801

ABSTRACT

ABSTRACT Objective To evaluate the effect of the COVID-19 impacts on the activities of researchers in the field of Oral Medicine (OM) and Oral Pathology (OP). To assess the research activities and training of human resources by Brazilian productivity fellows in research (BPFR) in OM and OP in the COVID-19 Era. Material and Methods Thirty-six BPFR in OM and OP areas, funded by National Council for Scientific and Technological Development (CNPq), received a virtual structured questionnaire by e-mail, on the Google Forms (Google®) platform, with questions regarding research activities and training of human resources (supervision of undergraduate and postgraduate students), during the COVID-19 pandemic. From the thirty-six BPFR in OM and OP, twenty-seven (75.0%) answered the questionnaire. Results Most of them were males (n=20; 74.1%) and were distributed in four Brazilian regions and ten states of the federation, including the Federal District. Twenty-four (88.9%) BPFR reported having suspended clinical activities, while sixteen (59.3%) answered that histopathology practices are suspended. Twenty-five (92.6%) BPFR mentioned difficulties in conducting research projects and 55.5% stated having no difficulties in the supervision of undergraduates, master's and PhD students. Conclusion The current scenario may significantly impact the diagnosis of oral diseases in Brazil. Moreover, a decrease in the scientific production of BPFR in OM and OP in the coming years is also considered.


Subject(s)
Humans , Male , Female , Pathology, Oral , Research Support as Topic , Training Support , Brazil/epidemiology , Oral Medicine , COVID-19 , Research , Technological Development , Surveys and Questionnaires , Data Interpretation, Statistical , Workforce , Mentoring
5.
Medicine (Baltimore) ; 101(5): e28749, 2022 Feb 04.
Article in English | MEDLINE | ID: covidwho-1672391

ABSTRACT

BACKGROUND: Exponential-like infection growth leading to peaks (denoted by inflection points [IP] or turning points) is usually the hallmark of infectious disease outbreaks, including coronaviruses. To determine the IPs of the novel coronavirus (COVID-19), we applied the item response theory model to detect phase transitions for each country/region and characterize the IP feature on the temporal bar graph (TBG). METHODS: The IP (using the item difficulty parameter to locate) was verified by the differential equation in calculus and interpreted by the TBG with 2 virtual and real empirical data (i.e., from Collatz conjecture and COVID-19 pandemic in 2020). Comparisons of IPs, R2, and burst strength [BS = ln() denoted by the infection number at IP(Nip) and the item slope parameter(a) in item response theory were made for countries/regions and continents on the choropleth map and the forest plot. RESULTS: We found that the evolution of COVID-19 on the TBG makes the data clear and easy to understand, the shorter IP (=53.9) was in China and the longest (=247.3) was in Europe, and the highest R2 (as the variance explained by the model) was in the US, with a mean R2 of 0.98. We successfully estimated the IPs for countries/regions on COVID-19 in 2020 and presented them on the TBG. CONCLUSION: Temporal visualization is recommended for researchers in future relevant studies (e.g., the evolution of keywords in a specific discipline) and is not merely limited to the IP search in COVID-19 pandemics as we did in this study.


Subject(s)
COVID-19 , Models, Theoretical , COVID-19/epidemiology , Data Interpretation, Statistical , Disease Outbreaks , Europe , Humans , Pandemics , SARS-CoV-2
6.
Sci Rep ; 12(1): 598, 2022 01 12.
Article in English | MEDLINE | ID: covidwho-1621269

ABSTRACT

After a year of living with the COVID-19 pandemic and its associated consequences, hope looms on the horizon thanks to vaccines. The question is what percentage of the population needs to be immune to reach herd immunity, that is to avoid future outbreaks. The answer depends on the basic reproductive number, R0, a key epidemiological parameter measuring the transmission capacity of a disease. In addition to the virus itself, R0 also depends on the characteristics of the population and their environment. Additionally, the estimate of R0 depends on the methodology used, the accuracy of data and the generation time distribution. This study aims to reflect on the difficulties surrounding R0 estimation, and provides Spain with a threshold for herd immunity, for which we considered the different combinations of all the factors that affect the R0 of the Spanish population. Estimates of R0 range from 1.39 to 3.10 for the ancestral SARS-CoV-2 variant, with the largest differences produced by the method chosen to estimate R0. With these values, the herd immunity threshold (HIT) ranges from 28.1 to 67.7%, which would have made 70% a realistic upper bound for Spain. However, the imposition of the delta variant (B.1.617.2 lineage) in late summer 2021 may have expanded the range of R0 to 4.02-8.96 and pushed the upper bound of the HIT to 90%.


Subject(s)
COVID-19/immunology , Immunity, Herd , Data Interpretation, Statistical , Differential Threshold , Humans , Models, Biological , Spain
7.
Pesqui. bras. odontopediatria clín. integr ; 21: e0185, 2021. tab, graf
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1596303

ABSTRACT

ABSTRACT Objective: To analyze the difference in the on-line searches for terms related to hand hygiene during the COVID-19 pandemic in developed and middle-income countries. Material and Methods: The cross-sectional study analyzed the digital data through the Google Trends website to obtain the variation of the relative search volume (RSV) through the terms "alcohol gel" and "handwashing." According to socio-economic development, the countries were divided into two groups: countries from different continents and hemispheres, with more than 15 million inhabitants, with more than 50% of the population with access to the Internet network and over 1,000 confirmed cases of infected with COVID-19. The paired t-test was applied to compare the means. The significance value adopted was p<0.010. Results: The searches related to the term "hand washing" were more significant when compared to the term "alcohol gel," and the term "alcohol gel" presented a higher average volume of research in developed countries (p<0.010). The developed countries had a higher average relative volume of research than middle-income countries (p<0.010). Developed countries sought more for the term "alcohol gel," and the term "hand washing" showed no difference in the volume of research about the country's socio-economic aspect. Conclusion: Developed countries have a higher volume of search for hand hygiene terms. The middle-income countries must create proposals for raising awareness outside the on-line environment so that this information reaches the entire population during the pandemic.


Subject(s)
Humans , Socioeconomic Factors , Developed Countries , Hand Hygiene , Internet Access , COVID-19 , Primary Prevention , Brazil/epidemiology , Cross-Sectional Studies/methods , Data Interpretation, Statistical , Ethanol
9.
PLoS One ; 16(11): e0258649, 2021.
Article in English | MEDLINE | ID: covidwho-1528716

ABSTRACT

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.


Subject(s)
Anosmia/epidemiology , COVID-19/diagnosis , Cough/epidemiology , Datasets as Topic , Fatigue/epidemiology , Adult , Aged , COVID-19/epidemiology , Data Interpretation, Statistical , Female , Humans , Male , Middle Aged
10.
PLoS One ; 16(11): e0259601, 2021.
Article in English | MEDLINE | ID: covidwho-1526684

ABSTRACT

INTRODUCTION: Cases of the novel coronavirus disease (COVID-19) continue to spread around the world even one year after the declaration of a global pandemic. Those with weakened immune systems, due to immunosuppressive medications or disease, may be at higher risk of COVID-19. This includes individuals with autoimmune diseases, cancer, transplants, and dialysis patients. Assessing the risk and outcomes of COVID-19 in this population has been challenging. While administrative databases provide data with minimal selection and recall bias, clinical and behavioral data is lacking. To address this, we are collecting self-reported survey data from a randomly selected subsample with and without COVID-19, which will be linked to administrative health data, to better quantify the risk of COVID-19 infection associated with immunosuppression. METHODS AND ANALYSIS: Using administrative and laboratory data from British Columbia (BC), Canada, we established a population-based case-control study of all individuals who tested positive for SARS-CoV-2. Each case was matched to 40 randomly selected individuals from two control groups: individuals who tested negative for SARS-CoV-2 (i.e., negative controls) and untested individuals from the general population (i.e., untested controls). We will contact 1000 individuals from each group to complete a survey co-designed with patient partners. A conditional logistic regression model will adjust for potential confounders and effect modifiers. We will examine the odds of COVID-19 infection according to immunosuppressive medication or disease type. To adjust for relevant confounders and effect modifiers not available in administrative data, the survey will include questions on behavioural variables that influence probability of being tested, acquiring COVID-19, and experiencing severe outcomes. ETHICS AND DISSEMINATION: This study has received approval from the University of British Columbia Clinical Research Ethics Board [H20-01914]. Findings will be disseminated through scientific conferences, open access peer-reviewed journals, COVID-19 research repositories and dissemination channels used by our patient partners.


Subject(s)
COVID-19/epidemiology , /statistics & numerical data , British Columbia , Data Interpretation, Statistical , Female , Health Care Surveys/statistics & numerical data , Humans , Male , Self Report/statistics & numerical data
11.
Pesqui. bras. odontopediatria clín. integr ; 21: e210018, 2021. tab
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1511868

ABSTRACT

ABSTRACT Objective To evaluate the effect of the COVID-19 impacts on the activities of researchers in the field of Oral Medicine (OM) and Oral Pathology (OP). To assess the research activities and training of human resources by Brazilian productivity fellows in research (BPFR) in OM and OP in the COVID-19 Era. Material and Methods Thirty-six BPFR in OM and OP areas, funded by National Council for Scientific and Technological Development (CNPq), received a virtual structured questionnaire by e-mail, on the Google Forms (Google®) platform, with questions regarding research activities and training of human resources (supervision of undergraduate and postgraduate students), during the COVID-19 pandemic. From the thirty-six BPFR in OM and OP, twenty-seven (75.0%) answered the questionnaire. Results Most of them were males (n=20; 74.1%) and were distributed in four Brazilian regions and ten states of the federation, including the Federal District. Twenty-four (88.9%) BPFR reported having suspended clinical activities, while sixteen (59.3%) answered that histopathology practices are suspended. Twenty-five (92.6%) BPFR mentioned difficulties in conducting research projects and 55.5% stated having no difficulties in the supervision of undergraduates, master's and PhD students. Conclusion The current scenario may significantly impact the diagnosis of oral diseases in Brazil. Moreover, a decrease in the scientific production of BPFR in OM and OP in the coming years is also considered.


Subject(s)
Humans , Male , Female , Pathology, Oral , Research Support as Topic , Training Support , Brazil/epidemiology , Oral Medicine , COVID-19 , Research , Technological Development , Surveys and Questionnaires , Data Interpretation, Statistical , Workforce , Mentoring
12.
Ann Intern Med ; 174(8): 1151-1158, 2021 08.
Article in English | MEDLINE | ID: covidwho-1481184

ABSTRACT

The development of the National Institutes of Health (NIH) COVID-19 Treatment Guidelines began in March 2020 in response to a request from the White House Coronavirus Task Force. Within 4 days of the request, the NIH COVID-19 Treatment Guidelines Panel was established and the first meeting took place (virtually-as did subsequent meetings). The Panel comprises 57 individuals representing 6 governmental agencies, 11 professional societies, and 33 medical centers, plus 2 community members, who have worked together to create and frequently update the guidelines on the basis of evidence from the most recent clinical studies available. The initial version of the guidelines was completed within 2 weeks and posted online on 21 April 2020. Initially, sparse evidence was available to guide COVID-19 treatment recommendations. However, treatment data rapidly accrued based on results from clinical studies that used various study designs and evaluated different therapeutic agents and approaches. Data have continued to evolve at a rapid pace, leading to 24 revisions and updates of the guidelines in the first year. This process has provided important lessons for responding to an unprecedented public health emergency: Providers and stakeholders are eager to access credible, current treatment guidelines; governmental agencies, professional societies, and health care leaders can work together effectively and expeditiously; panelists from various disciplines, including biostatistics, are important for quickly developing well-informed recommendations; well-powered randomized clinical trials continue to provide the most compelling evidence to guide treatment recommendations; treatment recommendations need to be developed in a confidential setting free from external pressures; development of a user-friendly, web-based format for communicating with health care providers requires substantial administrative support; and frequent updates are necessary as clinical evidence rapidly emerges.


Subject(s)
COVID-19/therapy , Pandemics , Practice Guidelines as Topic , Advisory Committees , COVID-19/drug therapy , COVID-19/epidemiology , Child , Data Interpretation, Statistical , Drug Approval , Evidence-Based Medicine , Female , Humans , Interprofessional Relations , National Institutes of Health (U.S.) , Pregnancy , SARS-CoV-2 , Stakeholder Participation , United States
13.
Sci Rep ; 11(1): 20098, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462023

ABSTRACT

Access to online information has been crucial throughout the COVID-19 pandemic. We analyzed more than eight million randomly selected Twitter posts from the first wave of the pandemic to study the role of the author's social status (Health Expert or Influencer) and the informational novelty of the tweet in the diffusion of several key types of information. Our results show that health-related information and political discourse propagated faster than personal narratives, economy-related or travel-related news. Content novelty further accelerated the spread of these discussion themes. People trusted health experts on health-related knowledge, especially when it was novel, while influencers were more effective at propagating political discourse. Finally, we observed a U-shaped relationship between the informational novelty and the number of retweets. Tweets with average novelty spread the least. Tweets with high novelty propagated the most, primarily when they discussed political, health, or personal information, perhaps owing to the immediacy to mobilize this information. On the other hand, economic and travel-related information spread most when it was less novel, and people resisted sharing such information before it was duly verified.


Subject(s)
COVID-19/epidemiology , Information Dissemination/methods , Pandemics/statistics & numerical data , Psychological Distance , Social Media/statistics & numerical data , Data Interpretation, Statistical , Humans , Machine Learning , Pandemics/prevention & control , Poisson Distribution
15.
MMWR Morb Mortal Wkly Rep ; 70(37): 1267-1273, 2021 Sep 17.
Article in English | MEDLINE | ID: covidwho-1456567

ABSTRACT

Native Hawaiian and Pacific Islander populations have been disproportionately affected by COVID-19 (1-3). Native Hawaiian, Pacific Islander, and Asian populations vary in language; cultural practices; and social, economic, and environmental experiences,† which can affect health outcomes (4).§ However, data from these populations are often aggregated in analyses. Although data aggregation is often used as an approach to increase sample size and statistical power when analyzing data from smaller population groups, it can limit the understanding of disparities among diverse Native Hawaiian, Pacific Islander, and Asian subpopulations¶ (4-7). To assess disparities in COVID-19 outcomes among Native Hawaiian, Pacific Islander, and Asian populations, a disaggregated, descriptive analysis, informed by recommendations from these communities,** was performed using race data from 21,005 COVID-19 cases and 449 COVID-19-associated deaths reported to the Hawaii State Department of Health (HDOH) during March 1, 2020-February 28, 2021.†† In Hawaii, COVID-19 incidence and mortality rates per 100,000 population were 1,477 and 32, respectively during this period. In analyses with race categories that were not mutually exclusive, including persons of one race alone or in combination with one or more races, Pacific Islander persons, who account for 5% of Hawaii's population, represented 22% of COVID-19 cases and deaths (COVID-19 incidence of 7,070 and mortality rate of 150). Native Hawaiian persons experienced an incidence of 1,181 and a mortality rate of 15. Among subcategories of Asian populations, the highest incidences were experienced by Filipino persons (1,247) and Vietnamese persons (1,200). Disaggregating Native Hawaiian, Pacific Islander, and Asian race data can aid in identifying racial disparities among specific subpopulations and highlights the importance of partnering with communities to develop culturally responsive outreach teams§§ and tailored public health interventions and vaccination campaigns to more effectively address health disparities.


Subject(s)
COVID-19/ethnology , Health Status Disparities , /statistics & numerical data , COVID-19/mortality , Community Health Services/organization & administration , Data Interpretation, Statistical , Hawaii/epidemiology , Humans
17.
PLoS One ; 16(10): e0258205, 2021.
Article in English | MEDLINE | ID: covidwho-1450732

ABSTRACT

BACKGROUND: How effective have lockdowns been at reducing the covid-19 infection and mortality rates? Lockdowns influence contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. The purpose of the present study was to assess the impact of lockdown measures on the spread of covid-19 and test a theoretical model of the covid-19 pandemic employing structural equation modelling. METHODS: Lockdown variables, population demographics, mortality rates, infection rates, and health were obtained for eight countries: Austria, Belgium, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The dataset, owid-covid-data.csv, was downloaded on 06/01/2020 from: https://github.com/owid/covid-19-data/tree/master/public/data. Infection spread and mortality data were depicted as logistic growth and analyzed with stepwise multiple regression. The overall structure of the covid-19 data was explored through factor analyses leading to a theoretical model that was tested using latent variable path analysis. RESULTS: Multiple regression indicated that the time from lockdown had a small but significant effect (ß = 0.112, p< 0.01) on reducing the number of cases per million. The stringency index produced the most important effect for mortality and infection rates (ß = 0.588,ß = 0.702, ß = 0.518, ß = 0.681; p< 0.01). Exploratory and confirmatory analyses resulted in meaningful and cohesive latent variables: 1) Mortality, 2) Infection Spread, 3) Pop Health Risk, and 4) Health Vulnerability (Comparative Fit Index = 0.91; Standardized Root Mean Square Residual = 0.08). DISCUSSION: The stringency index had a large impact on the growth of covid-19 infection and mortality rates as did percentage of population aged over 65, median age, per capita GDP, diabetes prevalence, cardiovascular death rates, and ICU hospital beds per 100K. The overall Latent Variable Path Analysis is theoretically meaningful and coherent with acceptable fit indices as a model of the covid-19 pandemic.


Subject(s)
COVID-19/prevention & control , Models, Theoretical , Quarantine , COVID-19/epidemiology , COVID-19/pathology , COVID-19/virology , Data Interpretation, Statistical , Databases, Factual , Humans , Logistic Models , Pandemics , Prevalence , SARS-CoV-2/isolation & purification , Survival Analysis
18.
Stroke ; 52(11): 3739-3747, 2021 11.
Article in English | MEDLINE | ID: covidwho-1443690

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges to stroke care and research internationally. In particular, clinical trials in stroke are vulnerable to the impacts of the pandemic at multiple stages, including design, recruitment, intervention, follow-up, and interpretation of outcomes. A carefully considered approach is required to ensure the appropriate conduct of stroke trials during the pandemic and to maintain patient and participant safety. This has been recently addressed by the International Council for Harmonisation which, in November 2019, released an addendum to the Statistical Principles for Clinical Trials guidelines entitled Estimands and Sensitivity Analysis in Clinical Trials. In this article, we present the International Council for Harmonisation estimand framework for the design and conduct of clinical trials, with a specific focus on its application to stroke clinical trials. This framework aims to align the clinical and scientific objectives of a trial with its design and end points. It also encourages the prospective consideration of potential postrandomization intercurrent events which may occur during a trial and either impact the ability to measure an end point or its interpretation. We describe the different categories of such events and the proposed strategies for dealing with them, specifically focusing on the COVID-19 pandemic as a source of intercurrent events. We also describe potential practical impacts posed by the COVID-19 pandemic on trials, health systems, study groups, and participants, all of which should be carefully reviewed by investigators to ensure an adequate practical and statistical strategy is in place to protect trial integrity. We provide examples of the implementation of the estimand framework within hypothetical stroke trials in intracerebral hemorrhage and stroke recovery. While the focus of this article is on COVID-19 impacts, the strategies and principles proposed are well suited for other potential events or issues, which may impact clinical trials in the field of stroke.


Subject(s)
COVID-19 , Clinical Trials as Topic/methods , Data Interpretation, Statistical , Research Design , Stroke/therapy , Clinical Trials as Topic/standards , Guidelines as Topic , Humans , Implementation Science , SARS-CoV-2
19.
Psychol Med ; 51(13): 2145-2147, 2021 10.
Article in English | MEDLINE | ID: covidwho-1442671
20.
Sci Rep ; 11(1): 17744, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1397902

ABSTRACT

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Models, Statistical , Pandemics/statistics & numerical data , Data Interpretation, Statistical , Feasibility Studies , Global Burden of Disease , Gross Domestic Product/statistics & numerical data , Humans , Normal Distribution , Population Density
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