1,721,273 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Socioeconomic Factors and the 2014-16 Ebola Virus Disease Outbreak in Guinea, Liberia, and Sierra Leone
Full text linkSOCIOECONOMIC FACTORS AND THE 2014-16 EBOLA VIRUS DISEASE OUTBREAK IN GUINEA, LIBERIA, AND SIERRA LEONE
INTRODUCTION: Ebola virus disease (EVD) is an infectious disease transmitted by close contact with an estimated case fatality rate fluctuating around 50%. The most affected countries by the 2013-16 West African Ebola outbreak were Guinea, Liberia, and Sierra Leone. These countries reported a total of 28616 probable, suspected and confirmed cases. However, we are still learning about the sociodemographic factors that contributed to the outbreak characteristics at the subnational level.
METHODS: Data were collected from the World Health Organization, Demographic Health Surveys, and Global Data Lab for 37 districts (8 for Guinea, 15 for Liberia, and 14 for Sierra Leone). The outcome of interest was epidemic size at the district level for Guinea, Liberia, and Sierra Leone (cumulative number of EVD patient confirmed and probable cases). Socio-demographic predictors included household density, sanitation level, mobility, and wealth status. We also controlled for the timing of the start of the outbreak across districts. Pearson’s correlation and multiple linear regression were employed in our analyses. Model building was informed by a review of the relevant literature. Sensitivity analyses were conducted to assess the impact of potential outliers.
RESULTS: In the final multivariable regression model, wealth status and household density were positively associated with the epidemic size while sanitation level and the difference in the outbreak start dates were negatively associated with the outcome. These results did not change in the sensitivity analyses. The regression model explained 57% of the variance in epidemic size (Adj R-Sq=0.57), with the largest contribution from the international wealth index (semi-partial R-square=0.22).
CONCLUSION: District sociodemographic characteristics such as household density, wealth and sanitation levels contributed to the EVD outbreak in Guinea, Liberia, and Sierra Leone, which is in agreement with recent studies. However, further research should consider other sociodemographic indicators as well as the role of migration and connectivity among regions.Master of Public Health (MPH)Public Healt
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
2020 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Full text linkSchedule and abstract book for the Twelfth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Date: October 31 - November 1, 2020Location: The 2020 conference was conducted remotely due to COVID-19 concerns, utilizing the sococo platform that allows personal avatars to move between rooms and sessions, interact in small groups and also participate in zoom sessions.Keynote Speaker: Gerardo Chowell, Population Health Sciences, Georgia State Univ. School of Public Health, AtlantaFeatured Speaker: Olivia Prosper, Mathematics, Univ. of Tennessee, Knoxvill
An Epidemiological Study of West Nile Virus in Maricopa County, Arizona
Full text linkIntroduction: Vector-borne infectious diseases represent a major public health problem in both developing and developed nations. In particular, West Nile Virus (WNV), a mosquito-borne disease that can lead to severe disease and death in humans, caused over 2,100 reported cases in the United States last year (CDC, 2016). In Maricopa County, Arizona WNV has caused 474 reported cases during the last five years, with a case-fatality rate at 7.8%.
Aim: To examine the association between weather patterns and incidence of WNV in Maricopa County, AZ from 2007 to 2013.
Methods: We analyzed weekly data on climatological variables and WNV incidence from Maricopa County, AZ. The specific independent variables of interest were precipitation, minimum temperatures, mean temperatures, and maximum temperatures. A full model was generated using multiple linear regression, and a stepwise selection procedure yielded a minimal model.
Results: The full multiple linear regression model explains 45.30% of the observed variance in WNV incidence. The variable showing a significant impact on WNV incidence in this model was rainfall (p
Conclusion: Climatic variables, particularly the amount of rainfall and maximum temperatures, significantly influence WNV dynamics in Maricopa County, Arizona. These findings are in line with prior studies and could be useful to guide mosquito control programs in the state of Arizona.Master of Public Health (MPH)Public Healt
Sub-Epidemic Generalized Logistic-Growth Model Performance for Influenza Season in the United States, October 2015–April 2019
Full text linkINTRODUCTION: The public health response to an emerging infectious disease epidemic is based on risk assessments that predict the severity of the health threat. Although infectious disease surveillance data is often limited in scope, mathematical models can provide meaningful information about epidemic growth dynamics to inform development of public health interventions.
AIM: This investigation aims to validate application of a sub-epidemic version of the generalized logistic-growth model (GLM) to a delineated period of epidemic growth representing the influenza season, using national surveillance data for incidence of influenza-like illness (ILI) in the United States.
METHODS: Surveillance data for ILI case counts were obtained from the Centers for Disease Control and Prevention (CDC) website, FluView. GLM models with one, two, and three sub-epidemics were fit to four epidemic growth periods across four years, each containing 30 weekly ILI incidence counts. Parameter estimates were obtained through nonlinear least squares curve-fitting and sub-epidemic curves were aggregated into the best fit model. Model performance was evaluated using calculation of performance metrics, bootstrapping, and visual analysis.
RESULTS: Model performance consistently improved across all four seasons as the number of sub-epidemics incorporated into the GLM increased (n=1 to n=3). The parameter and sub-epidemic estimates provided information about the growth dynamics of the epidemic period, identifying trends specific to each season.
DISCUSSION: The sub-epidemic GLM provides useful results about epidemic dynamics using national case count data. In addition, while logistic growth models are often applied to discrete outbreaks, the results of this investigation support application of the model to periods of epidemic growth within seasonal trends as well. The findings support the continued use of this model for academic and other public health application
Characterizing Multiple Spatial Waves of the 1991-1997 Cholera Epidemic in Peru
Full text linkBackground
Due to a lack of sanitary infrastructure and a highly susceptible population, Peru experienced a historic outbreak of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease for several years. Though case-fatality was low, the epidemic put massive strain on healthcare and governmental resources. Here we explore the transmission dynamics and spatiotemporal variation of cholera in Peru using mathematical models and statistical analyses that account for environmental conditions favoring the persistence of bacteria in the environment.
Methods
The authors use dynamic transmission models that incorporate seasonal variation in temperature, concentration of vibrios in the environment, as well as separate human and environmental transmission pathways. The model is fit to weekly department level data obtained from the cholera surveillance system in Peru. The authors also assess the spatial patterns of cholera transmission and correlations between case incidence, time of epidemic onset, and department level variables. Reproductive numbers are compared across departments.
Results
Our findings indicate that the epidemic first hit the coastal departments of Peru and later spread through the highlands and jungle regions. There was high seasonal variation in case incidence, with three clear waves of transmission corresponding to the warm seasons in Peru. Department level variables such as population size and elevation also played a role in transmission patterns. Finally, basic reproductive numbers most often ranged from one to eleven depending on department and time of year. Lima had the largest reproductive number, likely due to its population density and proximity to the coast.
Conclusions
Incorporating environmental variables into an epidemic model predicts the multiple waves of transmission characteristic of \textit{V. cholerae}, and effectively differentiates transmission patterns by geographic region even in the absence of unique parameter estimates. Mathematical models can provide valuable information about transmission patterns and should continue to be used to inform public health decision making
Systematic Comparison of Parameter Estimation Approaches Using the Generalized-growth Model for Prediction of Epidemic Outbreaks
Full text linkBackground- Many different mathematical models are used to assess and predict the outbreaks. The model is selected by the characteristics of the outbreaks. Here, we utilize the generalized growth model (GGM), one of the simplest mathematical models, with the real outbreaks to compare two parameter estimation methods.
Materials and Methods- 25 outbreaks are used to analyze. We use GGM with the ascending phase of each outbreak and estimated the r and p parameters with both the least square (LSQ) and maximum likelihood estimation (MLE) methods. For both parameter estimation methods, we conduct the parametric bootstrap method to construct the confidence interval of parameters. We compare the two estimation methods by the RMSE, Anscombe residual, and prediction coverage.
Results- The result shows that most outbreaks have similar r and p parameters, RMSE, Anscombe, and prediction coverage for LSQ and MLE. Although Anscombe values for LSQ are higher than the values for MLE, the difference between results of the two methods are minimal for the most outbreaks.
Conclusion- The study is shown that LSQ and MLE do not result in different values of the parameter estimation, RMSE, Anscombe, and prediction coverage with GGM.Master of Public Health (MPH)Public Healt
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