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Mathematical Models of Vaccine-Preventable Disease in California
Get PDFIn chapter 1, using a Galton-Watson branching-process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number among post-elimination California measles cases. Genotype B3 is found to be a significant predictor of transmissibility.In chapter 2, we determine whether data from FDA-cleared wired and Bluetooth smart thermometers sold by a San Francisco-based company aid influenza forecasting efforts. We compare this smart thermometer data to regional influenza and ILI surveilance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Our results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.In chapter 3, we examine whether changes in varicella transmissibility may have occurred following the change from one- to two-dose vaccination scheduling in 2007. Following the change in ACIP recommendations for varicella vaccination in 2007, the median outbreak size decreased. However, while the number of outbreaks has continued to decrease following 2008, we do not find evidence that the distribution of sizes has changed since 2008. Using insights from branching process models assuming both subcritical and supercritical transmission and with and without depletion of susceptibles, we cannot rule out that varicella transmission is supercritical in school-based settings
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Modeling the transmission of Ebola and GU Chlamydia in Sub-Saharan African countries under both epidemic and endemic settings
Get PDFIn this dissertation, I used various methods to model the transmission of two infectious diseases, Ebola in an epidemic setting and GU Chlamydia in an endemic setting within in Sub-Saharan Africa. Since 2015, there have been five outbreaks of Ebola Virus Disease (EVD) in several dierent countries in Sub-Saharan Africa, one of which became the second largest EVD outbreak in history in the setting of a longstanding conflict zone. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. In my first chapter, I used spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak. In my second chapter, I also sought to use modeling to make outbreak projections, looking at the 2020 outbreak in The Democratic Republic of Congo. I made short and long-term projections for the outbreak in an eort to assess the potential to provide more accurate forecasting for an ongoing outbreak. I also evaluated how the outbreak’s timing and course affected the accuracy of such forecasts. Lastly in my third chapter, I focused on trachoma endemic areas of Sub-Saharan Africa and modeling the impact of annual Trachoma Mass Drug Administration (MDA) with azithromycin upon the prevalence of genitourinary (GU) chlamydia using a compartmental model. Communities that are especially hard hit with Trachoma are almost exclusively poor commu- nities with poor access to sanitation, screening and antibiotics to treat the infection; conditions that may allow for STDs to maintain a high chain of transmission. The dosing of azithromycin for the Trachoma MDA is consistent with dosing given clinically to treat GU chlamydial (GUC) disease, and recent evidence has suggested it reduces the population prevalence.In my first chapter investigating the potential impact of violent events upon local instability and increased EVD transmission, I collected daily EVD case counts from DRC Ministry of Health for the 2018 outbreak in the Democratic Republic of Congo (DRC). A time-varying indicator of recent violence in each health zone was derived from events documented in the WHO situation reports. I used the Wallinga-Teunis technique to estimate the reproduction number R for each case by day per zone in the 2018–2019 outbreak. I fit an exponentially decaying curve to estimates of R overall and by health zone, for comparison to past outbreaks.As of 16 April 2019, the mean overall R for the entire outbreak was 1.11. I found evidence of an increase in the estimated transmission rates in health zones with recently reported violent events versus those without (p = 0.008). The average R was estimated as between 0.61 and 0.86 in regions not aected by recent violent events, and between 1.01 and 1.07 in zones aected by violent events within the previous 21 days, leading to an increase in R between 0.17 and 0.53. Within zones with recent violent events, the mean estimated quenching rate was lower than for all past outbreaks except the 2013–2016 West African outbreak. The difference in the estimated transmission rates between zones affected by recent violent events suggests that violent events contributed to increased transmission and the prolonged nature of the second largest EVD outbreak in history.In my second chapter performing EVD outbreak projections, several mathematical models were used to predict the final outbreak size and weekly incidence for the 2020 DRC outbreak. Projections were commenced prospectively mid-way through the outbreak, and retrospectively applied for the early out- break. Short-term forecasts were made using two different models: (i) a particle-filter branching-process model and (ii) a naive auto-regression. Final outbreak size predictions were made using four different models: (i) the particle-filter branching-process model, (ii) Theil-Sen regression, (iii) Gott’s Law and (iv) a novel Bayesian branching process model parameterized using prior outbreak sizes and contingent on the current outbreak size. The Bayesian model examined final size distributions across a range of current outbreak sizes, allowing for an examination of parameter fits.Overall, there were reasonable amounts of variability in the forecasts created by different models. For short-term, auto-regression models showed relatively stable steady-state growth in the outbreak, with somewhat larger confidence intervals while the particle-filter branching model projected an outbreak slowly ending in the same period. Final outbreak size predictions increased overall as the outbreak continued. The median expectation among models increased between 2.5–4.0 fold in September over initial expectations from June as the outbreak grew from 34 to 128 cases. The branching-process model was overall the most stable consistent performer, though the Bayesian model was a close second. Including the West Africa outbreak, easily the largest to date, increased the range of predicted outcomes for the DRC outbreak between 40–50%.In predicting the 2020 Ebola outbreak, the most consistent performing model was the branching process particle-filter model though the Bayesian model did nearly as well, despite being agnostic to the trajectory of the outbreak. Our short-term models consistently predicted the outbreak would grow, though models disagreed over the slowing pace; it will be important to evaluate the performance of these models in future outbreaks to understand these uncertainties. The growth of the outbreak to well over a hundred cases underscores the real risk EBOV poses to the region and the need for improved understanding of outbreak trajectories even with the presence of three approved vaccines.In my third chapter, I analyzed the impact of Trachoma MDA upon GUChlamydia prevalence using an extended compartmental SIS model, accounting for the natural history of GUC, risk structure, and gender. The model includes slowly developing partial immunity. MDA was modelled as an impulsively forced treatment with varying coverage and efficacy.My model showed that three years of MDA at current levels reduced the prevalence of GUC in all populations by at least 15%. Between annual MDA, the prevalence partially rebounded to pre-treatment levels. With Coverage x Efficacy ≥ 0.80, the time between MDA treatments was insufficient to sustain transmission, allowing for GUC burden to be suppressed below 1 in 10,000 after 5 rounds for starting prevalence less than 9.2%. When serial non-compliance is increased from 20% to 80%, this target is achieved for starting prevalences below 4.7%, down from 9.2%. Targeting azithromycin treatment only to high-risk individuals reduces the starting prevalences for which target is reached to 1.8%.My model suggests that MDA could reduce the prevalence of GUC to less than 1 in 10,000 within 5 years time. This reinforces the suggestions of potential additional health benefits of trachoma MDA and points to potential value of screening and disease treatment even in impoverished areas, and suggests testable hypotheses regarding prevalence in endemic areas under treatment
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“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
Get PDFWe 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
A MODEL OF THE ENZOOTIOLOGY OF LYME DISEASE IN THE ATLANTIC NORTHEAST OF THE UNITED STATES
No full textDispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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