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on 18-Nov-2019 (Mon)

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Flashcard 4539176586508

Question
In AWS, for Route 53, when you register a new domain (like kkhosravi.com), and the corresponding Hosted Zone with the same name is created (that contains all the A records, CNAME records, etc for routing traffic for your domain and subdomains), two records are created automatically: the [...] records and the SOA record.
Answer

NS

^^ the NS record identifies the name servers for the hosted zone (e.g. samsungknox.com NS ns-650.awsdns-17.net 172800TTL). Note if you switch DNS service away from Route53 to another service, the TTL becomes a factor and it could take 172800 seconds (48 hours) for the changes to propegate across the internet.
^^^ the SOA (Start of Authority) record provides information about a domain and the corresponding Amazon Route 53 hosted zone. (e.g. samsungknox.com SOA ns-650.awsdns-17.net awsdns0hostmaster.amazon.com 1 7200 900 1209600 86400
^^^^ You do not need to ever edit these as they are created automatically when you register your domain with Route53 (or are imported if you say use godaddy.com for the domain name registration)


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Flashcard 4594442046732

Question
it’s important that you provide all of the appropriate metadata so that your data frame is self-*** and your plots will be self-***
Answer
it’s important that you provide all of the appropriate metadata so that your data frame is self-describing and your plots will be self-documenting

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The influenza study by Reich et al. (13) in PNAS is a unique example of multiyear infectious disease forecasts featuring a variety of modeling approaches, with consistent model formulations and forecasting tar- gets throughout the 7-y study period (13). This is a major improvement over previous model comparison studies that used different targets and time horizons and sometimes different epidemiological datasets.
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The time horizon at which flu predictions are generally offered is in the order of 2 to 4 wk, which is generally too short for action.
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Further, while recent work has shown the promises of ensemble forecasts that combine outputs from different models (12–14), there is no clear understanding of best practices for this type of analysis that could stabilize operational performance in routine forecasts.
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Reich et al. (13) consider a portfolio of statistical and mecha- nistic models and a range of datasets beyond the primary inci- dence targets, including digital surveillance and social media such as Google queries and Twitter. The study thus provides a broad perspective on most of the current approaches to influenza time series forecasting and is highly relevant to other infectious dis- eases. The study shows that the top-performing models in the statistical and mechanistic categories achieve similar perfor- mances. The study also stresses the importance of data quality, particularly because weekly flu data are regularly retroactively adjusted, even in nonpandemic seasons. The issue of retroactive data adjustment is even more pervasive during large-scale health emergencies such as the West African Ebola epidemic (6)
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Reich et al. (13) present a thorough comparison of prediction skills across seasons and US regions, shedding light on how forecasting performances vary with geography. Some regions appear more difficult to predict than others (e.g., a null model based on historical averages does poorly, indicating large interannual variability in ILI incidences). In contrast, other regions are easier to predict due to greater stability in observed historical patterns and substantial improvement of predictive models over historical averages. Moving forward, it will be important to understand whether regional differences in predictive skills are a reporting artifact or whether they reflect heterogeneities in influenza transmission dynamics. Demographic and environmental differences among regions, connectivity, and spatial extent could all affect predictive skills. This question could have practical implications because regions displaying consistently high predictive power could be used as sentinels for influenza surveillance.
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It is also useful to recall that weather forecasting went through a similar transition 60 y ago, from primarily statistical time series predictions to mechanistic models numerically solving the hydrodynamic and thermodynamic equations ruling atmospheric evolution. This transition took a careful understanding of the performances of different models, optimization of the spatial scales most appropriate for different components of the forecasts, and improvement in the quality and quantity of weather observations available to calibrate models (17, 18)
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The study by Reich et al. (13) points at an overall degradation of disease forecasts at the 4-wk time horizon, beyond which the average of historical incidences (i.e., a null model) becomes the most reliable option for prediction.

From a public health standpoint, a longer horizon, in the order of 2 mo or more, would be particularly useful to ramp up interventions and adjust hospital surge capacity.

Ideally, even longer timescales should be considered so that the prediction of epidemic intensity (epidemic size) and severity (total number of hospitalizations and deaths) aligns with the vaccine manufacturing process.

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Wearing et al. (2005) use analytical methods to show that

(i) ignoring the latent period

or

(ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period)

always results in underestimating the basic reproductive ratio of an infection from outbreak data

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unrealistic a priori assumptions concerning model structure give rise to systematically overoptimistic predictions on the outcome of potential management options
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A more sensible formulation of the infectious period would be to specify the probability of leaving a class as a function of the time spent within the class, such that initially the chance of leaving the class is small, but the probability increases as the mean infectious/latent period is reached.
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More realistic distributions for the length of the infectious period can be obtained by choosing p(t) to be a gamma probability density function [22–27]
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The dynamical consequences of differences in the distribution of infectious and latent periods have received some attention over the past two decades. It has been shown, for example, that the precise distribution of the infectious period has no qualitative effects on the asymptotic values or properties of the system [21,24], though perturbations to the endemic equilibrium take longer to die out as n increases [22,26]. When contact rates vary seasonally, for example, to mimic the aggregation of children in schools [28,29], changes in p(t) are known to have important consequences for the persistence likelihood of infections [25,26,30]. An issue that has received surprisingly little attention, despite its obvious applicability to emerging infections and possible ‘‘ deliberate exposure,’’ is the influence of latent and infectious period distributions on the invasion dynamics of an infection into a largely susceptible population.
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For the same basic reproductive ratio and average infectious period larger values of the Gamma distribution parameter n lead to a steeper increase in prevalence and an epidemic of shorter duration.
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Although a body of theoretical work [25,26,30] has demonstrated the importance of incorporating realistic distributions of latent and infectious periods into models of endemic disease, few studies have considered the effects associated with making predictions for an emerging disease [42].
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The large discrepancies between estimates of R0 from the exponentially distributed and gamma-distributed fits reiterate the importance of accurately determining the precise distributions of latent and infectious periods. Although the data required for such a task are often available from post hoc analyses of epidemics they are certainly lacking for a novel emerging infection. Instead, the uncertainty surrounding assumptions about the distributions should be incorporated into quantitative predictions made from epidemiological models, especially since this may well be greater than any uncertainty that arises from noise in the data. Of course, more sophisticated fitting methods than those used in this paper exist [43–46], but if the underlying structure of the model is inappropriate, the method of parameterization is largely irrelevant.
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Chretien et al. (2014)'s review suggests need for:

  • use of good practices in influenza forecasting (e.g., sensitivity analysis);
  • direct comparisons of diverse approaches;
  • assessment of model calibration;
  • integration of subjective expert input;
  • operational research in pilot, real-world applications; and
  • improved mutual understanding among modelers and public health officials
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a scoping review addresses broader topics and may include various study designs [9]
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QlikView Data (QVD) files are used to extract and store data into and from QlikView. This means that whichever table you read, from whichever database, you can store it in the QVD format before or after any transformations you perform on the table. The special characteristics of this file type are: • It contains only one logical table. • It uses a special algorithm to compress the data, achieving compression rates of up to 90 percent, depending on the fields' cardinality of the underlying data. • When reading a QVD table file in QlikView, the loading speed is anywhere from 10 to 100 times faster than when loading from a database. When the table file is being read without applying any transformations, QlikView performs an optimized load (super-fast mode). One of the main advantages of using QVD files is that, once you have a QVD on your disk, the table can be exploited by more than one QlikView application. This reduces the load on the database server, and optimizes QlikView resources and development time. This process is called QVD staging and is discussed further in Chapter 12, Advanced Data Transformation.
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QVX files QlikView data eXchange (QVX) files are used for data input from external systems into QlikView. The main difference with respect to the QVD file is that QVX is a public format and can be created from external interfaces. It can be considered as the format in which custom data sources (described earlier in the chapter) send data to QlikView via the custom connector. Data retrieval becomes optimized when complying with QVX specifications, although not as optimized as QlikView's own QVD.
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Chretien et al. (2014) included 35 publications in the review [10–44] (Figure 1). Twenty six (74%) of the studies were published in 2009 or later, with more than one-third published in 2012 or 2013. The studies fell into 3 categories based on the epidemiological application: population-based seasonal influenza forecasting (N = 27 publica- tions), medical facility-based forecasting of patient counts for seasonal or pandemic influenza (N = 4), and regional or global spread forecasting for pandemic influenza (N = 4) (Table 1). Most studies included areas of North America (N = 15) or Europe (N = 14 publications), while a few included areas in the Asia- Pacific region (N = 4) or had global scope (N = 3) (Table 1). Twenty-eight studies employed temporal forecasting without a spatial component, while 7 made forecasts in time and space (Table 1). The studies used diverse forecasting methods, with 18 using statistical approaches without models for epidemiological processes and 17 employing epidemiological models (Table 1).
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Among the statistical approaches, methods included time series models (N = 9 publications), generalized linear models (N = 4), Bayesian networks (N = 2), classification methods (N = 2), survival analysis (N = 1), and a prediction market (N = 1) (Table 1). The mechanistic approaches included compartmental models, which model transitions across various sub-populations (susceptible-infectious-removed [SIR] models and variants) (N = 12 publications); and agent-based models (ABMs), which model exposure, infection, transmission and behaviors for each individual in the population (N = 5) (Table 1).
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Several studies coupled mechanistic models with methods to update parameter estimates and forecasts as new influenza-related surveillance data becomes available. Nsoesie et al. [11,13] developed a simulation-optimization algorithm for their ABM, which iteratively proposes estimates of key epidemiological parameters, uses those estimates to simulate the future course of the epidemic, and compares observed surveillance data to forecasts to revise the parameter estimates. Ong et al. [25] and Shaman et al. [10,16] used data assimilation techniques to incorporate influenza-related surveillance data into their compartmental models and update parameter estimates and forecasts.
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The studies used dynamic virological (N = 14), syndromic influenza-like illness (ILI; N = 13) and other influenza-related surveillance data to forecast influenza activity (Table 2). Birrell et al. [19] included serological data to model pre-existing immunity, as well as virological and syndromic data. Four studies included internet search query data (Google Flu Trends) [10,11,16,37]. Six studies considered meteorological data [10,16,26,37–39], with 3 including the meteorological predictors in the final forecasting model [10,16,26].
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Among the 27 population-based forecasting studies, 16 used weekly predictions of weekly incidence 1 or more weeks into the future in the validation (Table 3). Nine studies predicted the timing of the epidemic peak or incidence at the peak; all performed validation using at least some forecasts made at least 4 weeks before the actual peak [10–13,16–18,29,31]. The facility-based forecasting studies used 1-step-ahead [37–39] or n-step-ahead [40] predictions of visit counts over step sizes of 1 day [40] to 1 month [39]. The regional or global pandemic spread forecasting studies used early data from the 2009 influenza A(H1N1)pdm09 pandemic to predict outcomes at national level across countries, including pandemic arrival, and peak incidence and time of peak.
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mean (or median) absolute error and mean absolute percent error are the most common metrics for forecasts of incidence (i.e., daily, weekly, or monthly incidence; peak incidence; or cumulative incidence)
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Among all studies, only 2 reported accuracy as a function of estimated forecast variance [10,16].
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Shaman et al. [10] compared their susceptible-infectious-recovered-susceptible (SIRS) compartmental model, coupled to an ensemble-adjusted Kalman filter (SIRS-EAKF), to various resampling approaches using previous influenza seasons. The SIRS-EAKF model was consid- erably more accurate in predicting ILI peak week for the 2012– 2013 season across 108 US cities. Merler et al. [43] compared the performance of an ABM and a simpler compartmental model in predicting the course of the 2009 influenza A(H1N1)pdm09 pandemic in Europe, and found the simpler model failed to predict pandemic dynamics and attack rate accurately across countries.
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included only studies that validated models against independent data, a crucial part of predictive model development since using the same data for model fitting and testing inflates estimates of predictive skill [46]
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Flashcard 4595445009676

Question
What is syndromic surveillance?
Answer
Collecting, analysing and interpreting health-related data to provide an early warning of human or veterinary public health threats, which require public health action.

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2019 Chretien et al: "it is not sufficient merely [for a model] to predict an event; the likelihood of that prediction should also be ascribed"
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future operational forecasting efforts should develop explicit approaches that incorporate additional expertise and analysis from scientists and public health officials. (The only documented systematic elicitation of expert judgment, for any type of modeling approach, in our review was the prediction market of Polgreen et al. [32].) Similar methods exist in weather and climate forecast (e.g., [50]). For example, meteorological forecasts are typically statistically post-processed to account for inherent model biases, and new methods for this post-processing are still being developed [51]. These combined results are then further vetted by meteorologists to monitor anomalous prediction behavior, and communicated to the public and decision makers.
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Dimensional modeling When ER models get too complicated to query, dimensional modeling can offer a practical solution. A dimensional data model is composed of a single fact table. This fact table contains a compound primary key, with separate keys linking the fact table to the dimension tables. These dimension tables contain descriptions and attributes that provide context to the metrics stored in the fact table. Dimensions often contain data on multiple hierarchical levels that are "flattened" (or denormalized) into a single table. For example, in our Aircraft Types dimension table, we have both Aircraft and Aircraft Manufacturer in the same table.
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The intent of this primer is to provide a brief introduction to the formulation, numerical simulation, and analysis of stochastic epidemic models for a newcomer to this field.
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This primer is restricted to two types of stochastic settings, continuous-time Markov chains (CTMCs) and stochastic differential equations (SDEs).
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Stochastic modeling of epidemics is important when the number of infectious individuals is small or when the variability in transmission, recovery, births, deaths, or the environment impacts the epidemic outcome. The variability associated with individual dynamics such as transmission, recovery, births or deaths is often referred to as demographic variability.
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In CTMCs and SDEs, the time variable is continuous, but the state variables are either discrete (CTMC) or continuous (SDEs).
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Flashcard 4596634881292

Question
In CTMCs and SDEs, the time variable [...], but the state variables are either discrete (CTMC) or continuous (SDEs).
Answer
is continuous

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Parent (intermediate) annotation

Open it
In CTMCs and SDEs, the time variable is continuous, but the state variables are either discrete (CTMC) or continuous (SDEs).

Original toplevel document (pdf)

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[unknown IMAGE 4596639862028] #has-images
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The branching process is the linear approximation of the SIR stochastic process near the disease-free equilibrium. For a few initial infectious individuals, the branching process either grows exponentially or hits zero.
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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2).

Three important assumptions underlie the branching process approximation:

  1. Each infectious individual behavior is independent from other infectious individuals. Reasonable if a small number of infectious individuals is introduced into a large homogeneously-mixed population (assumption (3)).
  2. Each infectious individual has the same probability of recovery and the same probability of transmitting an infection. Reasonable in a homogeneously-mixed population with constant transmission and recovery rates, b and g.
  3. The susceptible population is sufficiently large.
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In this Comment, we discuss major limitations of the current set of tools used in forecasting outbreaks and highlight existing and emerging technologies that have the potential to significantly enhance forecasting capabilities. We focus on forecasting for outbreak management, specifically the capacity to predict short- term (i.e., days to weeks) trends of disease activity or incidence (i.e., the number and location of new cases) in an ongoing out- break. We do not address the prediction of outbreak emergence, which is a separate endeavor with its own opportunities 6 and challenges 7 , nor do we consider projecting multi-year trends of disease burden 8 .
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Technologies that translate raw, unprocessed data into struc- tured formats would be particularly useful. For instance, software could extract data from line lists of cases or clinical notes in electronic health records, or convert data stored in non-standard formats into machine-readable data. Digitizing handwritten text reliably, quickly and securely from clinical or epidemiological records will be a persistent need for the foreseeable future.
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There have been instances where epidemiological data are available via informal networks of people sharing spreadsheets (D. B. George, personal communication); secure CSV file trans- fers 13 ; or unofficial APIs 14,15 . These approaches should be lauded, but they are not long-term, enterprise solutions. Open-science approaches to sharing data have shown promise in recent outbreaks. Epidemiologists and modelers have begun using publicly available repositories, such as GitHub, to aggregate and share digitized data in standardized formats 16–18 . This paradigm shift resulted in a rapid improvement in data-sharing capability during the 2014–2015 West Africa Ebola outbreak (D. B. George, personal communication). A team of influenza forecasters in the U.S. also has used GitHub to share forecast data to facilitate the creation of multi-model ensemble forecasts 19,20 . The shift from informal means of sharing data to robust tech- nologies using standardized, machine-readable formats enables more rapid and meaningful engagement of a broader group of analysts. Structured open-science approaches to data sharing that are speci fically tailored to forecasting applications should be further supported and explored.
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Over the past several years, academic research on infectious disease forecasting has grown and models have successfully generated predictions for pathogens such as influenza[19–2] , dengue[13], Zika[22], and Ebola[2]. But, scaling academic research to support public health decision-makers in real-time has received little attention and relatively scarce resources.
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In this Review, we start with an examination of real-time outbreak response using the West African Ebola epidemic as an example. Here, the challenges range from underreporting of cases and deaths, and missing information on the impact of control measures to understanding human responses.
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Methods that depart from simplistic assumptions about random mixing are yielding new insights into the dynamics of transmission and control. There is rapid growth in estimation of model parameters from mismatched or incomplete data, and in contrasting model output with real-world observations. New data streams on social connectivity and behavior are being used, and combining data collected from very different sources and scales presents important challenges.
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Thirty-five years ago, it was believed that the health burden of infectious diseases was close to becoming insignificant as hygiene, improved nutrition, drugs, and vaccines brought about a steady decline in overall mortality (1). In recent decades, however, it has become clear that the threat from serious infectious diseases will persist, and human mortality attributed to infection is projected to remain at current levels of 13 to 15 million deaths annually until at least 2030 (2).
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For infectious agents important to public health, a series of principles has emerged for modeling infection dynamics (Table 1 and Box 4). The basic reproduction number R0 ,for example, is a central concept characterizing the average number of secondary cases generated by one primary case in a susceptible population. This concept highlights what must be measured to interpret observed d isease patterns and to quantify the impact of selected control strategies (Fig. 1)
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Flashcard 4596665289996

Question

ggplot() from ggplot2 in R

To make a scatterplot we need add at least one ***, such as points.

Here we add the ***() function to create a traditional scatterplot.

g <- ggplot(maacs, aes(logpm25, NocturnalSympt))

g + ***()

Answer

ggplot() from ggplot2 in R

To make a scatterplot we need add at least one geom, such as points.

Here we add the geom_point() function to create a traditional scatterplot.

g <- ggplot(maacs, aes(logpm25, NocturnalSympt))

g + geom_point()


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Flashcard 4596669746444

Question

ggplot() from ggplot2 in R

add a smoother on top of the points

g <- ggplot(...)

g + geom_point() + ***()

Answer

ggplot() from ggplot2 in R

add a smoother on top of the points

g <- ggplot(...)

g + geom_point() + geom_smooth()


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[unknown IMAGE 4596668435724] #has-images
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Flashcard 4596674989324

Question

ggplot() from ggplot2 in R

the default smoother is a *** smoother

Answer

ggplot() from ggplot2 in R

the default smoother is a loess smoother


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Flashcard 4596677872908

Question

ggplot() from ggplot2 in R

Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying *** = "***" to geom_smooth().

g <- ggplot(...)

g + geom_point() + geom_smooth(*** = "***")

Answer

ggplot() from ggplot2 in R

Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying method = "lm" to geom_smooth().

g <- ggplot(...)

g + geom_point() + geom_smooth(method = "lm")


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For infectious disease dynamics, our world is clearly stochastic, in that chance events play a role in many of the processes involv ed. Certainly at lower levels of biological aggregation, chance dominates— for e xample, in infection of individual cells or in contacts individual hosts make. At higher aggreg ation leve l s , ma ny cells or individuals interact, and chance effects may aver age out to allow deterministic descriptions. There are pur ely stochastic models, purely deterministic models, and models that are mixe d. It is important to point out that, even though the world is stochastic, stochastic descrip- tions are not by definition better than deterministic descriptions. Both are still models of reality, and the fact that chance plays a role may have a far less significant influence on model outcome and prediction than choices made in the relations between ingredients and variables.
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Several classes of model are used, spanning the spectrum of information available. At one end of the range are detailed individual-based simulation models, where large numbers of distinct individual entities (with their own characteristic traits such as age, spatial location, sex, immune status, risk profile , or behavior pattern) are described in interaction with each other, possibly in a contact network, and with the infectious agent. At the other end are compartmental models where no individuals are recognized, but only states for individuals (for example: susceptible, infectious, immune) aggregated into compartments where every one has the same average characteristics and where interaction is typically uniform (everybody interacts with everybody else). Such models do not describe the disease history of single individuals, but rather the time evolution of aggregated variables, such as the number of individuals that are currently susceptible. Mathematical models can have both mechanistic parts in their description, based on assumptions about biological mechanisms involved, and more phenomenological parts, where there is a statistical or presumed relation between variables, without clear assumptions from which this relation can be derived. An example of the former is the assumption of mass action to describe interaction betw een individual hosts; an example of the latter is an empirical relation between the length of an infectious period in a mosquito and environmental temperature.
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The 2014–2015 outbreak of Ebola in West Africa serves to highlight both opportunities and challenges in modeling for public health. In the initial phase of this outbreak, real-time estimates of the reproduction number or simple exponential extrapolation (14) allowed short-term predictions of epidemic growth that were used, for example, to plan for necessary bed capacity. Quantitative phylogenetic tools applied to samples from initial victims provided important estimates of the origin of the outbreak (15). Early mechanistic models that explicitly took into account the roles played by different transmission routes or settings were informed by analysis of earlier outbreaks (16, 17).
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Flashcard 4596686261516

Question

ggplot() from ggplot2 in R

Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying method = "lm" to ***().

g <- ggplot(...)

g + geom_point() + ***(method = "lm")

Answer

ggplot() from ggplot2 in R

Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying method = "lm" to geom_smooth().

g <- ggplot(...)

g + geom_point() + geom_smooth(method = "lm")


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repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill






In any emerging epidemic, underreporting is a critical challenge for ongoing assessment of this epidemic and has had enormous impact on predictions of outbreak size, but also of outbreak impact—for example, in terms of the case-fatality ratio (the proportion of cases that lead to death). Early in any outbreak, this estimate of severity can suffer from imprecise information on both the numerator (if not all deaths due to the infection are identified as such; for example, because health services are overwhelmed caring for the sick) and the denominator (if cases are not reported or, conversely, noncases get reported as cases if they are not laboratory-confirmed). This caused problems early in the H1N1 influenza outbreak first reported in Mexico in 2009, as well as in the current Ebola outbreak. Although level of underreporting can be estimated from retrospective serological studies, it is usually not identifiable in real-time data. These limitations make it almost impossible to make reliable long-term predictions. Thus, modeling results are often based on scenarios in which a pathogen spreads unaltered by behavioral changes or the public health response. This rarely reflects reality, especially in such a devastating outbreak as Ebola, where the situation constantly changes owing to growing awareness in the community, as well as national and international intervention. Careful communication of findings is key, and data and methods of analysis (including code) must be made freely available to the wider research community. Only in this way can reproducibility of analyses and an open exchange of methods and results be ensured for maximal transparency and benefit to public health.
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Research on po- tential emerging zoonoses draws on concepts from across the spectrum of infectious disease dynamics, disease ecology, microbiology, and phylogenetic analysis. Particular challenges in- clude estimating human-to-human transmis- sibility against a backdrop of ongoing zoonotic spillover, detecting anomalous outbreaks, and assessing the risk that more dangerous strains may arise through pathogen evolution. The recently identified gap in methodology for zoonoses with weak human-to-human trans- mission (6) is being filled with new approaches for estimating R 0 and other transmission-related quantities from subcritical outbreak data (19–21). These studies address key public health concerns, but rely on strong assumptions regarding the quality and completeness of case observations. Better information on surveillance program effi- cacy could be gained through serological surveys (where blood and saliva samples reveal evidence of past and present infections) or sociological study, and modeling studies can help to design and characterize efficient surveillance programs (22).
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The greatest challenge—and the greatest prize—in modeling emerging zoonoses is to assess which diseases pose the most risk to humans and how these might change over time and in different localities (24). Such tasks, which will join molecular studies to experimental infections to epidemiological and ecological surveys, will drive empirical and theoretical efforts for decades to come.
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Flashcard 4596696222988

Question

ggplot() from ggplot2 in R

Here we want one row and two columns, one column for each weight category. So we specify bmicat on the right hand side of the forumla passed to ***().

g <- ggplot(...)

g + geom_point() + geom_smooth(method = "lm") + ***(. ~ bmicat)

Answer

ggplot() from ggplot2 in R

Here we want one row and two columns, one column for each weight category. So we specify bmicat on the right hand side of the forumla passed to facet_grid().

g <- ggplot(...)

g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ bmicat)


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Although sequence data are extremely valuable, to link these data fully to disease dynamics, it will be important to determine how sequence changes affect functions related to pathogen fitness, such as replication rate, transmissibility, and immune recognition. Molecular epidemiological studies often treat pathogen genetic variation as simply reflecting the underlying transmission process, whereas in reality such variation may play an important role in determining transmission dynamics, as exemplified by escape from herd immunity by influenza A virus (31)
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Flashcard 4596702514444

Question

for ggplot() from ggplot2 in R

You can use ***() for x-axis labels, ***() for y-axis labels, and ***() for specifying plot titles. The ***() function is generic and can be used to modify multiple types of labels at once.

Answer

for ggplot() from ggplot2 in R

You can use xlab() for x-axis labels, ylab() for y-axis labels, and ggtitle() for specifying plot titles. The labs() function is generic and can be used to modify multiple types of labels at once.


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Multiple agents infecting the same host individual have been shown to influence each other by increasing or decreasing susceptibility and/or infectivity of that individual, thereby influencing the population dynamics of these agents in ways that we have yet to explore and understand (34, 35)
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Flashcard 4596707757324

Question

ggplot2 in R

You can modify properties of geoms by specifying options to their respective *** functions.

Answer

ggplot2 in R

You can modify properties of geoms by specifying options to their respective geom_* functions.


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Increasing sophistication of contact network models (42), together with data on epidemiological contacts, creates opportunities for understanding and controlling transmission at a fundamental level (43) and opens up the possibility of independent study of relevant social factors (10). Recent years have seen exciting developments in the measurement of contact patterns and “who might infect whom” through advances in individual electronic identification technology.
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