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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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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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ILI is a medical diagnosis of possible influenza with a set of common symptoms: cough and measured or reported fever >38˚C, with onset within the last 10 days [21]
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#2018_Adalja_etal_pandemic_potential_pathogens #reading

Attributes likely to be essential components of any GCBR-level pathogen include:

  • efficient human-to-human transmissibility,
  • an appreciable case fatality rate,
  • the absence of an effective or widely available medical countermeasure,
  • an immunologically naïve population,
  • virulence factors enabling immune system evasion, and
  • respiratory mode of spread.

Additionally, the ability to transmit during incubation periods and/or the occurrence of mild illnesses would further augment spread.

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#2018_Adalja_etal_pandemic_potential_pathogens #reading
Most classes of microbe could evolve or be manipulated in ways that would cause a catastrophic risk to humans. However, viruses—especially RNA viruses—are the most likely class of microorganism to have this capacity.
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Flashcard 4607798545676

Question
In AWS, VPC endpoints allow you to connect your VPC resources (like instances in private subnet with no route to a NAT) to supported AWS services, like S3, without going through the internet. There are two types of VPC endpoints, [...] endpoints and [...] endpoints. <--two different occulsions
Answer

Interface endpoints and Gateway endpoints

^^ Interface Endpoints are a Network interface with Private IP that allow you to talk to certain AWS resouces (like Config, SNS, etc)
^^^ Gateway Endpoints are gateways that allow you to talk to S3 and DynamoDB
^^^^ You create both types of VPC Endpoints via the "Endpoints" section of the VPC console (during this creation process a route is added to your specified subnet Route Table to route traffic from your VPC subnet(s) bound for the said AWS service (like S3) to the Interface/Gateway (and the Interface/Gateway endpoint then connects with PrivateLink to the service without going over the internet).


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#reading
Likelihood: The likelihood for θ given observations x is LX(θ; x) = fX(x |θ), θ ∈ Θ regarded as a function of θ for fixed x.
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#reading
The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.
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#reading
Point estimators map from the sample space X to a point in the parameter space Θ.
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Typically, estimators can be divided into two types: point estimators and set estimators. A point estimator which maps from the sample space X to a point in the parameter space Θ. A set estimator which maps from X to a set in Θ.

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#reading
Set estimators map from the sample space X to a set in Θ.
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span> Typically, estimators can be divided into two types: point estimators and set estimators. A point estimator which maps from the sample space X to a point in the parameter space Θ. A set estimator which maps from X to a set in Θ. <span>

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Flashcard 4610423131404

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#reading
Question
The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that [...] then the inference about θ should be the same irrespective of whether X = x or X = y was observed.
Answer
LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ

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The weak likelihood principle: If X = x and X = y are two observations for the experiment EX = {X, Θ, fX(x |θ)} such that LX(θ; y) = c(x, y)LX(θ; x) for all θ ∈ Θ then the inference about θ should be the same irrespective of whether X = x or X = y was observed.

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#reading
A drawback with the bias is that it is not, in general, transformation invariant. For example, if T is an unbiased estimator of θ then T−1 is not, in general, an unbiased estimator of θ−1 as E(T−1 | θ) 1/E(T | θ) = θ−1 .
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#reading
For an estimator T, a better criterion to being unbiased is that T has small mean square error (MSE)
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#reading
If we accept, as our working hypothesis, that one of the elements in the family of distributions is true (ie: that there is a θ∗ ∈ Θ which is the true value of θ) then the corresponding predictive distribution fY |X(y |x, θ∗ ) is the true predictive distribution for Y . The classical solution is to replace θ∗ by plugging-in an estimate based on x.
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#reading
E(θ |X), the posterior expectation, minimises the posterior expected square error and the minimum value of this error is Var(θ |X), the posterior variance.
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Flashcard 4610871135500

Question
More realistic distributions for the length of the infectious period can be obtained by choosing p(t) to be a [...] probability density function [22–27]
Answer
Gamma

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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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#reading
Scoring rules (also called scoring functions) are the key measures for the evaluation of probabilistic forecasts.
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#reading
Scoring rules assign a numerical score based on the predictive density f(y) for the unknown quantity and on the true value yobs , that has later materialised.
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#reading
Scoring rules are called proper, if they do not provide any incentive to the forecaster to digress from her true belief, and strictly proper if any such digress results in a penalty, i. e. the forecaster is encouraged to quote her true belief rather than any other predictive distribution.
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#reading
Note that in the literature inappropriate scoring methods are still often used, e. g. correlation coefficients between point predictions and observations [8, 10]. It is well known in the medical literature that high correlation does not necessarily imply good agreement [36] and therefore a very poor forecasting method may have high correlation.
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#reading
usage of metrics incorporating the whole probabilistic forecast (rather than using only point predictions) is still rare [6].
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#reading
denote by Y the predictive distribution which we compare with the actual observation yobs
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#reading
The logarithmic score [37] is strictly proper and defined as LogS(Y, yobs) = − log f(yobs ),
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#reading
A strictly proper alternative is the ranked probability score [19], which can be written for count data as RPS(Y, y obs ) = ∞ X k=0 {Pr(Y ≤ k) − 1(y obs ≤ k)} 2 , the sum of the Brier scores for binary predictions at all possible thresholds k ∈ {0, 1, . . .} [20]. An equivalent definition is RPS(Y, y obs ) = E |Y − y obs | − 1 2 E |Y − Y 0 |, (10) here Y and Y 0 are independent realisations from f(y) [19].
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#reading
a z-statistic z = RPS − E 0 (RPS) Var 0 (RPS) 1/2 a ∼ H 0 N(0, 1) can be computed where the sign of z indicates if the observations are over-/underdispersed relative to the predictions (+/− sign of z) [23, 39]. A (two-sided) P -value can be computed to quantify the evidence for miscalibration.
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#reading
One possibility for a proper scoring rule is the multivariate Dawid-Sebastiani score [38] mDSS(Y , y obs ) = log|Σ| + (y obs − µ) > Σ −1 (y obs − µ), (11) that depends only on the mean vector µ and the covariance matrix Σ of the predictive distribution. The first term in (11) involves the determinant |Σ| of the covariance matrix Σ, here Σ is a d × d matrix. Transformed to DS = |Σ| 1/(2d) , (12) this is known as the determinant sharpness (DS) and recommended as a multivariate measure of sharpness [24], with smaller values corresponding to sharper predictions.
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#reading
evaluation of the Dawid-Sebastiani score based on Monte Carlo estimates of the first two moments is not recommended, since the determinant |Σ| is known to be very sensitive to Monte Carlo sampling error [25].
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The branching process approximation is a CTMC
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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2).

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Flashcard 4611102346508

Question
The branching process approximation has rates that are [...] near the disease-free equilibrium.
Answer
linear

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The branching process approximation is a CTMC, but near the disease-free equilibrium, the rates are linear (Table 2).

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#reading
the distribution of the estimator T is known as the sampling distribution
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it is properties of the distribution of the estimator T, known as the sampling distribution, across the range of possible values of θ that are used to determine whether or not T is a good inference rule

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Flashcard 4611107589388

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#reading
Question
the distribution of the estimator T is known as the [...]
Answer
sampling distribution

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the distribution of the estimator T is known as the sampling distribution

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Pathogen fitness is traits such as such as replication rate, transmissibility, and immune recognition
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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 importa

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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
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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 transmi

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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
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lly 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. <span>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) <span>

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Flashcard 4611117550860

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#reading
Question
[...] map from the sample space X to a point in the parameter space Θ.
Answer
Point estimators

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Point estimators map from the sample space X to a point in the parameter space Θ.

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Flashcard 4611118599436

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#reading
Question
Point estimators map from [...] to a point in the parameter space Θ.
Answer
the sample space X

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Point estimators map from the sample space X to a point in the parameter space Θ.

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Flashcard 4611119648012

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#reading
Question
Point estimators map from the sample space X to [...].
Answer
a point in the parameter space Θ

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Point estimators map from the sample space X to a point in the parameter space Θ.

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Flashcard 4611126463756

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#reading
Question
If we accept, as our working hypothesis, that one of the elements in the family of distributions is true (ie: that there is a θ∗ ∈ Θ which is the true value of θ) then the corresponding predictive distribution fY |X(y |x, θ∗ ) is the true predictive distribution for Y . The classical solution is [...].
Answer
to replace θ∗ by plugging-in an estimate based on x

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e (ie: that there is a θ∗ ∈ Θ which is the true value of θ) then the corresponding predictive distribution fY |X(y |x, θ∗ ) is the true predictive distribution for Y . The classical solution is <span>to replace θ∗ by plugging-in an estimate based on x. <span>

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Flashcard 4611128560908

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#reading
Question
[...] map from X to a set in Θ.
Answer
Set estimators

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Set estimators map from X to a set in Θ.

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Flashcard 4611130395916

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#reading
Question
Set estimators map from [...] to a set in Θ.
Answer
the sample space X

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Set estimators map from the sample space X to a set in Θ.

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Flashcard 4611131444492

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#reading
Question
Set estimators map from the sample space X to [...].
Answer
a set in Θ

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Set estimators map from the sample space X to a set in Θ.

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Hill et al. (2019) used data subsequent to the 2009 pandemic for England, and developed a dynamic multi-strain SEIR-type transmission model for seasonal influenza, explicitly incorporating immunity propagation mechanisms between influenza seasons.
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Hill et al. (2019) used data subsequent to the 2009 pandemic for England, and developed a dynamic multi-strain SEIR-type transmission model for seasonal influenza, explicitly incorporating immunity propagation mechanisms between influenza seasons. With a view to minimising the number of independent parameters, we fit a parsimonious mechanistic model to seasonal-level data on strain competition. In spite of the multi-strain comple

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Flashcard 4611146910988

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#reading
Question
For an estimator T, a better criterion to being unbiased is that T has [...]
Answer
small mean square error (MSE)

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For an estimator T, a better criterion to being unbiased is that T has small mean square error (MSE)

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#reading
The posterior expectation minimises the posterior expected square error
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Consequently d = E(θ |X), the posterior expectation, minimises the posterior expected square error and the minimum value of this error is V ar(θ |X), the posterior variance.

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Flashcard 4611150581004

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#reading
Question
[...] minimises the posterior expected square error
Answer
The posterior expectation

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The posterior expectation minimises the posterior expected square error

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Flashcard 4611151629580

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#reading
Question
The posterior expectation minimises the [...]
Answer
posterior expected square error

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The posterior expectation minimises the posterior expected square error

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#reading
The minimum value of the posterior expected square error is the posterior variance.
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E(θ |X), the posterior expectation, minimises the posterior expected square error and the minimum value of this error is Var(θ |X), the posterior variance.

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Flashcard 4611156872460

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#reading
Question
The minimum value of the posterior expected square error is [...].
Answer
the posterior variance

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The minimum value of the posterior expected square error is the posterior variance.

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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.
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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 su

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the uncertainty surrounding assumptions about the distributions of the latent and infectious periods 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
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nt 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, <span>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

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if the underlying structure of the model is inappropriate, the method of parameterization is largely irrelevant
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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 <span>if the underlying structure of the model is inappropriate, the method of parameterization is largely irrelevant. <span>

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