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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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#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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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

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#reading

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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.

#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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#reading

Question

Answer

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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.

#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 f_{Y |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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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]

#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 y_{obs} , 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 y_{obs}

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#reading

The logarithmic score [37] is strictly proper and defined as LogS(Y, y_{obs}) = − log f(y_{obs} ),

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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).

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).

#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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#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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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, 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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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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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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#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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#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.

Tags

#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 f_{Y |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>

Tags

#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.

Tags

#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.

Tags

#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. 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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#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)

#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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#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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#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

#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.

Tags

#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. 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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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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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>