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Annotation 4596644580620

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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Flashcard 4596734496012

Question
Define basic reproduction number, R0.
Answer
R0 characterises the average number of secondary cases generated by one primary case in a susceptible population

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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.

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Annotation 4607940103436

#reading
Cox and Hinkley (1974; p12) observe, if we are interested in comparing two possible values of θ using the likelihood, then we should consider the ratio of the likelihoods rather than, for example, the difference
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Flashcard 4611146910988

Tags
#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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Flashcard 4653034376460

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#reading
Question
Scoring rules (also called scoring functions) are the key measures for [...]
Answer
the evaluation of probabilistic forecasts.

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Scoring rules (also called scoring functions) are the key measures for the evaluation of probabilistic forecasts.

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Flashcard 4653035949324

Tags
#reading
Question
[...] are the key measures for the evaluation of probabilistic forecasts.
Answer
Scoring rules (also called scoring functions)

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Scoring rules (also called scoring functions) are the key measures for the evaluation of probabilistic forecasts.

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Annotation 4653051153676

A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.
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Annotation 4653052726540

we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecast
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Annotation 4653054299404

focus on long-term predictions rather than short- term forecasting
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Annotation 4653055872268

Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures.
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Annotation 4653057445132

In this paper, an epidemic forecast generated by a model/data-driven approach is quantified based on epidemiologically relevant features which we refer to as Epi-features. Further, the accuracy of a model’s estimate of a particular Epi-feature is quantified by evaluating its error with respect to the Epi-features extracted from the ground truth. This is enabled by using functions that capture their dissimilarity, which we refer to as error measures.
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Annotation 4653059017996

present a simple end to end framework for evalu- ating epidemic forecasts, keeping in mind the variety of epidemic features and error measures that can be used to quantify their performance
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Annotation 4653060590860

Based on experimental results of Horizon Ranking, Tabataba et al. (2017) notice that for a single Epi-feature, one method may show the best performance in early stages of the prediction, whereas another algorithm is the dominator in other time intervals.
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Annotation 4653066882316

HIll et al. (2019) find that susceptibility in the next influenza season to a given influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current influenza season
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HIll et al. (2019) find that susceptibility in the next influenza season to a given influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current influenza season, with residual vaccine immunity having a lesser role and inconsequential support for carry over type B cross-reactivity.

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Annotation 4653068455180

HIll et al. (2019) find inconsequential support for carry over type B cross-reactivity.
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influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current influenza season, with residual vaccine immunity having a lesser role and <span>inconsequential support for carry over type B cross-reactivity. <span>

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Annotation 4653072125196

#reading
A drawback with the bias is that it is not, in general, transformation invariant.
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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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Annotation 4653077368076

When Ið0Þ¼i > 1, the assumption of independence of infectious individuals, implies the probability of no outbreak is either 1 or 1/R0i .
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Annotation 4653079727372

A numerical method for simulation of CTMC models was developed by Gillespie (Gillespie, 1977). This method is known as the Gillespie algorithm or the Stochastic Simulation algorithm.
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Annotation 4653082086668

In general, for multivariate processes, it is difficult to find analytical solutions for the transition probabilities from the forward and backward Kolmogorov differential equations. For multivariate processes, it is often simpler to numerically simulate stochastic realizations (sample paths) of the process.
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Annotation 4653083659532

the SDEs are much easier to solve numerically than the Kolmogorov differential equations and faster than simulating sample paths of the CTMC model
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Flashcard 4653102796044

Question

In AWS, there are 3 types of Elastic Load Balancers: [...] Load Balancer, Network Load Balancer and Classic Load Balancer.

Answer

Application Load Balancer

^^ Application Load Balancer are for intelligent load balancing of http/https traffic, layer 7, and can do forwarding based on headers, url path, etc
^^^ Network Load Balancers are for TCP level, layer 4, load balancing, so they offer ultra fast performance and fixed IPs
^^^^ Classic Load Balancers are the old dumb load balancer, for when you have simple case (just few EC2s that you want to round robin traffic to) and want to save costs


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Flashcard 4653104893196

Question
In AWS, there are 3 types of Elastic Load Balancers: Application Load Balancer, [...] Load Balancer and Classic Load Balancer.
Answer

Network Load Balancer

^^ Application Load Balancer are for intelligent load balancing of http/https traffic, layer 7, and can do forwarding based on headers, url path, etc
^^^ Network Load Balancers are for TCP level, layer 4, load balancing, so they offer ultra fast performance and fixed IPs
^^^^ Classic Load Balancers are the old dumb load balancer, for when you have simple case (just few EC2s that you want to round robin traffic to) and want to save costs


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Flashcard 4653106990348

Question

In AWS, there are 3 types of Elastic Load Balancers: Application Load Balancer, Network Load Balancer and [...] Load Balancer.

Answer

Classic Load Balancer

^^ Application Load Balancer are for intelligent load balancing of http/https traffic, layer 7, and can do forwarding based on headers, url path, etc
^^^ Network Load Balancers are for TCP level, layer 4, load balancing, so they offer ultra fast performance and fixed IPs
^^^^ Classic Load Balancers are the old dumb load balancer, for when you have simple case (just few EC2s that you want to round robin traffic to) and want to save costs


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Flashcard 4653109087500

Question
In AWS, for ELBs (Elastic Load Balancers), if you need to have intelligent routing, based on things like url paths and http headers, you would choose an [...] Load Balancer.
Answer

Application Load Balancer

^^ In ALB you group your target EC2 instances (or Lambdas, etc) into Target Groups, and then setup complex routing rules (based on url paths, http headers, etc) to route to the correct group.

Eg. you can have one Target Group for your English language webserver, and one for your French webservers


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Flashcard 4653111184652

Question
In AWS, for ELBs (Elastic Load Balancers), if you don't have need for intelligent balancing (i.e. routing based on url path or http headers) but are more concerned about speed (and maybe having fixed IPs for your Load Balancer), you would choose a [...] Load Balancer.
Answer

Network Load Balancer

^^ Network Load Balancers operate at layer 4, tcp layer, so they offer faster performance.


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Flashcard 4653113281804

Question
In AWS, for ELBs (Elastic Load Balancers), if you have a very basic scenario where you just need simple round robin routing to a couple of EC2 instances and you want the cheapest ELB option you would choose a [...] Load Balancer
Answer
Classic Load Balancer

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