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Likelihood. 1 - Rare: has rarely if ever happened.
Likelihood. Cancer death counts yiap at area i, age a and period p are modelled as Poisson distribution with parameters rate λiap and population size Niap, i.e., yiap ~ Poisson(Niapλiap), where λiap is the relative risk for the αth age group and the pth calendar period in the ith area, and can be modeled as log(λiap) = νi + µi + θa + γp + δc + ϕip + ϕic. Therefore, we can write the likelihood as , P (y|λ) ∝ Qi,a,p eNiapλiap (Niapλiap)yiap ∝ Qi,a,p eλiap (λiap)yiap where λ = (λiap) includes all i, a, and p and represents all the parameters which need to have prior distributions. Niap is a known quantity which can be ignored here. Priors The joint prior distribution can be written as P (λ) = P (ν, µ, θ, γ, δ, ϕ1, ϕ2), where ν is the joint spatial unstructured effect, µ is the joint spatial structured effect, θ is the age effect, γ is the period effect, δ is the cohort effect, ϕ1 and ϕ2 are the joint spatial- period and spatial-cohort interactions, respectively. Age effects As we introduced before, we apply the carcinogenesis model into the AAPC model by replacing the age effects with hazard functions derived from the carcinogenesis model. For example, the age effect θa at age group a is given a noninformative prior as below: θa ∼ N (θ¯a, τa) where θ¯a = log(h(ta)), and τa ∼ Gamma(0.001, 0.001). Armitage and Doll [1954] assumed multiple transformations happened in stages for a cell to grow into a cancerous tumor. In the Armitage-Doll model, h(t) = cts—1 where s is the number of stages, ta is the age and c is constant. Therefore, we have the following age effects in a nonlinear function of age, θ¯a = c + (s — 1) ∗ log(ta). Hyperpriors are given to c and s at c ∼ N (0, τc) s ∼ N (5, τs) where Gamma hyperpriors are given to two precision terms as below: τc ∼ Gamma(0.001, 0.001) and τs ∼ Gamma(0.001, 0.001). The hazard function derived from the TSCE model is introduced to the AAPC model as well. In contrast to the simple format of the hazard function in the Armitage-Doll model, the hazard function from the TSCE model is more difficult. Four derived parameters will be summarized in the TSCE model, the rate of initiation, ν, the rate of division, α, and death, β, of initial cells, and the rate of malignant conversion, µ. The hazard function in the TSCE model is given by Xxxxxxxxxx [1979, 1981, 1990, 2009] as h(t) = ν e—qt e—pt pq , — α qe—pt — pe—qt where p and q are the roots of a quadratic equation, with p + q = —(α— β — µ) and pq = αµ. Three estimated parameters p, q, and r...
Likelihood. Healthwatch Peterborough is not effective in discharging it’s duties to the council and the local population. Likelihood Legal challenge delays implementation of ICAS service. Delays to implementation of Healthwatch. Matrix 3 x I m p a c t 5 2 x I m p a c t 5 2 x I m p a c t 5 4 3 2 1 x I m p a c t Further Mitigating Actions Identified • Alternative plans already agreed with NHS Peterborough to ensure delivery of the service • Engagement with HR underway to manage any issue pro- actively • Governance arrangements for Healthwatch CIC have been developed and agreed and will support performance management • PCC will provide performance management support initially • Regional and DH advice is that the proposed approach is reasonable • A standard response has been agreed by all LAs regionally, one challenge has been addressed and has been withdrawn • DH guidance has been consistent and there is no indication that any new secondary legislation will impact on Healthwatch procurement options. Key Review 12 months
Likelihood. Time (time needed to prepare the attack) - verify if the security measure increases the time needed to mount an attack. If this is the case, increase the time level. • Expertise (expertise needed to mount the attack) - verify if the security measure in- creases the needed expertise level to mount an attack. If this is the case, increase the expertise level. • Knowledge (knowledge needed to mount the attack) - verify if the security measure increases the level of required knowledge to mount an attack. If this is the case, increase the knowledge level. • Opportunity (window of opportunity to perform an attack (e.g., minutes/weeks/days/ years) - verify if the security measure reduces the window of opportunity. If this is the case, increase the opportunity level. • Equipment (Complexity of equipment and or tools needed to mount an attack) - verify if the security measure increases the complexity of the equipment and or tools needed to mount the attack. If this is the case, increase the equipment complexity level. • Threat level - this parameter cannot be influenced by implementing a technical security measure. These steps must be taken for each threat. The exact change in levels that the security measure will introduce is a matter of expert opinion. After this verification step, the new risk levels can be established by running a new workshop using the TVRA spreadsheet that contained the original threats and risk as a basis. As a result of this activity, a new document with adjusted factors for each of the threats listed in the spread- sheet is produced. Combined with the cost assessment of the security measure, this document provides the input that is needed to decide whether to implement the selected security meas- ure(s) or not.
