TUNISIE Aucun développement significatif n'est probable.
LIBYE Aucun développement significatif n'est probable.
(...) YÉMEN Des ailés en petits nombres peuvent probablement être présents dans le Tihama et une reproduction à petite échelle pourrait probablement voir lieu.
OMAN Aucun développement significatif n'est probable.
ÉMIRATS ARABES UNIS Aucun développement significatif n'est probable.
Language:English
Score: 754673.3

https://www.fao.org/ag/locusts/common/ecg/1394/fr/DL159f.pdf
Data Source: un
Page 38  Methodology for measurement of Quality of Service (QoS) Key Performance Performance Indicators (KPIs) for Digital Financial Services
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MTDR Rate/ Probability Money Transfer Duplication Rate MTLR Rate/ Probability Money Transfer Loss Rate MTAST Time Money Transfer Account Stabilization Time MTASSR Rate/ Probability Money Transfer Account Stabilization Success Rate MTFTRR Rate/ Probability Money Transfer Failed Transaction Resolution Rate Start MTFNR Rate/ Probability Money Transfer False Negative Rate MTFPR Rate/ Probability Money Transfer False Positive Rate Start MTCT Time Money Transfer completion time Start X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) MTCR Rate/ Probability Money Transfer completion rate enter start USSD Start Prompt to select TA X (2) enter 1 to select “Transfer Money” X (2) Prompt to select X (2) enter 1 to select “to Mobile Money user” X (2) Prompt to select category of recipient X (2) enter 1 to select “to X (2) Prompt to select X (2) Enter B number X (2) Prompt to select recipient ID again X (2) Enter B number again and continue TABLE E3: KPI/Trigger point reference Start DFS app DFS_P2P_ command AA_100 Prompt to select DFS_P2P_ type TA type AE_104 Select: Transfer DFS_P2P_ AA_108 Prompt to select DFS_P2P_ recipient type recipient type AE_112 Select: To mobile DFS_P2P_ user AA_116 0 DFS_P2P_ AE_120 0 DFS_P2P_ subscriber” AA_124 Prompt to select DFS_P2P_ recipient ID recipient ID AE_128 Enter B number DFS_P2P_ and continue and continue AA_132 P 36 • Methodology for measurement of Quality of Service (QoS) Key Performance Indicators (KPIs) for Digital Financial Services
33 34 35 36 37 38 39 40 41 42 43
Language:English
Score: 753754

https://www.itu.int/en/publica...t/files/basichtml/page38.html
Data Source: un
Kohler
Population Studies Center
University of Pennsylvania
Overall Structure of the Survey on Aging in SSA
2 major components:
• Household Interview
• Individual Interview
• Both will be linked based on HH ID and individual ID
Overall Sampling Design
Nationally representative stratified random sample of households that include at least 1 household member age 60 years and older
Household sample surveys: Key source for data on social phenomena
Are among the most flexible methods of data collections
In theory almost any populationbased subject can be investigated through household surveys
Only probability samples following wellestablished sampling procedures are suitable for making inferences from the sample population to the larger population that it is designed to represent Snowball or convenience samples are not suitable for this survey
Overall Sampling Design cont’d
Probability sampling in the context of household surveys: Refers to the means by which elements of the target population are selected
for inclusion in the survey
In order to be costeffective, most household surveys are not implemented as simple random samples
Sampling procedure usually includes stratification to ensure that the selected sample actually is spread over geographic subareas and population subgroups
This sampling design usually uses clusters of households in order to keep costs to manageable level
General Principals of the Survey on Aging in SSA
Target population: individuals age 60+ and older
Household sample: Nationally representative clustered random sample of households that include household members age 60+ yrs.
Selection of household members: All regular household members age 60+ in the sampled household and their spouses if these are ageeligible and coresident
General Principals of the Survey on Aging in SSA
Use of an existing sampling frame: clustered random sample of households can only be obtained from existing sampling frame which is a complete list of statistical units covering the target population
Census frame, complete list of villages/communities or sampling list from other nationally representative surveys
Sampling frame: is a complete list of sampling units that entirely covers the target population
Conventional sampling frame: list of enumeration areas (EA) from a recently completed census
EA: geographic area which usually groups a number of households together for convenient counting purposes
General Principals of the Survey on Aging in SSA
Stratification: process in which the sample is designed into sub groups or strata that are as homogeneous as possible;
Within each stratum the sample is designed and selected independently;
Twostage cluster sampling procedure: Cluster: a group of adjacent households which serves as the primary sampling unit (PSU)
General Principals of the Survey on Aging in SSA
Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded;
Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;
Only way to obtain unbiased estimation and to be able to evaluate the sampling errors
Excluded are purposive sampling, quota sampling, and other uncontrolled nonprobability methods because they cannot provide evaluation of precision and confidence of survey findings
General Principals of the Survey on Aging in SSA
Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded;
Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;
Only way to obtain unbiased estimation and to be able to evaluate the sampling errors
Excluded are purposive sampling, quota sampling, and other uncontrolled nonprobability methods because they cannot provide evaluation of precision and confidence of survey findings
Sample Size
Sample size must take into account competing needs so that costs and precisions are optimally balanced
Sample size must also address the needs of users who desire for subpopulations of subareas domains
Sample size is determined by the tradeoffs between survey precision, data quality, organizational capacities and survey budget;
In the case of Malawi this is about 2,000 respondents (men and women)
Conducting a household listing and preselection of households
Data quality is enhances if eligible households are preselected for participating in the study
In many SSA countries recent and reliable household listings in EAs that carefully enumerates older individuals is not available
Hence, we suggest to conduct a specific household listing in selected EAs that provides a wellgrounded basis for selecting respondents
Interviewers than interview only preselected eligible households
STEPS:
Household listing operation conducted before the survey
Preselection of households from this list
Selected Households are interviewed
Overall Sampling Design cont’d
Two stage sample design is wellestablished approach for implementing household surveys
1st stage: select a sample of EAs with probability proportional to size (PPS); Within each stratum a sample of predetermined number of EAs is selected
independently with probability proportional to size, where size is measured in terms of older individuals age 60+;
If size of pop age 60+ is not available, and variations in age structures are relatively modest, then total pop size can be used
All households in the EAs are listed
2nd stage: after complete listing in EAs, a fixed number of households with individuals age 60+ is selected by equal probability sampling in the EAs
Interviewing all individuals age 60+ in the HH
Advantages:
Maximize the number of respondents for a given sample of HH
Cost effective to achieve the sample size
Analytical advantages so that interactions among spouses, within and between household variation of outcomes can be investigated
Disadvantages:
Lower statistical power given the within household correlation of observations
Logistical challenges in the fieldwork
Sample Take per Cluster
How many eligible individuals to interview per EA
DHS recommends 2530 individuals
Because there will be more than 1 ageeligible individual per household, less than 2430 households per PA need to be selected
If a sampled HH has 1.5 ageeligible individuals on average, than a sample take per cluster of 2530 individuals results in the selection of 1720 households per cluster
With 2,000 individuals sample size: 6780 clusters have to be selected
If sample is stratified, these considerations should be conducted stratum specific
Sample Take per Cluster
This fixed sample take per cluster is:
Easy for survey management and implementation
But requires sampling weights that vary within clusters
Language:English
Score: 753537

https://www.un.org/development...07_ikohlersamplingdesign.pdf
Data Source: un
The numerical impact of the covariates is small probably because we considered what
respectively, as compared with the actual probability, 2.51 and 1.37 percent. (...) However,
in column (2), we find that if the firm is currently exporting, the firm’s probability of be
coming a domestic firm is only 5 percent, whereas its probability of remaining an exporter
is 91 percent. (...) In Table 9, column (1) indicates the actual probability that domestic
firms are in each status in the next year, and column (2) the predicted probability of the
average domestic firm in each industry.
Language:English
Score: 751295.9

https://www.wto.org/english/re...e/gtdw_e/wkshop09_e/todo_e.pdf
Data Source: un
To encode a binary decision (bin) after binarization, context modelling determines a probability of the next bit by using a finite state model with 64 states, and the bin encoder based on BAC encodes the bit by using the estimated probability; in order to decode the bin at the decoder, the same context modeling process is used to determine the probability, and then the bin decoder based on BAC recovers the bin by using the probability.
(...) This is however simpler as the probability associated to the first bin is applied to the following bins. (No probability update needed within the group of bins).
Language:English
Score: 750964.55

https://www.itu.int/wftp3/ava...010_07_B_Geneva/JCTVCB036.doc
Data Source: un
The example of Figure 1 shows the probability distribution of MBtype corresponding to three sequences together with the ideal probability distribution of UVLC codewords. (...) By default, a frame is classified as Normal. If the probability of the ‘16x16’ MBtype exceeds the probability of the ‘Skip’ MBtype, the frame is classified as High Motion. When the probability of ‘Skip’ dominates we compute the average run of ‘Skip’ symbols.
Language:English
Score: 750964.55

https://www.itu.int/wftp3/ava...deosite/0104_Aus/VCEGM14.doc
Data Source: un
PowerPoint Presentation
Decision Support for CostEffective Diagnosis and Treatment
by Inverting Bayesian Probability
Gerald E. Loeb, M.D., University of Southern California Jeremy A. (...) Frontiers in Neurorobotics, 2012
Intake Patient
Collect basic info: Demographic Complaints Vital signs
Create differential diagnosis (Dn) and probabilities P(Dn):
Diseases & Treated Conditions Null = well patient
For each Pconsideration < P(Dn) < Pconclusion:
Identify Actions that affect P(Dn)
Compute Cost of each Action
Present Cost/Benefit analysis to physician
Select Action(s) to be taken
Perform Actions and obtain
Results
algorithm
Confusion Matrices
Extracted from EHR
Database
Physician
Differential Diagnosis The process of identifying all possible causes of a patient’s condition and efficiently eliminating all but the most probable by acquiring diagnostic data
Usually depends on the personal experiences and memory of the individual practitioner
We propose a decision support system to inform the physician of
• The currently most probable causes
• The costs of various diagnostic and therapeutic interventions
• The benefit of each as a probability of achieving a definitive
diagnosis or outcome.
(...) Create a Markov Chain from the EHR to
• Estimate Probability P of obtaining
Results R that lead to P>0.99
certainty of diagnoses A, B, C…
• Compute Total Costs C of each
possible Action:
• Expense +
• Morbidity +
• Delay
Medical Cost/Benefit
2020™ Physician Interface
INTAKE VISIT Pt. #12345 Name: John Doe _ Sex: M DOB: 01/01/1970 Zipcode: 11111 Presenting complaint: headache for 3 days
Differential Diagnosis (code) % probability to consider:
Tension headache (D001) 90% Viral encephalitis (D002) 7% Meningioma (D003) 3%
Diagnostic Actions (code) benefit/cost to consider:
Spinal tap (T001) 5.4 MRI (T002) 3.1
2020™ Physician Interface
FOLLOWUP VISIT Pt. #12345 Name: John Doe Sex: M DOB: 01/01/1970 Zipcode: 11111 Presenting complaint: headache for 3 days Test results: MRI (T002) 4 cm diameter, wellcircumscribed anterior fossa tumor
Differential Diagnosis (code) % probability to consider:
Meningioma (D003) 99% Tension headache (D001) <1% Viral encephalitis (D002) <1%
Therapeutic Actions (code) benefit/cost to consider:
Craniotomy (R003) 5.4 Acyclovir (R002) 0.01 Aspirin (R001) 0.5
Strengths • Unifies overlapping concepts:
• disease vs. wellness
• diagnostic test vs. therapeutic trial
• Mines existing EHRs to benefit from all prior
experience with all patients, procedures,
diagnoses and treatments
• Automatically incorporates new diagnoses,
tests and treatments as they arise in practice
• Tolerant of noise and errors in the EHR
• Reveals inefficient behavior
• Avoids liability for controversial or outofdate
expert knowledge
Challenges • Needs an EHR that is reasonably complete:
• Timing and results of all tests and procedures
• Knowledge of final diagnoses and outcomes
Conundrum • If we had really good EHRs,
then we could use them to improve the efficiency of health care.
• If we could improve the efficiency of health care with EHRs,
then we might get really good EHRs.
Language:English
Score: 750597.3

https://www.itu.int/en/ITUT/W...s/Gerald_Loeb_Presentation.pdf
Data Source: un
S/5
Annex B
Evaluating probability of noncompliance
To evaluate the probability of noncompliance, i.e. the probability that a noncompliant product of a certain type can be found on the market, an enforcement authority should determine a list of factors that increase the likelihood of the event “noncompliant products present in the market” for each family of products, as shown in the picture below:
"Non compliant product present on the market"
"There has been a change in the
standard"
Vulnerability 2
"Compliance checks are very
expensive"
Vulnerability N
A vulnerability of a risk event “noncompliant product present on the market” can be called a probability factor PF. (...) Ranking the products according to levels of risk, using both the index and the predefined combinations
3. Calculating the probability index and choosing combinations of probability factors having specific value
2. Building a productnoncompliance likelihood matrix: evaluating each product in the list against each probability factor
1. Analyzing the vulnerabilities of the risk event "noncompliant product present at the market" and building a comprehensive list of probability factors
The process similar to that described in Annex A, similar approaches for ranking the products according to their probability of noncompliance levels can be applied.
Language:English
Score: 749714.5

https://unece.org/DAM/trade/wp6/Recommendations/Rec_S_en.pdf
Data Source: un
MTCT = T(AE_104, AE_300) – MTHI + TTHI MTHI stands for the measured and TTHI for the (as 5.2 Money Transfer Completion Rate (MTCR) sumed) typical time for all human interaction in this use 5.2.1 Functional description case. Probability that a money transfer can be completed The meaning of this expression is “take the measured successfully. overall duration of the transaction, eliminate times caused by actual human interaction (which can vary 5.2.2 Formal definition from instance to instance) and replace them by a gener alized (typical) value). (...) TABLE 51: KPI abbreviations and full names ABBREVIATION TYPE REFERENCE MTCR Rate/Probability Money Transfer completion rate MTCT Time Money Transfer completion time MTFPR Rate/Probability Money Transfer False Positive Rate MTFNR Rate/Probability Money Transfer False Negative Rate MTFTRR Rate/Probability Money Transfer Failed Transaction Resolution Rate MTASSR Rate/Probability Money Transfer Account Stabilization Success Rate MTAST Time Money Transfer Account Stabilization Time MTLR Rate/Probability Money Transfer Loss Rate MTDR Rate/Probability Money Transfer Duplication Rate 18 • Methodology for measurement of Quality of Service (QoS) Key Performance Indicators (KPIs) for Digital Financial Services
15 16 17 18 19 20 21 22 23 24 25
Language:English
Score: 748112.8

https://www.itu.int/en/publica...t/files/basichtml/page20.html
Data Source: un
Les forums régionaux sur l’évolution probable du climat sont encore une autre réussite de l’Afrique. (...) Le forum sur l’évolution probable du climat en Afrique du Nord (PRESANORD) a été lancé sous la direction de l’ACMAD. (...) Il est important de noter que le Forum régional sur l’évolution probable du climat en Afrique du Nord (PRESANORD), composé des cinq pays d’Afrique du Nord, a rejoint le Forum sur l’évolution probable du climat en Europe du SudEst (SEECOF) pour former le Forum sur l’évolution probable du climat dans
5
Forums régionaux sur l’évolution probable du climat en Afrique Les meilleures pratiques
la région méditerranéenne (MedCOF), coordonné par le Service météorologique national d’Espagne (AEMET), avec une contribution de l’ACMAD.
Language:English
Score: 748105.66

https://www.uneca.org/sites/de...ctices2_converted%20French.pdf
Data Source: un