Calculation of the probabilities of dying, q(2) and q(5)
For each class of previous births, the probabilities of dying are calculated according to equations 7.1 and 7.2 by dividing the entries of column 2 of table 20 by those of column I, as shown below:
e(2) = 679 = .1422 q 4,775
"(S) = 620 = .1659 q 3,737
If questions on the survival of the next-to-previous child are also posed, then the following will also be needed:
3. (...) Calculation of the probabilities of dying, q(2)
and q(5) The probability of dying by age 2, q( 2), is calculated
by dividing the number of women reporting a previous child who has died by the total number of women report- ing a previous child. (...) TABLE 21. ESTIMATION OF THE PROBABILITY OF DYING BY AGE 2, q(2), FOR SOLOMON ISLANDS, USING THE BRASS-MACRAE METHOD
Source: W.
Language:English
Score: 694431.54
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https://www.un.org/en/developm...s/estimate/childmort/chap7.pdf
Data Source: un
If d = - D*, what is the probability the project payoff > 0 ?
S VK
V-K
Pr ob
ab ili
ty
f(V )
Pa yo
ff : P
(V )
276.0−=−= σ
KSd
σ
Probability = 39%
13
Alleman, Suto, & Rappoport 73
4.4 Meaning of d and D*?
(...) S VK
K-V
Pr ob
ab ili
ty
f(V )
Lo ss
F un
ct io
n L(
V)
If d = D*, what is the probability the project payoff > 0 ?
276.0=−= σ
KSd
Probability = 61%
Alleman, Suto, & Rappoport 75
4.4 Meaning of d and D*?
Tradeoffs of Losses
-2 -1 0 1 2 d
Lo ss
0
0.2
0.4
0.6
0.8
1
Pr ob
ab ilit
y
Probability Payoff > 0
D *-D *
Expected Loss
Alleman, Suto, & Rappoport 76
4.4 Meaning of d and D*?
Language:English
Score: 694431.54
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https://www.itu.int/ITU-D/fina...ent/rappoport-presentation.pdf
Data Source: un
The light colored bars titled “Group-Specific Probabilities” show the proportion of the labour force that could work from home. (...) The darker bars entitled “Global Probabilities” show the proportion of workers that could work from home if all countries had the same occupation-specific work from home probabilities. (...) The last column, labelled Global Probabilities, is obtained by multiplying the average of all 23 estimates of home- based work probabilities by each region’s occupational structure.
Language:English
Score: 694324.76
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https://www.ilo.org/wcmsp5/gro...s/briefingnote/wcms_743447.pdf
Data Source: un
title of presentation
1
Biosecurity during hunting,
carcass disposal and population
management
Vittorio Guberti
FAO Consultant ISPRA, Italy
Reducing the viral load in the environment
Reduced number of infected wild boar
Reduced probability to indirectly introduce the virus into a pig
farm (back yard/non commercial)
Reducing the probability to observe the long-distance
geographical spread of the infection; JUMPS
BIOSECURITY IN FOREST
2
Aim of hunting: reduce the wild boar population size and
density
Aim of biosecurity during hunting: reduce the virus JUMPS;
If hunting will indirectly increase the long distance spread of
the virus: hunting is counteractive in respect to ASF
eradication/control;
It would be better to leave an infected dead wild boar die in
the forest, rather then to take the risk of spreading the virus
outside the infected forest.
Biosecurity during hunting
Infected wild boar carcasses are actively searched in order to reduce the
environmental load of the virus
Infected wild boar carcasses: maintain for long time the virus in the
environment (NO GEOGRAPHICAL SPREAD)
The virus, through infected carcasses, overcomes the low
density/absence of wild boars during certain periods of time;
Infected carcasses removal reduces the environmental load of the virus
=> less infected wild boars, less probability to have outbreaks in domestic
animals;
Reducing the environmental load of the virus: management
of infected carcasses
3
Driven hunt with dogs – effective method to reduce the population density
but also effective in contaminating hunting tools
•Awareness
•Economical incentives
•Public bodies involvement (Forest workers, Army etc.)
(...) All the possible infected material HAS to be confined inside the
infected hunting ground
Hunting shall minimize the spread of the virus outside the
infected forest
In general
18
Simulated situation: 10.000 ha 100 wild boar 2% weekly incidence
Walking 10.000 steps (1 hour; 6 km) there is 1/18.000 probability to step on an infected scat; 3 persons walking 8 hours: 1/750 probability to step on infected material
There is a weak probability, but it should be better assessed knowing n. of persons that go in the forest, how long, how often and how many of them have pigs at home;
Mushroom and forest fruits
Thank you
Language:English
Score: 693737.4
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https://www.fao.org/fileadmin/...vents2017/ASF_Kaunas/10_en.pdf
Data Source: un
In developing countries, the probability of participating in the workforce increases by 7.8 per cent; in emerging, by 6.4 per cent; in ASNA, two regions with the widest gap in participation rates, the probability increases further, at 12.9 per cent.
(...) In ASNA countries, it decreases the probability to participate by 6.2 percentage points; in developing countries by 4.8 percentage points; and in developed countries by 4.0 percentage points. (...) In developing countries, the probability to participate is substantially reduced by religion, a proxy indicator for more restrictive gender role conformity.
Language:English
Score: 693737.4
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https://www.ilo.org/beirut/med...WCMS_566891/lang--en/index.htm
Data Source: un
An outbreak is defined as a cluster of at least 2 or more suspect / probable / lab-confirmed[footnoteRef:2] COVID-19 cases linked in place and time . (...) Note that an outbreak is defined cluster of at least 2 or more suspect / probable / lab-confirmed[footnoteRef:4] cases linked in place and time. (...) · Share bathroom facilities with any suspect/probable/confirmed COVID-19 cases?
· Share dining facilities with any suspect/probable/confirmed COVID-19 cases?
Language:English
Score: 692862.65
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https://www.un.org/sites/un2.u...rus_outbreakreportingform.docx
Data Source: un
BOEING PROPRIETARY 6
18% Probability One-stop 8:05 Hrs
A
Y
Z
B 58% Probability One-stop 8:05 Hrs
42% Probability One-stop 8:55 Hrs
71% Probability Non-stop 6:35 Hrs
Note: Based on probability of business preference
11% Probability One-stop 8:55 Hrs
• GMAS forecasts probability of passenger choice for all worldwide known O&D paths
• GMAS models how passengers choose flights
• Passengers prefer: • Shortest elapsed times • Least number of stops • Efficient connections (Alliance) • Online connections • Time-of-day schedules
• Business travelers are schedule sensitive, while leisure travelers are relatively more price sensitive
• GMAS does not model for frequent-flyer attraction, bonus offers, marketing tactics, sales promotions and new market stimulations
Copyright © 2014 Boeing.
Language:English
Score: 692862.65
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https://www.icao.int/Meetings/...scar/Documents/S4-Andjorin.pdf
Data Source: un
Figure I
The Classification Process
(a) General signal processes
(b) Example of Probability of Environmental Approval (PREA). UNFC does not currently specify
probabilities to determine E axis categories. (...) Many terms can be used with modifiers, such as “high”, “low”, “very” that alter the
assigned probability as shown in the table below.
Table 4
The effect of modifiers on the term probability
Modifier Term Median % IQR
Very high probability 91 5.4
Very probable 85 8.9
High probability 81 10.1
Probable 69 13.0
Moderate probability 52 18.5
Low probability 16 14.5
Very low probability 6 5.7
65. (...) Verbal Description Range of Probability
High ≥ 80
Medium ≥ 50 to 80
Low < 50
67.
Language:English
Score: 691708.6
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https://unece.org/sites/defaul...01/ECE_ENERGY_GE.3_2020_3e.pdf
Data Source: un
Both pages consist of an upper panel showing the average number of children ever born (average parity), the average number of children surviving, the reported proportions dead and the estimates of the probability of dying by age x, q(x), yielded by the different versions of the Brass method using all available mortality models. (...) The lower panel of each page presents three sets of estimates: q(l), that is, the probability of dying between birth and exact age 1, also known as infant mortality; 4ql, the probability of dying between exact ages I and 5, termed "child mortality" in the Guide; and q(5), the probability of dying between birth and exact age 5, also called "under-five mortality". (...) For a further discussion on choice of model life table and interpretation of the esti- mates, see chapter VI of the Guide.
Although QFIVE probably provides more information than is strictly necessary for the esti- mation of child mortality, this feature gives it added flexibility and should make it possible to meet the needs of most users.
17
Example of the first page of the printed output:
IlIPUT DATA FOI BAIGLlDESH, 1974 IE'l'lOSPEC'lIVE SUIVEY mSEIFS EllUJlERATIOK DATE: lIAR 1974
------- -------- ------- --------- Age Group Klllber IlImber of HUlber of
of of Children Children iiolen iiolen Ever Born Surviving
-------- -------- ----- ------- 15-19 3014706. 1160919. 945554. 20-24 2653155. 4901382. 3903998. 25-29 2607009. 9085852. 7147897. 30-34 2015663. 9910256. 7649060. 35-39 1771680. 10384001. 7893833. 40-44 1479575. 9164329. 6749306. 45-49 1135129. 6905673. 4946129.
18
Example of the second page of the printed output:
IIDIIIC'l ESmATIOI OF EARLY AGE lIOiTWTY FOR WGLAD~, 1974 RmOSPECTIV!
Language:English
Score: 691699.76
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https://www.un.org/en/developm...nuals/estimate/qfive/chap3.pdf
Data Source: un
Adjustments shown using less well-
known methods
Age misreporting
Age misreporting (45+)
New method(s) based on:
Basic statistic: cmRx(T1,T2) computed using two
censuses (at T1 and T2) and intercensal deaths
between T1 and T2
A standard pattern of age misreporting
Alternative techniques to estimate magnitude of
age misreporting
Statistic: cmRx(T1,T2)
From previous studies (Dechter-Preston, Del
Popolo, Preston-Condran-Himes) using (a) two
census at T1 and T2 and intercensal deaths in (T1,T2)
Behavior of key statistic cmRx(T1,T2) under
different conditions
Main problems:
Unequal census completeness leads to statistic’s
behavior that mimics age over(under)statement
Intercensal migration leads to statistic’s behavior
that mimic age over(under)statement
Conditions :
Adjusted for relative completeness of census enumeration
Closed to migration (or adjusted for it)
Age patterns and levels of age
misreporting Main idea:
Detect problem with statistic
Reconstruct true population (matrix)
Age pattern of age misreporting
Level of age misreporting
From previous studies
India (Bhat)
Latin America (Ortega)
US: Medicare records (Preston et al)
We use Costa Rica 2002 matching study (census-voting register) and estimate standard patterns of
Population age misreporting
Probability of over(under) stating age at age x
Conditional probability of over(under) stating age by 1-10 years given over(under) statement at age x
The above is referred to as “standard pattern of age misstatement”
Generates a “standard matrix” of population transfers across ages
Main results from Costa Rica study
Gender differences in age misreporting: marginal
Age differences in prob. of misreporting: large
Overstatement overwhelms under statement
Age patterns of age misreporting
Outcome
Matrix of net “age transfers” is a standard pattern
of age misreporting that we assume prevails in all
countries
Observed patterns produced by identical
standard but different levels of age misreporting (age specific probability of misreporting)
Standard death and population patterns of age
misreporting are identical
Strategy
Estimate model predicting prob of age net
overstatement as a function of age
Estimate negative binomial model for conditional probability of overestimation
Generate the Costa Rican standard of age net
overstatement
Allow shifts in levels of net overstatement: the shifts
or magnitude of age misreporting are estimated
from data
Identification conditions
We can estimate both LEVELS of net overstatement of ages at death and population
BUT:
Cannot identify simultaneously population over
and under statement, only net overstatement
Must assume age patterns of over (under)
statement of ages at death and population are
identical
Must assume that standard is appropriate for
observed population
METHODS TO ESTIMATE MAGNITUDE OF AGE
MISREPORTING DEATH AND POPULATION
Brute force iterative procedure :
plausible but time consuming
Inverse regression based on regression models
estimated in simulated population. (...) Adjust mortality rates and construction life tables from age 5 on
Uncertainty
Evaluation study produces
Metapopulation====== error distributions of each
candidate method under different conditions
violating assumption
Can attach probability (of error) measure to each
candidate method
Can use them explicitly in estimation thus
generating bounds of uncertainty of target parameters
THANK YOU
Language:English
Score: 691506.15
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https://www.un.org/development...mber-2016-modified_palloni.pdf
Data Source: un
