Blood Transfusion and Spread of Variant Creutzfeldt-Jakob

Variant Creutzfeldt-Jakob disease (vCJD) may betransmissible by blood. To prevent secondary transmissionthrough blood components, several countries have startedto exclude as donors persons who have received a bloodtransfusion. We investigated the effectiveness of this meas-ure by using a dynamic age-structured model. It is the firstsuch model based on epidemiologic data: 1) blood donoractivities, 2) a case-control study on CJD, 3) age distribu-tion of recipients, and 4) death of recipients of blood trans-fusions. The model predicts that an infection like vCJD,which has been introduced into the population by the ali-mentary route, could not become endemic by transfusionalone and that only <1% of cases would be avoided byexcluding from blood donation those persons who havereceived a transfusion.

Recent studies of variant Creutzfeldt-Jakob disease(vCJD) indicate that this disease is transmissible by

blood. One case of probable transfusion-transmitted vCJDinfection has been reported, and 1 case of subclinical infec-tion has been detected (1,2). On February 9, 2006, a thirdcase was announced by the UK Health Protection Agency(www.hpa.org.uk/hpa/news/articles/press_releases/2006/060209_cjd.htm). Each of the 3 patients had receiveda blood transfusion from a donor who subsequently devel-oped clinical vCJD, which indicates that transfusioncaused the infection. However, a policy to exclude poten-tial donors who had received a transfusion would not haveprevented at least the first 2 cases because the correspon-

ding donors had not received any blood transfusion.Diagnostic tools to detect prions in blood are under devel-opment (3), but no routine test for the presence of theinfectious agents of vCJD is available. Therefore, the ques-tions arise as to whether an infection like vCJD couldbecome endemic through blood donation alone and to whatextent exclusion of potential donors with a history of trans-fusion would influence the transmission of such an infec-tion (i.e., how many deaths due to the infection could beprevented?). The following mathematical model is thefirst to address these questions on the basis of epidemio-logic data and realistic and epidemiologically justifiedassumptions.

Methods

Model StructureFigure 1A shows the transitions of a person through

the basic states of potential donor activities and receipt ofblood transfusion. After birth a person is in the state of nothaving received any transfusion and not yet being an activedonor (S00). The first index refers to the person’s state as atransfusion recipient; the second index, to the person’s sta-tus as a donor. Persons in state S00 can change to state S01by becoming a donor or to state S100 or S101 by receiving ablood transfusion. The third index indicates whether a per-son with a transfusion history can actually be identifiedand excluded from donating blood (deferred) (index 1) ornot (index 0). The states S111 and S110 can be reached byeither transfusion recipients who start donating blood oractive donors who receive a blood transfusion. Blooddonors who become inactive are transferred into the statesof ex-donors S02 and S12, depending on their transfusionhistory. Ex-donors can also become transfusion recipients;i.e., they are transferred from S02 to S12. Donor exclusiontransfers a certain proportion of transfusion recipients into

Blood Transfusion and Spread ofVariant Creutzfeldt-Jakob Disease

Klaus Dietz,* Günter Raddatz,* Jonathan Wallis,† Norbert Müller,‡ Inga Zerr,§ Hans-Peter Duerr,*Hans Lefèvre,¶ Erhard Seifried,# and Johannes Löwer**

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 13, No. 1, January 2007 89

*University of Tübingen, Tübingen, Germany; †Freeman Hospital,Newcastle upon Tyne, United Kingdom; ‡University HospitalEssen, Essen, Germany; §University of Göttingen, Göttingen,Germany; ¶DRK Blood Donor Service West, Hagen, Germany;#DRK Blood Donor Service Baden-Württemberg, Hessen,Frankfurt am Main, Germany; and **Paul-Ehrlich-Institute,Langen, Germany

the state of ex-donors. For all susceptible states, Figure 1Bshows the transitions to the corresponding infected states.Table 1 provides a list of all input parameters together withdescriptions and sources. The details of the model with allthe numerical parameter estimates and the equations aregiven in the online Appendix (available from www.cdc.gov/ncidod/EID/13/1/89-app.htm). The computer programis available upon request. This article summarizes themajor features of the model, the data sources, and the esti-mation of the model parameters.

DemographyTo simplify the model, we did not attempt to describe

the demographics of the population during the next 150years. Doing so would involve predicting changes in ratesof birth, death, and immigration. It is assumed that in theabsence of infection, the population is demographically

stationary. We assumed a constant inflow of newborns andan age-specific death rate. The latter was estimated as aweighted mean of the age-specific female and male deathrates. Because this study was initiated in Germany, weused the corresponding demographic data. To start the sim-ulation in a demographically stationary state, the modelwas run for 100 years without infection. Thus, the age dis-tribution of the population was identical to the life table ofGermany 2002/2004 averaged over both sexes (www.destatis.de/download/d/bevoe/sterbet04.xls).

Modeling Blood DonorsBlood donors in Germany are >18 and <68 years of

age. The rates for becoming a new donor and terminatingthe period as an active donor are age dependent. The cor-responding parameters were estimated by using data from262,071 donors registered with the German Red Cross

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90 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 13, No. 1, January 2007

Figure 1. A) States and transitions for themodel of blood transfusion in the absence ofan infection. B) Routes of infection. Thearrows representing deaths out of all statesare omitted. Paths of donor exclusion areplotted by dotted arrows. S00, nonrecipientswho do not donate; S01, nonrecipients whodonate; S02, nonrecipients who are excludedfrom donating; S100, recipients who do notdonate; S101, recipients who become exclud-ed from donating; S110, recipients who donate;S111, recipients who become excluded fromdonating; S12, recipients who are excludedfrom donating. Indices replaced by a dot(panel B) represent all other possible states(e.g., S0• represents S00, S01, or S02).

(DRK) Blood Service West in Hagen, Germany, includingage, sex, age at first donation, number of donations, anddate of last donation.

The age-specific prevalence of active donors peaks at≈24 years of age and subsequently declines monotonical-ly to zero by age 68. The overall prevalence in the popu-lation is 3%, i.e., 2.4 million donors in a population of≈80 million.

Modeling Transfusion Recipients The model takes into account that persons may

receive >1 transfusion throughout their lifetime, but it doesnot track the number of transfusions received per person.Persons with >1 transfusion continue to be at risk for infec-tion from further transfusions. The age-specific risk ofreceiving a transfusion was estimated from data for allpatients hospitalized at the University Hospital in Essenduring March 2003. Of 4,867 patients, 1,343 (27.6%)received >1 transfusion. The number of persons receivinga blood transfusion in each 5-year age group was dividedby the corresponding number of persons in the generalpopulation. The observed rates were fitted with a simplemodel that assumes initially an exponential decline andsubsequently a unimodal peak, which is proportional to thedensity function of the normal distribution. These age-spe-cific ratios were properly scaled to balance the yearly num-ber of transfusions per capita. To limit the complexity ofthe model, we did not take into account persons in sub-groups, such as those with hemophilia, who obtain bloodproducts from pools of donors. Because for medical rea-sons these subgroups are excluded from donating blood,they cannot contribute to persistence of the infection.

Independence of Receiving and Donating BloodThe events of receiving a blood transfusion and of

donating blood are assumed to be independent of eachother. This assumption is supported by the results of acase-control study of potential risk factors for CJD, whichwas coordinated by the Clinical Surveillance Centre forCJD, Department of Neurology in Göttingen, Germany(7). Table 2 shows the joint distribution for the controlgroup of having received and donated blood. According tothe Fisher exact test, the p value for the hypothesis of noassociation is 0.43.

Heterogeneity in the risk of receiving a blood transfu-sion is modeled by the assumption that only a proportionof the population are at risk, whereas the remaining pro-portion never receives a transfusion. This assumption wasintroduced to be consistent with data from the case-controlstudy, in which ≈18% of the population reported havingever received a blood transfusion. Without this assump-tion, the model would predict that eventually 100% of acohort would receive a blood transfusion because the aver-

age annual risk of receiving a blood transfusion is about5%, i.e., ≈4 million in a population of 80 million.

Modeling Transfusion-associated Death RatesThe transfusion-associated death rate has been

described in detail by Wallis et al. (4). A good fit to the dataassumes that at all ages a certain proportion of transfusionrecipients have a higher rate of dying and the remainingproportion has a survival rate that corresponds to that ofpersons of the same age group in the general population.This age-dependent proportion of transfusion recipientswith an increased risk for death is described by a general-ized logistic function with a positive value at birth and anasymptote <100% for old age. The transfusion-associateddeath rate increases linearly with age. The increased deathrate appears to be concentrated in the first 2 years after atransfusion. Wallis et al. report that 2,888 patients wereobserved as long as 7.4 years after transfusions received inJune 1994 (4). The sex-specific rates were averaged for thesimulation model.

Modeling the InfectionUsually the incubation period refers to the time

between the infection and disease. In the context of CJD,however, disease can refer to onset, diagnosis, or death.Like Bacchetti, we also focused on death rates (8–10).The incubation period is assumed to be gamma distributedwith a mean duration of 16 years and a standard deviationof 4 years, which conforms to estimates of Valleron et al.and Ghani et al. (5,6). Because of great uncertainty aboutthe length of the incubation time, we also considered amuch higher value of 50 years in the absence of the com-peting risk for death. The coefficient of variation isassumed to be the same, such that the standard deviationis 12.5 years. Because of competing risks, the actualsojourn in the incubation period is 15.3 for an incubationperiod of 16 years and 34.0 years for an incubation periodof 50 years. The proportions of infected persons whowould die with disease symptoms are 79% and 37% forthe incubation periods of 16 and 50 years, respectively.This means that for an incubation time of 50 years, near-ly two thirds would die without disease symptoms.Hereafter we refer to these values of 15 and 50 years asshort and long incubation periods.

Blood Transfusion and Variant Creutzfeldt-Jakob Disease


We distinguish between 2 modes of transmission.Initially, the infection is introduced into the population bythe alimentary route. In the United Kingdom the number ofinfected animals entering the food supply peaked in 1989;most were concentrated within a period of 10 years (11),which we take as the assumed period of alimentary infec-tion. After this period, this mode of transmission was inter-rupted so that further transmissions are possible onlythrough blood transfusions.

A study to detect the presence of abnormal prion pro-tein in appendix and tonsil tissues has suggested a preva-lence of 235 infections per million in the United Kingdom(12). We arbitrarily assumed the prevalence of infectionsin Germany to be ≈1 order of magnitude lower, yielding acumulative incidence of 25 per million, which was thevalue used for the simulations.

We made 2 contrasting assumptions about the infec-tivity of blood preparations and evaluated the results ofthese 2 simulations: each transfusion (100% infectivity) orno blood transfusion (0% infectivity) from an infecteddonor leads to infection of the recipient. In the model theinfection probability (probability of receiving blood froman infected donor) is proportional to the proportion ofinfected donors among all donors. Thus, we can calculatethe number of infections from blood transfusions com-pared with the number of infections from alimentary trans-mission alone.

Modeling Donor ExclusionThe model distinguishes between persons with and

without transfusion history, termed recipients and nonre-cipients; these terms are applied to persons whether theyhave or have not donated blood. The model allows recipi-ents to be excluded from donating blood. In modeling theexclusion of recipients, we took into account that thismeasure may be imperfect and that a certain proportion ofrecipients may not be excluded.

ResultsFor the parameter estimates obtained from the sources

described above, the infection cannot become endemic(Figure 2). If we assume no further spread through bloodtransfusions after 10 years of infections by the alimentaryroute, the maximum prevalence reached is ≈1,860 (1,434for nonrecipients plus 426 for recipients) because some ofthe infected persons die of other causes during the incuba-tion period. If transmission is assumed to be possiblethrough blood transfusions (100% infectivity), then themaximum prevalence among recipients is increased by≈78 infections after 4 more years for the short incubationperiod and by 193 infections after 23 more years for thelong incubation period.

We assumed that donor exclusion is implementedimmediately at the beginning of the alimentary infectionrisk period, which reduced the original number of 2.55million donors by ≈20% to a value of 2.05 million donors.Because the model does not account for the stock of blooddonations, this reduction in the number of donors must becompensated for with an increased rate of donations perdonor to satisfy the demand; i.e., the average number ofdonations would have to increase from 1.6 to 2 per donorper year. Figure 2A shows that donor exclusion has almostno effect when the incubation period is assumed to be 16years. The absolute prevalence (i.e., the actual number ofinfected persons) differs at most by 9. For a long incuba-tion, differences are visible (59 persons at most) but smallin view of the long time intervals and the size of the total

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Figure 2. Absolute prevalence of infection for an incubation periodof 16 (A) and 50 (B) years, for nonrecipients of blood transfusion(solid, black), recipients under the assumption of no infectivity(dashed, gray), of 100% infectivity without donor exclusion (dotted,black), and 100% infectivity with donor exclusion (solid, gray). Theprevalence declines after the alimentary route of transmission isinterrupted, i.e., after 10 years. Prevalence differs only slightly ifthe infection probability of a transfusion from an infected donor isincreased from 0% to 100%. Donor exclusion produces negligiblereductions.

population (Figure 2B). The reason for these small differ-ences is described below.

The cumulative numbers of deaths from the infectionare given in Table 3. The numbers are considerably small-er for the long than for the short incubation period becausea long incubation period implies more deaths from othercauses. The numbers are given separately for cases inpatients with and without a history of blood transfusion.The route of infection for nonrecipients is alimentary only,whereas the route of infection for recipients is unclear. Ifwe compare the simulations at 100% and 0% infectivity ofblood transfusions, we observe 172 and 224 additionalcases for the short and the long incubation periods, respec-tively. These numbers represent 11% of 1,557 and 31% of725 cases, which would be expected for 0% infectivity forthe short and long incubations periods, respectively. Forthe short incubation period we expect a higher absolutenumber of alimentary cases but a smaller proportion oftransfusion cases than for the long incubation period. Theexclusion of donors would prevent only 15 and 50 cases,i.e., ≈15 (0.9%) of 1,729 and 50 (5%) of 949, respectively,at the end of the epidemic. The epidemic lasts for ≈50 or≈150 years for the short and the long incubation periods,respectively.

The predicted yearly incidence of deaths due to vCJD,separated by transfusion history, is shown in Figure 3. Theyearly peak incidence of total deaths would be 128 and 29for the short and the long incubation periods at 23 and 51years after the beginning of the epidemic, respectively. For0% infectivity the peak incidence would be only 5 and 3cases less for the short and long incubation periods,respectively, which implies that the exclusion of donorswith a transfusion history does not effectively preventinfection.

Figure 4 shows the predicted yearly incidence ofdeaths according to the route of infection. The time lagsbetween the peaks of deaths due to alimentary infectionand due to transfusion clearly differ and are 9 and 20 yearsfor short and long incubation periods, respectively.

Finally, we considered the absolute prevalence ofinfected donors according to their history of blood transfu-sion (Figure 5). Most infected donors do not have a trans-fusion history, which explains the negligible effect of apolicy excluding transfusion recipients from donation.

To determine whether the same model could also pre-dict transition into a positive endemic equilibrium of theinfection, we made the unrealistic assumptions that therates of donor recruitment and donor loss are constantbetween the ages of 18 and 67 and that the rate of receiv-ing a blood transfusion is constant throughout life. Thenthe model showed an extremely long time (>2,000 years)



Figure 3. The yearly incidence of deaths for an incubation periodof 16 (A) and 50 (B) years. The black curves show nonrecipientsof blood transfusion who were infected only by the alimentaryroute. These curves are independent of the infection probabilityand the rate of donor exclusion. The lower 3 curves represent thedeaths of recipients originating from 0% infectivity of blood trans-fusions (dashed gray), 100% infectivity without donor exclusion(solid gray), and 100% infectivity of blood transfusions with donorexclusion (dotted black, almost indistinguishable from solid grayline in A). The differences between the solid and dashed graycurves represent the cases due to blood transfusion.

before positive equilibrium would be reached (results notshown).

DiscussionOur model is the first attempt to describe in a realistic

way the transmission of infections through blood transfu-sions. In 1994, Velasco-Hernández proposed a model forthe spread of Chagas disease by vectors and blood transfu-sion (13). His model was used by Roberts and Heesterbeekto introduce their new concept to estimate the effort toeradicate an infectious disease (14). Huang and Villasanaincluded transmission through blood transfusion in anAIDS model (15). All these models have in common whatInaba and Sekine state about their extension of Velasco-Hernández’s Chagas model: “…here we assume that blooddonors are randomly chosen from the total population, andso there is no screening and the recipients of blood dona-tions are donating blood themselves at the same rate asanybody else. This is an unrealistic assumption, but wewill use it.” (16). These models implicitly describe trans-mission through blood transfusion exactly like person-to-person transmission by droplet infections.

The key innovation in our model is the simultaneousincorporation of 6 functions that all depend explicitly onthe age of a person: 1) natural death rate, 2) rate of receiv-ing a blood transfusion, 3) rates of donor recruitment, 4)donor loss, 5) death rate associated with transfusions, and6) proportion of transfusion recipients at increased risk fordeath. The age-dependent effects of these processes cannotbe ignored. Peak ages of donor activity (≈22 years) and ofreceiving a blood transfusion (≈70 years) are quite distinctand ≈50 years apart. This age pattern does not favor thespread of infection by blood transfusion. Another factorthat acts against the infection becoming endemic is thetransfusion-associated death rate. The good quality of thefollow-up data of nearly 3,000 patients helped to incorpo-rate realistic assumptions about the survival probabilitiesof transfusion recipients (4). The only data available aboutthe joint distribution of blood donor activity and history ofa blood transfusion was the CJD case-control study per-formed in Göttingen, Germany (7).

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Figure 4. Yearly incidence of deaths caused by alimentary trans-mission (solid line) and by blood transfusion (dashed line).The 2peaks differ by 9 and 20 years, depending on the incubation peri-od: 16 (A) and 50 (B) years, respectively.

Figure 5. Absolute prevalence of infected donors for an incubationperiod of 16 (A) and 50 (B) years. The solid black curves show theinfected donors without transfusion history. These curves are iden-tical for 0% and 100% infectivity and are independent of donorexclusion. The gray curves show infected donors with transfusionhistory for 100% (solid) and 0% (dashed) infectivity, respectively,without donor exclusion. The dotted black curves show the effectof donor exclusion starting at the beginning of alimentary risk.Most infected donors have no transfusion history and cannot,therefore, be excluded from blood donation.

The length of the incubation period plays a major rolein transmission dynamics and hence was subject to a sen-sitivity analysis. The model does not account for possiblechanges of infectivity during the incubation period. Themodel represents a worst-case scenario because it assumes100% infectivity throughout the period of infection. Evenunder this extreme assumption, donor exclusion can pre-vent only 0.9% (or 5%) of the expected deaths, assumingthe incubation period has a mean duration of 16 (or 50)years. The main explanation for this surprising result isthat most infected donors have been infected by the ali-mentary route and never received any blood transfusionand, therefore, are not eligible for donor exclusion.

The present simulations have arbitrarily assumed acumulative incidence of alimentary infection, about 25 permillion (2,000 per 80 million). With pessimistic assump-tions, the model predicts either 19.5 deaths per million forthe short incubation period or 9 deaths per million for thelong incubation period in the absence of spread throughblood transfusion. This corresponds to at least 9 (36%) of25 deaths attributable to the infection, which is ≈2 ordersof magnitude higher than expected for vCJD in the UnitedKingdom. As of July 2006, the number of vCJD cases inthe United Kingdom was 160. If we assume that the totalnumber of cases will be 200, then our assumption corre-sponds to about 3.3 cases per million. Thus, at most, 1.4%of infected persons would die from the infection (unless asecond wave of vCJD cases with a long incubation periodoccurs). According to our model, 0.9% of the deaths couldbe prevented by donor exclusion under the assumption ofthe short incubation period. In absolute numbers thiswould be ≈2 cases.

In France, the total number of vCJD cases recordedthrough July 2006 is 18. Even under the assumption thatthis number represents only 35% of the total number ofcases (17), the absolute expected number of preventedcases would be <1. In 1998, France decided to excludedonors with a transfusion history, primarily to reduce thespread of viruses. The present model could be modified toassess the effectiveness of excluding donors with transfu-sion history for preventing emerging infections with differ-ent modes of transmission and additional epidemiologicstates, e.g., latent or immune.

Our worst-case scenario assumptions of the epidemi-ology might seem similar to the situation in the UnitedKingdom. In Germany, no case of vCJD has been report-ed, which indicates that the expected number of cases inGermany is at least 2 orders of magnitude less than that inthe United Kingdom. This latter aspect was considered inthe interpretation of our model by a working group com-missioned by the German Federal Minister of Health,which recommended in April 2006 that persons with atransfusion history not be excluded from donating blood

(18). Our analysis enables different countries to performtheir own risk assessment and choose a strategy accordingto the absolute number of cases observed or expected.

The German CJD Surveillance study was supported by agrant from the German Ministry of Health (Az 325-4471-02/15to Inga Zerr and H. A. Kretzschmar). Helpful discussions aboutprevious versions of the model took place with the WorkingGroup Overall Blood Supply Strategy with regard to vCJD,Germany (Chairman R. Seitz).

Dr Dietz is head of the Department of Medical Biometry atthe University of Tübingen, Germany. His main interest is theapplication of mathematical models in the field of infectiousdiseases, in particular malaria and other parasitic diseases.

References

1. Llewelyn CA, Hewitt PE, Knight RS, Amar K, Cousens S,Mackenzie J, et al. Possible transmission of variant Creutzfeldt-Jakob disease by blood transfusion. Lancet. 2004;363:417–21.

2. Peden AH, Head MW, Ritchie DL, Bell JE, Ironside JW. PreclinicalvCJD after blood transfusion in a PRNP codon 129 heterozygouspatient. Lancet. 2004;364:527–9.

3. Castilla J, Saa P, Soto C. Detection of prions in blood. Nat Med.2005;11:982–5.

4. Wallis JP, Wells AW, Matthews JN, Chapman CE. Long-term sur-vival after blood transfusion: a population based study in the Northof England. Transfusion. 2004;44:1025–32.

5. Valleron AJ, Boelle PY, Will R, Cesbron JY. Estimation of epidem-ic size and incubation time based on age characteristics of vCJD inthe United Kingdom. Science. 2001;294:1726–8.

6. Ghani AC, Donnelly CA, Ferguson NM, Anderson RM. Updatedprojections of future vCJD deaths in the UK. BMC Infect Dis.2003;3:4.

7. Zerr I, Brandel JP, Masullo C, Wientjens D, de Silva R, Zeidler M,et al. European surveillance on Creutzfeldt-Jakob disease: a case-control study for medical risk factors. J Clin Epidemiol.2000;53:747–54.

8. Bacchetti P. Unexamined assumptions in explorations of upper limitfor cases of variant Creutzfeldt-Jakob disease. Lancet.2001;357:3–4.

9. Bacchetti P. Age and variant Creutzfeldt-Jakob disease. EmergInfect Dis. 2003;9:1611–2.

10. Bacchetti P. Uncertainty due to model choice in variant Creutzfeldt-Jakob disease projections. Stat Med. 2005;24:83–93.

11. Collins SJ, Lawson VA, Masters CL. Transmissible spongiformencephalopathies. Lancet. 2004;363:51–61.

12. Clarke P, Ghani AC. Projections of the future course of the primaryvCJD epidemic in the UK: inclusion of subclinical infection and thepossibility of wider genetic susceptibility. J R Soc Interface.2004;2:19–31.

13. Velasco-Hernandez JX. A model for Chagas disease involvingtransmission by vectors and blood transfusion. Theor Popul Biol.1994;46:1–31.

14. Roberts MG, Heesterbeek JA. A new method for estimating theeffort required to control an infectious disease. Proc R Soc Lond BBiol Sci. 2003;270:1359–64.

15. Huang XC, Villasana M. An extension of the Kermack-McKendrickmodel for AIDS epidemic. Journal of the FranklinInstitute–Engineering and Applied Mathematics. 2005;342:341–51.



16. Inaba H, Sekine H. A mathematical model for Chagas disease withinfection-age-dependent infectivity. Math Biosci. 2004;190:39–69.

17. Chadeau-Hyam M, Alperovitch A. Risk of variant Creutzfeldt-Jakob disease in France. Int J Epidemiol. 2005;34:46–52.

18. German Federal Ministry of Health Working Group. Overall bloodsupply strategy with regard to variant Cruetzfeldt-Jakob disease(vCJD). Transfusion Medicine and Hemotherapy. 2006;33(Suppl2):1–39.

Address for correspondence: Klaus Dietz, Department of MedicalBiometry, University of Tübingen, Westbahnhofstr. 55, Tübingen,Germany; email: [email protected]

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Blood Transfusion and Spread of Variant Creutzfeldt-Jakob Disease

Klaus Dietz, Günter Raddatz, Jonathan Wallis, Norbert Müller, Inga Zerr, Hans-Peter

Duerr, Hans Lefèvre , Erhard Seifried, Johannes Löwer

Technical Appendix

Modelling the age-specific mortality:

These death rates are described as a sum of three parametric functions which refer

to (i) infancy and early childhood, (ii) late childhood and early adulthood, and (iii) the

remaining life (a function which corresponds to a Gompertz model of aging with

exponentially increasing death rate) (1). This idea of expressing the mortality

throughout the whole life analytically is well-known since more than 100 years

(Figure S 1) (2).

0.00001

0.0001

0.001

0.01

0.1

1

0 20 40 60 80Age(years)

Ann

ual d

eath

rate

100

Figure S 1: Age-specific death rates in the general population. Dots: observed values; line: fitted death rates according to a parametric model involving three functions as described in the sections about demography. Beyond the age of 40 years the observations are well described by a Gompertz model which predicts that the death rates increase exponentially corresponding to a straight line on the logarithmic scale which is used here. The death rates for males and females are averaged in the present model.

1


The death rate initially declines very fast and reaches a minimum around the age of

10 years. Afterwards there is a unimodal peak which is well described by a lognormal

distribution with a peak around 21 years. The corresponding life table, i.e. the

probability of surviving a given age yields a life expectancy of 79 years (Figure S 2).

0.00

0.25

0.50

0.75

1.00

0 25 50 75Age(years)

Surv

ival

pro

babi

lity

100

Figure S 2: Life table for the stationary population used in the present model. This corresponds to the age-distribution in the absence of the infection

The three functions μk describe the age-specific mortality during infancy and early

childhood ( k =0), during late childhood and early adulthood ( k =1) and during late

adulthood ( k =2).

μB a( )= μ0 a( )+ μ1 a( )+ μ2 a( )μ0 a( )= exp(α0 )β0δaδ −1 exp(−β0a

δ )

μ1 a( )=β1

2πσexp −

12

log aA

⎛⎝⎜

⎞⎠⎟

2

σ 2

⎡

⎣

⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥

μ2 a( )= β2 exp β3a( )

The following parameter values were fitted by nonlinear least squares regression to

the age-specific mortality of Germany, where the data from both sexes were

averaged using the age specific survival function as weights

(http://www.destatis.de/download/d/bevoe/sterbet04.xls).

2

http://www.destatis.de/download/d/bevoe/sterbet04.xls


Parameter Value α0 -6.315 β0 0.4425 δ 0.7939 β1 0.0001649 σ 0.1716 A 20.61 β2 0.00002867 β3 0.09524

Each year a fixed number of susceptible newborns is added to the population, such

that the total size of the population is about 80 million.

Modelling donor activities

The yearly rate of becoming an active donor (the donor recruitment rate) is denoted

by ρ(a) . It is zero below 18 and above 67 years. The age-specific number of first

time donors of the German Red Cross (DRK) Blood Service was divided by the

corresponding age-specific number of individuals in the population as provided by the

Federal Statistical Office (to obtain an age specific rate of becoming a new active

donor. This rate has to be multiplied by an age-independent scaling factor which

ensures that the calculated total number of active donors corresponds to the

observed number of about 2.5 million. The logarithm of the empirical yearly rate is

approximated by a polynomial of fifth degree with the following parameters:

ρ a( )= 10 r0 +r1a+r2 a−42( )2 +r3 a−42( )3 +r4 a−42( )4 +r5 a−42( )5( ) for 18 ≤ a ≤ 67

Parameter Value r0 -2.96 r1 -0.0145 r2 -0.0015 r3 0.0000784 r4 0.0000023 r5 -0.000000236

The rate of terminating active donation is denoted by σ (a) . Depending on the age of

recruitment we estimate the average time as active donor by fitting an exponential

distribution to the age-distribution of active donors. For these age specific average

3


sojourn times we fit a polynomial of fourth degree. The inverse of these averages we

take as the age-specific rate σ (a) :

σ a( )=1

s0 + s1a + s2 a − 42.5( )2 + s3 a − 42.5( )3 + s4 a − 42.5( )4

with parameters

Parameter Value s0 25.92 s1 -0.38 s2 -0.00254 s3 0.000289 s4 -0.000006

The donor recruitment rate is estimated from the age distribution of first-time donors

in our sample (Figure S 3A) and from the age distribution of the population (Figure S

3B). The age-specific ratios of these frequencies are multiplied by a scaling factor in

order to obtain a realistic size of the donor population. The recruitment rate is highest

at the age of 18 years, declines by more than one order of magnitude up to the age

of 28, remains almost constant up to the age of 50 and declines substantially in aged

donors (Figure S 3C).

4


1

10

100

1000

10000

15 20 25 30 35 40 45 50 55 60 65Age(years)

Num

ber o

f firs

t tim

e do

nors

A

0

250000

500000

750000

1000000

1250000

15 20 25 30 35 40 45 50 55 60 65Age(years)

Pop

ulat

ion

(199

8)

B

0.0001

0.001

0.01

0.1

10 20 30 40 50 60 70Age(years)

Ann

ual r

ate

to b

ecom

e an

act

ive

dono

r C

Figure S 3: Estimation of the rate of becoming an active donor. A) Age-distribution of first-time donors, B) Age-distribution of the population of Germany C) observed (dots) and fitted (line) logarithm of the age-specific rate to become an active donor.

5


Donor loss is estimated from the age distributions of active donors as a function of

the youngest age as active donor. An exponential distribution is fitted to each of

these distributions for all yearly age classes from 18 to 66 years (Figure S 4A

provides an example). The corresponding mean durations are fitted by a polynomial

(Figure S 4B).

0.00

0.25

0.50

0.75

1.00

0 10 20 30Duration (years)

Pro

porti

on o

f don

ors

still

activ

e

A

0

2

4

6

8

10

12

14

15 25 35 45 55 65Age (years)

Dur

atio

n (y

ears

)

B

Figure S 4: Estimation of the rate of terminating active donation. a) The observed distribution of the time since the first donation for 9539 donors who were 22 years old when they started to donate blood (black line). We fit an exponential distribution to this curve and obtain a mean duration of 13.6 years as active donors (grey line). In b) a polynomial is fitted to these average durations. The inverse values of these durations are used as the age-specific transition rates to become an ex-donor.

The duration as active donor increases from 11 years in 18 year old donors to 13.7

years in 23 year old donors, and subsequently declines nearly linearly to zero until

the maximum age of active donors.

6


Modelling the age-specific prevalence of active donors

The estimates of donor recruitment and loss of donors produces the age-specific

prevalence of active donors (Figure S 5). The prevalence peaks at ≈24 years of age

and subsequently declines monotonically to zero by age 67. The overall prevalence

in the population is 3%, i.e., 2.4 million donors in a population of ≈80 million.

0.00

0.02

0.04

0.06

0.08

15 25 35 45 55 65Age (years)

Pro

porti

on o

f act

ive

dono

rs a

mon

g th

e to

tal

popu

latio

n

Figure S 5: Predicted age-specific prevalence of active blood donors, calculated on the basis of the parameter estimates of the rates of donor recruitment and donor loss.

Modelling the risk of receiving transfusions

The yearly age-specific risk of receiving a transfusion εB (a) among the fraction of

the population with a positive transfusion risk is estimated from the age-specific

transfusion risk at the University Hospital Essen, Germany during one month. The

function has a peak at birth which declines exponentially and another peak at about

65 years of age:

bε

εB (a) = μ0 + μ1 exp −μ2a( )+μ3

σexp −

a − μ4( )2

2σ 2

⎡

⎣⎢⎢

⎤

⎦⎥⎥

.

A graphical representation is given by Figure S 6.

7


0.000

0.025

0.050

0.075

0.100

0.125

0 20 40 60 80Age(years)

Ann

ual t

rans

fusi

on ri

sk

100

Figure S 6: The observed (dots) and fitted (line) age-specific transfusion risk.

The parameter values for the fitted function are given in the following Table:

Parameter Value μ0 0.0155 μ1 0.0412 μ2 0.259 μ3 1.133 σ 11.16 μ4 65.35

The cumulative transfusion risk is given by:

ΕB a( )= εB s( )0

a

∫ ds .

The following expression describes the age-specific risk of transfusion for the total

population:

ε a( )=bεεB a( )exp −ΕB a( )( )

1− bε 1− exp −ΕB a( )( )( ),

which is obtained by calculating the hazard rate for the proportion of individuals

without transfusion in the denominator of the previous expression.

8


Modelling the transfusion associated mortality

The proportion of transfusion recipients with increased mortality is described by the

generalised logistic function: q(a) = p0 +p1 − p0

1+Aq

a⎛⎝⎜

⎞⎠⎟

c .

Sex-specific fits are presented in Figure S 7.

0.0

0.2

0.4

0.6

0 25 50 75 100Age (years)

Pro

porti

on a

t hig

h ris

k to

die

0.0

0.5

1.0

1.5

2.0

2.5

Ann

ual t

rans

fusi

on a

ssoc

iate

d de

ath

rate

Figure S 7: Age-and sex-specific parameter values for the proportion q(a) of recipients with an increased risk of death (males: dotted in black, females: dashed in grey) and the linearly increasing death rates β(a) for those at increased risk of dying (males: solid-black, females: solid-grey).

Parameters for both sexes combined are fitted from original data (published in (3)),

and are given in the following Table:

Parameter Value p0 0.170 p1 0.4824 Aq 42.1 c 11.8

The generalised logistic function predicts that the proportion of transfusion recipients

with increased mortality increases from 17% at birth to about 48% in old age. The

point of inflection is at the age of about 42 years. The high value of nearly 12 for the

exponent indicates a steep slope at the point of inflection. For those individuals

with a high transfusion associated death rate this rate increases linearly with age:

c

β a( )= β4 + mβ a with the following parameters

9


Parameter Value β4 0.374 mβ 0.0125

This means that at birth the life expectancy for those affected by an increased death

rate is about 2.5 years. This value decreases to about 0.5 years for age 80 years.

The cumulative death rate is given by Β a( )= β4a + mβa2

2.

The survival probability for a 3 year old girl is much closer to the survival of the

general population compared to a 70 year old man (Figure S 8).

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7No. years

Sur

viva

l pro

babi

lity

A

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7No. years

Sur

viva

l pro

babi

lity

B

Figure S 8: The predicted survival curves (grey) for two patients together with the expected survival without transfusion-associated risk (black). A) 3 year old girl, B) 70 year old man.

10


Furthermore, the asymptote is approached more slowly for a 3 year old girl indicating

a longer survival. The goodness of fit of the model has been evaluated by dividing

the 1284 males and the 1605 females into deciles according to age such that there

are about 130 and 160 individuals, respectively, in each age group (Figure S 9).

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7No. years

Sur

viva

l pro

babi

lity

A

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7No. years

Sur

viva

l pro

babi

lity

B

Figure S 9: Goodness of model fit for the lowest (A) and the highest (B) sex-specific age deciles of survival after a transfusion together with the expected survival of those without transfusion. The lower curves are for male, the intermediate curves for females. Black: observed, grey: predicted. The upper dashed curves show the expected survival without transfusion (males: dashed in black, females: dotted in grey; in (A) both approximate the value of one and are almost identical).

The goodness of fit of this model is further illustrated in Figure S 10:

11


0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7No. years

Sur

viva

l pro

babi

lity

Figure S 10: Observed (grey) and predicted (black) sex-specific survival curves after a blood transfusion (all ages combined). The lower curves are for males; the intermediate curves are for females; the upper curves show the expected survival rates without transfusion (dashed for males, dotted for females; both are almost identical).

Since we model explicitly the transfusion associated death rates we have to

decrease the general death rates according to the following expression in order to

keep the total survival equal to the observations

μ a( )= μB a( )−bε β a( ) q s( )εB s( )exp −ΕB s( )− Β a( )− Β s( )( )( )ds

0

a

∫1− bε q s( )εB s( )exp −ΕB s( )( ) 1− exp − Β a( )− Β s( )( )( )( )ds

0

a

∫.

Modelling the infection

The sojourn time in the infected stage before an individual dies due to the infection

has a gamma distribution with shape parameter n = 16 and α = 0.0625 per year

which corresponds to an average duration of 16 years. This value is reduced to 0.02

to explore the sensitivity of the results with respect to this parameter. The density of

the incubation time distribution is not very different from a lognormal distribution with

the same mean and standard deviation (Figure S 11).

12


0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 5 10 15 20 25 30No. years

Den

sity

func

tion

Figure S 11: Density of the gamma distributed incubation time (grey line) in comparison to a lognormal density with the same mean and variance (black line).

The risk of an infection via the alimentary route is given by the following parameters:

λ t( )= λ0 für 0 ≤ t < 10,λ t( )= 0 für t ≥ 10.

The probability that a transfusion from an infected donor is infective is denoted by the

parameter which is assigned to the values of zero and one. f

Modelling donor exclusion

Donor exclusion is modelled by two parameters: coverage and a time-dependent

exclusion rate

b

γ (t) which allows determining the “velocity” of donor exclusion. By

means of the coverage parameter the model may take into account that a donor

exclusion program may not succeed to exclude 100% of potential and active donors

with transfusion history. The time-dependent parameter γ (t) allows determining the

timing of a donor exclusion program in relation to the period during which there is an

alimentary infection risk.

13


Model Equations

All variables S(t,a) and I(t,a) depend both on time t and year , both of which are

updated every two months, i.e. we always follow a cohort by increasing simulta-

neously time and age by two months. All 26 variables are distinguished by appropri-

ate indices of the numbers of susceptible (

a

S(t,a) ) and infective ( I(t,a) ) individuals.

Explanation of the indices of the 26 variables

• 1st index: without history of transfusion = 0 (non-recipients); with history

transfusion = 1 (recipients);

• 2nd index: potential donor = 0; active donor = 1, past donor or excluded from

donation = 2;

• 3rd index (for recipients, i.e. 1st index=1, and 2nd index <2): not deferrable=0,

deferrable=1;

• 3rd index (for recipients, i.e. 1st index=1, and 2nd index =2): without transfusion

associated risk of death = 0; with transfusion associated risk of death =1;

• 3rd index for individuals infected via the alimentary route (1st index = 0): number

of the fictitious incubation state (j=1, ... , n);

• 4th index (for recipients, i.e. 1st index = 1, and 2nd index <2): without transfusion

associated risk of death =0; with transfusion associated risk of death =1;

• 4th index (for recipients, i.e. 1st index = 1, and 2nd index =2) for infected

individuals: number of the fictitious incubation state (j=1, ... , n);

• 5th index (for recipients, i.e. 1st index = 1, and 2nd index <2) for infected

individuals: number of the fictitious incubation state (j=1, ... , n)

14


15

With this notation, the 26 variables represent proportions in the population with

following attributes:

1st index 2nd index 3rd index 4th index

1 S00 non-recipient no donor

2 S01 non-recipient donor

3 S02 non-recipient ex-donor

4 S1000 recipient no donor deferrable no extra mortality

5 S1001 recipient no donor deferrable extra mortality

6 S1010 recipient no donor not deferrable no extra mortality

7 S1011 recipient no donor not deferrable extra mortality

8 S1100 recipient donor deferrable no extra mortality

9 S1101 recipient donor deferrable extra mortality

10 S1110 recipient donor not deferrable no extra mortality

11 S1111 recipient donor not deferrable extra mortality

12 S120 recipient ex-donor no extra mortality

13 S121 recipient ex-donor extra mortality

14 I00j non-recipient no donor

15 I01j non-recipient donor

16 I02j non-recipient ex-donor

17 I1000j recipient no donor not deferrable no extra mortality

18 I1001j recipient no donor not deferrable extra mortality

19 I1010j recipient no donor deferrable no extra mortality

20 I1011j recipient no donor deferrable extra mortality

21 I1100j recipient donor not deferrable no extra mortality

22 I1101j recipient donor not deferrable extra mortality

23 I1110j recipient donor deferrable no extra mortality

24 I1111j recipient donor deferrable extra mortality

25 I120(j-1) recipient ex-donor no extra mortality

26 I121j recipient ex-donor extra mortality


For the indices we use the so called “+”-Notation: if an index is replaced by a “+”,

then this means that we have summed all variables with different indices at this

position.

The model is illustrated with Figure S 12:

S101 S111

I101 I111

γ γ

γ γ

λεpfλεpf

ρ

σ

σ

ρ

b b

b b

(1-b) (1-b)

(1-b) (1-b)

S00 S01 S02

S100 S110 S12

I100 I110 I12

I00 I01 I02

Active donors Ex-donors

With

trans

fusi

onW

ithou

ttra

nsfu

sion

ρ

ρ

σ

σ

ρ

ρ

σ

σ

ε

ε

ε ε

ε ε

λ λ λλ

λ λ

ν

ε ε εεε ε(1-pf)

(1-pf)

(1-pf)

pf pf pf

pf pf

pf

Potential donors

Figure S 12: States and transitions of the epidemiological model. For the transfusion recipients (all states for which the 1st index equals 1) the model distinguishes individuals with and without increased risk of dying. In the model equations this aspect is expressed with the last index of the variables. In this figure this aspect is not included. The death rates associ-ated with each state are also not shown. Parameters: ν : birth rate, λ : rate of infection during the period of alimentary transmission, ε : rate of receiving blood transfusions, ρ : donor recruitment rate, σ : donor loss rate, γ : rate of donor exclusion, b: proportion of deferrable donors with transfusion history, p: proportion of infected donors among all donors, f: probability that an infected blood transfusion leads to infection of the recipient.

16


Model equations

Since our step size is two months and some rates are high we use the exponential

function in all equations for calculating how many individuals are still in the initial

state after two months. The argument of the exponential function is the sum of all

rates which describe a transition out of the corresponding variable. This approach

avoids negative variables which would arise when more individuals are removed than

were present at the beginning of the interval which could happen for large rates. E.g.

in the second equation (for the active susceptible donors without transfusion history)

we use the theory of competing risks to determine which proportion of the potential

donors are added to this category: in the denominator we add all transition rates and

in the numerator we select the relevant transition rate out of those from the numera-

tor. The number of individuals in the previous state is multiplied by the probability to

survive the previous year and the probability to make a transition which is the

complement to the probability to make no transition.

The following expression is the time-dependent prevalence of infected active donors

among all active donors:

p(t) =I01+ a, t( )+ I11+++ a, t( )⎡⎣ ⎤⎦

a=18

67

∑

I01+ a, t( )+ I11+++ a, t( )+ S01 a, t( )+ S11++ a, t( )⎡⎣ ⎤⎦a=18

67

∑.

If the duration of an infection is short, i.e. the parameter α is high, then it may

happen that a newly infected individual at the end of the first iteration step is not only

in the first fictitious state of the infection but already in the second state or even in a

higher state or with a small probability the individual may even have died during the

iteration step. The following expressions determine the probabilities according to a

gamma-distribution of finding a newly infected individual in state j = 1,...,n. The state

j = n +1 corresponds to the death of the individual due to the infection:

qj = exp −nα( )(nα ) j−1

( j −1)!, j = 1,....,n,

qn+1 = 1− qjj=1

n

∑ .

17


(1) S00 t +1,a +1( )= S00 t,a( )exp − λ t( )+ ρ a( )+ ε(a) + μ a( )⎡⎣ ⎤⎦{ }

(2) S01 t +1,a +1( )= S01 t,a( )exp − λ t( )+ σ a( )+ ε(a) + μ a( )⎡⎣ ⎤⎦{ }+

ρ a( )λ t( )+ ρ a( )+ ε(a)

S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ ε(a)⎡⎣ ⎤⎦{ }

(3) S02 t +1,a +1( )= S02 t,a( )exp − λ t( )+ ε(a) + μ a( )⎡⎣ ⎤⎦{ }+

σ a( )λ t( )+ σ a( )+ ε(a)

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)⎡⎣ ⎤⎦{ }

(4) S1000 t +1,a +1( )= S1000 t,a( )exp − λ t( )+ ρ a( )+ fp(t)ε(a) + μ a( )⎡⎣ ⎤⎦{ }+

ε(a) 1− fp(t)( ) 1− b( ) 1− q(a)( )λ t( )+ ρ a( )+ ε(a)


(5) S1001 t +1,a +1( )= S1001 t,a( )exp − λ t( )+ ρ a( )+ fp(t)ε(a) + μ a( )+ β(a)⎡⎣ ⎤⎦{ }+

ε(a) 1− fp(t)( ) 1− b( )q(a)λ t( )+ ρ a( )+ ε(a)


(6) S1010 t +1,a +1( )= S1010 t,a( )exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t) + μ a( )⎡⎣ ⎤⎦{ }+

ε(a) 1− fp(t)( )b 1− q(a)( )λ t( )+ ρ a( )+ ε(a)


(7) S1011 t +1,a +1( )= S1011 t,a( )exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t) + μ a( )+ β a( )⎡⎣ ⎤⎦{ }+

ε(a) 1− fp(t)( )bq(a)λ t( )+ ρ a( )+ ε(a)


(8) S1100 t +1,a +1( )= S1100 t,a( )exp − λ t( )+ σ a( )+ fp(t)ε(a) + μ a( )⎡⎣ ⎤⎦{ }+

ρ a( )λ t( )+ ρ a( )+ fp(t)ε(a)

S1000 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } +

ε(a) 1− fp(t)( ) 1− b( ) 1− q(a)( )λ t( )+ σ a( )+ ε(a)


(9) S1101 t +1,a +1( )= S1101 t,a( )exp − λ t( )+ σ a( )+ fp(t)ε(a) + β(a) + μ a( )⎡⎣ ⎤⎦{ }+

ρ a( )λ t( )+ ρ a( )+ fp(t)ε(a)

S1001 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } +

ε(a) 1− fp(t)( ) 1− b( )q(a)λ t( )+ σ a( )+ ε(a)


18


(10) S1110 t +1,a +1( )= S1110 t,a( )exp − λ t( )+ σ a( )+ fp(t)ε(a) + γ (t) + μ a( )⎡⎣ ⎤⎦{ }+

ρ a( )λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)

S1010 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)⎡⎣ ⎤⎦{ } +

ε(a) 1− fp(t)( )b 1− q(a)( )λ t( )+ σ a( )+ ε(a)


(11) S1111 t + 1,a + 1( )= S1111 t,a( )exp − λ t( )+ σ a( )+ fp(t)ε(a) + γ (t) + μ a( )+ β a( )[ ]{ }+

ρ a( )λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)

S1011 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)[ ]{ } +

ε(a) 1− fp(t)( )bq(a)λ t( )+ σ a( )+ ε(a)

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)[ ]{ }

(12) S120 t +1,a +1( )= S120 t,a( )exp − λ t( )+ fp(t)ε(a) + μ a( )⎡⎣ ⎤⎦{ }+

ε(a) 1− fp(t)( ) 1− q(a)( )λ t( )+ ε(a)

S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε(a)⎡⎣ ⎤⎦{ }σ a( )

λ t( )+ σ a( )+ fp(t)ε(a)S1100 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } +

σ a( )+ γ (t)λ t( )+ σ a( )+ fp(t)ε(a) + γ (t)

S1110 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ fp(t)ε(a) + γ (t)⎡⎣ ⎤⎦{ }γ (t)

λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)S1010 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t⎡⎣ ⎤⎦{ }

(13) S121 t + 1,a + 1( )= S121 t,a( )exp − λ t( )+ fp(t )ε(a) + μ a( )+ β a( )[ ]{ }+

ε(a) 1− fp(t)( )q(a)λ t( )+ ε(a)

S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε(a)[ ]{ }

σ a( )λ t( )+ σ a( )+ fp(t )ε(a)

S1101 t,a( )exp(−μ(a) − β(a)) 1 − exp − λ t( )+ σ a( )+ fp(t )ε(a)[ ]{ } +

σ a( )+ γ (t)λ t( )+ σ a( )+ fp(t )ε(a) + γ (t )

S1111 t,a( )exp(−μ(a) − β(a)) 1 − exp − λ t( )+ σ a( )+ fp(t)ε(a) + γ (t)[ ]{ }

γ (t )λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)

S1011 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)[ ]{ }

19


(14) I00 j t +1,a +1( )= I00 j t,a( )exp − ρ a( )+ ε(a) + μ a( )+ nα⎡⎣ ⎤⎦{ }+

nαρ a( )+ ε(a) + nα

I00( j−1) t,a( )exp(−μ(a)) 1− exp − ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ } +

λ t( )λ t( )+ ρ a( )+ ε(a)

S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj

(15) I01 j t +1,a +1( )= I01 j t,a( )exp − σ a( )+ ε(a) + μ a( )+ nα⎡⎣ ⎤⎦{ }+

nασ a( )+ ε(a) + nα

I01( j−1) t,a( )exp(−μ(a)) 1− exp − σ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ } +

ρ a( )ρ a( )+ ε(a) + nα

I00 j t,a( )exp(−μ(a)) 1− exp − ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ } +

λ t( )λ t( )+ σ a( )+ ε(a)

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj

(16) I02 j t +1,a +1( )= I02 j t,a( )exp − ε(a) + μ a( )+ nα⎡⎣ ⎤⎦{ }+

σ a( )σ a( )+ ε(a) + nα

I01 j t,a( )exp(−μ(a)) 1− exp − σ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ } +

nαε(a) + nα

I02( j−1) t,a( )exp(−μ(a)) 1− exp − ε(a) + nα[ ]{ } +

λ t( )λ t( )+ ε(a)

S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε(a)⎡⎣ ⎤⎦{ } qj

(17) I1000 j t +1,a +1( )= I1000 j t,a( )exp − ρ a( )+ μ a( )+ nα⎡⎣ ⎤⎦{ }+

nαρ a( )+ nα

I1000( j−1) t,a( )exp(−μ(a)) 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

1− b( ) fp(t)ε(a) 1− q a( )( )λ t( )+ ρ a( )+ ε(a)

S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ ρ a( )+ fp(t)ε(a)

S1000 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a⎡⎣ ⎤⎦{ } qj

+ε(a)(1− b)(1− q(a))

ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦I00 j (t,a)exp(−μ(a)) 1− exp − ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ }

(18) I1001 j t +1,a +1( )= I1001 j t,a( )exp − ρ a( )+ μ a( )+ β a( )+ nα⎡⎣ ⎤⎦{ }+

nαρ a( )+ nα

I1001( j−1) t,a( )exp(−μ(a) − β(a)) 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

1− b( ) fp(t)ε(a)q a( )λ t( )+ ρ a( )+ ε(a)

S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ ρ a( )+ fp(t)ε(a)

S1001 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } qj

+ε(a)(1− b)q(a)

ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦I00 j (t,a)exp(−μ(a)) 1− exp − ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ }

20


(19) I1010 j t + 1,a + 1( )= I1010 j t,a( )exp − ρ a( )+ γ (t) + μ a( )+ nα[ ]{ }+

nαρ a( )+ γ (t ) + nα

I1010( j−1) t,a( )exp(−μ(a)) 1− exp − ρ a( )+ γ (t ) + nα[ ]{ } +

bfp(t)ε(a) 1 − q(a)( )λ t( )+ ρ a( )+ ε(a)

S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ ε(a[ ]{ } qj +

fp(t )ε(a) + λ t( )λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)

S1010 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ρ a( )+ fp(t)ε(a) + γ (t)[ ]{ } qj

ε(a)b(1 − q(a))ρ a( )+ ε(a) + nα[ ]

I00 j (t,a)exp(−μ(a)) 1 − exp − ρ a( )+ ε(a) + nα[ ]{ }

(20) I1011 j t + 1, a + 1( ) = I1011 j t, a( )exp − ρ a( )+ γ (t ) + μ a( )+ β a( )+ nα[ ]{ }+

nα

ρ a( )+ γ (t ) + nαI1011( j −1) t, a( )exp(−μ(a) − β(a)) 1 − exp − ρ a( )+ γ (t ) + nα[ ]{ } +

bfp(t )ε(a)q(a)

λ t( )+ ρ a( )+ ε(a)S00 t, a( )exp(−μ(a)) 1 − exp − λ t( )+ ρ a( )+ ε(a)[ ]{ } qj +

fp(t )ε(a) + λ t( )λ t( )+ ρ a( )+ fp(t )ε(a) + γ (t )

S1011 t, a( )exp(−μ(a) − β(a)) 1 − exp − λ t( )+ ρ a( )+ fp(t )ε(a) + γ (t )[ ]{ } qj

+ε(a)bq(a)

ρ a( )+ ε(a) + nα[ ]I 00 j (t, a) exp(−μ(a)) 1 − exp − ρ a( )+ ε(a) + nα[ ]{ }

(21) I1100 j t +1,a +1( )= I1100 j t,a( )exp − σ a( )+ μ a( )+ nα⎡⎣ ⎤⎦{ }+

nασ a( )+ nα

I1100( j−1) t,a( )exp(−μ(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ } +

ρ a( )ρ a( )+ nα

I1000 j t,a( )exp(−μ(a)) 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

1− b( ) fp(t)ε(a) 1− q a( )( )λ t( )+ σ a( )+ ε(a

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ σ a( )+ fp(t)ε(a)

S1100 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } qj

+ε(a)(1− b)(1− q(a))

σ a( )+ ε(a) + nα⎡⎣ ⎤⎦I01 j (t,a)exp(−μ(a)) 1− exp − σ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ }

(22) I1101 j t +1,a +1( )= I1101 j t,a( )exp − σ a( )+ μ a( )+ β a( )+ nα⎡⎣ ⎤⎦{ }+

nασ a( )+ nα

I1101( j−1) t,a( )exp(−μ(a) − β(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ } +

ρ a( )ρ a( )+ nα

I1001 j t,a( )exp(−μ(a) − β(a)) 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

1− b( ) fp(t)ε(a)q a( )λ t( )+ σ a( )+ ε(a)

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ σ a( )+ fp(t)ε(a)

S1101 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ σ a( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } qj

+ε(a)(1− b)q(a)

σ a( )+ ε(a) + nα⎡⎣ ⎤⎦I01 j (t,a)exp(−μ(a)) 1− exp − σ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ }

21


(23) I1110 j t + 1,a + 1( )= I1110 j t,a( )exp − σ a( )+ μ a( )+ γ (t ) + nα[ ]{ }+

nασ a( )+ γ (t ) + nα

I1110( j−1) t,a( )exp(−μ(a)) 1− exp − σ a( )+ γ (t) + nα[ ]{ } +

ρ a( )ρ a( )+ γ (a) + nα

I1010 j t,a( )exp(−μ(a)) 1 − exp − ρ a( )+ γ (t ) + nα[ ]{ } +

bfp(t)ε(a) 1− q a( )( )λ t( )+ σ a( )+ ε(a

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ σ a( )+ ε(a)[ ]{ } qj +

fp(t )ε(a) + λ t( )λ t( )+ σ a( )+ fp(t)ε(a) + γ (t)

S1110 t,a( )exp(−μ(a)) 1 − exp − λ t( )+ σ a( )+ fp(t )ε(a) + γ (t )[ ]{ } qj

+ε(a)b(1 − q(a))

σ a( )+ ε(a) + nα[ ]I01 j (t,a)exp(−μ(a)) 1− exp − σ a( )+ ε(a) + nα[ ]{ }

(24) I1111 j t + 1, a + 1( ) = I1111 j t, a( )exp − σ a( )+ μ a( )+ β a( )+ γ (t ) + nα[ ]{ }+

nα

σ a( )+ γ (t ) + nαI1111( j −1) t, a( )exp(−μ(a) − β(a)) 1 − exp − σ a( )+ γ (t ) + nα[ ]{ } +

ρ a( )ρ a( )+ γ (a) + nα

I1011 j t, a( )exp(−μ(a) − β(a)) 1 − exp − ρ a( )+ γ (t ) + nα[ ]{ } +

bfp(t )ε(a)q a( )λ t( )+ σ a( )+ ε(a

S01 t, a( )exp(−μ(a)) 1 − exp − λ t( )+ σ a( )+ ε(a)[ ]{ } qj +

fp(t )ε(a) + λ t( )λ t( )+ σ a( )+ fp(t )ε(a) + γ (t )

S1111 t, a( )exp(−μ(a) − β(a)) 1 − exp − λ t( )+ σ a( )+ fp(t )ε(a) + γ (t )[ ]{ } qj

+ε(a)bq(a)

σ a( )+ ε(a) + nα[ ]I 01 j (t, a) exp(−μ(a)) 1 − exp − σ a( )+ ε(a) + nα[ ]{ }

(25) I120 j t +1,a +1( )= I120 j t,a( )exp − μ a( )+ nα⎡⎣ ⎤⎦{ }+

I120( j−1) t,a( )exp(−μ(a)) 1− exp − +nα[ ]{ } +

fp(t)ε(a) 1− q a( )( )λ t( )+ ε(a)

S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ fp(t)ε(a)

S120 t,a( )exp(−μ(a)) 1− exp − λ t( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } qj +

γ t( )γ t( )+ ρ a( )+ nα

I1010 j t,a( )exp(−μ(a)) 1− exp − γ t( )+ ρ a( )+ nα⎡⎣ ⎤⎦{ } +

γ t( )+ σ a( )γ t( )+ σ a( )+ nα

I1110 j t,a( )exp(−μ(a)) 1− exp − γ t( )+ σ a( )+ nα⎡⎣ ⎤⎦{ }

+σ (a)

σ a( )+ nα⎡⎣ ⎤⎦I1100 j (t,a)exp(−μ(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ }

+ε(a)(1− q(a))

ε(a) + nα[ ] I02 j (t,a)exp(−μ(a)) 1− exp − ε(a) + nα[ ]{ }

22


(26) I121 j t +1,a +1( )= I121 j t,a( )exp − μ a( )+ β a( )+ nα⎡⎣ ⎤⎦{ }+

I121( j−1) t,a( )exp(−μ(a) − β(a)) 1− exp − nα[ ]{ } +

fp(t)ε(a)q a( )λ t( )+ ε(a)

S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε(a)⎡⎣ ⎤⎦{ } qj +

fp(t)ε(a) + λ t( )λ t( )+ fp(t)ε(a)

S121 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ fp(t)ε(a)⎡⎣ ⎤⎦{ } qj +

γ t( )γ t( )+ ρ a( )+ nα

I1011 j t,a( )exp(−μ(a) − β(a)) 1− exp − γ t( )+ ρ a( )+ nα⎡⎣ ⎤⎦{ } +

γ t( )+ σ a( )γ t( )+ σ a( )+ nα

I1111 j t,a( )exp(−μ(a) − β(a)) 1− exp − γ t( )+ σ a( )+ nα⎡⎣ ⎤⎦{ }σ a( )

σ a( )+ nαI1101 j t,a( )exp(−μ(a) − β(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ }

+ε(a)q(a)ε(a) + nα[ ]I02 j (t,a)exp(−μ(a)) 1− exp − ε(a) + nα[ ]{ }

In addition to the 26 variables we also calculate the cumulative number of individuals

who have died due to the infection. These numbers we do not calculate for the

individual ages separately, i.e. we sum all age groups. Since we have 13 types of

infected individuals we define those who died in the corresponding 13 categories:

D00 t +1( )= D00 t( )+nα

ρ a( )+ ε(a) + nαI00n t,a( )

a=1

100

∑ exp(−μ(a)) 1− exp − ρ a( )+ ε(a) + nα⎡⎣ ⎤⎦{ } +

qn+1λ t( )

λ t( )+ ε a( )+ ρ a( )S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ ρ a( )⎡⎣ ⎤⎦{ }a=1

100

∑

D01 t +1( )= D01 t( )+nα

σ a( )+ ε(a) + nαI01n t,a( )exp(−μ(a))

a=1

100

∑ 1− exp − σ a( )+ ε(a + nα⎡⎣ ⎤⎦{ } +

qn+1λ t( )

λ t( )+ ε a( )+ σ a( )S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )⎡⎣ ⎤⎦{ }a=1

100

∑

D02 t +1( )= D02 t( )+nα

ε(a) + nαI02n t,a( )

a=1

100

∑ exp(−μ(a)) 1− exp − ε(a) + nα[ ]{ } +

qn+1λ t( )

λ t( )+ ε a( )S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )⎡⎣ ⎤⎦{ }a=1

100

∑

23


D1000 t +1( )= D1000 t( )+nα

ρ a( )+ nαI1000n t,a( )exp(−μ(a))

a=1

100

∑ 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

qn+1

1− b( ) fp t( )ε a( ) 1− q a( )( )λ t( )+ ε a( )+ ρ a( ) S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ ρ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑ +

qn+1fp t( )ε a( )+ λ t( )

λ t( )+ ε a( )+ ρ a( )S1000 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ ρ a( )⎡⎣ ⎤⎦{ }a=1

100

∑

D1001 t +1( )= D1001 t( )+nα

ρ a( )+ nαI1001n t,a( )

a=1

100

∑ exp(−μ(a) − β(a)) 1− exp − ρ a( )+ nα⎡⎣ ⎤⎦{ } +

qn+11− b( ) fp t( )ε a( )q a( )λ t( )+ ε a( )+ ρ a( ) S00 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ ρ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ ε a( )+ ρ a( )S1001 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ε a( )+ ρ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑

D1010 t + 1( )= D1010 t( )+nα

ρ a( )+ γ t( )+ nαI1010n t,a( )

a=1

100

∑ exp(−μ(a)) 1− exp − ρ a( )+ γ t( )+ nα[ ]{ } +

qn+1

bfp t( )ε a( ) 1− q a( )( )λ t( )+ ε a( )+ ρ a( )

S00 t,a( )exp(−μ(a)) 1 − exp − λ t( )+ ε a( )+ ρ a( )[ ]{ }a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ fp(t )ε a( )+ ρ a( )+ γ t( )

S1010 t,a( )exp(−μ(a)) 1 − exp − λ t( )+ fp(t)ε a( )+ ρ a( )+ γ t( )[ ]{ }a=1

100

∑

D1011 t + 1( ) = D1011 t( )+nα

ρ a( )+ γ t( )+ nαI1011n t, a( )exp(−μ(a) − β(a))

a=1

100

∑ 1 − exp − ρ a( )+ γ t( )+ nα[ ]{ }

+qn+1

bfp t( )ε a( )q a( )λ t( )+ ε a( )+ ρ a( )

S00 t, a( )exp(−μ(a)) 1 − exp − λ t( )+ ε a( )+ ρ a( )[ ]{ }a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ fp(t )ε a( )+ ρ a( )+ γ t( )

S1011 t, a( )exp(−μ(a) − β(a)) 1 − exp − λ t( )+ fp(t )ε a( )+ ρ a( )+ γ t( )[ ]{ }a=1

100

∑

D1100 t +1( )= D1100 t( )+nα

σ a( )+ nαI1100n t,a( )

a=1

100

∑ exp(−μ(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ } +

qn+1

1− b( ) fp t( )ε a( ) 1− q a( )( )λ t( )+ ε a( )+ σ a( ) S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑ +

qn+1fp t( )ε a( )+ λ t( )

λ t( )+ ε a( )+ σ a( )S1100 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )⎡⎣ ⎤⎦{ }a=1

100

∑

D1101 t +1( )= D1101 t( )+nα

σ a( )+ nαI1101n t,a( )

a=1

100

∑ exp(−μ(a) − β(a)) 1− exp − σ a( )+ nα⎡⎣ ⎤⎦{ } +

qn+11− b( ) fp t( )ε a( )q a( )λ t( )+ ε a( )+ σ a( ) S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ ε a( )+ σ a( )S1101 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ε a( )+ σ a( )⎡⎣ ⎤⎦{ }

a=1

100

∑

24


D1110 t + 1( )= D1110 t( )+nα

σ a( )+ γ t( )+ nαI1110n t,a( )

a=1

100

∑ exp(−μ(a)) 1 − exp − σ a( )+ γ t( )+ nα[ ]{ } +

qn+1

bfp t( )ε a( ) 1− q a( )( )λ t( )+ ε a( )+ σ a( )

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )[ ]{ }a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ ε a( )+ σ a( )+ γ t( )

S1110 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )+ γ t( )[ ]{ }a=1

100

∑

D1111 t + 1( )= D1111 t( )+nα

σ a( )+ γ t( )+ nαI1111n t,a( )

a=1

100

∑ exp(−μ(a) − β(a)) 1− exp − σ a( )+ γ t( )+ nα[ ]{ } +

qn+1

bfp t( )ε a( )q a( )λ t( )+ ε a( )+ σ a( )

S01 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )+ σ a( )[ ]{ }a=1

100

∑ +

qn+1

fp t( )ε a( )+ λ t( )λ t( )+ ε a( )+ σ a( )+ γ t( )

S1111 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ ε a( )+ σ a( )+ γ t( )[ ]{ }a=1

100

∑

D120 t +1( )= D120 t( )+ I120n t,a( )a=1

100

∑ exp(−μ(a)) 1− exp − nα[ ]{ } +

qn+1

fp t( )ε a( ) 1− q a( )( )λ t( )+ ε a( ) S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )⎡⎣ ⎤⎦{ }

a=1

100

∑ +

qn+1p t( )λ t( )

λ t( )+ fp t( )ε a( )S120 t,a( )exp(−μ(a)) 1− exp − λ t( )+ fp t( )ε a( )⎡⎣ ⎤⎦{ }a=1

100

∑

D121 t +1( )= D121 t( )+ I121n t,a( )exp(−μ(a) − β(a))a=1

100

∑ 1− exp − nα[ ]{ } +

qn+1fp t( )ε a( )q a( )

λ t( )+ ε a( ) S02 t,a( )exp(−μ(a)) 1− exp − λ t( )+ ε a( )⎡⎣ ⎤⎦{ }a=1

100

∑ +

qn+1p t( )λ t( )

λ t( )+ fp t( )ε a( )S121 t,a( )exp(−μ(a) − β(a)) 1− exp − λ t( )+ fp t( )ε a( )⎡⎣ ⎤⎦{ }a=1

100

∑

25


26

References (Appendix)

1. Mode CJ. Stochastic processes in demography and their computer implementation. Berlin ; New York: Springer-Verlag; 1985. xvii, 389 p. p. (Biomathematics ; v. 14).

2. Thiele PN. On a mathematical formula to express the rate of mortality throughout the whole life. J Inst Actuaries. 1872; 16:213-39.

3. Wallis JP, Wells AW, Matthews JN, Chapman CE. Long-term survival after blood transfusion: a population based study in the North of England. Transfusion. 2004; 44:1025-32.

Documents

Blood Transfusion and Spread of Variant Creutzfeldt-Jakob