To forecast demand for HCV medication under several scenarios (over a 10-year period)

Objectives To forecast demand for HCV medication under several scenarios (over a 10-year period) To assess the responsivenness of the demand for HCV medication to changes in prices of the medicines How are changes in the prices of drugs (either through the introduction of generics or subsidization by a third party) will affect the demand? 2

Outline Methodological Approach o Mathematical Model Two Base Cases for Treatment Cascade o Current Best Regimen vs. Future Best Regimen Two Financing Scenarios o Free-Market vs. Third Party Coverage Working Assumptions Main Results: o Low-income, Lower-Middle & Upper-middle income countries Sensitivity Analysis Discussion & Policy Recommendations 3

Methodological Approach A Susceptible-Infected-Removed (SIR) Model (e.g. Elbasha 2013): New infections are assumed to be of the naïve nature (No reinfection) Population is assumed to mix homogeneously N Susceptible S Acutely infected I Immune R Chronically infected F m 4

Methodological Approach ν Susceptible S(t) x Acute HCV I(t) ε 0 F0(t) ε 1 F1(t) ε 2 F2(t) ε 3 F3(t) ε 4 F4(t) λ σ μ μ μ μ μ μ Recovered R(t) μ Mortality Unrelated to HCV μ 5

Methodological Approach The chronically infected go through the diagnosis cascade starting with HCV detection and ending once treatment is accepted. Outline of the Stages of Detection, Referral to Care and Treatment Chronically Infected α 1 HCV Detected α 2 Referred to Care α 3 Fibrosis Staging α 4 Medically Eligible Patients (based on scenarios) α' 5 Health System Absorption Capacity α" 5 Price α'" Elasticity Ability-to- 5 of Demand α 5 Pay (Among those with ATP) Treatment Received 6

Methodological Approach: Diagnosis Rate (r 0 = α 1 *α 2 *α 3 ) Diagnosis rate reflects the probability that a chronically infected individual would have had HCV detected been referred to care and gone through fibrosis staging. For the current best case, this rate (r 0 ) is assumed to be 5% in the year 2013 Stage 2013 to 2014 to 2015 to 2016 to 2017 to 2018 to 2019 to 2020 to 2021 to 2022 to 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Acute 0,1% 0,1% 0,1% 0,2% 0,2% 0,2% 0,2% 0,3% 0,3% 0,4% F0 0,5% 0,6% 0,7% 0,8% 0,9% 1,0% 1,2% 1,3% 1,5% 1,8% F1 1,0% 1,2% 1,3% 1,5% 1,7% 2,0% 2,3% 2,7% 3,1% 3,5% F2 2,5% 2,9% 3,3% 3,8% 4,4% 5,0% 5,8% 6,7% 7,6% 8,8% F3 10,0% 11,5% 13,2% 15,2% 17,5% 20,1% 23,1% 26,6% 30,6% 35,2% F4 15,0% 17,3% 19,8% 22,8% 26,2% 30,2% 34,7% 39,9% 45,9% 52,8% For the future best case, this rate (r 0 ) is assumed to be 15% in the year 2013 Stage 2013 to 2014 to 2015 to 2016 to 2017 to 2018 to 2019 to 2020 to 2021 to 2022 to 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Acute 5,0% 5,8% 6,6% 7,6% 8,7% 10,1% 11,6% 13,3% 15,3% 17,6% F0 10,0% 11,5% 13,2% 15,2% 17,5% 20,1% 23,1% 26,6% 30,6% 35,2% F1 15,0% 17,3% 19,8% 22,8% 26,2% 30,2% 34,7% 39,9% 45,9% 52,8% F2 20,0% 23,0% 26,5% 30,4% 35,0% 40,2% 46,3% 53,2% 61,2% 70,4% F3 25,0% 28,8% 33,1% 38,0% 43,7% 50,3% 57,8% 66,5% 76,5% 80,0% F4 35,0% 40,3% 46,3% 53,2% 61,2% 70,4% 80,0% 80,0% 80,0% 80,0% 7

Methodological Approach: Eligibility(α 4 ) The probability that an individual will be offered treatment is based on their eligibility for treatment. All individuals eligible for treatment are offered treatment after fibrosis staging. α 4 is the probability that the individual will fulfil the eligibility criteria (depending on WHO (2014) guidelines). Scope of eligibility for treatment: Initial analysis: only fibrosis stage F3 & F4 Sensitivity analysis: treatment of everyone who is chronically infected (F0 to F4). 8

Methodological Approach: Treatment Rate (α 5 ) Individuals are at liberty to either accept or refuse treatment based on conditions: Side effects of the drug (acceptability of treatment) Costs involved (Ability-to-Pay (ATP)) Sensitivity analysis to take into account differential acceptability of treatment & various thresholds for ATP In the Two Cases: of people willing and able to accept treatment depends on three factors: o Health System s Absorption Capacity (AC) α 5 o Individuals Ability-To-Pay (ATP) α 5 Treatment rate α 5 o Price elasticity of demand (ξ d q) α 5 9

1 0 Objectives Methodology Results Discussion Methodological Approach: Treatment Rate (α 5 ) For the current best case, the initial absorbtion capacity of the health system is assumed to be 5% in the year 2013 and increasing (annually) by 5% over time, i.e. AC t = AC t-1 +0.05. For the future best case, the initial absorbtion capacity of the health system is assumed to be 5% in the year 2013 and increasing (annually) by 15% over time, i.e. AC t = AC t-1 +0.15 (with a maximum 80% capacity at end of period) The price elasticity of demand measures the change in demand as a result of a 1 percent decrease in prices. For the current best case, it is assumed that elasticity is 0.9 for the highest income quintile and reduces to 0.5 for the lowest income quintile in each country. For the future best case, it is assumed that elasticity is 0.9 irrespective of the income quintile. Individuals are considered as able-to-pay for medication if the cost of the regimen represents at most 40% of their per-capita GDP.

1 1 Objectives Methodology Results Discussion Methodological Approach: Prices & Treatment For the initial analysis drugs are assumed to cost the same in all countries irrespective of their income levels. Current lowest prices are used as the reference prices Prices are assumed to fall by an annual rate of 3.4% as in the price of brand ARVs. The total cost of medication will depend on the genotype. Genotype Cost (12 Weeks) in USD 1, 4 Sofosbuvir 840 Ribavirin 53 Peg-IFN 500 2 Sofosbuvir 840 Ribavirin 53 3 Sofosbuvir 840 Ribavirin 53 All Genotypes Sofosbuvir 840 Another DAA 420 Recommended Duration (Weeks) 12 12 24 Current Best WHO (2014) 12 Future Best

1 2 Objectives Methodology Results Discussion Treatment Module: Two FINANCING Scenarios: (Free Market vs. Subsidised) Free Market Prices: Demand for drugs depends on ATP Subsidised: Third party willing to cover a fraction of the avg. costs (say, e.g., 50%) Demand for drugs based on ATP after subsidies have been applied. Major sensitivity analysis Changes in Eligibility Criteria Changes in third-party subsidies Changes in ATP thresholds Changes in price reduction rates

Countries Used in Analysis Low Income Countries ($664) Lower-Middle Income Countries ($2751) Upper Middle Income Countries ($9025) Included Benin, Burkina Faso, Gambia, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Sierra Leone, Togo, Central African Republic, Chad, Democratic Republic of Congo, Burundi, Comoros, Ethiopia, Kenya, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zimbabwe, Haiti, Afghanistan, Bangladesh, Nepal, Cambodia, Kyrgyz Republic, Tajikistan Cape Verde, Côte d Ivoire, Ghana, Nigeria, Senegal, Cameroon, Congo, São Tomé & Principe, Lesotho, Swaziland, Djibouti, Zambia, Egypt, Morocco, Iraq, Sudan, Yemen, Bolivia, Paraguay, Belize, El Salvador, Guatemala, Honduras, Nicaragua, Guyana, Pakistan, India, Bhutan, Sri Lanka, Indonesia, Lao People s Democratic Republic, Philippines, Vietnam, Fiji, Papua New Guinea, Armenia, Georgia, Moldova, Ukraine, Uzbekistan, Albania, Mongolia Angola, Gabon, Botswana, Namibia, South Africa, Seychelles, Algeria, Islamic Republic of Iran, Jordan, Tunisia, Argentina, Brazil, Chile, Colombia, Ecuador, Peru, Uruguay, Venezuela, Costa Rica, Mexico, Panama, Dominican Republic, Jamaica, Suriname, Maldives, Malaysia, Thailand, China, Azerbaijan, Belarus, Kazakhstan, Turkmenistan, Russia, Bosnia & Herzegovina, Serbia, Montenegro, The Former Yugoslav Republic of Macedonia, Turkey, Bulgaria, Latvia, Lithuania, Romania Excluded Eritrea, Democratic Republic of Korea, Myanmar, Somalia Kiribati, Kosovo, Marshall Islands, Federal States of Micronesia, Samoa, Solomon Islands, South Sudan, Syrian Arab Republic, Timor-Leste, Tonga, Vanuatu, West Bank & Gaza Cuba, Dominica, Grenada, Lebanon, Libya, Mauritius, Palau, St. Lucia, St. Vincent and the Grenadine, Tuvalu 1 3

1 4 Objectives Methodology Results Discussion Data Sources Data & Variables Economic-related Data Share of GDP held by each quintile (income per capita (share of GDP /# of i's in each q) GDP overtime Population related Data: size, mortality, growth rate Prices of medication Recommended therapy per genotype Transition probabilities Incidence rates Sensitivity Parameters: treatment option, rate & type of third party coverage, & rate of price decrease) Data Sources World Development Indicators (WDI) International Monetary Fund s World Economic Outlook Database (April 2014) World Development Indicators (WDI) WB Projection Tables; World Health Statistics (2013) WHO guidelines (2014), Open Society pharmaceutical companies & other media sources exogenous & given by the natural history of the disease exogenous and obtained from literature exogenously determined but allowed to vary based on assumptions made NA: extrapolated using geography, genotypes, GDP

Main Results Some Definitions Expenditure: This refers to the total (cumulative) expenditure on drugs at the end of the period. Regimen: This is the average cost of the drug regimen at the end of the period for enduser (patient). 15

Results: Base case of Low Income Countries (LICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] of Individuals (Annual) in 000s of Individuals in 000s Current Best Future Best LIC Regimen ($) LIC Regimen ($) By 2018 32 075 37 798 657 1 166 By 2018 137 451 154 849 029 1 097 Free Market Free Market By 2023 382 208 401 162 603 981 By 2023 950 173 948 104 095 923 By 2018 299 055 362 184 232 583 Subsidised By 2018 693 806 798 787 520 549 Subsidised By 2023 1 600 895 1 716 199 586 490 50% By 2023 2 171 704 2 245 180 280 461 50% 16

Lessons from LIC Base Case Scenarios LIC markets are and will remain limited in size - there are fewer and fewer LICs - HVC prevalence is relatively low In the most pessimistic scenario (current best regimen, free market) there will still be over 380.000 patients treated by 2023, for 400 Million USD over 10 years In the most optimistic scenario (future best regimen, 50% subsidy) there would be almost 2.2 million people treated by 2013, for 2.2 Billion USD over 10 years. In none of 4 scenarios we see the number of new patients treated annually decline over time. Alternative actions (steeper price declines, higher levels of subsidy, improved diagnostic access and health system performance are needed to realize meaningful impact on the epidemic in LIC. 17

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] of Individuals (Annual) in 000s of Individuals in 000s Current Best Future Best Cummulative LMIC Regimen ($) Cummulative LMIC Regimen ($) By 2018 2 996 937 3 815 860 410 1 233 By 2018 8 613 412 9 965 213 852 1 097 Free Market Free Market By 2023 13 085 821 14 923 572 220 1 037 By 2023 19 660 396 20 902 839 829 923 By 2018 6 606 014 8 505 262 636 617 Subsidised By 2018 16 193 401 18 830 253 427 549 Subsidised By 2023 24 539 091 28 684 844 550 519 50% By 2023 30 070 869 32 627 246 899 461 50% 18

Lessons from LMIC Base Case Scenarios LMIC markets will be massive In the most pessimistic scenario (current best regimen, free market) over 13 Million patients will have been treated by 2023, for a total of almost 15 Billion USD over 10 years. In the most optimistic scenario (future best regimen, 50% subsidy) over 30 Million patients will have been treated, for a total of almost 33 Billion USD over 10 years The number of new patients treated decline over time only in the future best regimen case scenarios with 50% subsidy (most optimistic) For public health impact either lower prices than projected, or subsidy for treatment will be needed, along with improved access to diagnostics and health system performance. 19

Results: Base case of Upper-Middle Income Countries (UMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] of Individuals (Annual) in 000s of Individuals in 000s Current Best Future Best UMIC Regimen ($) UMIC Regimen ($) By 2018 7 930 697 10 095 540 115 1 204 Free By 2018 19 553 810 22 930 786 148 1 097 Free Market By 2023 23 122 496 26 712 822 039 1 012 Market By 2023 28 459 792 31 761 059 115 923 By 2018 9 775 460 12 421 029 900 602 Subsidised By 2018 23 552 170 27 660 696 916 549 Subsidised By 2023 25 650 001 29 882 590 436 506 50% By 2023 29 540 482 33 692 538 796 461 50% 20

Lessons from UMIC Base Case Scenarios UMIC markets will be massive too and may reach saturation of demand by 2023 In the most pessimistic scenario (current best regimen, free market) over 23 Million patients will have been treated by 2023, for a total of over 26 Billion USD over 10 years. In the most optimistic scenario (future best regimen, 50% subsidy) over 29 Million patients will have been treated, for a total of over 33 Billion USD over 10 years The number of new patients treated declines over time in all scenarios. For impact on the epidemic, better performance in case finding and health system organization will be needed than in the current best regimen scenario. Subsidy would improve the impact marginally. 21

Results: Base case of All Countries [price decrease = 3.4, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] of Individuals (Annual) in 000s of Individuals in 000s Current Best Future Best Low-to-Middle Income Regimen ($) CumUlative Low-to-Middle Income Regimen ($) By 2018 10 959 710 13 949 199 182 1 208 Free By 2018 28 304 673 33 050 849 029 1 107 Free By 2023 36 590 525 42 037 556 862 1 016 Market By 2023 49 070 361 53 612 003 039 931 Market By 2018 16 680 529 21 288 476 768 604 Subsidised By 2018 40 439 377 47 289 737 863 553 Subsidised By 2023 51 789 987 60 283 634 573 508 50% By 2023 61 783 055 68 564 965 975 465 50% 22

Summary: Lessons from Base Case Scenarios LIC markets will be small - additional price decreases, better diagnostic/health system performance and high levels of subsidy are needed to have a meaningful public health impact LMIC and UMIC markets will be massive In LMIC, third party coverage would boost demand (X2.0 to X3.0) In LMIC, impact on the epidemic will require both greater price decreases than projected, better access to diagnostics and health system organization; In UMIC saturation of demand, high coverage, and impact on the epidemic may be reached if diagnostics and health system organization can be improved, and subsidy will impact demand in a limited way. Comparison of "current" and "future" best regimen case scenarios suggests that simplification in drug regimens and access to diagnostics might boost LIC, LMIC and UMIC markets in the next 5 years (X 2.5 to X4.0) and increase demand at the 2023 horizon. 23

Sensitivity Analysis: Expand eligibility for treatment from F3-F4 to F0-F4 Main Lesson In all cases, demand would be very sensitive to an extension of current treatment eligibility guidelines. If it happened now, there would be, in L+MIC combined, by the end of 2023: 57 million people treated, compared to 36.5 million in the current best regimen case/free market baseline scenario 198 million people treated, compared to 62 million in the future best regimen case/50% subsidy baseline scenario 24

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 50%, Treatment Group = F0 to F4, ATP =40%] Sensitivity F0 to F4 of Individuals (Annual) in 000s Base Case F3 to F4 of Individuals (Annual) in 000s Current Best Future Best LMIC Regimen ($) LMIC Regimen ($) By 2018 4 164 082 5 280 333 248 1 233 By 2018 25 093 120 28 829 997 752 1 097 Free Free Market By 2023 22 677 558 25 448 559 537 1 037 By 2023 79 299 298 82 336 253 704 923 Market By 2018 9 370 928 11 997 202 592 617 Subsidised By 2018 51 745 204 59 670 005 575 549 Subsidised By 2023 43 763 614 50 128 875 779 519 50% By 2023 131 398 116 138 651 692 810 461 50% 25

Results: Base case of All Countries [price decrease = 3.4, subsidy = 50%, Treatment Group = F0 to F4,, ATP =40%] Sensitivity F0 to F4 of Individuals (Annual) in 000s Base Case F3 to F4 of Individuals (Annual) in 000s Current Best Future Best Low-to-Middle Income Regimen ($) Low-to-Middle Income Regimen ($) By 2018 14 329 479 18 194 647 956 1 208 By 2018 66 775 765 77 468 459 288 1 107 Free Market Free Market By 2023 57 409 964 65 130 922 767 1 016 By 2023 155 415 975 165 042 152 868 931 By 2018 22 256 235 28 317 898 252 604 Subsidised By 2018 105 133 961 122 000 893 547 553 Subsidised By 2023 84 458 350 96 800 688 559 508 50% By 2023 197 920 762 204 900 065 654 465 50% 26

Sensitivity Analysis: Subsidy Rate 0% - 100% (25% Interval) Main Lesson Free of charge drugs for end-users at the point of delivery (100% subsidy) would be required to have a significant impact on the number of annual cases in need of treatment in LIC. High-level of third-party coverage ( 75% subsidy) would be needed to boost demand (X2.0 or more) in LMIC. This is shown in the current best regimen case scenarios in the next 3 slides. 27

Results: Sensitivity Analysis of Low Income Countries (LICs) [price decrease = 3.4, subsidy = 0% - 100%, Treatment Group = F3 to F4, ATP =40%] Current Best of Individuals (Annual) in 000s of Individuals in 000s Current Best Current Best By 2018 LIC By 2023 LIC Subsidy Expenditure Subsidy Regimen ($) ($) Regimen ($) 0% 32 609 38 423 765 1 166 0% 397 495 416 914 125 981 25% 102 772 123 913 117 874 25% 775 667 820 740 040 735 50% 299 055 362 184 232 583 50% 1 600 895 1 716 199 586 490 75% 931 776 1 130 756 487 291 75% 4 201 765 4 540 084 705 245 100% 4 122 755 4 978 565 682 0 100% 10 705 603 11 836 440 879 0 28

Results: Sensitivity Analysis of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, subsidy = 0% - 100%, Treatment Group = F3 & F4] Current Best of Individuals (Annual) in 000s of Individuals in 000s Current Best Current Best By 2018 LMIC By 2023 LMIC Subsidy Subsidy Regimen ($) Regimen ($) 0% 3 060 995 3 894 338 396 1 233 0% 13 593 409 15 468 255 426 1 037 25% 4 609 513 5 926 929 458 925 25% 17 594 130 20 272 215 366 778 50% 6 606 014 8 505 262 636 617 50% 24 539 091 28 684 844 550 519 75% 11 070 840 14 581 070 999 308 75% 34 377 849 40 968 808 505 259 100% 14 072 595 18 545 573 989 0 100% 36 955 349 44 499 146 459 0 29

Sensitivity Analysis: Rate of Price Reduction 25% (2014-2016) then 5.2% Main Lesson In LIC, an initial price shock would help create a significant market The value of the LIC market will reach almost 1billion US$ in the most pessimistic scenario (current best case/free market) compared to 400 Million over 10 years in the base case, without "price shock In LMIC, it will increase access to treatment by a factor of 1.5 to 2.0, while maintaining total value of market over the next ten years In UMIC, it will increase demand by 30% to 50% in the next five years (without affecting total $ value of market). It will reach similar levels of coverage at the 2023 horizon (with circa 15% to 25% decrease in total $ market value) Prices would still be well above the marginal costs at the 2023 horizon for DAA-only treatment 30

Results: Base case of Low Income Countries (LICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] Sensitivity 25% decrease (2014-2016) then 5.2% price decrease of Individuals (Annual) in 000s Base Case 3.4% price decrease of Individuals (Annual) in 000s Current Best Future Best LIC Regimen ($) LIC Regimen ($) By 2018 227 882 166 402 918 677 By 2018 626 556 435 200 510 637 Free Market Free Market By 2023 1 617 499 951 020 996 518 By 2023 2 743 006 1 570 501 423 488 By 2018 913 270 680 810 757 338 Subsidised By 2018 1 859 570 1 350 129 733 318 Subsidised By 2023 4 302 163 2 612 181 020 259 50% By 2023 5 202 940 3 157 593 207 244 50% 31

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] Sensitivity 25% decrease (2014-2016) then 5.2% price decrease of Individuals (Annual) in 000s Base Case 3.4% price decrease of Individuals (Annual) in 000s Current Best Future Best LMIC Regimen ($) LMIC Regimen ($) By 2018 6 602 310 5 283 687 069 716 By 2018 14 943 103 11 281 629 789 637 Free Market Free Market By 2023 24 157 270 16 113 287 505 548 By 2023 30 654 679 19 885 788 946 488 By 2018 12 599 899 10 426 181 482 358 Subsidised By 2018 24 119 479 18 849 357 477 318 Subsidised By 2023 35 089 176 24 503 565 392 274 50% By 2023 38 503 177 26 776 480 939 244 50% 32

Results: Base case of Upper-Middle Income Countries (UMICs) [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] Sensitivity 25% decrease (2014-2016) then 5.2% price decrease of Individuals (Annual) in 000s Base Case 3.4% price decrease of Individuals (Annual) in 000s Current Best Future Best UMIC Regimen ($) UMIC Regimen ($) By 2018 11 777 910 9 553 864 619 699 By 2018 23 494 442 19 227 713 680 637 Free Market Free Market By 2023 26 398 485 18 461 980 178 535 By 2023 29 563 287 22 622 144 642 488 By 2018 12 933 821 10 602 263 121 349 Subsidised By 2018 23 756 967 20 081 649 149 318 Subsidised By 2023 26 418 170 18 844 086 380 268 50% By 2023 29 467 175 23 268 590 605 244 50% 33

Results: Base case of All Countries [price decrease = 25% (2014-2016) then 5.2%, subsidy = 50%, Treatment Group = F3 & F4, ATP =40%] Sensitivity 25% decrease (2014-2016) then 5.2% price decrease of Individuals (Annual) in 000s Base Case 3.4% price decrease of Individuals (Annual) in 000s Current Best Future Best Low-to-Middle Income Regimen ($) Low-to-Middle Income Regimen ($) By 2018 18 608 102 15 003 954 605 701 By 2018 39 064 101 30 944 543 980 642 Free Market Free Market By 2023 52 173 254 35 526 288 679 537 By 2023 62 960 972 44 078 435 011 492 By 2018 26 446 989 21 709 255 361 351 Subsidised By 2018 49 736 015 40 281 136 359 321 Subsidised By 2023 65 809 509 45 959 832 793 268 50% By 2023 73 173 292 53 202 664 751 246 50% 34

Sensitivity Analysis: Ability-to-Pay 10% - 50% (10% Interval) Main Lesson Results are quite robust to economic uncertainties on the demand-side : they are only moderately sensitive to assumptions about elasticity of demand to price under households budgetary constraints (ability-to-pay). However, monitoring of patients actual preferences and behaviors will remain key for scaling up access to treatment. 35

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, Treatment Group = F3 & F4, ATP = 10% - 50%] Free Market Current Best of Individuals (Annual) in 000s of Individuals in 000s Current Best Current Best By 2018 LMIC By 2023 LMIC ATP Expenditure ATP ($) Regimen ($) Regimen ($) 10% 102 412 129 393 235 1 233 10% 1 600 011 1 726 601 121 1 037 20% 878 543 1 106 010 591 1 233 20% 5 112 034 5 690 018 998 1 037 30% 1 601 150 2 007 741 934 1 233 30% 9 667 006 10 861 429 547 1 037 40% 2 996 937 3 815 860 410 1 233 40% 13 085 821 14 923 572 220 1 037 50% 4 154 053 5 326 640 212 1 233 50% 16 246 509 18 686 068 921 1 037 36

Results: Base case of Lower-Middle Income Countries (LMICs) [price decrease = 3.4, Treatment Group = F3 & F4, ATP = 10% - 50%] Free Market Future Best of Individuals (Annual) in 000s of Individuals in 000s Future Best Future Best By 2018 LMIC By 2023 LMIC ATP Expenditure ATP ($) Regimen ($) Regimen ($) 10% 141 202 160 422 457 1 097 10% 3 640 066 3 591 103 257 923 20% 2 492 557 2 890 841 608 1 097 20% 9 952 737 10 158 847 036 923 30% 5 299 542 6 072 426 421 1 097 30% 15 790 197 16 455 983 462 923 40% 8 613 412 9 965 213 852 1 097 40% 19 660 396 20 902 839 829 923 50% 11 013 613 12 800 267 111 1 097 50% 23 318 367 24 966 796 915 923 37

Some limitations of the forecasting exercise Model does not take into account potential re-infections and is limited to initial treatment initiation. Model does not fully take into account the impact of treatment on rates of transmission (incidence remains exogenous). There is an intrinsic difficulty in predicting ex-ante human behaviours and market dynamics Heterogeneity and uncertainty on epidemiological data used? Some assumptions on price dynamics derived from the experience of HIV/AIDS drugs? 38

Some major lessons of the forecasting exercise There is a significant opportunity for a massive scale up of access to HCV treatment in health system absorption capacity will be required. in treatment eligibility from F3-F4 to F0-F4 would greatly increase demand. a significant in initial drug prices is needed for LIC and LMIC, and should be possible because it would increase the total value of the LIC and LMIC markets over a 10 year horizon. significant subsidy will be needed in LIC markets and some subsidy in LMIC to decrease the number of new cases needing treatment. The willingness to subsidize will be greater if the drugs are more affordable and would increase the value of the market. There is a rationale for adapting policies for HCV treatment access according to countries level of development. 39