GMAP

[Full Table of Contents]
[Executive Summary]

[Appendices] PDF version

  1. Acknowledgement of contributors
  2. Glossary
  3. Assumptions behind current burden, coverage and funding estimates
  4. Assumptions behind country implementation cost estimates
  5. Assumptions behind R&D cost estimates
  6. Compilation of WHO References

Appendices

Appendix 4. Assumptions behind Country Implementation Cost Estimates

Appendix 4 explains the methodology used to estimate the cost of the country implementation strategies through 2040 recommended by the GMAP. The model estimates the full cost to deliver interventions through scale-up, sustained control, and elimination across 109 malarious countries. It includes country malaria program and systems costs, but does not include global costs such as operational research or monitoring and evaluation (M&E) at an international level. The research and development cost estimates were determined separately and are included in Appendix 5.

Scope of model. The estimates were developed using a financial model to aid the planning and budgeting for malaria program implementation and to inform resource mobilization efforts. This analysis is not intended to assess the efficiency, sustainability, or feasibility of implementing programs in certain settings or countries.

The estimates are based on the recommendations laid out in the GMAP. They are aspirational in that they assume coverage targets are met by the end of 2010, that all suspected cases are diagnosed, and that all confirmed malaria cases are treated appropriately.

Baseline estimates are in 2008 US dollars. Future cost estimates do not incorporate individual country inflation rates, because of the difficulty assessing the international prices of many interventions across countries, the variety of funding sources used, and the unavailability of accurate projections for inflation rates for most of the countries evaluated. Below, a section is included on what projected costs would be in the future using an estimated inflation rate.

Process to date. Cost estimates were built from the country level up, using country-specific data and assumptions whenever possible. The major source of data for this model is the WHO World Malaria Report 2008. Other major data sources used for the model include the UNICEF’s 2007 Malaria and Children: Progress in intervention coverage report and work authored by Kiszewski A and Johns B, et al. The cost estimates have been formally reviewed in collaboration with the RBM Resources Working Group and Virginia Wiseman of the London School of Tropical Hygiene and Medicine.

This Appendix includes the following information

Model Methodology

Prevention. The model uses population at risk (PAR) estimates at low and high transmission levels from the WHO World Malaria Report 2008 to determine the quantity of preventive interventions needed within each country. The average population growth rate per country from 2005 to 2050 was used.[12]Country data based on information collected by the US Census Bureau. See US Census Bureau webpage.

Click for source Preventive interventions required are expected to increase by annual population growth rates.

Fevers. For this model, the incidence of fevers was used as a proxy for suspected malaria cases and the number of diagnostics needed. Fever estimates were taken from the WHO World Malaria Report 2008, and were estimated based on the inverse of the country’s slide positivity rate.

Incidence. Quantity of treatments needed was based on estimated malaria cases per country from the 2008 World Malaria Report. To account for over-treatment due to non- or mis-diagnosis, a multiplier of 1.25 was applied.

Although populations at risk will increase due to population growth, incidence is assumed to start decreasing in the sustained control stage due to the high intervention coverage rates. Modeling by Richard Cibulskis, WHO, as well as recent country experiences indicate that reaching 80% utilization can reduce incidence by 75% over a 5 year period.[13]Presented by Richard Cibulskis. WHO Informal Consultation on Global malaria control and elimination: A Technical Review. Geneva, World Health Organization, 17-18 January, 2008. Hence, the first five years of sustained control reflect a linear 75% reduction. This simplifying assumption was used because the complexity needed to adequately model the interlinking dynamics between incidence, populations at risk, intervention use, etc., was beyond the scope of this model. The remaining time in sustained control reflects a linear reduction in incidence to 5 cases per 1000, the point at which a country can consider moving into the elimination stage (according to indicative WHO recommendations).[14]Malaria Elimination: A Field Manual for Low and Moderate Endemic Countries. Geneva, World Health Organization, 2007.

Click for source During elimination, incidence decreases linearly from .5% to 0% incidence. (More detail on stages is listed below.)

The breakdown between P. falciparum and non P. falciparum cases, to determine quantities of ACTs for P. falciparum and chloroquine and primaquine for P. vivax, was based on percentages listed in the 2008 World Malaria Report.

Table A.2: Regional breakdown of P. falciparum and non P. falciparum cases

Region P. falciparum Non P. falciparum
Africa 98% 2%
Americas 29% 71%
Eastern Mediterranean 76% 24%
Europe 2% 98%
Southeast Asia-Pacific 56% 44%
Western Pacific 67% 33%

Source: World Malaria Report 2008. Geneva, World Health Organization, 2008.

As the burden of P. ovale and P. malariae is significantly lower than that of P. vivax and P. falciparum, these have not been included in the model. The model also assumes that non-P. falciparum cases were P. vivax.

One percent of total cases are assumed to turn into severe malaria requiring higher cost care.

Intervention Coverage Assumptions

Target coverage and utilization assumptions. The GMAP target is to achieve universal coverage (100%). Therefore, all target coverage levels are for 100% of the populations at risk with appropriate interventions. However, as not all interventions are appropriate to each setting, the percent of the population at risk targeted for a particular intervention could be below 100% based on the best available evidence.

Prevention. Preventive interventions include LLINs, IRS, IPTp, and vaccines.

LLINs. All malarious regions were considered appropriate for LLIN usage unless otherwise stated by the malaria control program manager or indicated in the 2008 World Malaria Report as not part of the country’s strategy. This includes all of sub-Saharan Africa. Target coverage was assumed to be 100%, unless otherwise indicated in the WMR or by the country program manager.

The model assumes a three year active life for LLINs, at one net per two people at risk. For the scale-up period, the total number of nets needed was calculated. Then the number of nets recently deployed were subtracted from this amount and the result divided by the number of years in scale-up. In the baseline scenario, scale-up was assumed for two years (2009-2010). After the scale-up period, nets are replaced every three years; however, the replacement cost is averaged over the years as the replacement times will vary. Hence, after scale-up, the annual cost per person at risk covered by an LLIN is 1/6 the cost of the LLIN (see below for specific intervention costs).

IRS. There is much debate on which settings are most appropriate for IRS. Some feel that IRS is most suitable in urban areas where homes are closer together; others believe that IRS is very suitable for some rural settings. Consequently, country-stated strategies and current usage of IRS, based on information in the World Malaria Report 2008 as well as interviews and other sources, were incorporated into the model to determine the appropriate target coverage and the ongoing annual costs. Based on expert recommendation, the model assumes that countries that are currently using IRS would scale-up further, and that countries not using IRS would continue not using IRS.

IPTp. Eighty percent utilization of IPTp for pregnant women in high transmission settings is recommended. Therefore, IPTp utilization targets for high transmission areas in sub-Saharan Africa were 80%, but 0% for pregnant women in low transmission areas within sub-Saharan Africa and the rest of the world.

Vaccines. It was assumed that a vaccine would be launched in 2013, with scale-up by 2015 to 80% coverage of all infants less than one year old.

Treatment assumptions. Treatment assumptions were made for drugs, diagnostics, and severe case management.

Diagnostics. P. falciparum RDTs are modeled for Africa, and combination P. falciparum / P. vivax RDTs are modeled for the rest of the world. The model optimistically assumes that every fever case suspected of malaria is parasitologically diagnosed, 50% with an RDT and 50% with microscopy. Microscopy costs are included in the malaria control program costs.

Drugs. All malaria cases are assumed to be treated with anti-malarials (but not suspected fever cases as diagnostics have been to confirm cases). P. falciparum cases are treated with ACTs and P. vivax cases is treated with chloroquine and primaquine. The model takes into account the different cost and dosing regimens across age cohorts. They are split into pediatric dosing (for children under the age of 5), and for those over the age of 5.

Severe case management. The model assumes 1% of cases will turn into severe cases, resulting in treatment costs of US$ 29.50.[15]Kiszewski A, Johns B, et al. Estimated global resources needed to attain international malaria control goals. Bulletin of the World Health Organization, 2007, 85:623-630.

Click for source

Additionally, some coverage levels change over time. It is assumed that severe cases are treated 50% of the time in scale-up, 75% of the time in sustained control and 100% of the time in elimination.

Impact on Morbidity and Mortality

The field effectiveness of preventive interventions was used to determine reduction in cases; however, based on discussions with experts, additive benefits were not applied when multiple interventions were used together. For example, if a region uses both LLINs and IRS, the higher effectiveness level (60%) was applied. ACT effectiveness levels were applied to the resulting number of cases to determine the reduction in mortality.

Table A.3: Effectiveness level by intervention

Intervention Effectiveness level
Long-lasting insecticidal nets (LLINs)a,b 50% reduction in cases
Indoor residual spraying (IRS)c 60% reduction in cases
Intermittent preventive treatment in pregnancy (IPTp)d 56% reduction in cases

a) Lengeler C. Insecticide-treated bednets and curtains for preventing malaria. In: Cochrane Library, issue 1. Oxford: Update Software, 2001.
b) Morel, CM et al. Cost effectiveness analysis strategies to combat malaria in developing countries. BMJ, doi:10/1136/bmj.38639.702384.AE (published 10 November 2005).
c) Curtis CF. Should the use of DDT be revived for malaria vector control? BioMedica, 2002. 22 (4): 455-61.
d) Parise M et al. Efficacy of Sulfadoxine-pyrimethamine for prevention of placental malaria in an area of Kenya with a high prevalence of malaria and Human Immunodeficiency Virus infection.
American Journal of Tropical Medicine and Hygiene, 1998. 59 (5): 813-22.

As the model was not intended to estimate impact on morbidity and mortality outside of the impact on the treatment needed, another model was used to determine the impact of the GMAP strategy. A consortium of organizations led by the Institute of International Programs at Johns Hopkins Bloomberg School of Public Health developed an IMPACT model measuring child survival based on work by the Child Health Epidemiology Reference Group (CHERG) and using software developed by the Futures Institute. It determined the impact of preventive interventions, diagnosis and treatment on mortality due to P. falciparum in 20 high burden African countries. The model did not evaluate interventions which have not been launched, including vaccines. All future burden estimates in the GMAP are from the IMPACT model.

Intervention Costs

Intervention costs plus costs for distribution, warehousing, etc., were used to determine fully-loaded cost estimates. Most intervention costs were based on UNICEF average cost estimates, which incorporate an additional ~35% to account for distribution costs (based on interviews with experts), except for RDTs[16]Yoell Lubell, the London School of Hygiene and Tropical Medicine and Joshua Yukich, Swiss Tropical Institute, personal communication, 2008. and IRS[17]USAID / the President-s Malaria Initiative (PMI). Also see PMI webpage.

Click for source.

Table A.4: Cost per intervention

Intervention Intervention costs Fully-loaded costs
LLINs (3-year) $4.75 $6.41
IRS (one round) n/a $7.50
IPTp $0.20 $0.30
Pf RDTs $0.60 $0.78
Pf + Pv Combination RDTs $0.90 $1.17
ACTs (adult) $1.50 $2.025
ACTs (pediatric) $0.80 $1.08
Severe case management n/a $29.50
Chloroquine and Primaquine $0.20 $0.30
Vaccine $21 $5

Source(s): UNICEF, USAID/PMI, London School of Hygiene and Tropical Medicine, Swiss Tropical Institute

It is widely recognized that fully-loaded costs to deliver interventions vary significantly by country as well as within countries based on many factors such rural vs. urban settings, routine distribution vs. campaigns, public vs. private sector distribution, infrastructural and seasonal issues, etc. The model has been built so that country-specific information can be incorporated when available. For example, country-specific LLIN delivery costs were used for some countries, although the average cost was applied to most countries.

For the baseline, intervention costs were assumed to stay static over time; this is a simplifying assumption, as costs could increase due to more expensive raw materials, or decline due to improved manufacturing. Specifically there are expectations that ACT prices will come down, and some believe that pesticides prices will increase when current tools are lost due to resistance. Therefore, sensitivity analyses portraying these scenarios are detailed towards the end of this appendix.

Malaria Control Program Costs

Country-specific malaria control program costs developed by Kiszewski, Johns, et. al. were input into the model. For countries they did not evaluate, a uniform percentage based on country-location, population at risk and burden, were used to approximate costs. The approximate percentage of control program costs to overall intervention costs are as follows:

Table A.5: Malaria program costs as percent of overall country costs

Program cost components Africa Rest of world
Training / communication 3% 4%
Community health workers 2% 2%
Operational research / M&E 3% 2%
Infrastructure / institutional strengthening 12% 6%
Total 19% 14%

Source(s): Kiszewski A, Johns B, et al. Estimated Global resources needed to attain international malaria control goals. Bulletin of the World Health Organization, 2007.

The components of the specific categories are as follows:

Specific annual costs were determined through 2015. Post-2015, program costs were increased by the population growth rate through the end of the sustained control stage.

To account for the extensive surveillance required in the elimination stage, M&E is increased by 50% during the last two years of sustained control. While this may seem high, it reflects the significant costs associated with surveillance during the elimination stage. The other systems costs decline to 80% of their prior levels due to the absorption of malaria control program activities and staff into the general health system.

Scale-up, Sustained Control, and Elimination Stages

The model assigns a baseline starting point for each country based on burden levels. WHO classifications were used to designate countries in the elimination stage.

Duration of time spent in each stage was based primarily on natural-state transmission. Cost estimates are highly sensitive to the length of time a country spends in scale-up, sustained control and elimination. See Exhibit 1 for specific details on countries’ current status as well as anticipated duration in each stage.

Scale-up. The model assumes three durations for scale-up: 2 years, 4 years, and 7 years. However, all countries were assumed to achieve scale-up objectives in 2 years (by 2010). In the sensitivity analysis section, a more conservative scenario was also applied in which 15% of countries achieve scale-up in 2 years, 35% achieve it in 4 years, and 50% achieve it in 7 years.

Sustained control. Five potential durations were considered for sustained control. Countries currently in sustained control as well as several low transmission countries are assumed to move through the stage in 5 or 10 years. Most high transmission countries are assumed to move through sustained control in 15 or 20 years, assuming a new tool is developed in 10 or 15 years which will allow elimination in all settings. There is also a 30 year assumption for sustained control, to show the impact on costs if a new tool enabling elimination is not developed in the next 15-20 years.

Elimination. Low transmission countries as well as those currently in elimination are assumed to achieve elimination in 10 years, the minimum time in which a country can reach zero incidence.[18]Informal consultation on malaria elimination: setting up the WHO agenda. Tunis, World Health Organization, February 2006. High transmission countries are assumed to need 20 years to achieve zero incidence levels.

Inflation

As mentioned previously, all cost estimates are in 2008 US dollars. In reality, over time the costs will be impacted by the inflation rates of currencies in the malarious countries as well as those of the countries of international donors and manufacturers which set intervention prices.

However, to understand the potential impact on costs, the projected US inflation rate was applied in order to determine how costs could increase over time.[19]Estimates of the projected US inflation rate were obtained from Bureau of Labor Statistics - Consumer Price Index (http://www.bls. gov/CPI/) and Moody-s Economy.com.

Click for source This oversimplifies the impact of inflation, but the lack of accurate projections for most of the countries under consideration necessitated a simplified approach.

Projected impact on cost is detailed in the table below:

Table A.6: Estimated impact of inflation on country implementation costs

Year Real dollars (US$ billions) Nominal dollars (US$ billions)
2010 $5.6 $5.8
2020 $4.8 $6.0
2030 $2.4 $3.6
2040 $1.2 $2.1

Source(s): Bureau of Labor Statistics, Consumer Price Index, Moody’s Economy.com

Sensitivity Analyses: What will impact estimated costs?

As indicated above, there are many different factors and uncertainties which can impact the costs. Different factors such as operational effectiveness of interventions, time to scale-up, and duration of stages can cause costs to increase or decrease. To understand the extent of the impact, several sensitivity analyses were completed to quantify the impact of different factors on the required investment.

What could decrease projected costs?

A. Decreasing intervention costs. Recently the Clinton Foundation announced an agreement that will stabilize the market for ACTs and reduce the price of one key product. Additionally, one of the key research priorities for vector control and drugs is lower intervention costs. Therefore, the impact of a 50% reduction in cost of ACTs was modeled. Unfortunately, as treatment, including diagnostics, drugs, and severe case management costs, is only about 15% of the total malaria control costs, the 50% cost reduction for ACTs translated into a 3% cost reduction overall, or an average US$ 153 million annually over the 2011-20 period The change becomes even lower after scale-up, when incidence declines rapidly. Still, while not the largest impact on costs, any amount saved in resource-constrained environments will be beneficial to countries. In fact, costs decreases to preventive interventions are likely to have an even bigger impact on costs. Implications: Investment in R&D for lower cost tools, as well as increased advocacy for lower intervention prices, can save costs.

B. Increased effectiveness of preventive interventions. The field effectiveness of preventive interventions is a key driver of treatment costs. This includes the effective application of IRS and/or appropriate utilization of LLINs. Increasing operational effectiveness of LLINs and IRS from 50-60% (their current field effectiveness) up to 98% can theoretically reduce incidence and therefore treatment costs, by almost 50%. Modeling a 98% effectiveness rate showed an average annual savings globally of ~US$ 109 million every year through 2020. This underscores the value of programs that focus on increasing appropriate use of interventions. Implications: In the near term, invest in in-country communication programs and operational research that improve field effectiveness of current tools. In the long run, support R&D for more effective tools.

C. Slower scale-up by 2015, not 2010. Currently, the model estimates that all control countries achieve the scale-up targets by 2010 at a total cost of US$ 38.4 million from 2009 to 2015. However, if more conservative assumptions were used[20]Assumptions based on discussions with endemic country representatives and anticipated activity if intense scale-up efforts were not undertaken. (approximately 20% of the countries scaling up by 2010, ~50% scaling up by 2012 and 30% scaling up by 2015), the total cumulative costs through 2015 are US$ 33.8 billion, approximately US$ 4.6 billion less in total.

This is due to the frontloading of costs in a 2 year ramp-up and the high cost of maintaining preventive measures in the 2010 scenario. While treatment costs are decrease more rapidly due to the impact of preventive measures in the rapid scale-up scenario, they do not offset the high cost of sustaining IRS and LLINs. However, the lives saved, 4.2 million vs. 2.8 million, are a powerful argument in favor of faster scale-up despite the higher cost (discussed in Section II, Chapter 5: Why invest in malaria: the costs and benefits). Implications: Slower scale-up may lower costs, but fewer lives will be saved.

Table A.7: Cost comparison of scale-up by 2010 and 2015

Costs (US$ millions) 2009 2010 2011 2012 2013 2014 2015 TOTAL
Scenario A: Rapid scale-up of all countries by 2010
Prevention costs 3,687 3,941 3,487 3,543 3,592 3,643 3,693 25,587
Case management costs 968 1,359 1,385 1,186 980 767 550 7,195
Program costs 638 839 810 748 782 792 764 5,373
Total costs 5,335 6,180 5,710 5,506 5,383 5,232 5,038 38,384
Lives saved per year 360,000 626,000 636,000 638,000 644,000 652,000 655,000 4,211,000
Scenario B: Slower scale-up of 20% of countries by 2010, 50% by 2012 and 30% by 2015
Prevention costs 2,105 2,492 2,832 3,241 3,372 3,504 3,638 21,185
Case management costs 597 803 997 1,186 1,242 1,160 1,075 7,059
Program costs 638 839 810 748 782 792 762 5,372
Total costs 3,353 4,153 4,662 5,202 5,424 5,485 5,505 33,786
Lives saved per year 113,000 224,000 324,000 418,000 506,000 584,000 656,000 2,825,000

Source: GMAP costing model

What could increase projected costs?

D. Decreasing diagnosis and increasing presumptive treatment in Africa. The baseline cost estimate assumes that each fever case suspected of malaria is diagnosed and only confirmed cases are treated with an anti-malarial drug. This is very different from the practice in many African countries. Currently, parasitological diagnosis is under-used, and suspected malaria cases are treated presumptively. Not only does this increase the risk of drug resistance, but overall case management costs increase significantly as well. A sensitivity analysis was conducted for Africa assuming presumptive treatment with fewer diagnostics. When applying a 75% lower usage rate of RDTs than the baseline “aspirational” scenario and subsequent treatment of all fever cases, overall diagnosis and treatment costs are ~40% higher than when all cases are diagnosed and only confirmed malaria cases are treated. Implications: Appropriate diagnosis and treatment saves significant costs. The scale-up of diagnostics should be a priority, as well as ACT scale-up.

E. Slow development of new tools. Countries in high transmission settings will likely not be able to move into an elimination program unless new tools are developed. Currently, the model assumes that a new tool will be developed in 10-15 years, allowing the most highly-endemic countries to move into elimination shortly thereafter (for a total of 15 or 20 years in sustained control.) However, if it takes 25 years to develop an elimination-enabling tool (so that sustained control lasts 30 years for high transmission countries), costs will gradually increase to 50% higher than the baseline scenario (as countries must maintain the expensive preventive measures until elimination feasibility can be proved.) Implications: Support R&D efforts to develop tools which will enable elimination in all transmission settings.

F. Elimination takes longer than anticipated. Approximately 60 countries are assumed to be able to achieve elimination in 10 years after beginning the phase, and the remaining in 20 years. If all countries not currently in elimination were assumed to need 20 years, the additional costs from today through 2050 would be approximately US$ 16.3 billion. Implications: Support operational research to determine optimal elimination approaches in all transmission settings.

G. Increasing intervention costs. Some experts are concerned that increasing resistance to current pesticide classes will leave the community with no other options than to utilize more expensive pesticides for vector control purposes. Hence a 50% increases in the costs of both IRS and LLINs was modeled. Due to the high percentage of costs comprised by vector control, this change resulted in almost a 40% increase in overall global costs, peaking at US$ 7.9 billion in 2011. Implications: Promote R&D for new lower cost tools and active ingredient classes to minimize resistance pressure on current insecticides.

Current country positioning and duration country spends in each stage.

Table A.8 outlines the assumptions that were used for modeling. These were for modeling only and are not intended to imply country targets. While some members of the malaria community and endemic country representatives reviewed the list, not every country was consulted regarding its current position or expected length of time spent in each stage.

Table A.8: Country positioning

Country Region Current framework stage Length of stage (years)
SUFI SC Elimination
Afghanistan Middle East and Eurasia Control 2 10 10
Algeria Africa Elimination n/a n/a 10
Angola Africa Control 2 20 20
Argentina The Americas Elimination n/a n/a 10
Armenia Middle East and Eurasia Elimination n/a n/a 10
Azerbaijan Middle East and Eurasia Elimination n/a n/a 10
Bangladesh Asia-Pacific Control 2 10 10
Belize The Americas Control 2 10 10
Benin Africa Control 2 20 20
Bhutan Asia-Pacific Control 2 15 10
Bolivia The Americas Control 2 10 10
Botswana Africa Control 2 15 10
Brazil The Americas Control 2 10 10
Burkina Faso Africa Control 2 15 20
Burundi Africa Control 2 15 20
Cambodia Asia-Pacific Control 2 10 10
Cameroon Africa Control 2 15 20
Cape Verde Africa Control 2 5 20
CAR Africa Control 2 20 20
Chad Africa Control 2 20 20
China Asia-Pacific Control 2 10 10
Colombia The Americas Control 2 10 10
Comoros Africa Control 2 15 20
Congo Africa Control 2 20 20
Costa Rica The Americas Control 2 10 10
Cote d'Ivoire Africa Control 2 20 20
Djibouti Africa Control 2 20 20
Dom. Republic The Americas Control 2 5 10
DRC Africa Control 2 20 20
Ecuador The Americas Control 2 10 10
Egypt Middle East and Eurasia Elimination n/a n/a 10
El Salvador The Americas Elimination n/a n/a 10
Equatorial Guinea Africa Control 2 20 20
Eritrea Africa Control 2 15 20
Ethiopia Africa Control 2 15 20
French Guiana The Americas Control 2 10 10
Gabon Africa Control 2 15 10
Gambia Africa Control 2 20 20
Georgia Middle East and Eurasia Elimination n/a n/a 10
Ghana Africa Control 2 15 20
Guatemala The Americas Control 2 15 10
Guinea Africa Control 2 15 20
Guinea-Bissau Africa Control 2 15 20
Guyana The Americas Control 2 10 10
Haiti The Americas Control 2 10 10
Honduras The Americas Control 2 10 10
India Asia-Pacific Control 2 10 20
Indonesia Asia-Pacific Control 2 10 20
Iran Middle East and Eurasia Elimination n/a n/a 10
Iraq Middle East and Eurasia Elimination n/a n/a 10
Jamaica The Americas Prevention of Reintroduction n/a n/a n/a
Kenya Africa Control 2 15 20
Korea DPR Asia-Pacific Elimination n/a n/a 10
Kyrgyz Republic Middle East and Eurasia Elimination n/a n/a 10
Lao PDR Asia-Pacific Control 2 10 10
Liberia Africa Control 2 15 20
Madagascar Africa Control 2 15 20
Malawi Africa Control 2 15 20
Malaysia Asia-Pacific Elimination n/a n/a 10
Mali Africa Control 2 15 20
Mauritania Africa Control 2 20 20
Mauritius Africa Prevention of Reintroduction n/a n/a n/a
Mexico The Americas Elimination n/a n/a 10
Morocco Africa Prevention of Reintroduction n/a n/a n/a
Mozambique Africa Control 2 15 20
Myanmar Asia-Pacific Control 2 20 20
Namibia Africa Control 2 15 20
Nepal Asia-Pacific Control 2 10 10
Nicaragua The Americas Control 2 10 10
Niger Africa Control 2 20 20
Nigeria Africa Control 2 20 20
Oman Middle East and Eurasia Prevention of Reintroduction n/a n/a n/a
Pakistan Middle East and Eurasia Control 2 10 10
Panama The Americas Control 2 10 10
Papua New Guinea Asia-Pacific Control 2 10 20
Paraguay The Americas Elimination n/a n/a 10
Peru The Americas Control 2 10 10
Philippines Asia-Pacific Control 2 5 10
Republic of Korea Asia-Pacific Elimination n/a n/a 10
Russian Federation Middle East and Eurasia Elimination n/a n/a 10
Rwanda Africa Control 2 15 20
Sao Tome and Principe Africa Control 2 10 10
Saudi Arabia Middle East and Eurasia Elimination n/a n/a 10
Senegal Africa Control 2 20 20
Sierra Leone Africa Control 2 20 20
Solomon Islands Asia-Pacific Control 2 5 10
Somalia Africa Control 2 20 20
South Africa Africa Control 2 5 10
Sri Lanka Asia-Pacific Elimination n/a n/a 10
Sudan Africa Control 2 20 20
Suriname The Americas Control 2 10 10
Swaziland Africa Control 2 5 10
Syrian Arab Republic Middle East and Eurasia Prevention of Reintroduction n/a n/a n/a
Tajikistan Middle East and Eurasia Elimination n/a n/a 10
Tanzania Africa Control 2 15 20
Thailand Asia-Pacific Control 2 10 10
Timor-Leste Asia-Pacific Control 2 10 10
Togo Africa Control 2 15 20
Turkey Middle East and Eurasia Elimination n/a n/a 10
Turkmenistan Middle East and Eurasia Elimination n/a n/a 10
Uganda Africa Control 2 15 20
Uzbekistan Middle East and Eurasia Elimination n/a n/a 10
Vanuatu Asia-Pacific Control 2 5 10
Venezuela The Americas Control 2 10 10
Vietnam Asia-Pacific Control 2 10 10
Yemen Middle East and Eurasia Control 2 5 10
Zambia Africa Control 2 15 20
Zimbabwe Africa Control 2 15 20

Source: GMAP Costing Model