science.sciencemag.org/content/369/6502/379/suppl/DC1
Supplementary Materials for
Ecology and economics for pandemic prevention
Andrew P. Dobson*, Stuart L. Pimm*, Lee Hannah, Les Kaufman, Jorge A. Ahumada,
Amy W. Ando, Aaron Bernstein, Jonah Busch, Peter Daszak, Jens Engelmann,
Margaret F. Kinnaird, Binbin V. Li, Ted Loch-Temzelides, Thomas Lovejoy, Katarzyna Nowak,
Patrick R. Roehrdanz, Mariana M. Vale
*Corresponding author. Email: dobber@princeton.edu (A.P.D.); stuartpimm@me.com (S.L.P.)
Published 24 July 2020, Science 369, 379 (2020)
DOI: 10.1126/science.abc3189
This PDF file includes:
Supplementary text
References
Authors’ affiliations
Other supplementary material for this manuscript includes:
Probability calculations (Excel file)
Supplemental Material: Overview of Elements of Costs and Benefits of Prevention
Table 1 (All $ numbers in Millions)
Item
In 2020$
Expenditures on preventive measures
1. Annual funding for monitoring of wildlife trade
2. Annual cost of programs to reduce spillovers from wildlife
$250— $750
$120 — $340
3. Annual cost of programs for early detection and control
$217 — $279
4. Annual cost of programs to reduce spillover via livestock
$476— $852
5. Annual cost of reducing deforestation by half in the most critical
parts of the tropics (Brazil policy plus forest conservation
payments — payments only)
6. Annual cost of ending wild meat trade in China
$1,530 —
$9,590
$19,400
7. ANNUAL TOTAL GROSS PREVENTION COSTS (C)
$21,993—
$31,211
Benefit of carbon sequestration from reduced deforestation prevention
8. Social cost of carbon
$36.5/ton
9. Annual CO2 emissions reduced from less deforestation (million
tons)
10. ANNUAL ANCILLARY BENEFITS FROM NET CO2 REDUCTION
11. ANNUAL NET PREVENTION COSTS
118
$4,307
$17,686 —
$26,904
Damages from COVID-19
12. Lost Gross Domestic Product (GDP) in the world from COVID19
(IMF)
13. Value of a statistical life (V), low
Value of a statistical life (V), high
14. Number of deaths in world from COVID-19 (QD)
2.5th percentile
50th percentile
97.5th percentile
Cost of deaths in world from COVID-19 = QD*V
15. Low ($5.34 * 2.5th percentile death forecast)
16. Middle ($10 * 50th percentile death forecast)
17. High ($10 * 97.5th percentile death forecast)
1
$5,600,000
$5.34
$10
473,209
590,643
1,019,078$2,526,938
$5,906,430
$10,190,780
TOTAL DISEASE DAMAGES IN WORLD
18. Lowest
19. Middle
20. Highest
$8,126,938
$11,506,430
$15,790,780
Data sources and details:
1. Expenditure on Preventive Measures
•
Annual funding for monitoring of wildlife trade
International conventions, such as CITES, regional networks and national agencies devoted to monitoring
wildlife trade, and enforcing regulations are severely underfunded (1). CITES’ annual budget for 2020 is
$6 million, they were $4.2 million short of this by late April. Some Nations are decades in arrears (2, 3).
The annual budget of one Regional Wildlife Enforcement Network WEN, that of the Association for
Southeast Asian Nations (ASEAN), is some $30,000, part of which gets allocated for travel to meetings of
the Convention on International Trade in Endangered Species of Fauna and Flora (CITES). Given the
enormous global economic losses of the current COVID-19 pandemic, helping to prevent the next one by
spending $500 million to combat the illegal wildlife trade (valued at $7 to $23 billion (4)) would be costeffective. This amount would raise budgets for agencies charged with enforcing laws on wildlife trade to
US$250-750 million per year.
Alternatively, given that there is no global agency with a remit to conduct surveillance on the wildlife
trade, we started with the annual operating budget of the World Organization for Animal Health (OIE)
($30 M Euros = $34 million) https://www.oie.int/about-us/director-general-office/strategic-plan/.
OIE has a remit to assess disease risk in livestock trade without conducting testing. We then assumed a
similar budget for an organization to assess disease risk in wildlife trade ($30 million per year). Next, we
added costs of disease surveillance based on USAID PREDICT budgets for disease monitoring in 20
countries ($20 million per year). USAID PREDICT collected, tested and built capacity for around 100,000
wildlife specimens in 20 countries. We assumed that this would need to be scaled 10-fold, to account for
the likely volume of shipments that would need to be tested. This assumes that only a portion of the
likely tens of millions of shipments per year would be targeted (5). This results in a cost of approximately
$500 million per year. We then gave a range of costs because of the uncertainty of how much
surveillance would need to be conducted to reduce risk significantly.
In sum, we estimate a range of US$250-750 million per year, with a mid-point of $500 million.
2. Annual cost of programs to reduce spillovers from wildlife:
There are a variety of programs to address spillovers from emerging disease hotspots (6). They include
the specific focus of various USA funded programs like USAID’s PREDICT(7), DARPA’s PREEMPT (8) and
USAID EPT programs (9). A decade-long global effort would encompass these and other nation’s efforts.
That is the idea behind the Global Virome Project (10). There is vigorous debate about the need and its
efficacy (11). We follow that reference’s annual cost estimates over the decade as $120 to $340 million
per year and enter the midpoint as $230 million per year.
2
The annual costs of running Wildlife Enforcement Networks (WENs) varies regionally and many are
currently in early stages of development
(https://cites.org/sites/default/files/eng/prog/enforcement/WEN%20Meeting%20Report%20%20Final%
2014_1_2020.pdf). We have not included these costs in our calculation.
In sum, we estimate a range of $120 to $340 million per year and enter the midpoint as $230 million.
3. Annual cost of programs to provide early detection and control:
To provide cost effective early detection and control, we used the country surveillance targets of the
10year long USAID PREDICT project that were identified due to their high risk of disease emergence from
(6, 12), and were located in Latin America, Africa, South and Southeast Asia. PREDICT-1 worked in 20
countries for 5 years (Bangladesh, Bolivia, Brazil, Cambodia, Cameroon, China, Democratic Republic of
Congo, Gabon, Indonesia, Lao PDR, Malaysia, Mexico, Nepal, Peru, Republic of Congo, Rwanda, Tanzania,
Thailand, Uganda, and Vietnam) (13). PREDICT-2 worked in a further 11 countries (Cote d’Ivoire, Egypt,
Ethiopia, Ghana, Guinea, Jordan, Kenya, Liberia, Myanmar, Senegal, Sierra Leone), with extremely limited
work in 2 others (India, Mongolia) (14). We therefore assumed all programs in this section would need to
run in each one of these 31 high risk countries.
We identified pilot research projects that successfully identified spillover events for Nipah virus in
Bangladesh (15) and SARS-related coronaviruses in China (16). We analyzed budgets of the cited grant
numbers in these papers by searching the US National Institutes of Health database (17), and estimated
the amount spent on surveillance in the field. To maximize the likelihood of early detection of small
numbers of cases of spillover, we estimated that these programs would need to be scaled up by an order
of magnitude. This scaling is based on the three Nipah virus spillover events identified in Bangladesh by
(15), and the geographical coverage of the ‘Nipah belt’ that this project funded for syndromic hospital
surveillance. We used the published budgets in the request for proposal document for NIAID CREID
contracts (previously called “Emerging Infectious Disease Research Centers”), which are specifically
designed to identify early spillover in emerging disease hotspot countries (18). We then estimated the
cost of control programs for these early outbreaks to include testing, isolation and quarantine of small
numbers of cases to reduce transmission, based on costs from the budgets that funded (15), available in
(17), and of partial budgets allocated for (18).
•
•
•
Pilot projects ($500-700 thousand per year 10 per country for 31 countries) = $155 – 217
million.
NIAID CREID contracts ($1.5 million per year, for 31 countries) = $46.5 million per year.
Isolation and quarantine ($500 thousand per year, for 31 countries) = $15.5 million per year.
Summing these three programs, the Total cost of Early Detection and Control Programs for the 31 high
risk countries = $217 – 279 million per year.
4. Annual cost of programs to reduce spillover via livestock:
Following (19), we used data from the World Bank One World One Health farm biosecurity intervention
program (20) on the cost of implementing enhanced biosecurity for zoonoses around farming systems in
low to middle income countries. The annual cost for 139 low to middle income countries is US$1.9 billion
for low disease prevalence increasing to US$3.4 billion for high disease prevalence. We then took the
3
proportional costs for the 31 countries with high risk of wildlife viral spillover risk from (6, 12), i.e.
(31/139)*US$1.9 billion = $424 million and (31/139)* US$3.4 billion = $758 million (midpoint $591
million), all in 2012 US dollars.
To obtain the range of annual costs we then converted these to 2020 dollars to get the range of $476 $842 million.
5. Annual cost of reducing deforestation:
Busch and Engelmann (21) estimate that 289 million hectares of tropical forest will be cleared in the 34
years from 2016 to 2050, of which 80 million hectares (8.0 million per year) would be cleared between
2020-2030. Using the results of the land-use change model from (21) for the subset of tropical area
identified as 10% highest risk of virus spillover (6), we estimate that 5.53 million hectares (just under
7%) of this would the priority for action.
We obtained the reductions in deforestation arising from two interventions—(a) payments for avoided
deforestation only; and (b) Brazil policy combined with (smaller) payments for avoided deforestation
(21).
(a)
To calculate an upper limit, we calculated the cost of payments for avoided deforestation based
on a one-time lump-sum payments for avoided deforestation—$33,600 per hectare in 2014 dollars, or
$36,600 in 2020 dollars—which would reduce deforestation by half. This assumes that land-users’
changes in deforestation in response to payments would be equivalent to their observed changes in
response to historical variations in agricultural prices (21). We inflated these costs to 2020 US$ using CPI.
Combined with the area to avoid deforestation, this gives an upper estimate of $9,590 million
(b)
For a lower limit, we estimated that the Brazil policy would reduce deforestation by half. We
calculated the cost of the Brazil policy to be $646 per hectare of deforestation avoided per year in 2014
dollars. This number comes from the expenditures by federal, state, and municipal governments in the
Amazon of $1.0 billion per year (22) to avoid 12.4 million hectares of deforestation over the eight years
2005-2012 (21). We transferred this cost-per-hectare-of-deforestation-avoided to the study area and
assumed that other countries could implement policies as effectively as did Brazil. The Brazil policy was
supplemented with one-time lump-sum payments for avoided deforestation of $14,600 per hectare in
2014 dollars, or $15,900 in 2020 dollars.
Combined with the area to avoid deforestation, this gives a lower estimate of $1,530 million
6. Annual cost of ending wild meat trade in China:
The annual cost of ending the wild meat trade in China was taken from a study by the Chinese Academy
of Engineering.
According to these statistics, wildlife consumed as food has annual value of $17.8 billion in 2014 dollars,
or $19.4 billion in 2020 dollars.
4
Annual monetary value is the price of the goods multiplied by the quantity sold. To calculate the true loss
to society of eliminating the sale of wildlife for food in these venues, we would ideally have estimates of
the supply and demand curves of this industry and calculate the consumer surplus and producer surplus
gained from the market existing. We do not have those estimates. Total market revenue is likely to be an
upper bound on the true social cost of eliminating trade in wild meat because wild meat consumption in
many parts of the world is very sensitive to price (23-27). Thus, the demand curve for wild meat is likely
to be fairly flat. The variable costs of production (included in revenue but not social surplus) are likely to
be larger than the difference between the demand curve and the market price (included in social surplus
but not revenue).
7. Total gross prevention costs are the sum of items 1-6 above: $21,993 - $31,211 Million.
8. Social cost of carbon.
Economists estimate the value of net changes in CO2 emissions using the Social Cost of Carbon (SCC).
Estimates vary from zero to several hundred dollars per ton. See Nordhaus (2007) (28) or Auffhammer
(2018) (29) for a more recent review. We consider two scenarios. In the first, we completely ignore these
ancillary benefits. In the second, we assume a mid-range SCC of $36.5/ton, taken from Nordhaus.
9. Annual ancillary benefits from net CO2 reduction:
To calculate the total ancillary benefits from the net reduction in CO2 emissions as a result of reduced
deforestation, we multiplied the reduction in area deforested (223,000 hectares per year— see item 5
above), times the CO2 emissions per hectare (of about 450 tons CO2 per hectare), to get 118 million tons
of reduction per year.
10. Multiplying this by $36.5 per ton gives $4,307 million per year.
11. ANNUAL NET PREVENTION COSTS. These are given by subtracting the carbon benefits of reduced
deforestation (11) from the Total Gross Prevention costs (7) to give an annual net prevention cost
of $17,686 – $26,904 million.
5
152
153
Figure 1. The costs and ancillary benefits of these zoonotic prevention efforts are shown below.
II. Damages from COVID-19
12. Lost Gross Domestic Product (GDP) in the world from COVID-19.
We relied on recent statistics and forecasts by the IMF (30) for these calculations. World GDP stood at
$87 trillion in 2019. As a result of COVID-19, the IMF is projecting a -3% growth rate in 2020, implying a
$2.6 trillion loss. In its last economic outlook prior to the pandemic, the IMF forecasted 3.4% global
growth for 2020.
If we take this into consideration, the global GDP loss from the pandemic in 2020 amounts to $5.6
trillion.
13. Value of a statistical life
Federal agencies in the US like the EPA and U.S. Department of Transportation use a value of a statistical
life (VSL) of $10 million (31, 32). However, we also model a more conservative approach and adjust the
VSL used in these calculations for the age structure of COVID-19 mortality as follows. We downloaded
mortality counts in the U.S. by age group as of April 14, 2020 in “Provisional Death Counts for
6
Coronavirus Disease (COVID-19)” from https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm. Aldy
and Viscusi (31, 32) estimated the VSL by age group in millions of year 2000 dollars.
We use those two columns of data on age-dependent deaths due to Covid-19 (Table 2) to calculate a
weighted average of the VSL for premature deaths due to COVID-19 as $3.56 million year 2000 dollars,
which is equivalent to $5.34 million year 2020 dollars.
All ages
Under 1 year
1–4 years
5–14 years
15–24 years
25–34 years
35–44 years
45–54 years
55–64 years
65+ years
Weighted average
Table 2: U.S. COVID-19 Deaths and VSL by Age Group
# Deaths
VSL by Age Group ( year 2000 $M)
9,681
0
2
1
9
90
217
558
1,271
7,533
3.74
3.74
3.74
3.74
9.43
9.66
8.07
3.43
3
3.56
14. Numbers of deaths
We use forecasts of COVID-19 mortality counts from the “COVID-19 Confirmed and Forecasted Case
Data” website maintained by Los Alamos National Labs (https://covid-19.bsvgateway.org/). We
accessed the data on June 15, 2020 and tally the forecast cumulative number of deaths in the world
from COVID-19 up to July 28, 2020. We extract cumulative numbers for the 50th percentile model as
the most likely case, but we also extract the 2.5th and the 97.5th percentile forecasts to explore
robustness of our calculations to different scenarios. These numbers are likely to rise, as they do not
capture COVID-19 related deaths after July 28, 2020. In this sense, our calculations of the pandemic
costs are conservative.
15-17 Costs of deaths
The value of deaths in the entire world from COVID-19 is the product of the VSL and the total number of
deaths. Figure 2 shows the range of damage estimates in comparison to the range of total gross cost
estimates. Any uncertainty in the values of cost numbers is dwarfed in this analysis by the scale of
damage estimates in comparison to costs.
7
Figure 2: COVID-19 Damages and Total Gross Prevention Costs
(million $)
$11,000,000
$10,000,000
$9,000,000
$8,000,000
$7,000,000
$6,000,000
$5,000,000
$4,000,000
$3,000,000
$2,000,000
$1,000,000
$0
Lost global GDP
Global mortality costs
Low
Middle
Total gross costs
High
18-20 Scenarios for total disease damages
These numbers take the lowest, middle, and highest numbers from the preceding sections. For
example, the lowest takes the low value of lost global GDP and the low value of VSL.
8
Probability calculations
The various combinations of low and high costs and probabilities of outcomes are also presented in an
Excel spreadsheet that accompanies these supplementary materials.
Supporting Calculations for Box 1 in Text
Define notation for our calculations as follows.
C = total net costs of pandemic prevention
D = total damages from pandemic like COVID-19
P0 = baseline probability that a pandemic like COVID-19 occurs in a given year
P1 = probability that a pandemic like COVID-19 occurs in a given year after prevention actions
ΔP = P0 - P1.
In order for prevention actions to yield net positive returns, we must have:
C = (P0 – P1) * D
which implies
P1 = (P0*D - C)/D.
From that we can calculate the break-even percentage by which prevention must reduce the annual
probability of a severe pandemic like COVID to be worthwhile
%ΔP = (ΔP/P0)*100.
Results of calculations for various scenarios are in Table 3.
Main Scenario
In the first row of Table 3, we show what we consider the most reasonable case: P0 = 0.01 (a pandemic
like COVID-19 arrives only about once in one hundred years), we have the highest gross costs so
C=$31,322 million; and we have the 50th percentile mortality count valued at the standard $10 million
per life, so D=$11,506,430 million.
In that case, P1 = 0.0073 and ΔP = 0.0027. Thus, the required percentage change in the pandemic
probability is 27%.
Main Scenario with Ancillary CO2 Benefits
The second row of Table 3 shows the main scenario with ancillary benefits. If we use the parameters for
the main scenario but also account for ancillary carbon sequestration benefits from reducing
deforestation, C=$27,561 million, and the required percentage change is only 24%.
Other Scenarios
In the third through the tenth rows, we carry out these calculations for a range of other parameter
combinations: lowest and highest costs, lowest and highest damages, P0 = 0.01 and P0 = 0.005 (an
extreme pandemic is only half as likely.) The percentage change in pandemic probability needed to
make the investment worthwhile is as low as 12% and largely less than 50%. Only in one extreme
9
scenario where costs are high, damages are exceptionally low, and pandemics are not likely must
prevention be as much as 75% effective to be worthwhile.
Table 3: Break-Even Reduction in Pandemic Probability, Multiple Scenarios
P0
C (B$)
D (B$)
P1
ΔP
%ΔP
0.01
$31,211
$11,506,430
0.0073
0.0027
27.12
0.01
0.01
0.01
0.01
0.005
0.01
0.005
0.005
0.005
$26,904
$17,686
$31,211
$17,686
$17,686
$31,211
$31,211
$17,686
$31,211
$11,506,430
$15,790,790
$15,790,780
$8,126,938
$15,790,780
$8,126,938
$15,790,780
$8,126,938
$8,126,938
0.0077
0.0089
0.0080
0.0078
0.0039
0.0062
0.0030
0.0028
0.0012
0.0023
0.0011
0.0020
0.0022
0.0011
0.0038
0.0020
0.0022
0.0038
23.38
11.20
19.77
21.76
22.40
38.40
39.53
45.52
76.81
NOTE: C = total net costs of prevention; D = total damages from pandemic;
P0 = baseline annual probability of pandemic; P1 = probability of pandemic after
prevention actions; P1 = (P0*D - C)/D; ΔP = P0 - P1; %ΔP = (ΔP/P0)*100.
III. Multiple periods
For a present value interpretation, consider a calculation with a discounting of 5% per year. Then, if we
do nothing, and the probability of the pandemic is 1% per year, the discounted present value of the
society’s cost is:
∞
∑𝑡=0(0.95)𝑡 (0.01 ∗ 11,506,430) = 2,301,286 $𝑚𝑖𝑙𝑙𝑖𝑜𝑛 .
If we pay the yearly cost ($31,211 million) to reduce the probability of a pandemic by a value p, the
society’s cost is:
∞
∞
∑(0.95)𝑡 (31,211) + ∑(0.95)𝑡 [(0.01 − 𝑝) ∗ 11,506,430]
𝑡=0
𝑡=0
To find the critical p that reduces the probability of the pandemic by just enough to justify the
mitigation cost, we need to set the above two expressions equal to each other, and then solve for p.
This gives p = 0.0027. The other cases work similarly. Thus, present value calculations are consistent
with the simpler formula used above.
10
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12
Author names, affiliations and e-mail addresses.
Andrew P. Dobson1*, Stuart L. Pimm2*, Lee Hannah3, Les Kaufman4, Jorge A. Ahumada3,
Amy W. Ando5, Aaron Bernstein6, Jonah Busch7, Peter Daszak8, Jens Engelmann9, Margaret
Kinnaird10, Binbin V. Li11, Ted Loch-Temzelides12, Thomas Lovejoy13, Katarzyna Nowak14,
Patrick R. Roehrdanz3, Mariana M. Vale15
1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
08544, USA. dobber@princeton.edu and Santa Fe Institute, Hyde Park Road, Santa Fe,
New Mexico, USA.
2. Nicholas School of the Environment, Duke University, Durham, NC 27708, USA,
stuartpimm@me.com
3. Moore Center for Science, Conservation International, Arlington VA 22202, USA.
a. lhannah@conservation.org; b. jahumada@conservation.org; c.
proehrdanz@conservation.org
4. Department of Biology and Pardee Center for the Study of the Longer-Range Future,
Boston University, MA, USA. lesk@bu.edu
5. Department of Agricultural and Consumer Economics, University of Illinois
UrbanaChampaign, IL, USA. amyando@illinois.edu
6. Boston Children’s Hospital and the Center for Climate, Health and the Global
Environment at the Harvard TH Chan School of Public Health, Boston, MA, USA.
aaron_bernstein@hms.harvard.edu
7. Earth Innovation Institute, 98 Battery Street #250, San Francisco, CA, 94111, USA.
jbusch@earthinnovation.org
8. Ecohealth Alliance, 460 West 34th Street, New York, NY 10001-2320, USA.
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daszak@ecohealthalliance.org
9. Department of Agricultural and Applied Economics, University of Wisconsin-Madison,
Madison, WI, USA. engelmann@wisc.edu
10. Practice Leader, Wildlife, WWF International, The Mvuli, Mvuli Road, Westlands, Kenya.
mkinnaird@wwfint.org
11. Duke Kunshan University, Kunshan, Jiangsu Province, China 215316.
binbin.li@dukekunshan.edu.cn
12. Department of Economics and Baker Institute for Public Policy, Rice University,
Houston, TX 77005, USA. tedt@rice.edu
13. Environmental Science and Policy, George Mason University, Fairfax VA 22030, USA.
tlovejoy@unfoundation.org
14. The Safina Center, 80 North Country Road, Setauket, NY 11733, USA.
knowak02@gmail.com
15. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. National Institute of
Science and Technology in Ecology, Evolution and Biodiversity Conservation, Goiania,
Brazil mvale.eco@gmail.com
*Addresses for correspondence
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