At 3:47 a.m. on March 16, 2020, researchers at Imperial College London stared at a number that froze them in place: 2.2 million. Their model warned that, without intervention, COVID-19 could kill that many people in the United States alone. Within two days, those projections were in the hands of world leaders. Borders closed. Cities emptied. One-third of humanity went into lockdown. Even so, millions died.
Those models — grounded in mathematics, not guesswork — helped avert an even greater catastrophe. They gave us our best shot at survival. Yet, even though the IMF estimates the global economy will suffer more than $12.5 trillion in losses through 2024, we continue to ignore the very tools that kept the death toll from being far worse (Koch).
We cannot ignore the certainty that another pandemic will occur — only its timing and nature remain unknown. The next threat could be a completely new pathogen emerging from an untracked animal population, or a mutated version of an existing one, such as bird flu, which has already spread worldwide. Climate change is forcing pathogens into new, unexplored areas, while deforestation, international travel, and rapid urban development create ideal conditions for their proliferation.
However, governments continue to underfund the very systems that could warn us before the next sucker punch lands. You know the saying, “Don’t bring a knife to a gunfight.” At this rate, we’ll walk into the next pandemic armed with nothing but an Excel spreadsheet and crossed fingers.
The Models That Saved Lives, and Could Again
Many people might think that models are just academic exercises involving dry text, but that is not the case. In 2009, the H1N1 influenza pandemic spread rapidly worldwide. As Germann et al. (2006) showed during the H1N1 pandemic, prioritizing children for vaccination slowed transmission and saved lives. Similarly, in January 2020, when Chinese authorities reported fewer than 300 confirmed cases of COVID-19, Imperial College’s model suggested that there were already thousands of infections in Wuhan. This early warning prompted a change in responses worldwide.
These are practical tools with a real-world impact, not just hypothetical thought experiments. At the core of these forecasts is a surprisingly simple mathematical concept known as the SIR model, first developed by Kermack and McKendrick in 1927. This model divides a population into three categories: Susceptible (S), Infected (I), and Recovered (R). It employs a set of parameters and differential equations to illustrate how infections spread within a population. Although the equations may appear technical:
- dS/dt = -βSI/N
- dI/dt = βSI/N - γI
- dR/dt = γI
The underlying logic of epidemic models conveys fundamental biological principles. The shift from susceptible to infected individuals—referred to as "generating infections"—is influenced by two main factors: the "rate of transmission (β)" and the "distribution of close contacts between susceptible and infected populations (SI/N)." The transition from infected to recovered individuals is determined solely by the recovery rate (γ), which typically depends on the natural progression of the disease and the pathogen involved. When these dynamics play out over a population of 1 million people (or more) and over time, they generate the epidemic curves that have become familiar through news cycles and media coverage during the COVID-19 pandemic.
The SIR modeling framework is valuable partly because it simplifies complex epidemic dynamics using a minimal number of parameters. For instance, the basic reproduction number (R₀), calculated as R₀ = β/γ, indicates the average number of secondary infections caused by a single infected individual in a fully susceptible population. If R₀ is greater than 1, the infections will grow exponentially. Conversely, if interventions can reduce the effective reproduction number below 1, we can expect a decline in the epidemic, moving towards eventual extinction.
Why Our Forecasting System Feels Inadequate
If models are powerful, why did our pandemic response often seem improvised? Here are three key reasons:
- Garbage In, Garbage Out: Models need accurate and timely data. During COVID, many local health departments relied on manual reporting, resulting in incomplete or delayed data. This forced teams to waste time cleaning records instead of providing real-time guidance.
- Computers and Infrastructure: High-resolution simulations require significant computing power. The CDC Forecast Hub, which aggregates short-term forecasts from various groups, is a step forward but needs more investment to become a fully operational national system.
- Translating Math into Policy: There are not enough modelers in public health who understand both math and policy. Analysts often worked with makeshift solutions, which are not sustainable. We need formal hiring and training programs, along with cross-disciplinary teams.
Models make assumptions, and when reality differs, forecasts can be off. However, timely forecasts can prevent exponential growth and save lives. The important question is: Should we invest in better systems for earlier and more reliable warnings, or continue risking lives because forecasts are not perfect?
Establishing a global pandemic modeling system is essential and is estimated to cost between $10 billion and $20 billion per year. This aligns with calculations by the World Bank and WHO, which estimate annual costs at $10.3 to $11.5 billion under a One Health framework. Broader preparedness projections suggest costs could reach $30 billion to $35 billion annually, according to the Brownstone Institute.
In comparison, the global losses projected from COVID-19 are around $12.5 trillion, as reported by Reuters. Thus, this annual expenditure is a bargain and serves as a vital insurance policy against far greater catastrophes.
Here’s where this funding must be directed:
- Implement Real-Time, Electronic Case Reporting: Mandate immediate data reporting across states and countries to ensure modelers have the critical information they need for accurate predictions.
- Create a High-Performance Computing Network: Invest in a network capable of running complex outbreak simulations in hours, not days.
- Embed Disease Modelers in Public Health Agencies: Train and integrate experts into public health systems to convert forecasts into actionable policy.
Consequently, cutting the impact of the next pandemic by even 10% will yield economic savings that far exceed the investment, while saving countless lives. The time to act is now: We must build this infrastructure to ensure we are never caught unprepared again.
Beyond economic considerations, think of the lives we could save with just a few days of early warning. This isn’t just about adjusting budgets; it’s about protecting the people we care about.
We’ve seen the cards before. The question is whether we’ll keep playing blind or finally stack the deck in our favor.
Works cited:
Ferguson, N., Laydon, D., Gilani, G. N., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Perez, Z. C., Cuomo-Dannenburg, G., Dighe, A., Dorigatti, I., Fu, H., Gaythorpe, K., Green, W., Hamlet, A., Hinsley, W., Okell, L., Van Elsland, S., . . . Ghani, A. (2020). Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. 20. https://doi.org/10.25561/77482
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London Series a Containing Papers of a Mathematical and Physical Character, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118
Germann, T. C., Kadau, K., Longini, I. M., & Macken, C. A. (2006). Mitigation strategies for pandemic influenza in the United States. Proceedings of the National Academy of Sciences, 103(15), 5935–5940. https://doi.org/10.1073/pnas.0601266103
Reuters. (2022, January 20). IMF sees cost of COVID pandemic rising beyond $12.5 trillion estimate. Reuters. https://www.reuters.com/business/imf-sees-cost-covid-pandemic-rising-beyond-125-trillion-estimate-2022-01-20/
World Bank Group. (2023, February 13). Prevent Rather than Fight the Next Pandemic with a One Health Approach: World Bank. World Bank. https://www.worldbank.org/en/news/press-release/2022/10/24/prevent-rather-than-fight-the-next-pandemic-with-a-one-health-approach-world-bank
The Pandemic Treaty’s true cost | Think Global Health. (2025, February 17). Think Global Health. https://www.thinkglobalhealth.org/article/pandemic-treatys-true-cost