When an epidemic strikes, the financial consequences can spread almost as quickly as the disease itself. As infections multiply, hospitals face mounting demand, treatment costs escalate, and health insurers struggle to predict how much money they will need to cover future claims. The COVID-19 pandemic highlighted just how difficult these calculations can become when conventional insurance forecasting methods encounter a rapidly evolving public health crisis.

Traditional actuarial models rely heavily on historical claims data, generally assuming that illnesses occur independently and that claim patterns evolve gradually. Epidemics overturn these assumptions. Infections spread from person to person, case numbers can surge within weeks, and medical expenses often increase dramatically as patients progress from diagnosis to hospitalisation or intensive care.

A new study published in the KeAi journal Risk Sciences offers a potential solution. Researchers from Tsinghua University, Meta and Middle Tennessee State University have developed a framework centred on “infection age” — the time elapsed since an individual became infected. This measure helps capture how both infectiousness and healthcare costs change throughout the course of an illness.

“Policyholders with different infection ages will produce different medical claims,” explains corresponding author Runhuan Feng, a professor at Tsinghua University’s School of Economics and Management. “The concept of infection age introduces heterogeneity in individual infectivity into the population, and at a micro level this is highly useful for actuaries performing loss reserving.”

To translate disease progression into financial projections, the researchers adapted a technique commonly used in property and casualty insurance: the loss triangle. Their “infection triangle” tracks how infections develop among successive groups of policyholders, while a corresponding “payment triangle” translates those patterns into expected medical expenses. Together, these tools help insurers estimate outstanding liabilities for patients who remain infected and have not yet completed treatment.

The findings reveal a close relationship between disease transmission and insurance finances. The effective reproduction number, which indicates how widely an infection is spreading, can influence reserve requirements, premium adequacy and ultimate claims costs. Financial liabilities may climb sharply during an outbreak’s expansion before stabilising or declining as transmission slows. However, mild cases may generate more frequent claims, while severe cases requiring hospitalisation or intensive care account for a disproportionate share of overall costs.

Using a numerical illustration based on COVID-19 dynamics, the researchers demonstrate how insurers could apply the framework to stress-test their financial exposure under different outbreak scenarios. Changes in transmission rates or disease severity could be translated into revised reserve estimates, giving insurers an opportunity to anticipate financial pressures rather than react after claims accumulate.

“Our work establishes a formal linkage between epidemiological modelling and financial risk quantification,” says Feng. He suggests that sudden changes in reproduction numbers could trigger parametric catastrophe insurance or signal the need for reinsurance. By incorporating real-time public health data into financial planning, the approach could help insurers, regulators and healthcare institutions better withstand the economic shocks of future epidemics.

More information: Runhuan Feng et al, From disease progression to healthcare demand: A temporal model for epidemic cost and insurance projection, Risk Sciences. DOI: 10.1016/j.risk.2026.100061

Journal information: Risk Sciences Provided by KeAi Communications Co., Ltd.