LANL research suggests COVID-19 spreads faster than previously thought

The COVID-19 outbreak is spreading much faster than initial estimates reported, according to research from Los Alamos National Laboratory published by Emerging Infectious Diseases, an open access Centers for Disease Control and Prevention journal. The findings underscore that a combination of strong control measures, including early and active surveillance, quarantine, and especially strong social distancing efforts, are needed to slow down or stop the spread of the virus.

"If these measures are not implemented early and strongly, the virus has the potential to spread rapidly and infect a large fraction of the population, overwhelming healthcare systems," the authors wrote. "Fortunately, the decline in newly confirmed cases in China and South Korea in March 2020 and the stably low incidences in Taiwan, Hong Kong, and Singapore strongly suggest that the spread of the virus can be contained with early and appropriate measures."

Initial estimates of the early dynamics of the outbreak in Wuhan, China, suggested a doubling time of the number of infected persons of 6–7 days and a basic reproductive number (R0) of 2.2–2.7. R0 is defined as the average number of secondary cases attributable to infection by an index case after that case is introduced into a susceptible population.

The LANL scientists collected extensive individual case reports across China and estimated key epidemiologic parameters, including the incubation period. They then designed two mathematical modeling approaches to infer the outbreak dynamics in Wuhan by using high-resolution domestic travel and infection data. Results show that the doubling time early in the epidemic in Wuhan was 2.3–3.3 days. Assuming a serial interval of 6–9 days, they calculated a median R0 value of 5.7 (95% CI 3.8–8.9).

The estimated R0 values have important implications for predicting the effects of pharmaceutical and nonpharmaceutical interventions, the authors wrote. For example, the threshold for combined vaccine efficacy and herd immunity needed for disease extinction is calculated as 1 – 1/R0. At R0 = 2.2, this threshold is only 55%. But at R0 = 5.7, this threshold rises to 82%. This means that more than 82% of the population has to be immune, through either vaccination or prior infection, to stop transmission.

LANL postdoctoral research associates Steven Sanche and Yen Ting Lin led the investigative team. Sanche's primary research interest lies in complex disease dynamics inferred from data science and mathematical modeling. Lin's primary research interest lies in applied stochastic processes, biological physics, statistical inference, and computational system biology.

Read the study here: