Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.


When a new infectious disease emerges, appropriate case definitions are important for clinical diagnosis and for public health surveillance. Tracking case numbers over time is important to establish the speed of spread and the effectiveness of interventions. We aimed to assess whether changes in case definitions affected inferences on the transmission dynamics of coronavirus disease 2019 (COVID-19) in China.

Health and Medical Research Fund, Hong Kong.

We examined changes in the case definition for COVID-19 in mainland China during the first epidemic wave. We used exponential growth models to estimate how changes in the case definitions affected the number of cases reported each day. We then inferred how the epidemic curve would have appeared if the same case definition had been used throughout the epidemic.

From Jan 15 to March 3, 2020, seven versions of the case definition for COVID-19 were issued by the National Health Commission in China. We estimated that when the case definitions were changed, the proportion of infections being detected as cases increased by 7·1 times (95% credible interval [CrI] 4·8-10·9) from version 1 to 2, 2·8 times (1·9-4·2) from version 2 to 4, and 4·2 times (2·6-7·3) from version 4 to 5. If the fifth version of the case definition had been applied throughout the outbreak with sufficient testing capacity, we estimated that by Feb 20, 2020, there would have been 232 000 (95% CrI 161 000-359 000) confirmed cases in China as opposed to the 55 508 confirmed cases reported.

The case definition was initially narrow and was gradually broadened to allow detection of more cases as knowledge increased, particularly milder cases and those without epidemiological links to Wuhan, China, or other known cases. These changes should be taken into account when making inferences on epidemic growth rates and doubling times, and therefore on the reproductive number, to avoid bias.

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