OBJECTIVES

: Models of COVID-19 transmission have been heavily relied upon during the current pandemic to make short- and long-term policy decisions. As such, it is important that model outcomes are reported clearly and transparently to allow for comparison between modelling results.

There are multiple possible definitions of a case:

• An infected person
• An infectious person
• A symptomatic person
• A person with a positive test
• A symptomatic person with a positive test
• A symptomatic person seeking care with a positive test

If transmission models predict infections but are calibrated to reported cases, models must then make the jump from infections to symptomatic and confirmed cases. But there are a number of factors at play, such as who can get tested, changes in testing over time, proportion seeking care, and proportion asymptomatic. Calibrating the model to confirmed cases is also impacted by substantial changes in testing practices over time.

METHODS

: We performed a targeted literature review of COVID-19 modelling papers for the United Kingdom published between January-July 2020 and compared the definitions of a ‘case’ of COVID-19.

RESULTS

: We identified 19 modelling papers. The definition of a ‘case’ varies between papers. The majority of papers defined a case as a confirmed COVID infection; however, the majority of models did not factor in testing and how it may have changed over time. In many instances, ‘cases’ were not defined. A few papers used ‘cases’ to mean different things within the text. Many models used reported confirmed case data for model calibration, but did not account for factors affecting confirmed cases over time.

CONCLUSIONS

: It is very important to be clear on how a case is defined, especially when models are under such scrutiny and used extensively to shape policies with huge public health implications. However, this often is not clearly reported.