  The spread of COVID-19 makes it essential to investigate its prevalence. In
such investigation research, as far as we know, the widely-used sampling
methods didn't use the information sufficiently about the numbers of the
previously diagnosed cases, which provides a priori information about the true
numbers of infections. This motivates us to develop a new, two-stage sampling
method in this paper, which utilises the information about the distributions of
both population and diagnosed cases, to investigate the prevalence more
efficiently. The global likelihood sampling, a robust and efficient sampler to
draw samples from any probability density function, is used in our sampling
strategy, and thus, our new method can automatically adapt to the complicated
distributions of population and cases. Moreover, the corresponding estimating
method is simple, which facilitates the practical implementation. Some
recommendations for practical implementation are given. Finally, several
simulations and a practical example verified its efficiency.
