Governments worldwide are currently deliberating the feasibility of a national shutdown strategy to contain and mitigate the effect of coronavirus disease 2019 (COVID-19) on their country's population. Testing for COVID-19 is mainly being done among at-risk individuals (eg, those with influenza-like symptoms, people who have had contact with an individual testing positive for COVID-19, health-care professionals, or those with a travel history to an affected region), thus an accurate value for how many individuals are truly infected is not known. Since at-risk individuals are not representative of the general population, it is impossible to obtain the true prevalence of COVID-19 in the population. Yet, establishing this value is vital to understand the morbidity and mortality risk in the population, particularly in low-income and middle-income countries (LMICs) such as India, which cannot absorb the socioeconomic and public-health fallout resulting from national shutdowns.

In the absence of universal testing, a random-sample-based population surveillance framework is urgently needed. The scientists behind this article propose using the well established Demographic and Health Survey (DHS) framework as a solution to ascertain the true prevalence of COVID-19. We use as an example the National Family Health Survey (NFHS), India's version of the DHS that is led by the International Institute of Population Sciences under the Ministry of Health and Family Welfare.