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?:abstract
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The challenges of gathering data about displaced people and host communities are further complicated in the context of the COVID-19 pandemic However, the need to assess the impact of the pandemic is also driving innovations in collection, methodology, analysis and the sharing of expertise In mid-May 2020, two cases of COVID-19 were reported in the Cox\'s Bazar refugee camp in Bangladesh The news caused great concern because of the potentially devastating implications Several features characterising the living conditions of forcibly displaced persons can facilitate a fast spread of the virus: the population density in refugee camps;limited access to health services;and existing levels of malnutrition, poor health and limited financial resources In the first four months of the COVID-19 pandemic the reported incidence of infection among displaced people was quite limited However, a precise assessment of incidence of the disease in the context of displacement is constrained by the persistence of a long-known phenomenon: the paucity of reliable, publicly available data on the living conditions of displaced people, both within and outside camps Some of the defining characteristics of the disease have made the need for the collection and analysis of data on displaced people even more relevant Several features of COVID-19 make it particularly hard to estimate its true spread across any studied population, even in developed economies Symptoms are common to many other illnesses, a high percentage of infected individuals may not show any symptoms, and many of those who have died after contracting the virus already had severe underlying health conditions This has led many experts to call for the strengthening of the collection and analysis of data in order to build more reliable and comparable systems for monitoring and forecasting infection A study conducted by researchers from the London Business School indicates how testing random samples of the population, recording their socio-demographic characteristics and inferring the characteristics most likely to predict whether or not an individual in the population as a whole is infected can be a valid approach to limiting the spread of the virus and ultimately reducing deaths
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