The INFORM COVID-19 Risk Index is an experimental adaptation of the INFORM Epidemic Risk Index and aims to identify: "countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance". It can be used to support prioritization of preparedness and early response actions for the primary impacts of the pandemic, and identify countries where secondary impacts are likely to have the most critical humanitarian consequences. The main scope of the INFORM COVID-19 Risk Index is global and regional risk-informed resource allocation, i.e. where comparable understanding of countries' risk is important. It cannot predict the impacts of the pandemic in individual countries.

Although they have been selected on the basis of their reliability, consistency, continuity and completeness, most of indicators used in INFORM Global Risk Index do not have global coverage and neither are issued regularly every year. This results in a significant number of missing values, irregularly distributed among countries, time and indicators. The main motivations for imputing missing values arise from the need to create consistent trends that would otherwise not be possible due to the lack of data in the indicator's time series, and to increase the reliability of the single compound release. In the presented study we focus on better understanding the patterns and mechanisms of missing values in the INFORM GRI model, and on evaluating their impact on the model's outputs. The scope is to develop a missing data imputation strategy to be implemented in the INFORM GRI that will strongly depend on the reason why data is missing.