The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1)demonstration of the predictive power of alternative data variables such as Google search trendsand mobile payments, and (2) implementation of two types of modelling methodologies, machinelearning and parametric factor models, that have flexibility to incorporate mixed-frequency datavariables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa,Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast andforecast GDP for other SSA countries with limited data availability and shorter timeframes.