The COVID-19 pandemic has shown how quickly the economic situation can change. In a few short months, ASEAN+3 trade has evolved from the downturn as a result of the US-China trade tensions; to nascent recovery on the back of the Phase One deal; to a sudden stop as the coronavirus spread and the region went into lockdown; to trade diversion as importers and exporters of intermediate and final goods sought to diversify their sources and markets; and more recently, rebounding as economies gradually reopened. Through these phases, many analysts and policymakers have had to rely on lagging (of sometimes more than 6 months’) official trade data to decipher trade developments and the attendant outlook for growth and strategize on next steps. Against this backdrop, alternative data may be able to improve often-crucial decision-making. Given the importance of trade for this region’s economies, alternative shipping indicators may be able to provide timely insights into the current fast-changing environment amid heightened uncertainty. This note utilizes near real-time marine traffic “big data,” incorporating machine-learning techniques, in an effort to “nowcast” export performance. Backtests show that the high-frequency shipping indicators for several member economies are strongly correlated with official export statistics.