ABSTRACT
Background: The South-East Asia (SEA) region bears a significant proportion of the world's communicable disease burden. The onset of the COVID-19 pandemic has further affected the situation. A well-established laboratory-based surveillance (LBS) can reduce the burden of infectious diseases. In light of this, the review collated the existing literature on LBS system in the region and the modifications adopted by the surveillance systems during the pandemic. Methodology: We followed the guidelines for scoping review as prescribed by Arskey and O'Malley. We comprehensively searched three databases (PubMed, Scopus and CINAHL) and supplemented it with grey literature search. The screening of the articles was conducted at the title and abstract followed by full-text screening. This was followed by data extraction using a pre-tested data extraction tool by two independent reviewers. The results were presented narratively. Results: Including 75 relevant articles and documents, we compiled a list of surveillance systems. A shift from paper to dual (paper and electronic) modalities was identified across the countries. This largely low- and middle-income countries (LMIC) area face challenges in reporting, resources, and collaboration-related issues. While some countries have well-established National Reference Laboratories; others have more private than public-owned laboratories. Given the COVID-19 pandemic, modifications to the existing laboratory capacities to enable real-time surveillance was identified. Laboratory capacity complemented with genomic surveillance can indubitably aid in disease detection and control. Limitations due to inaccessible government portals, and language barriers are acknowledged. This review identified a comprehensive list of surveillance systems in the region, challenges faced in using these surveillance systems and inform the decision makers about the benefits of integrating fragmented surveillance systems. Conclusion: Regionally and nationally integrated genomic and laboratory surveillance systems justify capital investments, as their payoffs rationalise such costs owing to economies of scale over time. Further, as data flows are harmonized and standardized, algorithm- and computing-based pattern recognition methods allow for targeted and accurate disease prediction when integrated with, potentially, climate and weather systems data. Trained human resources are a sine qua non to optimize such investments, but in the medium to long run, such investments will buttress initiatives in other arenas at the regional level.