improving the detection of emerging zoonotic pathogens in forest fringe populations: can we achieve high quality spatio-temporal sampling?

Context: Many emerging infections (such as COVID-19, HIV), have arisen where people live in close proximity to wildlife. When a new infection crosses over from the wildlife to humans (zoonotic infections/pathogens), it usually takes time to establish, circulating in remote populations before entering urban environments. Fevers are a starting point for recognising an emerging infection. The development of new pathogen variants, which may make people sicker, often bring it to the attention of health care workers. Indigenous communities living in and around forest-fringes, such as those found in Indonesia, Malaysia, and many parts of Southeast Asia, are particularly vulnerable to emerging infections: these populations live near wildlife and their daily activities (such as farming/hunting) bring them into close contact with them. Insect-vectors also facilitate the spread of vector-borne diseases. These populations often have limited access to healthcare and can delay seeking care when unwell. Even when they seek care, diagnostic tests are limited. Further, fevers are commonly reported, but the causes of fevers are often not identified. Challenge: The recent COVID-19 pandemic is a stark reminder of the human and economic consequences of newly emerging infections. To prevent future epidemics/pandemics and their devastating consequences, we must engage with communities most-at-risk of emerging infections and develop context-specific, acceptable, feasible ways to rapidly identify emerging infections with epidemic/pandemic potential as they arise. This would facilitate early, rapid clinical and public health action at source. But how to do this is unclear. Given the high-risk of zoonotic infections, burden of reported fevers, and limited access to healthcare, innovative ways to diagnose fevers as they arise in high-risk indigenous forest-fringe communities are warranted. A community-led model of sample collection and testing to detect the like cause, may be appropriate. Combining this with newer genetic (DNA/RNA) techniques such as sequencing for detecting pathogens, may help us identify known and unknown pathogens. But we do not know if a community-led approach is feasible, acceptable, and achieves high coverage among indigenous forest-fringe populations and would give enough genetic material from samples for testing. Vector-borne diseases (e.g. malaria, dengue), transmitted by mosquitoes, are also common in these settings. Therefore, trapping and testing vectors for pathogens, may complement testing in humans. But how best to trap vectors in and around forest areas is not known. Aims and objectives: Therefore, we propose to co-develop a decentralised community-led community-sampling intervention package for fevers in two indigenous forest-fringe communities in Indonesia and Malaysia. Rapid diagnostic tests will be used for diagnosis with linkage-to-care for managing people with fevers. Acceptable sample-types will be collected, processed, and stored for sequencing. Using implementation research, we will determine if this approach is feasible, acceptable, and achieves high-coverage. We will also determine if this provides high-quantities of high-quality DNA/RNA required for sequencing, compared to collecting samples at healthcare facilities by trained research staff. Finally, we will evaluate different mosquito traps to identify the optimum trap for use in these communities. Benefits: Through this work, we aim to identify the ways to deliver services to detect emerging infections to indigenous forest-fringe communities across similar geographies. Our work, combined with the rapidly developing accessible sequencing technologies (and analysis methods), could inform on how to detect pathogens in a meaningful, sustainable way in high-risk, hard-to-reach populations.