74 Technology and Data in Zoonotic Surveillance

Modern technologies have revolutionized the way zoonotic risks get identified while being monitored and managed. Scientists can quickly detect new pathogens and analyze their genetic structures through next-generation sequencing (NGS) which helps track their origins and genetic changes (Cummings et al., 2021). Real-time disease surveillance systems powered by cloud computing and global data-sharing platforms enable health authorities to detect and respond to outbreaks at unprecedented speeds (World Health Organization [WHO], 2023). Geographic information systems (GIS) together with artificial intelligence (AI) improve situational awareness through outbreak hotspot mapping and transmission modeling and future zoonotic spillover event prediction (Morse et al., 2019). (E.B)

Digital health tools which include mobile phone GPS data and online symptom reporting and wearable devices provide detailed information about human movement patterns to detect possible transmission pathways (Oliver et al., 2020). The COVID-19 pandemic demonstrated how digital contact tracing together with wastewater surveillance and AI-assisted diagnostic tools contributed to early case detection and outbreak control (Kretzschmar et al., 2020; Medema et al., 2020). These approaches have the potential to transform zoonotic disease surveillance because they provide earlier warnings and improved targeted public health interventions. The successful implementation of these technologies throughout low- and middle-income countries demands substantial funding for digital infrastructure development along with reliable internet connectivity and data sharing policies and trained personnel (Rothstein et al., 2021). The lack of equal access to surveillance tools creates a widening divide between high-resource and low-resource settings in global surveillance efforts. (E.B.)