Waterborne Infections: AI-Driven Pathogen Surveillance and 3D-Printed Diagnostic Sensors for Viral, Bacterial, And Parasitic Diseases Across Megacities in India and Worldwide – A Comprehensive Review

Abstract

In rapidly urbanising megacities, where population growth, poor sanitation, environmental pollution, climate change, and ageing water infrastructure all contribute to increased disease transmission, waterborne diseases continue to be a significant global public health concern. Norovirus, Rotavirus, Hepatitis A virus, Vibrio cholerae, Salmonella Typhi, Giardia lamblia, and Cryptosporidium spp. are among the bacterial, viral, and parasitic infections that continue to cause considerable morbidity and mortality globally, with developing nations bearing a disproportionate burden. This paper looks at the prevalence of waterborne illnesses worldwide, the difficulties megacities in India and around the world confront, and the epidemiological traits of the main waterborne pathogens. It also assesses modern methods of detection and surveillance, such as pathogen surveillance powered by artificial intelligence (AI), molecular diagnostic methods, wastewater-based epidemiology, and newly developed 3D-printed diagnostic sensors. While 3D-printed biosensors provide quick, portable, and affordable pathogen detection capabilities, the reviewed literature shows that AI technologies can greatly improve outbreak prediction, disease mapping, and real-time decision-making. A possible approach to improving water quality monitoring and public health readiness is the merging of artificial intelligence (AI), Internet of Things (IoT) devices, wastewater surveillance, and sophisticated biosensors. However, issues with field validation, data quality, technology standardisation, and large-scale adoption continue to be major obstacles.