PREVENTIVE WATER–HEALTH INTELLIGENCE LABORATORY

1. Executive Summary

This proposal seeks to establish a Preventive Water–Health Intelligence Laboratory using urban Haridwar as the initial demonstration landscape. The facility will integrate drinking-water chemistry, heavy-metal surveillance, environmental microbiology, molecular diagnostics, microbial genomics, GIS, population exposure assessment and predictive modelling.

The central objective is to move beyond periodic water-quality testing toward a continuously learning system that can identify persistent and emerging contamination hotspots, characterize their biological signatures, estimate population exposure and generate evidence for preventive intervention.

The Roorkee Health Intelligence Pilot (RHIP) provided the initial proof of concept. IITRoorkee and Ullas Charitable Trust (UCT) collaborated upon the project which inculcated thorough geographically structured water surveillance. RHIP demonstrated the feasibility of linking water-quality findings with spatial intelligence and identified heavy-metal contamination in sampled water sources. It also highlighted the need for dedicated laboratory capacity to support timely, standardised and longitudinal surveillance. The laboratory and surveillance programme will be implemented through two complementary cost components:

The proposed Preventive Water–Health Intelligence Laboratory will integrate wastewater-based viral surveillance to strengthen infectious disease monitoring, early outbreak detection and public health preparedness. Surveillance will focus on priority seasonal and emerging viral pathogens, including influenza, Respiratory Syncytial Virus (RSV), SARS-CoV-2 (COVID-19), mpox, Zika and measles, based on epidemiological significance and technical feasibility.

Untreated wastewater samples will undergo targeted molecular analysis to detect viral genetic material, monitor community-level viral circulation and identify emerging transmission trends. Surveillance findings will be integrated with GIS-based mapping, aggregated health data and predictive analytics to generate early-warning signals, identify potential outbreak hotspots and support timely, evidence-based public health interventions.

This integrated approach will complement existing clinical and epidemiological surveillance systems, strengthen preparedness for seasonal and emerging viral outbreaks, and establish the laboratory as a comprehensive environmental and infectious disease surveillance and early-warning platform. Pathogen-specific surveillance feasibility will be assessed during the initial implementation phase.

2. Problems to be Addressed

3. Phases of the project: Over 36 months’ time period

Phase 01 – Setting up of laboratory → Phase 02 – Haridwar (Urban) → Phase 03 – Haridwar (Semi-Urban) → Phase 04 – GIS mapping of secondary disease data → Preventive Water–Health Intelligence Dashboard → Phase 05 – Rishikesh (Urban) → Phase 06 – Rishikesh (Semi-Urban) → Phase 07 – GIS mapping of secondary disease data → Preventive Water–Health Intelligence Dashboard

4. Long-Term Vision

The laboratory will function as a permanent institutional platform in Indian Institute of Technology, Roorkee (IITR). Haridwar will provide the initial urban and semi urban as the test bed, but the infrastructure, methods, data architecture and models will be expanded to Rishikesh urban and semi-urban regions, followed by other regions of Uttarakhand and India.

Operating cycle: SURVEILLANCE → DATA INTEGRATION → HOTSPOT DETECTION → MOLECULAR INVESTIGATION → EXPOSURE ASSESSMENT → RISK PREDICTION → PREVENTIVE ACTION → VERIFICATION → MODEL UPDATE

5. Laboratory Architecture

6. Phase-I Equipment and Capital Budget

Phase-I estimated capital requirement: ₹4.253 Cr.

7. Structured Sampling Approach:

Contingency & Unforeseen Costs (1%): ₹0. 14873 Cr Phase (II – VII) estimated capital requirement: ₹15.02173 Cr = ~ ₹ 15.02 Cr

8. Why These Equipment Are Needed

9. Initial Consumables and Recurring Cost Structure

A recurring annual operating budget should be planned separately for field sampling, consumables, instrument AMC/calibration, outsourced sequencing and specialized analyses. The capital purchase alone will not sustain a continuous intelligence platform.

10. Core KPIs

11. Expected Outcomes

12. Three-Year Development Pathway

13. Data and Governance Principles

• All samples will carry standardized identifiers, GPS coordinates, source metadata, date/time and analytical batch information.
• QA/QC, calibration, blanks, duplicates, spikes and reference materials will be incorporated into routine workflows.
• Health information will be handled only in aggregated/de-identified form and subject to appropriate institutional and ethical permissions.
• Genomic detection will be interpreted as environmental surveillance evidence and not automatically as proof of clinical causation.
• Predictive models will be validated across time and/or geography and will report uncertainty and model limitations.
• Raw data, processed datasets, metadata, model versions and intervention records will be retained to support reproducibility.

14. Wastewater Surveillance

The proposed intervention will integrate wastewater surveillance as a complementary strategy for environmental monitoring and early identification of potential public health risks. Wastewater treatment facility operators will periodically collect untreated wastewater samples from designated inlet points, prior to treatment, and transport them to the designated laboratory in accordance with established sampling, transportation and biosafety protocols.

The collected samples will undergo microbiological, molecular and, where relevant, chemical analyses to detect priority pathogens, antimicrobial-resistance markers and environmental contaminants. Surveillance findings will be integrated with GIS-based mapping and environmental health intelligence systems to identify emerging threats, monitor contamination trends, assess potential public health risks and inform timely, evidence-based preventive interventions.

Molecular analysis will also include targeted surveillance of priority respiratory and other viral pathogens, such as influenza, RSV, SARS-CoV-2, measles and mpox. Because many seasonal outbreaks are viral and viral shedding in wastewater often precedes clinical case reporting, this will serve as an early-warning system for community-level transmission.

15. Wastewater Surveillance: Seasonal and Emerging Viral Pathogens

The proposed intervention will integrate wastewater-based viral surveillance as a key component of environmental monitoring and infectious disease early-warning systems. Surveillance will prioritise seasonal and emerging viral pathogens, including influenza, Respiratory Syncytial Virus (RSV), SARS-CoV-2 (COVID-19), mpox, Zika and measles, based on epidemiological significance, seasonal trends and technical feasibility. Wastewater treatment facility operators will periodically collect untreated wastewater samples from designated inlet points before treatment and transport them to the laboratory following standardised sampling, cold-chain and biosafety protocols. Samples will undergo targeted molecular analysis using RT-qPCR/qPCR and other validated techniques to detect viral genetic material and monitor community-level circulation patterns.

Surveillance findings will be integrated with GIS-based mapping, aggregated disease surveillance data and predictive analytics to identify unusual viral signals, monitor geographical and seasonal variations, and generate early-warning indicators of potential outbreaks. These insights will facilitate timely reporting to public health authorities, targeted epidemiological investigations, strengthened disease surveillance and appropriate preventive interventions. The initiative will complement existing clinical and epidemiological surveillance systems, enhance preparedness and response to seasonal and emerging viral threats, and establish a sustainable wastewater-based infectious disease early-warning system for Haridwar and Rishikesh. Pathogen-specific feasibility, analytical methods and surveillance protocols will be assessed during the initial implementation phase.

16. Upstream and Downstream Linkages

The proposed Preventive Water–Health Intelligence Laboratory will establish strategic upstream and downstream linkages to strengthen environmental surveillance, integrate water-quality and public health intelligence, and facilitate evidence-based preventive interventions.

Upstream linkages will involve collaboration with municipal water authorities, environmental agencies, research institutions, diagnostic laboratories and healthcare facilities to support systematic water-quality monitoring, contamination assessment, laboratory investigations and integration of environmental and aggregated health data.

Downstream linkages will facilitate the translation of surveillance findings, genomic analysis, GIS-based risk mapping and predictive intelligence into actionable public health interventions, including early identification of contamination hotspots, targeted mitigation measures, risk communication and post-intervention monitoring.

Together, these linkages will establish an integrated surveillance-to-action framework, supporting informed decision-making, improved water safety and sustainable public health outcomes in Haridwar and Rishikesh, with potential for replication and scale-up across other regions.

17. Institutional Value

• Creates a distinctive interdisciplinary research facility at the interface of environmental biotechnology, microbial genomics, GIS, AI and public health.
• Provides a permanent platform for PhD, M.Tech and project training.
• Creates a longitudinal dataset that becomes more valuable with every sampling cycle.
• Supports competitive proposals to national agencies, public-health programmes, environmental agencies and industry.
• Enables collaborations with municipal water authorities, hospitals, diagnostic laboratories and environmental monitoring agencies.
• Provides a scalable framework that can be transferred from Haridwar to other urban, semi urban and rural regions.

18. Final Institutional Outcome

The principal outcome will not be a completed Haridwar water-quality survey. The principal outcome will be the establishment of a continuously operating Water–Health Intelligence Laboratory whose evidence base, analytical capability and predictive models improve every year.

In practical terms, the platform should progressively answer five questions:

This creates a closed-loop system from surveillance to prevention.

19. Logical Framework (Logframe)

The Logical Framework provides a structured, results-based approach to monitoring project implementation, measuring deliverables and assessing outcomes. It links laboratory establishment, environmental surveillance, molecular investigations, GIS-based analysis and predictive modelling with the overarching objective of strengthening drinking-water safety, environmental health intelligence and evidence-based preventive public health interventions.