Data Pipelines for Disease Surveillance

Timely, accurate, and actionable data has long been the cornerstone of effective public health surveillance. Whether in detecting an uptick in febrile illness, monitoring stockouts of essential medicines, or tracing the spread of a new pathogen, every intervention begins with data. But raw data alone is not enough, the speed and structure with which it is processed can determine whether a health system responds in days or in hours.

Surveillance is not just about collecting data, it is about understanding and acting on it, often in real time or close to it. This is where the concept of data pipelines comes into play.

A well-designed pipeline ensures that information moves smoothly from the point of generation (a remote health facility, a lab, or even a mobile app) to the desks of district surveillance officers, national response teams, and global health bodies. Without it, data remains scattered, delayed, and difficult to trust, weakening preparedness and response.

What Is a Data Pipeline?

A data pipeline is a series of steps through which data flows from its origin to its final use, with each stage designed to transform, clean, enrich, or analyze the data in meaningful ways. In the context of disease surveillance, a pipeline may begin at a primary health center, where cases are logged into a facility register or system. From there, the data could move through several steps:

  1. Collection: The initial step where data is entered manually, scanned, or submitted via digital tools like mobile apps or online forms.
  2. Validation: Ensures that the incoming data meets expected standards, i.e. dates are valid, diagnoses follow coding standards, or values fall within acceptable ranges.
  3. Cleaning: Handles errors, duplicates, or missing fields. This is a surprisingly common step that often gets overlooked.
  4. Transformation: Converts raw data into a more usable format. For instance, aggregating case counts per district, standardizing terminology, or tagging metadata like age group or sex.
  5. Analysis & Visualization: The heart of surveillance; trends are detected, maps plotted, and alerts generated.
  6. Dissemination: Dashboards, automated reports, and alerts are sent to decision-makers.

Pitfalls and Inefficiencies in Current Systems

Despite the good intentions behind most surveillance efforts, the systems in place often fall short of their potential.

  1. Fragmentation Across Tools and Systems

    2. Many health systems use multiple, uncoordinated platforms: one for routine reporting, another for outbreak response, and yet others for supply chain or laboratory data. These systems often do not communicate well with each other, leading to silos of information that are difficult to reconcile or analyze holistically.

  2. Over-Reliance on Manual Processes

    3. A sizeable proportion of health facilities still submit data via paper forms or manually entered spreadsheets. Errors are common, and double-handling introduces inconsistencies. Even where digital systems are in place, poor connectivity and indadequate training hinder timely entry.

  3. Poor Data Quality Assurance

    4. Validation rules, such as catching impossible values or flagging missing data are often weak or absent. In practice, this means decision-makers are left working with erroneous or incomplete data, undermining trust and utility.

  4. Lack of Feedback to Data Providers

    5. Health workers input data, but rarely see how it is used or what insights are drawn. This disconnect weakens engagement between health workers and the data they generate, and leads to apathy

Building better pipelines

The shortcomings of current systems present an opportunity: building public health data pipelines that are fit for purpose, context-aware, and future-ready.

  1. Adopt Modular and interoperable Architecture

    2. Instead of monolithic systems, pipelines should be built in modular components, including data collection, validation, transformation, storage, and visualization, each capable of functioning independently and scaling as needed.

    A country can, for example, maintain DHIS2 for aggregated reporting while integrating a lightweight real-time surveillance tool for epidemic-prone diseases, both feeding into a shared analytics layer.

  2. Use Template-Based Data Parsers

    3. Especially where Excel or CSV forms are common, template-based parsers can reduce data entry errors and make ingestion of varied formats easier. These tools can be reused across regions with minimal modification, enabling consistency without rigidity.

  3. Investment in Real-Time or Near-Real-Time Reporting

    4. For priority conditions or emergency settings, reporting delays can be mitigated through SMS or USSD-based systems in low-connectivity areas, lightweight Android apps with offline synchrnonisation, and APIs for direct system-to-system integration. The goal is not to eliminate existing platforms but to complement them with faster, specialized tools.

  4. Building Data Validation into the Pipeline

    5. Embed checks at multiple points, especially during collection, parsing, and loading, to ensure incoming data is clean and logical.

  5. Automate where necessary

    6. Automation can streamline cleaning, analysis, and reporting. However, critical oversight remains essential, especially when data has policy implications. Build dashboards and alerts, but also allow analysts to verify outliers and interpret context.

  6. Design with Feedback Loops

    7. Ensure those generating the data receive visual summaries, trends, or alerts. When people see the value of their inputs, data quality improves. Feedback also builds trust and makes systems more collaborative than extractive.

At the core of effective disease surveillance is the ability to move data reliably from the point of collection to decision-makers. Purpose-built ETL pipelines can reduce errors, improve timeliness, and unlock insights that save lives.