Integrating Interpolated Weather Data into National Clinical Data Warehouse to Support Health Services

In many parts of the world, the weather does more than just dictate what we wear; it quietly shapes the spread of disease. When temperatures rise or heavy rains fall, the conditions can become perfect for mosquitoes to breed or for bacteria to multiply in water sources. This connection between the sky and human health is well known to scientists, but turning that knowledge into action requires a specific kind of information. To predict an outbreak, health officials need to know exactly what the weather is doing in every small corner of a country, not just in the few places where official weather stations happen to sit. The challenge is that these stations are often far apart, leaving vast areas in between with no direct measurements. Without a way to fill in those gaps, health planners are forced to guess, often too late to stop an epidemic before it starts.

A team of researchers in Bangladesh has built a new system to solve this problem, creating a bridge between the sparse network of weather stations and the detailed records of patient illnesses found in hospitals. Bangladesh is a country where diseases like dengue fever, cholera, and diarrhea are common and often tied to the monsoon seasons. The country has 42 official weather stations, but it is divided into 516 smaller administrative regions called upazilas. Many of these regions are far from any station, meaning their specific weather conditions were previously unknown to health authorities. The researchers set out to create a digital map that could estimate the temperature, humidity, and rainfall for every single one of those 516 regions, using only the data from the 42 stations. They then combined this estimated weather data with a massive national database of clinical records, linking the health of patients to the weather conditions in their specific neighborhoods.

The core of their work involved teaching a computer to act as a meteorologist. The team tested several different mathematical approaches to see which one could best guess the weather in a location where no station existed, based on the readings from nearby stations. They found that a method called Random Forest, which works by combining the predictions of many smaller decision-making models, was the most accurate. This system successfully estimated the daily minimum and maximum temperatures, humidity, and rainfall for every upazila in the country. The results were remarkably close to the actual readings where stations did exist, preserving the true patterns of the weather. For example, the system could accurately predict the daily temperature swings and the intensity of rainfall, even in areas hundreds of kilometers from the nearest observer.

Once the weather data was filled in for the entire country, the researchers merged it with the National Clinical Data Warehouse, a central repository containing anonymous health records from hospitals across Bangladesh. This new system, which they named Climate-NCDW, allowed them to look at patient data and weather data side by side. They focused specifically on dengue fever, a mosquito-borne illness that causes significant illness and death in the region. By analyzing four years of data, from 2019 to 2024, they discovered a strong link between rainfall and dengue cases. The data showed that rainfall had the highest association with the disease, particularly during the months following the monsoon season, from August to October. The connection was so clear that the researchers could see how spikes in rain were followed by increases in dengue cases, with the weather acting as a leading indicator for the outbreak.

The study also revealed that while temperature and humidity played roles, the immediate impact of heavy rain was the most powerful predictor of dengue risk. The researchers found that for every increase in rainfall, the likelihood of dengue cases rose significantly, with the strongest correlation appearing right after the rain stopped. This suggests that the water left behind by storms creates the breeding grounds for mosquitoes that carry the virus. The system they built does not just show what happened in the past; it provides a framework for the future. By integrating real-time weather estimates with clinical data, health officials can now see potential outbreaks forming before they spread widely. This allows them to prepare resources, such as medical supplies and mosquito control measures, for the specific regions that are most at risk.

The researchers did not stop at dengue. Their system is designed to be flexible, capable of creating specialized databases for other diseases like diarrhea, malaria, and pneumonia. They demonstrated how the same method could be used to track these illnesses, linking them to the specific weather patterns that drive their spread. For instance, they noted that diarrhea cases often rise with temperature and rainfall, while malaria is influenced by a combination of heat and humidity. The ability to generate these disease-specific views from a single, unified system means that health planners can move from a reactive stance, treating patients after they get sick, to a proactive one, preventing illness before it starts.

This work represents a significant step forward in how countries manage public health in a changing climate. By filling in the missing pieces of the weather puzzle and connecting them directly to human health records, the researchers have created a tool that turns raw data into actionable insight. The system proves that even with a limited number of weather stations, it is possible to understand the climate conditions of an entire nation with high precision. As climate change continues to alter weather patterns and shift the boundaries of disease, tools like this will become increasingly vital. They offer a way to see the invisible connections between the environment and human well-being, providing the clarity needed to protect communities from the next wave of infectious disease.