Climate-based Mapping Of A Rare But Emerging Disease In Odisha

A chance encounter between a microbiologist and a climate modelling scientist has led to the mapping of a neglected tropical disease across space and time in Odisha.

The study used weather and land factors, namely, rain, temperature, humidity, sunlight, soil wetness, and soil temperature to map the occurrence of melioidosis, a rare but emerging tropical health concern that spreads through water and soil contaminated with the bacterium Burkholderia pseudomallei.

The results show that melioidosis infections are most likely to occur during heavy rainfall and high humidity, with cloudy weather and low sunlight conditions, at temperatures of 23-29 degrees Celsius. The study also indicates that soil moisture levels between 200-420 L/m³ and soil temperatures of 24-31 degrees Celsius are associated with the highest rates of infections. In Odisha, these conditions prevail for half the year, starting with the monsoon months of June to September, and continuing into the post-monsoon months of October and November.

“Melioidosis can be considered an ‘environmentally driven infectious disease’, where climate and landscape play key roles in transmission,” says V. Vinoj from the Indian Institute of Technology (IIT) Bhubaneswar, who is one of the authors of this study.

Mapping the occurrence of melioidosis across Odisha shows that infections occur across 21 of 30 districts, and that coastal areas in the state, particularly, Puri, Cuttack, Nayagarh, and Khordha, reported more than half these occurrences. The districts of Balasore, Bhadrak, and Keonjhar in northern Odisha, reported roughly 20% of the cases. Unsurprisingly, another trend that emerged, was that areas with high population densities reported more cases.

Underdiagnosed and underreported

Although melioidosis is a rare disease globally, it is ubiquitous in south and southeast Asia, including India. Earlier studies have shown that most parts of India have highly suitable conditions for the occurrence and growth of B. pseudomallei.

But melioidosis in India is underdiagnosed and underreported, perhaps because it is not a notifiable disease (its occurrence need not be reported to the government), unlike cholera or malaria. Therefore, it is unsurprising that between 2000–2025, there are only 300 publications on melioidosis in India, mostly scattered reports of its occurrence in single hospitals or sites, as compared to the 2,500-odd publications on cholera or 7,100 publications on malaria.

However, the number of people in India, specifically Odisha, at risk of contracting melioidosis is quite large. Considering that the people in Odisha most at risk of infections are those in rural areas (80%) with diabetes (6.8%), chronic kidney disease (10%), and chronic alcoholism (30%), in this state alone, roughly 1.5 crore (15 million) people are exposed to B. pseudomallei.

Added to this, melioidosis is not easy to diagnose or cure. Known as the “great mimicker”, it often displays symptoms of other conditions, most commonly tuberculosis, septic arthritis, and skin infections. A clear diagnosis of melioidosis is usually only obtained by culturing blood, urine, or throat swabs, which can take up to four days. But most infected people do not have the luxury of that time.

The majority of melioidosis cases tend to occur as sudden and severe illnesses, with about 20% of them progressing to sepsis – a serious condition that may cause multi-organ failure. Without prompt action, up to 50% of such patients die. Only about 10% of B. pseudomallei infections are asymptomatic or mildly symptomatic, with fevers, skin sores, coughing, chest pains, and headaches; about 11% of these infections can become chronic.

Treatment for melioidosis is also long drawn and expensive and as of now, there are no available vaccines against melioidosis.

Currently, the true prevalence of melioidosis in India remains unknown due to low awareness of the disease. Further, a lack of laboratory capacity, advanced microbiology facilities, and trained personnel capable of diagnosing the disease in rural areas has led to missed or delayed identification and underreporting of melioidosis.

Gathering data was an uphill task

“I first encountered a clinical case of melioidosis when I worked in Secunderabad; knowledge about melioidosis during my MD-senior residency days at AIIMS Delhi was limited to textbooks”, says Bijayini Behera, Professor at the All India Institute of Medical Sciences (AIIMS), Bhubaneswar, who initiated this study. “Decades back, when I noticed that AIIMS Bhubaneswar was receiving several melioidosis cases every year, the disease caught my interest. Since then, I have been part of several projects on the clinical aspects of melioidosis,” she says.

But what Behera found even more fascinating in her explorations on the topic, was that the occurrence of melioidosis was associated with rainfall, extreme weather events, humidity, and wind speed. She wondered if an association between melioidosis and climatic variables could be found in Odisha.

On a casual visit to IIT Bhubaneswar where Behera’s sister works, she learned about Vinoj’s work on climate modelling. The two met, brainstormed ideas, and the stage was set for a collaboration.

However, it turned out that gathering data for the project would prove more difficult than they anticipated.

“Since we needed to match the weather data with the occurrence of cases, we had to know the exact dates when symptoms began to show, and not just when the patient sought care at AIIMS Bhubaneswar,” says Behera. In addition, for older records, patients who had moved between villages, would often report their current locations rather than the ones they lived in when they fell ill. This meant long hours tracing and following up with patients to obtain exact details. Since the research team was unable to get exclusive funding for this project, this also entailed considerable efforts outside of regular work hours.

Finally, a total of 144 cases that had been reported at AIIMS Bhubaneswar over nine years (January 2015 to September 2023) were gathered and deemed ready for use.

Data on weather conditions were obtained from the ECMWF Land Reanalysis data for grid points closest to the occurrence of each melioidosis infection and analysed. This again, was not a straightforward task, as the incubation period for melioidosis can vary from 1–21 days. Therefore, the team assumed incubation periods of 3, 7, 14, and 21 days, to estimate a range of values for weather conditions during which these infections occurred.

This range of values was then used to obtain a potential exposure map for melioidosis in Odisha. Weather data from each day between 2015 and 2023 (a total of 3195 days) was tested to see how close weather conditions were to the range of values that led to infections. These were set to vary between 0 and 1, where a higher value meant a greater risk of exposure to melioidosis.

While the associations with weather conditions have been studied for other diseases such as malaria, and most recently for dengue, this is one of the first studies mapping melioidosis risk in India, and the first for Odisha using meteorological data.