When Climate and Mosquitoes Adapt Faster than Policy: CHRIST University Bengaluru Study

The malaria cases in India are at a historic low and declining. A new vaccine AdFalciVax (developed by the Regional Medical Research Centre (RMRC), Bhubaneswar, under the Indian Council of Medical Research ( ICMR), brings showers of optimism. At a recent symposium on climate change–induced forced migration, a study was presented by researchers from the CHRIST University, Bengaluru, contemplating on the interplay of three possible actors - climate, vector adaptability, and migration.

The Indo-Gangetic Plains accounts for less than 10% of malaria transmission. This means, there was still the 10% transmission which has to be investigated for to attain complete elimination in the region. The researcher team examined this gap by extending the Climate-Disease modelling analysis beyond Odisha (primary area of interest as Odisha was studied first with VECTRI analysis), to the Indo-Gangetic Plains.

Migration was identified as a new variable of interest, deviating from regular VECTRI studies in the Indian geography. VECTRI (Vector borne disease community model of ICTP, Trieste) developed by Adrian Tompkins is a mathematic dynamical model for malaria transmission that accounts for the impact of climate variability and population.

The original study, “Potential Future Malaria Transmission in Odisha Due to Climate Change” (Ruchi Singh Parihar et al., Scientific Reports, 2022), throws light on how climate change is one of the most decisive players. For climate change alters local temperature and rainfall. This shifts malaria risk into higher-altitude and forested districts of Odisha (such as Kandhamal, Koraput, Rayagada, and Mayurbhanj), by the end of the century. These areas historically had less burden, but in the future may emerge as hotspots due to increase in temperature of the earth’s atmosphere; even though overall malaria transmission in most of Odisha is expected to decline.

We know that seasonal migration is a fact of India’s labour economy, where nearly 100 million migrants migrate for work each year, many of them seasonally. Imagine a worker from the Indo-Gangetic region, an area of low endemicity according to national Malaria records, travels to Odisha, Jharkhand, or Chhattisgarh, particularly regions of high endemicity. Unknowingly, they are exposed to the disease and may carry the malaria burden back home. This could be transmitted, and infect another member, who falls sick. Now they may not be diagnosed with Malaria. We often have a tendency to opt for self-medication, either waiting for it to go away or subscribing to over-the-counter treatment. What this enables is basically vector adaptability, and eventually the parasite becomes resistant and unresponsive to any treatment. This is Antimicrobial resistance (AMR), a future challenge India must watch out for.

Thought the NVBDCP (National Centre for Vector Borne Diseases Control Programme) measures ABER (Annual Blood Examination rate) and API (Annual Parasite Incidence) which help keep track of the disease, they capture mostly the static populations or those who voluntarily get their blood tested. This is a policy blind spot where the cause is dynamic and the treatment remains static.

India has set a very ambitious goal of eliminating malaria by 2030, in line with the World Health Organization’s Global Technical Strategy for Malaria (2016–2030). To get there, surveillance must become multidimensional and adaptive. And with investors now looking to put all their eggs in the basket of India’s “silver economy”, referring to the pharmaceuticals industry and healthcare services, what we need could be early warning systems that combine climate risk maps with human mobility data, Portable health coverage for migrants, ensuring test-and-treat access across state borders and Active case detection, to prevent a hidden reservoir case scenario.

We are close to eliminating one player and declaring a winner. May the force be with India’s people and its wise policymakers, and not with resurgence.

By  Dr. Ruchi Singh Parihar (Assistant Professor) & Ms. Architha A Murthy (Researcher)

About the Authors

Ruchi Singh Parihar is the co-author of the study paper, an assistant professor of the Department of Data science and statistics at CHRIST University.

Architha A Murthy, Former MSc student at Department of Data Science and Statistics, CHRIST University and co-author of the paper.