An assessment of remotely sensed environmental variables on Dengue epidemiology in Central India

• Published in: PLoS neglected tropical diseases Vol. 16; no. 10; p. e0010859
• Main Authors: Sarma, Devojit Kumar, Kumar, Manoj, Balabaskaran Nina, Praveen, Balasubramani, Karuppusamy, Pramanik, Malay, Kutum, Rintu, Shubham, Swasti, Das, Deepanker, Kumawat, Manoj, Verma, Vinod, Dhurve, Jigyasa, George, Sekar Leo, Balasundreshwaran, Alangar, Prakash, Anil, Tiwari, Rajnarayan R
• Format: Journal Article
• Language: English
• Published: San Francisco, CA USA Public Library of Science 17-10-2022; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Dengue; Diagnosis; Distribution; Earth Sciences; Epidemiology; Geospatial data; Medicine and Health Sciences; People and Places; Social Sciences; India
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0010859

In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity [greater than or equal to]60 g/m.sup.3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.