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印度的登革热负担：近期趋势及气候参数的重要性

Dengue burden in India: recent trends and importance of climatic parameters.

作者信息

Mutheneni Srinivasa Rao, Morse Andrew P, Caminade Cyril, Upadhyayula Suryanaryana Murty

机构信息

Biology Division, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad 500 007, Telangana, India.

Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, Merseyside L69 7ZT, UK.

出版信息

Emerg Microbes Infect. 2017 Aug 9;6(8):e70. doi: 10.1038/emi.2017.57.

DOI:10.1038/emi.2017.57
PMID:28790459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5583666/
Abstract

For the past ten years, the number of dengue cases has gradually increased in India. Dengue is driven by complex interactions among host, vector and virus that are influenced by climatic factors. In the present study, we focused on the extrinsic incubation period (EIP) and its variability in different climatic zones of India. The EIP was calculated by using daily and monthly mean temperatures for the states of Punjab, Haryana, Gujarat, Rajasthan and Kerala. Among the studied states, a faster/low EIP in Kerala (8-15 days at 30.8 and 23.4 °C) and a generally slower/high EIP in Punjab (5.6-96.5 days at 35 and 0 °C) were simulated with daily temperatures. EIPs were calculated for different seasons, and Kerala showed the lowest EIP during the monsoon period. In addition, a significant association between dengue cases and precipitation was also observed. The results suggest that temperature is important in virus development in different climatic regions and may be useful in understanding spatio-temporal variations in dengue risk. Climate-based disease forecasting models in India should be refined and tailored for different climatic zones, instead of use of a standard model.

摘要

在过去十年中，印度登革热病例数逐渐增加。登革热是由宿主、病媒和病毒之间复杂的相互作用驱动的，而这些相互作用受气候因素影响。在本研究中，我们重点关注印度不同气候区的外在潜伏期（EIP）及其变异性。通过使用旁遮普邦、哈里亚纳邦、古吉拉特邦、拉贾斯坦邦和喀拉拉邦的日平均气温和月平均气温来计算EIP。在所研究的邦中，利用日气温模拟得出喀拉拉邦的EIP较快/较低（在30.8℃和23.4℃时为8 - 15天），旁遮普邦的EIP通常较慢/较高（在35℃和0℃时为5.6 - 96.5天）。计算了不同季节的EIP，喀拉拉邦在季风期的EIP最低。此外，还观察到登革热病例与降水量之间存在显著关联。结果表明，温度在不同气候区的病毒发育中很重要，可能有助于理解登革热风险的时空变化。印度基于气候的疾病预测模型应针对不同气候区进行完善和调整，而不是使用标准模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/e5d12687356b/emi201757f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/3eb13a017108/emi201757f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/fdf0da57d889/emi201757f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/c0d76d1a58c5/emi201757f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/ab8ef086001f/emi201757f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/5662e2477222/emi201757f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/e5d12687356b/emi201757f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/3eb13a017108/emi201757f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/fdf0da57d889/emi201757f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/c0d76d1a58c5/emi201757f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/ab8ef086001f/emi201757f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/5662e2477222/emi201757f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5149/5583666/e5d12687356b/emi201757f6.jpg

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