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The University of Southampton
Geography and Environmental Science

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010 Seminar

Time:
12:00 - 13:00
Date:
16 November 2023
Venue:
On Teams

Event details

Geography and Environmental Science Seminar

Abstract: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. By using mechanistic models, we showed how the intensity of malaria transmission is explained by the temperature on the island with spatial heterogeneity. We found that a trend of increasing temperature over several decades has shaped current patterns of malaria transmission in Hainan. Specifically, the annual peak incidence came earlier in the highland regions but later in lowland regions. The increasing temperature is associated with fewer cases during the rainy season, but more cases in the dry season. This study further shows the diverse effects of rising temperatures on malaria epidemic trends in tropical regions with great heterogenicity in altitude. Our results indicate the specific and precise mitigation and control for malaria, both in low-altitude regions and in the central highland regions, with appropriate timing and strength of control measures.

article link: The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study - The Lancet Planetary Health

Speaker Information

Yonghong Liu

Bio: Yonghong Liu has a BSc in geography science from Henan University in 2017. In 2021, she has her MSc from Beijing Normal University with a research focus on spatial epidemiology. She is currently working at the Beijing Center for Disease Prevention and Control. Her main research focuses on infectious diseases such as COVID-19, malaria, and influenza. She utilizes mathematical and statistical models to understand the patterns of disease transmission, explore risk factors, predict the spread of infections, and assess the effectiveness of prevention and control measures.

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