Zengmiao Wang1, Yonghong Liu1, Yapin Li2, Guangze Wang3, José Lourenço4, Moritz Kraemer5, Qixin He6, Bernard Cazelles7, Yidan Li1, Ruixue Wang8, Dongqi Gao2, Yuchun Li3, Wenjing Song2, Dingwei Sun3, Lu Dong9, Oliver G Pybus10, Nils Chr Stenseth11, Huaiyu Tian12. 1. State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China. 2. Central Theater Center for Disease Control and Prevention of PLA, Beijing, China. 3. Hainan Center for Disease Control and Prevention, Haikou, China. 4. Biosystems and Integrative Sciences Institute, University of Lisbon, Lisbon, Portugal. 5. Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Harvard University, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA. 6. Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA. 7. Institut de Biologie de l'École Normale Supérieure, Unité Mixte de Recherche 8197, Centre National de la Recherche Scientifique et École Normale Supérieure, Paris, France; Unité Mixte Internationnale 209, Mathematical and Computational Modeling of Complex Systems, Institut de Recherche pour le Développement et Sorbonne Université, Bondy, France. 8. State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; School of National Security and Emergency Management, Beijing Normal University, Beijing, China. 9. Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China. 10. Department of Zoology, University of Oxford, Oxford, UK; Department of Pathobiology and Population Science, The Royal Veterinary College, London, UK. 11. Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway. Electronic address: n.c.stenseth@ibv.uio.no. 12. State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China. Electronic address: tianhuaiyu@gmail.com.
Abstract
BACKGROUND: 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. METHODS: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. FINDINGS: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. INTERPRETATION: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. FUNDING: Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.
BACKGROUND: 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. METHODS: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. FINDINGS: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. INTERPRETATION: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. FUNDING: Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.
Authors: Koh Kawaguchi; Elorm Donkor; Aparna Lal; Matthew Kelly; Kinley Wangdi Journal: Int J Environ Res Public Health Date: 2022-09-22 Impact factor: 4.614