Identifying Plasmodium falciparum transmission patterns through parasite prevalence and entomological inoculation rate

eLife

Michele van Vugt¹  Emanuele Giorgi²  Benjamin Amoah²  Peter J Diggle²  Paula Moraga³  Themba Mzilahowa⁴  Kamija S Phiri⁴  Alinune N Kabaghe⁵  Tinashe Tizifa⁵  Michael G Chipeta⁶  Dianne J Terlouw⁷  Willem Takken⁸  Henk van den Berg⁸  Steven Gowelo⁹  Monicah Mburu⁹  Robert S McCann^1,10 

[1] Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands;Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom;Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom;Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Malawi-Liverpool Wellcome Trust Research Programme, Blantyre, Malawi;Big Data Institute, University of Oxford, Oxford, United Kingdom;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Malawi-Liverpool Wellcome Trust Research Programme, Blantyre, Malawi;Liverpool School of Tropical Medicine, Liverpool, United Kingdom;Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands;Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands;Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi;Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, United States;

关键词: Plasmodium falciparum;    entomological inoculation rate;    parasite prevalence;    model-based geostatistics;    malaria;    disease mapping;    P. falciparum;   

DOI  :  10.7554/eLife.65682

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Background:Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities.Methods:Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models.Results:Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR.Conclusions:Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices.Funding:This work was supported by Stichting Dioraphte grant number 13050800.

【 授权许可】

CC BY