title: Estimating malaria transmission from humans to mosquitoes in a noisy landscape.
abstract: A basic quantitative understanding of malaria transmission requires measuring the probability a mosquito becomes infected after feeding on a human. Parasite prevalence in mosquitoes is highly age-dependent, and the unknown age-structure of fluctuating mosquito populations impedes estimation. Here, we simulate mosquito infection dynamics, where mosquito recruitment is modelled seasonally with fractional Brownian noise, and we develop methods for estimating mosquito infection rates. We find that noise introduces bias, but the magnitude of the bias depends on the 'colour' of the noise. Some of these problems can be overcome by increasing the sampling frequency, but estimates of transmission rates (and estimated reductions in transmission) are most accurate and precise if they combine parity, oocyst rates and sporozoite rates. These studies provide a basis for evaluating the adequacy of various entomological sampling procedures for measuring malaria parasite transmission from humans to mosquitoes and for evaluating the direct transmission-blocking effects of a vaccine.
authors.affiliation: Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Department of Entomology, University of California, Davis, CA, USA Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA rcreiner@indiana.edu.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Reiner
authors.affiliation: Center for Disease Dynamics, Economics and Policy, Washington, DC, USA.
authors.forename: Carlos
authors.initials: C
authors.lastname: Guerra
authors.affiliation: Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK Malaria Programme, Wellcome Trust Sanger Institute, Cambridge, UK.
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Donnelly
authors.affiliation: Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
authors.forename: Teun
authors.initials: T
authors.lastname: Bousema
authors.affiliation: Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Chris
authors.initials: C
authors.lastname: Drakeley
authors.affiliation: Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD, USA Department of Zoology, University of Oxford, Oxford, UK Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D000818:Animals; D000852:Anopheles / Q000469:parasitology*; D003140:Communicable Disease Control; D017753:Ecosystem; D005260:Female; D006801:Humans; D007303:Insect Vectors / Q000469:parasitology*; D008018:Life Cycle Stages; D008288:Malaria / Q000635:transmission*; D017780:Malaria Vaccines; D015233:Models, Statistical; D033661:Oocysts / Q000378:metabolism; D011157:Population Dynamics; D015203:Reproducibility of Results; D034101:Sporozoites / Q000378:metabolism
chemical_list: D017780:Malaria Vaccines
keywords: disease ecology; infectious disease dynamics; mosquito-borne pathogen; non-Markovian dynamics