Estimating malaria transmission from humans to mosquitoes in a noisy landscape

Reiner, Robert C., Guerra, Carlos, Donnelly, Martin ORCID: https://orcid.org/0000-0001-5218-1497, Bousema, Teun, Drakeley, Chris and Smith, David L. (2015) 'Estimating malaria transmission from humans to mosquitoes in a noisy landscape'. Journal of The Royal Society, Interface, Vol 12, Issue 111, p. 20150478.

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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.

Item Type:	Article
Additional Information:	The original, online version of this paper can be found at: http://dx.doi.org/10.1098/rsif.2015.0478
Uncontrolled Keywords:	disease ecology infectious disease dynamics mosquito-borne pathogen non-Markovian dynamics
Subjects:	QW Microbiology and Immunology > Immunotherapy and Hypersensitivity > QW 805 Vaccines. Antitoxins. Toxoids WA Public Health > WA 105 Epidemiology WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria WC Communicable Diseases > Tropical and Parasitic Diseases > WC 755 Epidemiology
Faculty: Department:	Biological Sciences > Vector Biology Department
Digital Object Identifer (DOI):	https://doi.org/10.1098/rsif.2015.0478
Depositing User:	Samantha Sheldrake
Date Deposited:	03 May 2016 14:17
Last Modified:	16 Sep 2019 09:17
URI:	https://archive.lstmed.ac.uk/id/eprint/5749

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