Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony

Abstract

AbstractDuring sporogony, malaria-causing parasites infect a mosquito, reproduce and migrate to the mosquito salivary glands where they can be transmitted the next time blood-feeding occurs. The time required for sporogony, or extrinsic incubation period (EIP), is a crucial determinant of malaria transmission intensity. The EIP is typically estimated as the time for a given percentile of infected mosquitoes to have salivary gland sporozoites (the infectious parasite life stage). Many mechanisms, however, affect the observed sporozoite prevalence including the human-to-mosquito transmission probability and possibly differences in mosquito mortality according to infection status. To account for these various mechanisms, we present a mechanistic mathematical model (“mSOS”), which explicitly models key processes at the parasite, mosquito and observational scales. Fitting this model to experimental data, we find greater variation in EIP than previously thought: we estimated the range between two percentiles of the distribution, EIP10–EIP90 (at 27°C), as 4.5 days, compared to 0.9 days using existing methods. This pattern holds over the range of study temperatures included in the dataset. Increasing temperature from 21°C to 34°C decreased the EIP50 from 16.1 to 8.8 days and the human-to-mosquito transmission probability from 84% to 42%. Our work highlights the importance of mechanistic modelling of sporogony to (1) improve estimates of malaria transmission under different environmental conditions or disease control programs and (2) evaluate novel interventions that target the mosquito life stages of the parasite.Author summaryAnopheles mosquitoes become infected with malaria-causing parasites when blood feeding on an infectious human host. The parasites then process through a number of life stages, which begin in the mosquito gut and end in the salivary glands, where the newly formed infectious parasites can be transmitted to another host the next time a mosquito blood-feeds. The large variability in parasite numbers and development times that exists between mosquitoes, environments and parasites, mean that understanding parasite population dynamics from individual mosquito dissections is difficult. Here, we introduce a mathematical model of the mosquito life stages of parasites that mimics key characteristics of the biology. We show that the model’s parameters can be chosen so that its predictions correspond with experimental observations. In doing so, we estimate key system characteristics that are crucial determinants of malaria transmission intensity. Our work is a step towards a realistic model of within-mosquito parasite dynamics, which is increasingly important given that many recently proposed disease interventions specifically target mosquito life stages of the parasite.