Biology subjects

Churcher, T. S.

Publications and source records attributed to Churcher, T. S..

8 recordsLinked to original sources

bioRxiv2025

Modelling the effects of adult emergence on the surveillance and age distribution of medically important mosquitoes

Entomological surveillance is an important component of mosquito-borne disease control. Mosquito abundance, infection prevalence and the entomological inoculation rate are the most widely reported entomological metrics, although these data are notoriously noisy and difficult to interpret. For many infections, only older mosquitoes are infectious, which is why, in part, vector control tools that reduce mosquito life expectancy have been so successful. The age structure of wild mosquitoes has been proposed as a metric to assess the effectiveness of interventions that kill adult mosquitoes, and age grading tools are becoming increasingly advanced. Mosquito populations show seasonal dynamics with temporal fluctuations. How seasonal changes in adult mosquito emergence and vector control could affect the mosquito age distribution or other important metrics is unclear. We develop stochastic mathematical models of mosquito population dynamics to show how variability in mosquito emergence causes substantial heterogeneity in the mosquito age distribution, with low frequency, positively autocorrelated changes in emergence being the most important driver of this variability. Fitting a population model to mosquito abundance data collected in experimental hut trials indicates these dynamics are likely to exist in natural Anopheles gambiae populations. Incorporating age structuring into an established compartmental model of mosquito dynamics and vector control, indicates that the use of mosquito age as a metric to assess the efficacy of vector-control tools will require an understanding of underlying variability in mosquito ages, with estimates affected by short-term and seasonal fluctuations in mosquito emergence.

ecology↗

bioRxiv2024

Integrating data from across insecticide resistance bioassay types using a mechanistic model incorporating mosquito genetic variation and behaviour

Malaria claims approximately 500,000 lives each year, and insecticide-treated nets (ITNs), which kill mosquitoes that transmit the disease, remain the most effective intervention. However, resistance to pyrethroids, the primary insecticide class used in ITNs, has risen dramatically in Africa, making it difficult to assess the current public health impact of pyrethroid-ITNs. Past work has modelled the relation between pyrethroid susceptibility measured in discriminating-dose susceptibility bioassays and ITN effectiveness in experimental hut trials. Here, we introduce a new predictive approach that accounts for heterogeneity in insecticide resistance within wild mosquito populations, for example, due to genetic variability, by incorporating data from newly recommended intensity-dose susceptibility bioassays. We fit our mathematical model to a comprehensive data set that combines discriminating dose bioassays from all over Africa, intensity dose bioassays from Burkina Faso, and concurrent experimental hut trials. Our analysis estimates location- and insecticide-specific variation in resistance heterogeneity in Burkina Faso and quantifies differences in insecticide exposure in bioassays and experimental huts. By providing a mechanistic understanding of these experimental data, our approach could be integrated into malaria transmission models to account for the public health impact of insecticide resistance detected by surveillance programmes. Author summaryBednets treated with insecticides that kill mosquitoes have been responsible for major reductions in malaria burden over recent decades. However, the spread of insecticide resistance in mosquito populations threatens to slow or reverse these gains. It is therefore important to be able to gauge insecticide resistance in local mosquito populations and the remaining effectiveness of bednets. Important tools for quantifying insecticide resistance include susceptibility bioassays, which expose mosquitoes to specific insecticide doses and measure mortality, and experimental hut trials, which aim to mimic how mosquitoes interact with insecticides on bednets in the field. Here, we develop a mathematical model that incorporates mechanistic features of how mosquitoes interact with insecticides in both types of experiments. We show that this model can accurately predict mosquito mortality in experimental hut trials using data from intensity-dose susceptibility bioassays. These bioassays are more sensitive than traditional discriminating-dose assays while being less costly and logistically demanding than experimental hut trials. Our model provides a more granular understanding of insecticide resistance measurements and could be embedded into malaria transmission models to predict the public health impact of insecticide resistance measured by surveillance programmes.

bioinformatics↗

bioRxiv2023

Temperature and transmission: novel estimates of the effects of temperature on the human malaria parasite, Plasmodium falciparum

Malaria transmission is strongly influenced by traits such as mosquito life expectancy, vector competence, and the Extrinsic Incubation Period (EIP, which is the development time of the parasite inside the mosquito). Even though these traits are known to be shaped by environmental temperature and there is considerable concern that climate change could increase disease risk in certain areas, surprisingly, these temperature dependencies are poorly characterized. Here, we use a mechanistic model fitted to novel experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, EIP and overall competence. We use these data, together with novel estimates of the survival of infected blood-fed mosquitoes, to explore the potential influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission efficiency due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

microbiology↗

bioRxiv2020

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

During 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{degrees}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{degrees}C to 34{degrees}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 models 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.

microbiology↗

bioRxiv2020

Functional data analysis techniques to improve the generalizability of near-infrared spectral data for monitoring mosquito populations

Near infrared spectroscopy is increasingly being used as an economical method to monitor mosquito vector populations in support of disease control. Despite this rise in popularity, strong geographical variation in spectra has proven an issue for generalising predictions from one location to another. Here, we use a functional data analysis approach--which models spectra as smooth curves rather than as a discrete set of points--to develop a method that is robust to geographic heterogeneity. Specifically, we use a penalised generalised linear modelling framework which includes efficient functional representation of spectra, spectral smoothing and regularisation. To ensure better generalisation of model predictions from one training set to another, we use cross-validation procedures favouring smoother representation of spectra. To illustrate the performance of our approach, we collected spectra for field-caught specimens of Anopheles gambiae complex mosquitoes - the most epidemiologically important vector species on the planet - in two sites in Burkina Faso. Using these spectra, we show how models trained on data from one site can successfully classify morphologically identical sibling species in another site, over 250km away. Whilst we apply our framework to species prediction, our unified statistical framework can, alternatively, handle regression analysis (for example, to determine mosquito age) and other types of multinomial classification (for example, to determine infection status). To make our methods readily available for field entomologists, we have created an open-source R package mlevcm. All data used is publicly also available.

bioinformatics↗

bioRxiv2020

Anopheles stephensi as an emerging malaria vector in the Horn of Africa with high susceptibility to Ethiopian Plasmodium vivax and Plasmodium falciparum isolates

Anopheles stephensi, an efficient Asian malaria vector, recently spread into the Horn of Africa and may increase malaria receptivity in African urban areas. We assessed occurrence, genetic complexity, blood meal source and infection status of An. stephensi in Awash Sebat Kilo town, Ethiopia. We used membrane feeding assays to assess competence of local An. stephensi to P. vivax and P. falciparum isolates from clinical patients. 75.3% of the examined waterbodies were infested with An. stephensi developmental stages that were genetically closely related to isolates from Djibouti and Pakistan. Both P. vivax and P. falciparum were detected in wild-caught adult An. stephensi. Local An. stephensi was more receptive to P. vivax compared to a colony of An. arabiensis. We conclude that An. stephensi is an established vector in this part of Ethiopia, highly permissive for local P. vivax and P. falciparum isolates and presents an important new challenge for malaria control. Summary of the articleAn. stephensi, a metropolitan malaria vector that recently expanded to the Horn of African, was highly susceptible to local P. falciparum and P. vivax isolates from Ethiopia and may increase malariogenic potential of rapidly expanding urban settings in Africa.

microbiology↗

bioRxiv2019

Thermal ecology of malaria transmission and the potential impact of behavioural resistance

Insecticide-treated bed nets reduce malaria transmission by limiting contact between mosquito vectors and human hosts when mosquitoes feed during the night. However, malaria vectors can also feed in the early evening and in the morning when people are not protected. Here, we explored how timing of blood feeding interacts with environmental temperature to influence the capacity of Anopheles mosquitoes to transmit the human malaria parasite, Plasmodium falciparum. We found no effect of biting time itself on the proportion of mosquitoes that became infectious (vector competence) at constant temperature. However, when mosquitoes were maintained under more realistic fluctuating temperatures there was a significant increase in competence for mosquitoes feeding in the evening, and a significant reduction in competence for those feeding in the morning, relative to those feeding at midnight. These effects appear to be due to thermal sensitivity of malaria parasites during the initial stages of parasite development within the mosquito, and the fact that mosquitoes feeding in the evening experience cooling temperatures during the night, whereas mosquitoes feeding in the morning quickly experience warming temperatures that are inhibitory to parasite establishment. A transmission dynamics model illustrates that such differences in competence could have important implications for disease endemicity, the extent of transmission that persists in the presence of bed nets, and the epidemiological impact of behavioural resistance. These results indicate the interaction of temperature and feeding behaviour to be a major ecological determinant of the vectorial capacity of malaria mosquitoes.

ecology↗

bioRxiv2018

Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy

BackgroundThe proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquitoes under a microscope or using molecular methods. These techniques are laborious, subjective, and require either expensive equipment or training. This study evaluates the potential of near infra-red spectroscopy (NIRS) to identify laboratory reared mosquitoes infected with rodent malaria.\n\nMethodsAnopheles stephensi mosquitoes were reared in the laboratory and fed on Plasmodium berghei infected blood. After 12 and 21 days post-feeding mosquitoes were killed, scanned and analysed using NIRS and immediately dissected by microscopy to determine the number of oocysts on the midgut wall or sporozoites in the salivary glands. A predictive classification model was used to determine parasite prevalence and intensity status from spectra.\n\nResultsThe predictive model correctly classifies infectious and uninfectious mosquitoes with an overall accuracy of 72%. The false negative and false positive rates are, respectively, 30% and 26%. While NIRS was able to differentiate between uninfectious and highly infectious mosquitoes, differentiating between mid-range infectious groups was less accurate. Multiple scans of the same specimen, with repositioning the mosquito between scans, is shown to improve accuracy. On a smaller dataset NIRS was unable to predict whether mosquitoes harboured oocysts.\n\nConclusionsWe provide the first evidence that NIRS can differentiate between infectious and uninfectious mosquitoes. Currently the method has moderate accuracy and distinguishing between different intensities of infection is challenging. The classification model provides a flexible framework and allows for different error rates to be optimised, enabling the sensitivity and specificity of the technique to be varied according to requirements.

epidemiology↗