Pareto rules for malaria super-spreaders and super-spreading

Cooper, Laura V; Kang, Su Yun; Bisanzio, Donal; Kilama, Maxwell; Rodríguez-Barraquer, Isabel; Greenhouse, Bryan; Drakeley, Chris; Arinaitwe, Emmanuel; Staedke, Sarah G.; Gething, Peter W.; Eckhoff, Philip A.; Reiner, Robert C.; Hay, Simon I.; Dorsey, Grant; Kamya, Moses R.; Lindsay, Steve W.; Grenfell, Bryan T.; Smith, David L.

doi:10.1038/s41467-019-11861-y

Cited by 51 publications

(72 citation statements)

References 38 publications

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“…As utilities for setting up simulations, MBITES has developed some simple parametric functions to compute these probabilities as a function of distance and an activity-specific search weight, ω x , where x denotes a particular blood feeding haunt or aquatic habitat [14]. These functions thus provide multiple ways of computing the probability of moving from one haunt to any other haunt, optionally conditional on behavioral state.…”

Section: Mbitesmentioning

confidence: 99%

“…While the Ross-Macdonald model and associated bionomic parameters are a useful way of summarizing overall transmission intensity, a weakness of the model is that it does not include many other parameters and metrics that are important for pathogen transmission dynamics, the measurement of transmission, and responses to vector control. These include metrics for heterogeneous biting by mosquitoes [14], the spatial dimensions of transmission or control [15][16][17], vector contact rates with and quantitative responses to various kinds of vector control deployed in myriad combinations and coverage levels [18], environmental factors that affect variance in the number of mosquitoes caught [19], and nuances of behavior affecting the accuracy of various field methods [8]. Models describing effect sizes of vector control rely on assumptions about the way interventions alter the basic bionomic parameters [20], but there has been very little theory developed to understand either what contextual factors determine baseline bionomic parameters or how contextual factors influence the effect sizes of control [21].…”

Section: Introductionmentioning

confidence: 99%

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Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Henry

et al. 2020

PLoS Comput Biol

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Mosquitoes are important vectors for pathogens that infect humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we reexamine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Boutbased and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While mosquito infection and pathogen development are one possible part of a mosquito's state, that is not our main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations' capacity to transmit pathogens.

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Section: Mbitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Henry

et al. 2020

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…While the Ross-Macdonald model and associated bionomic parameters are a useful way of summarizing overall transmission intensity, a weakness of the model is that it does not include many other parameters and metrics that are important for pathogen transmission dynamics, the measurement of transmission, and responses to vector control. These include metrics for heterogeneous biting [14], the spatial dimensions of transmission or control [15–17], contact rates with and quantitative responses to various kinds of vector control deployed in myriad combinations and at various coverage levels [18], environmental factors that affect variance in the number of mosquitoes caught [19], and nuances of behavior affecting the accuracy of various field methods [8]. Models describing effect sizes of vector control rely on assumptions about the way interventions alter the basic bionomic parameters [20], but there has been very little theory developed to understand either what contextual factors determine baseline bionomic parameters or how contextual factors influence the effect sizes of control [21].…”

Section: Background and Introductionmentioning

confidence: 99%

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Henry

et al. 2019

Preprint

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AbstractMosquitoes are important vectors for pathogens of humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we re-examine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Bout-based and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While infection of the mosquito and pathogen development are one possible part of a mosquito’s state, that is not the main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations’ capacity to transmit pathogens.

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“…control. An 80 − 20 "Pareto" rule has been proposed to describe this heterogeneity, whereby 80% of transmission is accounted for by 20% of individuals, called super-spreaders [32,33,34]. Vanderwaal et al [31] notes that criterion V (or R0) can be de ned as the number of new infections induced by one infected individual [35].…”

Section: Discussionmentioning

confidence: 99%

Real-time PCR on skin biopsies for super-spreaders’ detection in bovine besnoitiosis.

Grisez¹,

Bottari

Prévot

et al. 2020

Preprint

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Background: Bovine besnoitiosis, an emerging disease in Europe that can be transmitted by vectors, is caused by the Apicomplexa Besnoitia besnoiti. Bovine besnoitiosis is difficult to control due to the complexity of its diagnosis in the acute stage of the disease, poor treatment success and chronically asymptomatic cattle acting as parasite reservoirs. When serological prevalence is low, detection and specific culling of seropositive cattle is feasible; however, economic considerations preclude this approach when serological prevalence is high. The aims of this study were to evaluate the accuracy of detection of super-spreaders in highly infected herds and to test their selective elimination as a new control strategy for bovine besnoitiosis. Methods: Previous real-time PCR analyses performed on skin tissues from 160 asymptomatic animals sampled at slaughterhouses showed that the tail base was the best location to evaluate the dermal parasite DNA load. All seropositive animals (N = 518) from eight dairy or beef cattle farms facing a high serological prevalence of besnoitiosis were sampled at the tail base and their skin sample analysed by real-time PCR. A recommendation of rapid and selective culling of super-spreaders was formulated and provided to the cattle breeders. Subsequent serological monitoring of naïve animals was used to evaluate the interest of this control strategy over time.Results: Among the 518 seropositive animals, a low proportion of individuals (14.5%) showed Ct values below 36, 17.8% had doubtful results (36 < Ct ≤ 40) and 67.8% had negative PCR results. These proportions were grossly similar on the eight farms, regardless of their production type (beef or dairy cattle), size, geographic location or history of besnoitiosis. Within two weeks of the biopsy, the rapid culling of super-spreaders was implemented on only three farms. The numbers of newly infected animals were lower on these farms compared to those where super-spreaders were maintained in the herd. Conclusions: Real-time PCR analyses performed on skin biopsies of seropositive cattle showed huge individual variabilities in parasite DNA load. The rapid culling of individuals considered as super-spreaders seems to be a new and encouraging strategy for bovine besnoitiosis control.

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Pareto rules for malaria super-spreaders and super-spreading

Cited by 51 publications

References 38 publications

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology

Real-time PCR on skin biopsies for super-spreaders’ detection in bovine besnoitiosis.

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