Dataset Information

Empirical Dynamic Modelling Identifies different Responses of Aedes Polynesiensis Subpopulations to Natural Environmental Variables.

ABSTRACT: To control mosquito populations for managing vector-borne diseases, a critical need is to identify and predict their response to causal environmental variables. However, most existing attempts rely on linear approaches based on correlation, which cannot apply in complex, nonlinear natural systems, because correlation is neither a necessary nor sufficient condition for causation. Applying empirical dynamic modelling that acknowledges nonlinear dynamics on nine subpopulations of tiger mosquitos from three neighbouring reef islets of the Raiatea atoll, we identified temperature, precipitation, dew point, air pressure, and mean tide level as causal environmental variables. Interestingly, responses of subpopulations in close proximity (100-500?m) differed with respect to their causal environmental variables and the time delay of effect, highlighting complexity in mosquito-environment causality network. Moreover, we demonstrated how to explore the effects of changing environmental variables on number and strength of mosquito outbreaks, providing a new framework for pest control and disease vector ecology.

SUBMITTER: Grziwotz F

PROVIDER: S-EPMC6233190 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Empirical Dynamic Modelling Identifies different Responses of Aedes Polynesiensis Subpopulations to Natural Environmental Variables.

Grziwotz Florian F Strauß Jakob Friedrich JF Hsieh Chih-Hao CH Telschow Arndt A

Scientific reports 20181113 1

To control mosquito populations for managing vector-borne diseases, a critical need is to identify and predict their response to causal environmental variables. However, most existing attempts rely on linear approaches based on correlation, which cannot apply in complex, nonlinear natural systems, because correlation is neither a necessary nor sufficient condition for causation. Applying empirical dynamic modelling that acknowledges nonlinear dynamics on nine subpopulations of tiger mosquitos fr ...[more]

PMID: 30425277

Similar Datasets

Project description:BackgroundFrom October 2014 to March 2015, French Polynesia experienced for the first time a chikungunya outbreak. Two Aedes mosquitoes may have contributed to chikungunya virus (CHIKV) transmission in French Polynesia: the worldwide distributed Ae. aegypti and the Polynesian islands-endemic Ae. polynesiensis mosquito.MethodsTo investigate the vector competence of French Polynesian populations of Ae. aegypti and Ae. polynesiensis for CHIKV, mosquitoes were exposed per os at viral titers of 7 logs tissue culture infectious dose 50%. At 2, 6, 9, 14 and 21 days post-infection (dpi), saliva was collected from each mosquito and inoculated onto C6/36 mosquito cells to check for the presence of CHIKV infectious particles. Legs and body (thorax and abdomen) of each mosquito were also collected at the different dpi and submitted separately to viral RNA extraction and CHIKV real-time RT-PCR.ResultsCHIKV infection rate, dissemination and transmission efficiencies ranged from 7-90%, 18-78% and 5-53% respectively for Ae. aegypti and from 39-41%, 3-17% and 0-14% respectively for Ae. polynesiensis, depending on the dpi. Infectious saliva was found as early as 2 dpi for Ae. aegypti and from 6 dpi for Ae. polynesiensis. Our laboratory results confirm that the French Polynesian population of Ae. aegypti is highly competent for CHIKV and they provide clear evidence for Ae. polynesiensis to act as an efficient CHIKV vector.ConclusionAs supported by our findings, the presence of two CHIKV competent vectors in French Polynesia certainly contributed to enabling this virus to quickly disseminate from the urban/peri-urban areas colonized by Ae. aegypti to the most remote atolls where Ae. polynesiensis is predominating. Ae. polynesiensis was probably involved in the recent chikungunya outbreaks in Samoa and the Cook Islands. Moreover, this vector may contribute to the risk for CHIKV to emerge in other Polynesian islands like Fiji, and more particularly Wallis where there is no Ae. aegypti.

Project description:Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and require analysis approaches that temporally map between cell state distributions across time points. Current approaches to approximate the single-cell time series as a dynamical system suffer from too restrictive assumptions about the type of kinetics, or link together pairs of sequential measurements in a discontinuous fashion. We propose Dynamic Distribution Decomposition (DDD), an operator approximation approach to infer a continuous distribution map between time points. On the basis of single-cell snapshot time series data, DDD approximates the continuous time Perron-Frobenius operator by means of a finite set of basis functions. This procedure can be interpreted as a continuous time Markov chain over a continuum of states. By only assuming a memoryless Markov (autonomous) process, the types of dynamics represented are more general than those represented by other common models, e.g., chemical reaction networks, stochastic differential equations. Furthermore, we can a posteriori check whether the autonomy assumptions are valid by calculation of prediction error-which we show gives a measure of autonomy within the studied system. The continuity and autonomy assumptions ensure that the same dynamical system maps between all time points, not arbitrarily changing at each time point. We demonstrate the ability of DDD to reconstruct dynamically important cell states and their transitions both on synthetic data, as well as on mass cytometry time series of iPSC reprogramming of a fibroblast system. We use DDD to find previously identified subpopulations of cells and to visualise differentiation trajectories. Dynamic Distribution Decomposition allows interpretation of high-dimensional snapshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynamics analysis for a variety of biological processes by means of identifying their dynamically important cell states.

Project description:BackgroundAedes mosquitoes severely affect the health and wellbeing of human populations by transmitting infectious diseases. In French Polynesia, Aedes aegypti is the main vector of dengue, chikungunya and Zika, and Aedes polynesiensis the primary vector of Bancroftian filariasis and a secondary vector of arboviruses. Tools for assessing the risk of disease transmission or for measuring the efficacy of vector control programmes are scarce. A promising approach to quantify the human-vector contact relies on the detection and the quantification of antibodies directed against mosquito salivary proteins.Methodology/principal findingsAn ELISA test was developed to detect and quantify the presence of immunoglobulin G (IgG) directed against proteins from salivary gland extracts (SGE) of Ae. aegypti and Ae. polynesiensis in human populations exposed to either species, through a cross-sectional study. In Tahiti and Moorea islands where Ae. aegypti and Ae. polynesiensis are present, the test revealed that 98% and 68% of individuals have developed IgG directed against Ae. aegypti and Ae. polynesiensis SGE, respectively. By comparison, ELISA tests conducted on a cohort of people from metropolitan France, not exposed to these Aedes mosquitoes, indicated that 97% of individuals had no IgG directed against SGE of either mosquito species. The analysis of additional cohorts representing different entomological Aedes contexts showed no ELISA IgG cross-reactivity between Ae. aegypti and Ae. polynesiensis SGE.Conclusions/significanceThe IgG response to salivary gland extracts seems to be a valid and specific biomarker of human exposure to the bites of Ae. aegypti and Ae. polynesiensis. This new immuno-epidemiological tool will enhance our understanding of people exposure to mosquito bites, facilitate the identification of areas where disease transmission risk is high and permit to evaluate the efficacy of novel vector control strategies in Pacific islands and other tropical settings.

Dataset Information

Empirical Dynamic Modelling Identifies different Responses of Aedes Polynesiensis Subpopulations to Natural Environmental Variables.

Publications

Empirical Dynamic Modelling Identifies different Responses of Aedes Polynesiensis Subpopulations to Natural Environmental Variables.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets