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New methods for modeling Anopheles gambiae s.l. movement with environmental and genetic data



Models that simulate the effects of interventions on malaria vectors and transmission make assumptions about how mosquitoes move in the environment, such as isotropic behavior and no sex-related differences. These are applied to dispersal between households and villages and processes such as host-seeking and oviposition. Most models use mathematically convenient dispersal kernels based on these assumptions given the paucity of available data to better parameterize mosquito movement and the increase in complexity required. Consequently, there are few available methods to explore the effects of landscape and environmental factors on dispersal patterns. Understanding these patterns is key to optimizing control strategies, particularly for genetic control methods that involve releasing modified mosquitoes that compete with the natural mosquito population. We present a framework for modeling Anopheles gambiae s.l. movement mechanistically using available mark-release-recapture, biological, and ecological data and describe how it can be tailored for different locations and scenarios. We demonstrate its use for São Tomé and Príncipe and the Comoros, two candidate field sites for genetic control trials. Furthermore, we show the effects on these islands of elevation, land use, village/city proximity, and wind on predicted dispersal kernels and the implications for mosquito population dynamics. The resultant dispersal kernel is unique to the landscape of interest and is easily calibrated to field data measurements. Finally, we compare these results with genetic methods for inferring dispersal and connectivity between different mosquito populations on the islands and suggest future directions for the synthesis of these two data streams.
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... 31 This would be useful to assess monitoring requirements to both: i) accurately measure 32 effectiveness of genetic control (e.g., establishment and persistence of alleles at future 33 field sites), and ii) detect unintended spread of transgenes beyond the testing or trial expected to be a major cost driver for this technology. 38 Previously, our group developed MGDrivE (Mosquito Gene Drive Explorer) [5] to model 39 the spatial population dynamics of a variety of mosquito genetic control systems, and 40 MGDrivE 2 [15], incorporating simple models of malaria and arbovirus transmission, 41 seasonality in mosquito populations, and a novel formulation of mosquito and human 42 state space utilizing stochastic Petri nets (SPNs). Here, we present MGDrivE 3, a new 43 version of MGDrivE 2 that incorporates three major developments: i) a decoupled 44 sampling algorithm allowing the vector and human portions of the model to be readily 45 modularized, and hence for the mosquito portion of MGDrivE to be paired with a 46 more-detailed epidemiological framework, ii) a version of the Imperial College London 47 (ICL) malaria transmission model [10,11], which incorporates age structure, various 48 forms of immunity, human and vector interventions, and more meaningful disease 49 outcomes, and iii) surveillance functionality that tracks mosquitoes captured by traps 50 throughout the simulation. ...
... Output like this will be useful to model surveillance strategies for the 381 progression of field trials and interventions, and the emergence of alternative alleles that 382 could interfere with intervention effectiveness [14]. Mosquito population nodes represent villages and suburbs of comparable size with mosquito movement probabilities between localities derived from an ecology-motivated algorithm [39] and calibrated to mark-release-recapture data [40,41]. Simulation was restricted to the southern portion of the island, with population nodes including traps depicted in pink and other population nodes depicted in blue. ...
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Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN ( ) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community. Author summary Vector-borne diseases such as malaria cause massive morbidity and mortality throughout much of the world. Currently-available control measures, such as insecticide-based tools and antimalarial drugs, have limited impact and are waning in effectiveness, hence there is a need for novel tools to complement existing ones. Mosquito genetic control tools, such as gene drive systems and genetic versions of the sterile insect technique, offer a range of promising options, the development of which has greatly expanded since the advent of CRISPR-based gene-editing. Recently, we proposed MGDrivE 2 (Mosquito Gene Drive Explorer 2), which incorporates epidemiology into simulations of the dynamics of these systems in spatially-structured mosquito populations; however, that framework relied on simple model representations of vector-borne diseases. Here, we present MGDrivE 3, which decouples the vector portion of the model from the human portion, allowing the mosquito genetic control framework to be paired with more-detailed epidemiological frameworks. As an example, we implement the human transmission dynamics of the Imperial College London malaria model. We also incorporate a network of mosquito traps for surveillance. As genetic control technology edges closer towards field implementation, more detailed predictions of its epidemiological and biosafety implications are needed. We propose MGDrivE 3 to fulfill this role.
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