Distribution Modelling of Gene Drive-Modified Mosquitoes and Their Effects on Wild Populations
Abstract: Emerging technologies have the potential to bring numerous new opportunities and solutions to the existing challenges, including addressing sustainable development goals (SDGs). Particular hopes are given to areas of the life that require our urgent action, these include but are not limited to medicine and food security. Scientists investigate how these technological advances can be applied for the benefit of humanity by making more robust crops, eliminating diseases, or trying to extend our longevity. One technology that attracted and kept on attracting attention is the so-called “gene drives.” Genes in sexually reproducing organisms have, on average, a 50% chance of being inherited by the offspring. There are, however, genes that have a higher chance of being inherited. In a long-term, such dominant genes can affect the entire population by adding, replacing, suppressing, or editing genetic traits. Being able to eradicate invasive local species, alter mosquito genomes to eliminate Zika, dengue fever, and malaria or produce more environmentally robust to plant species is something to aim for. The study uses computational modelling techniques in which gene drive inheritance model are combined with distribution models of mosquito species to develop unified modelling approach to evaluate the factors related to gene drive altered species and their capability to eradicate population of wild species. The study shows how gene drive altered mosquitoes can influence wild mosquito populations to prevent them from vectoring malaria and other vector-borne diseases. The study focuses on malaria spread that is associated with one specific species - Anopheles mosquitoes. The study area is Kenya due to a number of reported cases of malaria. The proliferation of malaria mosquitoes was selected due to a number of spatial distribution models that have been developed over the years, as well as the availability of existing remote sensing data.
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