Adrien Ickowicz

Background: The dynamics of vector borne disease transmission depend on the abundances of vectors. The dominant malaria vector species complex of Anopheles gambiae sensu lato is a target of vector control strategies designed to reduce and eliminate malaria transmission. The three most widely distributed dominant malaria vectors within the species complex are An. arabiensis, An. coluzzii and An. gambiae sensu stricto . Methods: Previous studies across the extent of the species complex range have been restricted to annual predictions of species occurrence or relative abundance. This study incorporated relative abundance data at the species level and abundance data at the species complex level to estimate and predict daily equilibrium vector abundances of each species. Statistical models with interpretable parameters were used to gain insight into how factors such as meteorological conditions, insecticide treated net use, and human density affect the spatial and temporal predictions. The statistical models were used to predict daily equilibrium vector abundance that is an important factor in indices of malaria transmission such as the basic reproduction number and vectorial capacity. Results: Meteorological factors, such as precipitation and relative humidity, and human factors, such as population density and insecticide treated nets, were important predictors of these three malaria vector species in Africa. Insecticide treated net use was negatively associated with per capita vector abundance of the An. gambiae species complex and interacted with year to suggest an a negative effect on the ratio of An. gambiae s.s . to An. arabiensis at the end of the prediction period that extended from 2002 to 2020. A predicted increasing interannual trend for An. coluzzii was potentially caused by changing species identification methods. Conclusion: The predicted equilibrium abundances of the three species showed potentially high levels of geographical overlap, niche overlap, and evidence for stable coexistence despite substantial interspecific competition. Improving collection of longitudinal species abundance data across the spatial range of the An. gambiae species complex will facilitate future explorations of causal hypotheses that relate vector abundance to control measures, malaria interventions and meteorological conditions.

Malaria is one of the deadliest vector-borne diseases in the world. Researchers are developing new genetic and conventional vector control strategies to attempt to limit its burden. Novel control strategies require detailed safety assessment to ensure responsible and successful deployments. Anopheles gambiae sensu stricto (s.s.) and Anopheles coluzzii, two closely related subspecies within the species complex Anopheles gambiae sensu lato (s.l.), are among the dominant malaria vectors in sub-Saharan Africa. These two subspecies readily hybridise and compete in the wild and are also known to have distinct niches, each with spatially and temporally varying carrying capacities driven by precipitation and land use factors. We model the spread and persistence of a population-modifying gene drive system in these subspecies across sub-Saharan Africa by simulating introductions of genetically modified mosquitoes across the African mainland and its offshore islands. We explore transmission of the gene drive between the two subspecies that arise from different hybridisation mechanisms, the effects of both local dispersal and potential wind-aided migration to the spread, and the development of resistance to the gene drive. Given the best current available knowledge on the subspecies’ life histories, we find that an introduced gene drive system with typical characteristics can plausibly spread from even distant offshore islands to the African mainland with the aid of wind-driven migration, with resistance beginning to take over within a decade. Our model accounts for regional to continental scale mechanisms, and demonstrates a range of realistic dynamics including the effect of prevailing wind on spread and spatio-temporally varying carrying capacities for subspecies. As a result, it is well-placed to answer future questions relating to mosquito gene drives as important life history parameters become better understood.

Malaria is one of the deadliest vector-borne diseases in the world. Researchers are developing new genetic and conventional vector control strategies to attempt to limit its burden. Novel control strategies require detailed safety assessment to ensure responsible and successful deployments. Anopheles gambiae sensu stricto (s.s.) and Anopheles coluzzii , two closely related subspecies within the species complex Anopheles gambiae sensu lato (s.l.), are among the dominant malaria vectors in sub-Saharan Africa. These two subspecies readily hybridise and compete in the wild and are also known to have distinct niches, each with spatially and temporally varying carrying capacities driven by precipitation and land use factors. We model the spread and persistence of a population-modifying gene drive system in these subspecies across sub-Saharan Africa by simulating introductions of genetically modified mosquitoes across the African mainland and its offshore islands. We explore transmission of the gene drive between the two subspecies that arise from different hybridisation mechanisms, the effects of both local dispersal and potential wind-aided migration to the spread, and the development of resistance to the gene drive. Given the best current available knowledge on the subspecies’ life histories, we find that an introduced gene drive system with typical characteristics can plausibly spread from even distant offshore islands to the African mainland with the aid of wind-driven migration, with resistance taking over within a decade. Our model accounts for regional to continental scale mechanisms, and demonstrates a range of realistic dynamics including the effect of prevailing wind on spread and spatio-temporally varying carrying capacities for subspecies. As a result, it is well-placed to answer future questions relating to mosquito gene drives as important life history parameters become better understood.

Background: Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains.Methods: In this study, we: (i) describe a diffusion–advection–reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains.Results: If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location.Conclusions: Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes.Graphical

The relative risk of disease transmission caused by the potential release of transgenic vectors, such as through sterile insect technique or gene drive systems, is assessed with comparison with wild-type vectors. The probabilistic risk framework is demonstrated with an assessment of the relative risk of lymphatic filariasis, malaria and o'nyong'nyong arbovirus transmission by mosquito vectors to human hosts given a released transgenic strain of Anopheles coluzzii carrying a dominant sterile male gene construct. Harm is quantified by a logarithmic loss function that depends on the causal risk ratio, which is a quotient of basic reproduction numbers derived from mathematical models of disease transmission. The basic reproduction numbers are predicted to depend on the number of generations in an insectary colony and the number of backcrosses between the transgenic and wild-type lineages. Analogous causal risk ratios for short-term exposure to a single cohort release are also derived. These causal risk ratios were parametrized by probabilistic elicitations, and updated with experimental data for adult vector mortality. For the wild-type, high numbers of insectary generations were predicted to reduce the number of infectious human cases compared with uncolonized wild-type. Transgenic strains were predicted to produce fewer infectious cases compared with the uncolonized wild-type.

Synthetic gene drives could provide new solutions to a range of old problems such as controlling vector-borne diseases, agricultural pests and invasive species. In this paper, we outline methods to identify hazards and detect potentially adverse ecological outcomes at the individual (genotype, phenotype), population, community and ecosystem level, when progressing Gene Drive Modified Organisms through a phased test and release pathway. We discuss the strengths and weaknesses of checklists and structured hazard analysis techniques, identify methods to help meet some of the challenges of detecting adverse ecological outcomes in experiments and confined field trials, and discuss ways to improve the efficiency and statistical rigour of post-release monitoring strategies.

Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications. The checklist was applied to a sample of 100 academic population health publications distributed among 5 readers. Cohen’s κ was used to measure interrater agreement. Of the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good. This study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.