Malaria, dengue, Zika and other mosquito-borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide-based tools and antimalarial drugs. The advent of CRISPR/Cas9-based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has enabled a wide range of gene drive architectures to be realized, creating a need for their population-level and spatial dynamics to be explored. We present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: (a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying user-defined inheritance patterns, (b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pests, and (c) a landscape module generates the metapopulation model by which insect populations are connected via migration over space. Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9-based homing gene drive for: (a) driving a disease-refractory gene into a population (i.e. population replacement), and (b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing-resistant alleles in both cases. Further documentation and use examples are provided at the project's Github repository. MGDrivE is an open-source r package freely available on CRAN. We intend the package to provide a flexible tool capable of modelling novel inheritance-modifying constructs as they are proposed and become available. The field of gene drive is moving very quickly, and we welcome suggestions for future development.

Background

The discovery of CRISPR-based gene editing and its application to homing-based gene drive systems has been greeted with excitement, for its potential to control mosquito-borne diseases on a wide scale, and concern, for the invasiveness and potential irreversibility of a release. Gene drive systems that display threshold-dependent behavior could potentially be used during the trial phase of this technology, or when localized control is otherwise desired, as simple models predict them to spread into partially isolated populations in a confineable manner, and to be reversible through releases of wild-type organisms. Here, we model hypothetical releases of two recently engineered threshold-dependent gene drive systems-reciprocal chromosomal translocations and a form of toxin-antidote-based underdominance known as UD^MEL-to explore their ability to be confined and remediated.

Results

We simulate releases of Aedes aegypti, the mosquito vector of dengue, Zika, and other arboviruses, in Yorkeys Knob, a suburb of Cairns, Australia, where previous biological control interventions have been undertaken on this species. We monitor spread to the neighboring suburb of Trinity Park to assess confinement. Results suggest that translocations could be introduced on a suburban scale, and remediated through releases of non-disease-transmitting male mosquitoes with release sizes on the scale of what has been previously implemented. UD^MEL requires fewer releases to introduce, but more releases to remediate, including of females capable of disease transmission. Both systems are expected to be confineable to the release site; however, spillover of translocations into neighboring populations is less likely.

Conclusions

Our analysis supports the use of translocations as a threshold-dependent drive system capable of spreading disease-refractory genes into Ae. aegypti populations in a confineable and reversible manner. It also highlights increased release requirements when incorporating life history and population structure into models. As the technology nears implementation, further ecological work will be essential to enhance model predictions in preparation for field trials.

The sterile insect technique (SIT) is an environmentally safe and proven technology to suppress wild populations. To further advance its utility, a novel CRISPR-based technology termed precision guided SIT (pgSIT) is described. PgSIT mechanistically relies on a dominant genetic technology that enables simultaneous sexing and sterilization, facilitating the release of eggs into the environment ensuring only sterile adult males emerge. Importantly, for field applications, the release of eggs will eliminate burdens of manually sexing and sterilizing males, thereby reducing overall effort and increasing scalability. Here, to demonstrate efficacy, we systematically engineer multiple pgSIT systems in Drosophila which consistently give rise to 100% sterile males. Importantly, we demonstrate that pgSIT-generated sterile males are fit and competitive. Using mathematical models, we predict pgSIT will induce substantially greater population suppression than can be achieved by currently-available self-limiting suppression technologies. Taken together, pgSIT offers to potentially transform our ability to control insect agricultural pests and disease vectors.

Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.

Background

Malaria is a risk factor for adverse pregnancy outcomes. Indoor residual spraying with insecticide (IRS) reduces malaria infections, yet the effects of IRS on pregnancy outcomes are not well established. We evaluated the impact of a large-scale IRS campaign on pregnancy outcomes in Eastern Uganda.

Methods

Birth records (n = 59 992) were obtained from routine surveillance data at 25 health facilities from five districts that were part of the IRS campaign and six neighbouring control districts ∼27 months before and ∼24 months after the start of the campaign (January 2013-May 2017). Campaign effects on low birthweight (LBW) and stillbirth incidence were estimated using the matrix completion method (MC-NNM), a machine-learning approach to estimating potential outcomes, and compared with the difference-in-differences (DiD) estimator. Subgroup analyses were conducted by HIV and gravidity.

Results

MC-NNM estimates indicated that the campaign was associated with a 33% reduction in LBW incidence: incidence rate ratio (IRR) = 0.67 [95% confidence interval (CI): 0.49-0.93)]. DiD estimates were similar to MC-NNM [IRR = 0.69 (0.47-1.01)], despite a parallel trends violation during the pre-IRS period. The campaign was not associated with substantial reductions in stillbirth incidence [IRRMC-NNM = 0.94 (0.50-1.77)]. HIV status modified the effects of the IRS campaign on LBW [βIRSxHIV = 0.42 (0.05-0.78)], whereby HIV-negative women appeared to benefit from the campaign [IRR = 0.70 (0.61-0.81)], but not HIV-positive women [IRR = 1.12 (0.59-2.12)].

Conclusions

Our results support the effectiveness of the campaign in Eastern Uganda based on its benefit to LBW prevention, though HIV-positive women may require additional interventions. The IRS campaign was not associated with a substantively lower stillbirth incidence, warranting further research.

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Current confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Using a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy, we estimated 6,275,072 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) as of April 18, 2020. Accounting for uncertainty, the number of infections was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference was due to incomplete testing, while 14% (0.3-36%) was due to imperfect test accuracy. Estimates of SARS-CoV-2 infections that transparently account for testing practices and diagnostic accuracy reveal that the pandemic is larger than confirmed case counts suggest.

School closures may reduce the size of social networks among children, potentially limiting infectious disease transmission. To estimate the impact of K-12 closures and reopening policies on children's social interactions and COVID-19 incidence in California's Bay Area, we collected data on children's social contacts and assessed implications for transmission using an individual-based model. Elementary and Hispanic children had more contacts during closures than high school and non-Hispanic children, respectively. We estimated that spring 2020 closures of elementary schools averted 2167 cases in the Bay Area (95% CI: -985, 5572), fewer than middle (5884; 95% CI: 1478, 11.550), high school (8650; 95% CI: 3054, 15 940) and workplace (15 813; 95% CI: 9963, 22 617) closures. Under assumptions of moderate community transmission, we estimated that reopening for a four-month semester without any precautions will increase symptomatic illness among high school teachers (an additional 40.7% expected to experience symptomatic infection, 95% CI: 1.9, 61.1), middle school teachers (37.2%, 95% CI: 4.6, 58.1) and elementary school teachers (4.1%, 95% CI: -1.7, 12.0). However, we found that reopening policies for elementary schools that combine universal masking with classroom cohorts could result in few within-school transmissions, while high schools may require masking plus a staggered hybrid schedule. Stronger community interventions (e.g. remote work, social distancing) decreased the risk of within-school transmission across all measures studied, with the influence of community transmission minimized as the effectiveness of the within-school measures increased.

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.

As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3-5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns - a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively "absorbed" as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group - namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.