Mitigating the threat of insecticide resistance in African malaria vector populations requires comprehensive information about where resistance occurs, to what degree, and how this has changed over time. Estimating these trends is complicated by the sparse, heterogeneous distribution of observations of resistance phenotypes in field populations. We use 6,423 observations of the prevalence of resistance to the most important vector control insecticides to inform a Bayesian geostatistical ensemble modelling approach, generating fine-scale predictive maps of resistance phenotypes in mosquitoes from the Anopheles gambiae complex across Africa. Our models are informed by a suite of 111 predictor variables describing potential drivers of selection for resistance. Our maps show alarming increases in the prevalence of resistance to pyrethroids and DDT across sub-Saharan Africa from 2005 to 2017, with mean mortality following insecticide exposure declining from almost 100% to less than 30% in some areas, as well as substantial spatial variation in resistance trends.

20 Mitigating the threat of insecticide resistance in African malaria vector 21 populations requires comprehensive information about where resistance occurs, 22to what degree, and how this has changed over time. Estimating these trends is 23 complicated by the sparse, heterogeneous distribution of observations of 24 resistance phenotypes in field populations. We use 6423 observations of the 25 prevalence of resistance to the most important vector control insecticides to 26 inform a Bayesian geostatistical ensemble modelling approach, generating fine-27 scale predictive maps of resistance phenotypes in mosquitoes from the 28Anopheles gambiae complex across Africa. Our models are informed by a suite of 29 111 predictor variables describing potential drivers of selection for resistance. 30Our maps show alarming increases in the prevalence of resistance to pyrethroids 31 and DDT across Sub-Saharan Africa from 2005-2017 as well as substantial 32 spatial variation in resistance trends. 33 34

1.A promising strategy for reducing the transmission of dengue and other arboviral human diseases by Aedes aegypti mosquito vector populations involves field introductions of the endosymbiotic bacteria Wolbachia. Wolbachia infections inhibit viral transmission by the mosquito, and can spread between mosquito hosts to reach high frequencies in the vector population. Wolbachia spreads by maternal transmission, and spread dynamics can be variable and highly dependent on natural mosquito population dynamics, population structure and fitness components.2. We develop a mathematical model of an A. aegypti metapopulation that incorporates empirically validated relationships describing density-dependent mosquito fitness components. We assume that density dependent relationships differ across subpopulations, and construct heterogeneous landscapes for which modelpredicted patterns of variation in mosquito abundance and demography approximate those observed in field populations. We then simulate Wolbachia release strategies similar to that used in field trials.3. We show that our model can produce rates of spatial spread of Wolbachia similar to those observed following field releases. 4. We then investigate how different types of spatio-temporal variation in mosquito habitat, as well as different fitness costs incurred by Wolbachia on the mosquito host, influence predicted spread rates. We find that fitness costs reduce spread rates more strongly when the habitat landscape varies temporally due to stochastic and seasonal processes. Synthesis and applications:Our empirically based modelling approach represents effects of environmental heterogeneity on the spatial spread of Wolbachia. The models can assist in interpreting observed spread patterns following field releases and in designing suitable release strategies for targeting spatially heterogeneous vector populations. K E Y W O R D S arbovirus, dengue, gene drive, spatial spread, wAlbB, wMel, wMelPop, Zika | 1675 Journal of Applied Ecology HANCOCK et Al. S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Hancock PA, Ritchie SA, Koenraadt CJM, Scott TW, Hoffmann AA, Godfray HCJ. Predicting the spatial dynamics of Wolbachia infections in Aedes aegypti arbovirus vector populations in heterogeneous landscapes.

Several thousand people die every year worldwide because of terrorist attacks perpetrated by non-state actors. In this context, reliable and accurate short-term predictions of non-state terrorism at the local level are key for policy makers to target preventative measures. Using only publicly available data, we show that predictive models that include structural and procedural predictors can accurately predict the occurrence of non-state terrorism locally and a week ahead in regions affected by a relatively high prevalence of terrorism. In these regions, theoretically informed models systematically outperform models using predictors built on past terrorist events only. We further identify and interpret the local effects of major global and regional terrorism drivers. Our study demonstrates the potential of theoretically informed models to predict and explain complex forms of political violence at policy-relevant scales.