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.

Resistance in malaria vectors to pyrethroids, the most widely used class of insecticides for malaria vector control, threatens the continued efficacy of vector control tools. Target-site resistance is an important genetic resistance mechanism caused by mutations in the voltage-gated sodium channel (Vgsc) gene that encodes the pyrethroid target-site. Understanding the geographic distribution of target-site resistance, and temporal trends across different vector species, can inform strategic deployment of vector control tools. Here we develop a Bayesian statistical spatiotemporal model to interpret species-specific trends in the frequency of the most common resistance mutations, Vgsc-995S and Vgsc-995F, in three major malaria vector species Anopheles gambiae, An. coluzzii, and An. arabiensis. For nine selected countries, we develop annual predictive maps which reveal geographically-structured patterns of spread of each mutation at regional and continental scales. The results show associations, as well as stark differences, in spread dynamics of the two mutations across the three vector species. The coverage of ITNs was an influential predictor of Vgsc allele frequencies in our models. Our mapped Vgsc allele frequencies are a significant partial predictor of phenotypic resistance to the pyrethroid deltamethrin in An. gambiae complex populations, highlighting the importance of molecular surveillance of resistance mechanisms.

The primary malaria control intervention in high burden countries is the deployment of long-lasting insecticide-treated nets (LLINs) treated with pyrethroids, alone or in combination with a second active ingredient or synergist. It is essential to understand whether the impact of pyrethroid resistance can be mitigated by switching between different pyrethroids or whether cross-resistance precludes this. Structural diversity within the pyrethroids could mean some compounds are better able to counteract the resistance mechanisms that have evolved in malaria vectors. Here we consider variation in vulnerability to the P450 enzymes that confer metabolic pyrethroid resistance in Anopheles gambiae s.l. and Anopheles funestus. We assess the relationships among pyrethroids in terms of their binding affinity to key P450s and the percent dep­letion by these P450s, in order to identify which pyrethroids diverge from the others. We then investigate whether these same pyrethroids also diverge from the others in terms of resistance in vector populations. We found that etofenprox, which lacks the common structural moiety of other pyrethroids, potentially diverges from the commonly deployed pyrethroids in terms of P450 binding affinity and resistance in malaria vector populations, but not depletion by the P450s tested. These results are supplemented by an analysis of resistance to the same pyrethroids in Aedes aegypti populations, which also found etofenprox diverges from the other pyrethroids in terms of resistance in wild populations. In addition, we found that bifenthrin, which also lacks the common structural moiety of most pyrethroids, diverges from the commonly deployed pyrethroids in terms of P450 binding affinity and depletion by P450s. However, resistance to bifenthrin in vector populations is largely untested. The prevalence of resistance to the pyrethroids α-cypermethrin, cyfluthrin, deltamethrin, λ-cyhalothrin, and permethrin was correlated across malaria vector populations and switching between these compounds as a tool to mitigate against pyrethroid resistance is not advised without strong evidence supporting a true difference in resistance.

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office (WHO EMRO) recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data, and interpreting model findings from a decision-maker's perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO Eastern Mediterranean Region (EMR), as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesizing and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.