A Two-Locus Model of the Evolution of Insecticide Resistance to Inform and Optimise Public Health Insecticide Deployment Strategies.

We develop a flexible, two-locus model for the spread of insecticide resistance applicable to mosquito species that transmit human diseases such as malaria. The model allows differential exposure of males and females, allows them to encounter high or low concentrations of insecticide, and allows selection pressures and dominance values to differ depending on the concentration of insecticide encountered. We demonstrate its application by investigating the relative merits of sequential use of insecticides versus their deployment as a mixture to minimise the spread of resistance. We recover previously published results as subsets of this model and conduct a sensitivity analysis over an extensive parameter space to identify what circumstances favour mixtures over sequences. Both strategies lasted more than 500 mosquito generations (or about 40 years) in 24% of runs, while in those runs where resistance had spread to high levels by 500 generations, 56% favoured sequential use and 44% favoured mixtures. Mixtures are favoured when insecticide effectiveness (their ability to kill homozygous susceptible mosquitoes) is high and exposure (the proportion of mosquitoes that encounter the insecticide) is low. If insecticides do not reliably kill homozygous sensitive genotypes, it is likely that sequential deployment will be a more robust strategy. Resistance to an insecticide always spreads slower if that insecticide is used in a mixture although this may be insufficient to outperform sequential use: for example, a mixture may last 5 years while the two insecticides deployed individually may last 3 and 4 years giving an overall 'lifespan' of 7 years for sequential use. We emphasise that this paper is primarily about designing and implementing a flexible modelling strategy to investigate the spread of insecticide resistance in vector populations and demonstrate how our model can identify vector control strategies most likely to minimise the spread of insecticide resistance.

Insecticide resistance evolution with mixtures and sequences: a model-based explanation.

BACKGROUND: Insecticide resistance threatens effective vector control, especially for mosquitoes and malaria. To manage resistance, recommended insecticide use strategies include mixtures, sequences and rotations. New insecticides are being developed and there is an opportunity to develop use strategies that limit the evolution of further resistance in the short term. A 2013 review of modelling and empirical studies of resistance points to the advantages of mixtures. However, there is limited recent, accessible modelling work addressing the evolution of resistance under different operational strategies. There is an opportunity to improve the level of mechanistic understanding within the operational community of how insecticide resistance can be expected to evolve in response to different strategies. This paper provides a concise, accessible description of a flexible model of the evolution of insecticide resistance. The model is used to develop a mechanistic picture of the evolution of insecticide resistance and how it is likely to respond to potential insecticide use strategies. The aim is to reach an audience unlikely to read a more detailed modelling paper. The model itself, as described here, represents two independent genes coding for resistance to two insecticides. This allows the representation of the use of insecticides in isolation, sequence and mixtures. RESULTS: The model is used to demonstrate the evolution of resistance under different scenarios and how this fits with intuitive reasoning about selection pressure. Using an insecticide in a mixture, relative to alone, always prompts slower evolution of resistance to that insecticide. However, when resistance to both insecticides is considered, resistance thresholds may be reached later for a sequence relative to a mixture. Increasing the ability of insecticides to kill susceptible mosquitoes (effectiveness), has the most influence on favouring a mixture over a sequence because one highly effective insecticide provides more protection to another in a mixture. CONCLUSIONS: The model offers an accessible description of the process of insecticide resistance evolution and how it is likely to respond to insecticide use. A simple online user-interface allowing further exploration is also provided. These tools can contribute to an improved discussion about operational decisions in insecticide resistance management.

ResistanceSim: development and acceptability study of a serious game to improve understanding of insecticide resistance management in vector control programmes.

The use of insecticides is the cornerstone of effective malaria vector control. However, the last two decades has seen the ubiquitous use of insecticides, predominantly pyrethroids, causing widespread insecticide resistance and compromising the effectiveness of vector control. Considerable efforts to develop new active ingredients and interventions are underway. However, it is essential to deploy strategies to mitigate the impact of insecticide resistance now, both to maintain the efficacy of currently available tools as well as to ensure the sustainability of new tools as they come to market. Although the World Health Organization disseminated best practice guidelines for insecticide resistance management (IRM), Rollback Malaria's Vector Control Working Group identified the lack of practical knowledge of IRM as the primary gap in the translation of evidence into policy. ResistanceSim is a capacity strengthening tool designed to address this gap. The development process involved frequent stakeholder consultation, including two separate workshops. These workshops defined the learning objectives, target audience, and the role of mathematical models in the game. Software development phases were interspersed with frequent user testing, resulting in an iterative design process. User feedback was evaluated via questionnaires with Likert-scale and open-ended questions. The game was regularly evaluated by subject-area experts through meetings of an external advisory panel. Through these processes, a series of learning domains were identified and a set of specific learning objectives for each domain were defined to be communicated to vector control programme personnel. A simple "game model" was proposed that produces realistic outputs based on player strategy and also runs in real-time. Early testing sessions revealed numerous usability issues that prevented adequate player engagement. After extensive revisions, later testing sessions indicated that the tool would be a valuable addition to IRM training.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework.

OBJECTIVES: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). METHODS: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. RESULTS: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. CONCLUSIONS: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

The Residual Efficacy of SumiShield™ 50WG and K-Othrine® WG250 IRS Formulations Applied to Different Building Materials against Anopheles and Aedes Mosquitoes.

Insecticides with novel modes of action are required to complement the pyrethroids currently relied upon for controlling malaria vectors. One example of this is the neonicotinoid clothianidin, the active ingredient in the indoor residual spray (IRS) SumiShield™ 50WG. In a preliminary experiment, the mortality of insecticide-susceptible and resistant An. gambiae adults exposed to filter papers treated with this IRS product reached 80% by 3 days post-exposure and 100% by 6 days post-exposure. Next, cement, wood, and mud tiles were treated with the clothianidin or a deltamethrin-based IRS formulation (K-Othrine WG250). Insecticide resistant and susceptible Anopheles and Aedes were exposed to these surfaces periodically for up to 18 months. Pyrethroid resistant Cx. quinquefasciatus was also exposed at 9 months. Between exposures, tiles were stored in heat and relative humidity conditions reflecting those found in the field. On these surfaces, the clothianidin IRS was effective at killing both susceptible and resistant An. gambiae for 18 months post-treatment, while mortality amongst the resistant strains when exposed to the deltamethrin IRS was not above that of the negative control. Greater efficacy of clothianidin was also demonstrated against insecticide resistant strains of An. funestus compared to deltamethrin, though the potency was lower when compared with An. gambiae. In general, higher efficacy of the clothianidin IRS was observed on cement and mud compared to wood, though it demonstrated poorer residual activity against Ae.aegypti and Cx. quinquefasciatus.

The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors.

Persistent insecticides sprayed onto house walls, and incorporated into insecticide-treated bednets, provide long-acting, cost-effective control of vector-borne diseases such as malaria and leishmaniasis. The high concentrations that occur immediately postdeployment may kill both resistant and susceptible insects. However, insecticide concentration, and therefore killing ability, declines in the months after deployment. As concentrations decline, resistant insects start to survive, while susceptible insects are still killed. The period of time after deployment, within which the mortality of resistant individuals is lower than that of susceptible ones, has been termed the "window of selection" in other contexts. It is recognized as driving resistance in bacteria and malaria parasites, both of which are predominantly haploid. We argue that paying more attention to these mortality differences can help understand the evolution of insecticide resistance. Because insects are diploid, resistance encoded by single genes generates heterozygotes. This gives the potential for a narrower "window of dominance," within the window of selection, where heterozygote mortality is lower than that of susceptible homozygotes. We explore the general properties of windows of selection and dominance in driving resistance. We quantify their likely effect using data from new laboratory experiments and published data from the laboratory and field. These windows can persist months or years after insecticide deployments. Differential mortalities of resistant, susceptible and heterozygous genotypes, after public health deployments, constitute a major challenge to controlling resistance. Greater attention to mortality differences by genotype would inform strategies to reduce the evolution of resistance to existing and new insecticides.

A reproducible picture of open access health facility data in Africa and R tools to support improvement.

Background: Open data on the locations and services provided by health facilities have, in some countries, allowed the development of software tools contributing to COVID-19 response. The UN and WHO encourage countries to make health facility location data open, to encourage use and improvement. We provide a summary of open access health facility location data in Africa using re-useable R code. We aim to support data analysts developing software tools to address COVID-19 response in individual countries. In Africa there are currently three main sources of such open data; 1) direct from national ministries of health, 2) a database for sub-Saharan Africa collated and published by a team from KEMRI-Wellcome Trust Research Programme and now hosted by WHO, and 3) The Global Healthsites Mapping Project in collaboration with OpenStreetMap.      Methods: We searched for and documented official national facility location data that were openly available. We developed re-useable open-source R code to summarise and visualise facility location data by country from the three sources. This re-useable code is used to provide a web user interface allowing data exploration through maps and plots of facility type. Results: Out of 52 African countries, seven currently provide an official open facility list that can be downloaded and analysed reproducibly. Considering all three sources, there are over 185,000 health facility locations available for Africa. However, there are differences and overlaps between sources and a lack of data on capacities and service provision. Conclusions: These summaries and software tools can be used to encourage greater use of existing health facility location data, incentivise further improvements in the provision of those data by national suppliers, and encourage collaboration within wider data communities. The tools are a part of the afrimapr project, actively developing R building blocks to facilitate the use of health data in Africa.

Malaria Data by District: An open-source web application for increasing access to malaria information.

Preventable diseases still cause huge mortality in low- and middle-income countries. Research in spatial epidemiology and earth observation is helping academics to understand and prioritise how mortality could be reduced and generates spatial data that are used at a global and national level, to inform disease control policy. These data could also inform operational decision making at a more local level, for example to help officials target efforts at a local/regional level. To be usable for local decision-making, data needs to be presented in a way that is relevant to and understandable by local decision makers. We demonstrate an approach and prototype web application to make spatial outputs from disease modelling more useful for local decision making. Key to our approach is: (1) we focus on a handful of important data layers to maintain simplicity; (2) data are summarised at scales relevant to decision making (administrative units); (3) the application has the ability to rank and compare administrative units; (4) open-source code that can be modified and re-used by others, to target specific user-needs. Our prototype application allows visualisation of a handful of key layers from the Malaria Atlas Project. Data can be summarised by administrative unit for any malaria endemic African country, ranked and compared; e.g. to answer questions such as, 'does the district with the highest malaria prevalence also have the lowest coverage of insecticide treated nets?'. The application is developed in R and the code is open-source. It would be relatively easy for others to change the source code to incorporate different data layers, administrative boundaries or other data visualisations. We suggest such open-source web application development can facilitate the use of data for public health decision making in low resource settings.

Measuring, manipulating and exploiting behaviours of adult mosquitoes to optimise malaria vector control impact.

Residual malaria transmission can persist despite high coverage with effective long-lasting insecticidal nets (LLINs) and/or indoor residual spraying (IRS), because many vector mosquitoes evade them by feeding on animals, feeding outdoors, resting outdoors or rapidly exiting from houses after entering them. However, many of these behaviours that render vectors resilient to control with IRS and LLINs also make them vulnerable to some emerging new alternative interventions. Furthermore, vector control measures targeting preferred behaviours of mosquitoes often force them to express previously rare alternative behaviours, which can then be targeted with these complementary new interventions. For example, deployment of LLINs against vectors that historically fed predominantly indoors on humans typically results in persisting transmission by residual populations that survive by feeding outdoors on humans and animals, where they may then be targeted with vapour-phase insecticides and veterinary insecticides, respectively. So while the ability of mosquitoes to express alternative behaviours limits the impact of LLINs and IRS, it also creates measurable and unprecedented opportunities for deploying complementary additional approaches that would otherwise be ineffective. Now that more diverse vector control methods are finally becoming available, well-established entomological field techniques for surveying adult mosquito behaviours should be fully exploited by national malaria control programmes, to rationally and adaptively map out new opportunities for their effective deployment.

EpiJSON: A unified data-format for epidemiology.

Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce 'EpiJSON', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package 'repijson' which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.