Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world’s largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.

The effectiveness of vector-control tools is often assessed by experiments as a reduction in mosquito landings using human landing catches (HLCs). However, HLCs alone only quantify a single characteristic and therefore do not provide information on the overall impacts of the intervention product. Using data from a recent semi-field study which used time-stratified HLCs, aspiration of non-landing mosquitoes, and blood feeding, we suggest a Bayesian inference approach for fitting such data to a stochastic model. This model considers both personal protection, through a reduction in biting, and community protection, from mosquito mortality and disarming (prolonged inhibition of blood feeding). Parameter estimates are then used to predict the reduction of vectorial capacity induced by etofenpox-treated clothing, picaridin topical repellents, transfluthrin spatial repellents and metofluthrin spatial repellents, as well as combined interventions for Plasmodium falciparum malaria in Anopleles minimus. Overall, all interventions had both personal and community effects, preventing biting and killing or disarming mosquitoes. This led to large estimated reductions in the vectorial capacity, with substantial impact even at low coverage. As the interventions aged, fewer mosquitoes were killed; however the impact of some interventions changed from killing to disarming mosquitoes. Overall, this inference method allows for additional modes of action, rather than just reduction in biting, to be parameterised and highlights the tools assessed as promising malaria interventions.

Entomological evaluations of vector control tools often use human landing catches (HLCs) as a standard measure of a direct human-vector contact. However, some tools have additional characteristics, such as mortality, and HLCS are not sensitive for measuring other effects beyond landing inhibition. Therefore, additional measures may need to be considered when evaluating these tools for public health use. This study has two main aims (1) the evaluate the accuracy of HLCs as a proxy for feeding and (2) to compare the predicted reduction in vectorial capacity when we do and do not consider these additional characteristics. To achieve this, we analyse previously published semi-field data from an experiment which used HLCs and another where mosquitoes were allowed to feed in the presence of different dosages of the volatile pyrethroid spatial repellent, transfluthrin. We compare results for two mathematical models: one which only considers the reduction in feeding effect and one which also considers mortality before and after feeding (using data gathered by the aspiration of mosquitoes after the semi-field feeding/landing period and 24 h survival monitoring). These Bayesian hierarchical models are parameterised using Bayesian inference. We observe that, for susceptible mosquitoes, reduction in landing is underestimated by HLCs. For knockdown resistant mosquitoes the relationship is less clear; with HLCs sometimes appearing to overestimate this characteristic. We find HLCs tend to under-predict the relative reduction in vectorial capacity in susceptible mosquitoes while over-predicting this impact in knockdown-resistant mosquitoes. Models without secondary effects have lower predicted relative reductions in vectorial capacities. Overall, this study highlights the importance of considering additional characteristics to reduction in biting of volatile pyrethroid spatial repellents. We recommend that these are considered when evaluating novel vector control tools.

Members of several different virus families cause equine viral encephalitis, the majority of which are arthropod-borne viruses (arboviruses) with zoonotic potential. The clinical signs caused are rarely pathognomonic; therefore, a clinical diagnosis is usually presumptive according to the geographical region. However, recent decades have seen expansion of the geographical range and emergence in new regions of numerous viral diseases. In this context, this review presents an overview of the prevalence and distribution of the main viral causes of equine encephalitis and discusses their impact and potential approaches to limit their spread.

Background: In the Greater Mekong Subregion (GMS), new vector-control tools are needed to target mosquitoes that bite outside during the daytime and night-time to advance malaria elimination. Methods: We conducted systematic literature searches to generate a bionomic dataset of the main malaria vectors in the GMS, including human blood index (HBI), parity proportion, sac proportion (proportion with uncontracted ovary sacs, indicating the amount of time until they returned to host seeking after oviposition) and the resting period duration. We then performed global sensitivity analyses to assess the influence of bionomics and intervention characteristics on vectorial capacity. Results: Our review showed that Anopheles minimus, An. sinensis, An. maculatus and An. sundaicus display opportunistic blood-feeding behaviour, while An. dirus is more anthropophilic. Multivariate regression analysis indicated that environmental, climatic and sampling factors influence the proportion of parous mosquitoes, and resting duration varies seasonally. Sensitivity analysis highlighted HBI and parity proportion as the most influential bionomic parameters, followed by resting duration. Killing before feeding is always a desirable characteristic across all settings in the GMS. Disarming is also a desirable characteristic in settings with a low HBI. Repelling is only an effective strategy in settings with a low HBI and low parity proportion. Killing after feeding is only a desirable characteristic if the HBI and parity proportions in the setting are high. Conclusions: Although in general adopting tools that kill before feeding would have the largest community-level effect on reducing outdoor transmission, other modes of action can be effective. Current tools in development which target outdoor biting mosquitoes should be implemented in different settings dependent on their characteristics. Graphical

Most mathematical models that assess the vectorial capacity of disease-transmitting insects typically focus on the influence of climatic factors to predict variations across different times and locations, or examine the impact of vector control interventions to forecast their potential effectiveness. We combine features of existing models to develop a novel model for vectorial capacity that considers both climate and vector control. This model considers how vector control tools affect vectors at each stage of their feeding cycle, and incorporates host availability and preference. Applying this model to arboviruses of veterinary importance in Europe, we show that African horse sickness virus (AHSV) has a higher peak predicted vectorial capacity than bluetongue virus (BTV), Schmallenberg virus (SBV), and epizootic haemorrhagic disease virus (EHDV). However, AHSV has a shorter average infectious period due to high mortality; therefore, the overall basic reproduction number of AHSV is similar to BTV. A comparable relationship exists between SBV and EHDV, with both viruses showing similar basic reproduction numbers. Focusing on AHSV transmission in the UK, insecticide-treated stable netting is shown to significantly reduce vectorial capacity of Culicoides, even at low coverage levels. However, untreated stable netting is likely to have limited impact. Overall, this model can be used to consider both climate and vector control interventions either currently utilised or for potential use in an outbreak, and could help guide policy makers seeking to mitigate the impact of climate change on disease control.