Antigens of Plasmodium falciparum targeted by most current and future vaccines are generally not conserved. There are limited studies estimating the risk of parasite selection with vaccines. We adapted an individual-based model of malaria to assess which conditions favour genotypes having some degree of vaccine resistance and estimate the impact of resistance spread on vaccine effectiveness. Even parasite genotypes with low degrees of vaccine resistance are likely to spread relatively quickly if vaccines are implemented in children and even faster in a broader population. However, only highly resistant genotypes could strongly reduce vaccine effectiveness. These results highlight that it is essential to understand the degree to which genotypes exhibit reduced sensitivity to vaccines and monitor genotype frequency and vaccine effectiveness along with vaccine deployment. If some genotypes exhibit reduced efficacy to vaccines, our results further suggest that polyvalent or combination vaccines should be considered to limit resistance spread.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study was funded through the Swiss National Science Foundation Professorship grant of M.A.P. (PP00P3_203450).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.Yes

SummaryBackgroundSeasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine (SP) plus amodiaquine (AQ) prevents millions of clinical malaria cases in children under five in Africa’s Sahel region. However, parasites partially resistant to SP (with “quintuple” mutations) potentially threaten SMC protective effectiveness. We evaluated its spread and clinical consequences.MethodsAn individual-based malaria transmission model with explicit parasite dynamics and drug pharmacological models, was used to identify and quantify the influence of factors driving quintuple mutant spread and predict the time needed for the mutant to spread from 1% to 50% of inoculations for several SMC deployment strategies. We estimated the impact of this spread on SMC effectiveness against clinical malaria.FindingsHigher transmission intensity, SMC coverage, and expanded age range of chemoprevention promoted mutant spread. SMC implementation in a high transmission setting (40% parasite prevalence in children aged 2-10 years) with four monthly cycles to children aged three months to five years (with 95% initial coverage declining each cycle), the mutant requires 53·1 years (95% CI 50·5–56·0) to spread from 1% to 50% of inoculations. This time increased in lower transmission settings and reduced by half when SMC was extended to children under ten, or reduced by 10-13 years when an additional monthly cycle of SMC was deployed. For the same setting, the effective reduction in clinical cases in children receiving SMC was 79·0% (95% CI 77·8–80·8) and 60·4% (95% CI 58·6–62·3) during the months of SMC implementation when the mutant was absent or fixed in the population, respectively.InterpretationSMC with SP+AQ leads to a relatively slow spread of SP-resistant quintuple mutants and remains effective at preventing clinical malaria despite the mutant spread. SMC with SP+AQ should be considered in seasonal settings where this mutant is already prevalent.FundingSwiss National Science Foundation and Marie Curie Individual Fellowship.

When drug resistance is suspected to be in a region, patients in the region are sampled and the suspicion is confirmed. This biased sampling limits our ability to capture underlying dynamics, meaning strategies to lengthen the lifespan of drugs are reactionary, not proactive. Testing for drug resistant infections is becoming easier and cheaper, therefore we should revisit sampling decisions. We present a hierarchical mechanistic Bayesian model, and apply it to a simulated dataset, where we sample between 5% and 30% of the population in a biased and unbiased manner. We show that unbiased spatiotemporal data on the presence of drug resistant infections, combined with our model, highlights underlying dynamics. Our mechanistic model is more accurate than a generalised additive model with space and time components. Moreover, highlighting underlying dynamics creates novel strategies that lengthen the lifespan of drugs. In low to middle income countries, generally, drug resistance emerges into a population from hotspots such as treatment centres (perhaps the use of sub-standard drugs), or major transport hubs, and then resistance spreads throughout the population. Using our model, we rank resistance hotspots, enabling resources to be targeted - such as verifying the quality of drugs at a particular health care centre.

Ongoing support for an eradication program depends on clear information being provided to on‐ground operational and managerial personnel and funding agencies about the performance of control effort. This paper describes the methods used to track the success of control activities during goat eradication on Kangaroo Island, Australia, from 2006 to 2017. A range of techniques were used during the program to monitor the goat population, including helicopter surveys, Judas goats, walking transects, camera trapping, scat count transects, and opportunistic community observations. A Bayesian catch‐effort model contained within a Microsoft Excel spreadsheet enabled data from multiple monitoring techniques to be accommodated. The familiar spreadsheet format made it relatively easy for operational staff to add data and visualize the strength of individual or combined monitoring techniques to track population decline. This provided greater incentive to maintain accurate records of hunting and survey effort during the eradication program. Combined survey effort data indicated there was a 1% chance that feral goats remained extant at the beginning of 2017. This probability was reduced to 0.1% if in another 12 months no further feral goat activity was detected by hunting effort and landholder/manager monitoring.

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure—treatment properties, biological factors, transmission intensity, and access to treatment—obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

Background Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. Methods Patients’ parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). Results To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. Conclusion This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.

Antimicrobial resistance is a major health problem with complex dynamics. Resistance may occur in an area because treated infections mutated and developed resistance, and the proportion of infections in a population may then increase. We developed a novel and flexible model that captures several features of resistance dynamics and competition. The model is able to account for many antimicrobials and thus can generally explore competition dynamics and their impact on pathogens and bacteria.Unlike simpler models, our nested model allows the population of resistant pathogen to smoothly increase or decrease. Time dependent dynamics are incorporated into difference equations which examines the effects of 12 parameters. This enables us to explicitly include three key competition dynamics: the transmission cost of resistance that occurs between hosts, the fitness cost of resistance that occurs within untreated hosts, and the release of this competition (from the fitness cost) that occurs once a host is treated. For malaria, our results suggest that without competitive release, drug resistance does not emerge. However, once emerged, competitive release has little effect, and the best way to mitigate the spread is to ensure that treatment is very effective.

Collecting data for reports on online wildlife trade is resource-intensive and time-consuming. Learning often focuses on the main item traded by each country only. However, online trade is increasing, providing potential to update the conversation from a national scale to a global scale. We demonstrate how hierarchical clustering can identify wildlife items that follow similar trading patterns. We also ordinate the clusters, and seek correlations between the clusters and global measures, such as Worldwide Governance Indicators. We primarily use a sample dataset from a published report of online traded wildlife, covering 16 countries and 31 taxa or product types. Clustering provided immediate insights, such as rhinos and pangolins were traded similarly to ivory and suspected ivory. Five out of eight clusters represented items predominately traded by one country. An ordination of these clusters, and representation of global measures on the ordination axis, show a strong correlation of the ‘Voice and accountability' score with the clusters. Consequently, from the ‘Voice and accountability' score of the United States, a country not included in our dataset, we inferred that it traded elephant items (not ivory) and owl items during 2014.

Development of resistance to malaria treatments remains a great threat to continued malaria burden reduction and elimination. Quantifying the impact of key factors which increase the emergence and spread of drug resistance can guide intervention strategies. Whilst modelling provides a framework to understand these factors, we show that a simple of model with a sensitive-resistant dichotomy leads to incorrectly focusing on reducing the treatment rate as a means to prevent resistance. Instead we present a model that considers the development of resistance within hosts as a scale, and we then quantify the number of resistant infections that would arise from a single sensitive infection. By including just one step before full resistance, the model highlights that disrupting this development is more effective than reducing treatment rate. This result is compounded when the model includes the more realistic scenario of several intermediary steps. An additional comparison to transmission probabilities, where resistant infections are less likely to be transmitted (cost of resistance), confirms that preventing the establishment of resistance is more effective than controlling the spread. Our work strongly advocates for further studies into within-host models of resistance, including the potential of combination therapies to disrupt emergence. (C) 2018 Elsevier Ltd. All rights reserved.

More frequently protests are accompanied by an opposing group performing a counter protest. This phenomenon can increase tension such that police must try to keep the two groups separated. However, what is the best strategy for police? This paper uses a simple agent-based model to determine the best strategy for keeping the two groups separated. The 'thin blue line' varies in density (number of police), width and the keenness of police to approach protesters. Three different groups of protesters are modelled to mimic peaceful, average and volatile protests. In most cases, a few police forming a single-file 'thin blue line' separating the groups is very effective. However, when the protests are more volatile, it is more effective to have many police occupying a wide 'thin blue line', and police being keen to approach protesters. To the authors knowledge, this is the first paper to model protests and counter-protests.

Rhino populations are at a critical level due to the demand for rhino horn and the subsequent poaching. Wildlife managers attempt to secure rhinos with approaches to devalue the horn, the most common of which is dehorning. Game theory has been used to examine the interaction of poachers and wildlife managers where a manager can either 'dehorn' their rhinos or leave the horn attached and poachers may behave 'selectively' or 'indiscriminately'. The approach described in this paper builds on this previous work and investigates the interactions between the poachers, We build an evolutionary game theoretic model and determine which strategy is preferred by a poacher in various different populations of poachers. The purpose of this work is to discover whether conditions which encourage the poachers to behave selectively exist, that is, they only kill those rhinos with full horns. The analytical results show that full devaluation of all rhinos will likely lead to indiscriminate poaching. In turn it shows that devaluing of rhinos can only be effective when implemented along with a strong disincentive framework. This paper aims to contribute to the necessary research required for informed discussion about the lively debate on legalising rhino horn trade.

Extinction models vary in the information they require, the simplest considering the rate of certain sightings only. More complicated methods include uncertain sightings and allow for variation in the reliability of uncertain sightings. Generally extinction models require expert opinion, either as a prior belief that a species is extinct, or to establish the quality of a sighting record, or both. Is this subjectivity necessary? We present two models to explore whether the individual quality of sightings, judged by experts, is strongly informative of the probability of extinction: the ‘quality breakpoint method’ and the ‘quality as variance method’. For the first method we use the Barbary lion as an exemplar. For the second method we use the Barbary lion, Alaotra grebe, Jamaican petrel and Pohnpei starling as exemplars. The ‘quality breakpoint method’ uses certain and uncertain sighting records, and the quality of uncertain records, to establish whether a change point in the rate of sightings can be established using a simultaneous Bayesian optimisation with a non-informative prior. For the Barbary lion, there is a change in subjective quality of sightings around 1930. Unexpectedly sighting quality increases after this date. This suggests that including quality scores from experts can lead to irregular effects and may not offer reliable results. As an alternative, we use quality as a measure of variance around the sightings, not a change in quality. This leads to predictions with larger standard deviations, however the results remain consistent across any prior belief of extinction. Nonetheless, replacing actual quality scores with random quality scores showed little difference, inferring that the quality scores from experts are superfluous. Therefore, we deem the expensive process of obtaining pooled expert estimates as unnecessary, and even when used we recommend that sighting data should have minimal input from experts in terms of assessing the sighting quality at a fine scale. Rather, sightings should be classed as certain or uncertain, using a framework that is as independent of human bias as possible.

Rhino populations are at a critical level due to the demand for rhino horn and the subsequent poaching. Wild life managers attempt to secure rhinos with approaches such as devaluing the horn. The most common strategy of devaluing horns is dehorning. Game theory has been used before to examine the interaction of poachers and wild life managers. A manager can either `dehornu0027 their rhinos or leave the horn attached. Poachers may chose to to behave `selectivelyu0027 or `indiscriminatelyu0027. The approach described in this paper builds on this previous work and investigates the interactions between the poachers. Using evolutionary game theory, we determine which strategy is preferred by a poacher in various different populations of poachers. The purpose of this work is to discover whether conditions which encourage the poachers to behave selectively exist, that is, they only kill those rhinos with full horns. Notwithstanding, the analytical results prove that poachers will never adopt a selective strategy as long as there is gain from a partial horn. Additionally, poachers behaving indiscriminately is stable and robust. However, the model is adapted further to include a disincentive factor, which may represent factors such as harsher punishment, or lower demand for horn. With a disincentive, poachers can be encouraged to behave selectively, but only when there are few devalued rhinos. This paper aims to contribute to the necessary research needed for informed discussion about the lively debate on legalising rhino horn trade.

We consider a directed graph model for the human brain's neural architecture that is based on small scale, directed, strongly connected sub-graphs (SCGs) of neurons, that are connected together by a sparser mesoscopic network. We assume transmission delays within neuron-to-neuron stimulation, and that individual neurons have an excitable-refractory dynamic, with single firing 'spikes' occurring on a much faster time scale than that of the transmission delays. We demonstrate numerically that the SCGs typically have attractors that are equivalent to continual winding maps over relatively low-dimensional tori, thus representing a limit on the range of distinct behaviour. For a discrete formulation, we conduct a large-scale survey of SCGs of varying size, but with the same local structure. We demonstrate that there may be benefits (increased processing capacity and efficiency) in brains having evolved to have a larger number of small irreducible sub-graphs, rather than few, large irreducible sub-graphs. The network of SCGs could be thought of as an architecture that has evolved to create decisions in the light of partial or early incoming information. Hence the applicability of the proposed paradigm to underpinning human cognition.

Estimating the demand on the low voltage network is essential for the distribution network operator (DNO), who is interested in managing and planning the network. Such concerns are particularly relevant as the UK moves towards a low carbon economy, and the electrification of heating and transport. Furthermore, small to medium enterprises (SMEs) contribute a significant proportion to network demand but are often overlooked. The smart meter roll out will provide greater visibility of the network, but such data may not be readily available to the DNOs. The question arises whether useful information about customer demand can be discerned from limited access to smart meter data? We analyse smart meter data from 196 SMEs so that one may create an energy demand profile based on information which is available without a smart meter. The profile itself comprises of simply two estimates, one for operational power and another for non-operational power. We further improve the profile by clustering the SMEs using a simple Gaussian mixture model. In both cases, the average difference between the actual and predicted operational/non-operational power is less than 0.15 kWh, and clustering reduces the range around this difference. The methods presented here out perform the flat profile (akin to current methods).

© 2017 The Institution of Engineering and Technology. All rights reserved. Uptake of low carbon technologies could likely lead to increased demand in distribution networks and consequently could impose additional stress on the networks. Battery energy storage systems (BESS) are identified as a feasible alternative to traditional network reinforcement. This study analyses two BESS scheduling algorithms (model predictive control (MPC) and fixed schedule) supplied with forecasts from five methods for predicting demand on 100 low-voltage feeders. Results show that forecasting feeders with higher mean daily demand produce lower mean absolute errors and better peak demand reduction. MPC with simple error improves peak reduction over fixed schedule for feeders with lower mean daily demand.

Understanding domestic electricity consumption behaviour is very important for planners and operators of electrical networks. Distribution Network Operators (DNOs) are installing monitoring devices at the low-voltage (LV) substation level. However, monitoring devices need to be allocated with consideration. This paper seeks to quantify the relationship between key information from a substation electricity profile, and the type of properties it supplies. Often the number of properties is the most significant predictor for key substation features. Additionally, the proportion of properties which are small-to-medium enterprises (SMEs) affects nearly all key features considered: from the overall load mean and variance, to the annual trend, to peak behaviour. Knowing substation key features is of direct use, for example where to prioritise monitoring. However we also demonstrate how these key features can be used within a so-called ‘buddying’ framework to estimate the load every half-hour for individual properties.

Distribution network operators (DNOs) are increasingly concerned about the impact of low carbon technologies on the low voltage (LV) networks. More advanced metering infrastructures provide numerous opportunities for more accurate load flow analysis of the LV networks. However, such data may not be readily available for DNOs and in any case is likely to be expensive. Modelling tools are required which can provide realistic, yet accurate, load profiles as input for a network modelling tool, without needing access to large amounts of monitored customer data. In this paper we outline some simple methods for accurately modelling a large number of unmonitored residential customers at the LV level. We do this by a process we call buddying, which models unmonitored customers by assigning them load profiles from a limited sample of monitored customers who have smart meters. Hence the presented method requires access to only a relatively small amount of domestic customers' data. The method is efficiently optimised using a genetic algorithm to minimise a weighted cost function between matching the substation data and the individual mean daily demands. Hence we can show the effectiveness of substation monitoring in LV network modelling. Using real LV network modelling, we show that our methods perform significantly better than a comparative Monte Carlo approach, and provide a description of the peak demand behaviour.