Systematic Review and Geospatial Modeling of Molecular Markers of Resistance to Artemisinins and Sulfadoxine-Pyrimethamine in Plasmodium falciparum in India

Surveillance for genetic markers of resistance can provide valuable information on the likely efficacy of antimalarials but needs to be targeted to ensure optimal use of resources. We conducted a systematic search and review of publications in seven databases to compile resistance marker data from studies in India. The sample collection from the studies identified from this search was conducted between 1994 and 2020, and these studies were published between 1994 and 2022. In all, Plasmodium falciparum Kelch13 (PfK13), P. falciparum dihydropteroate synthase, and P. falciparum dihydrofolate reductase (PfDHPS) genotype data from 2,953, 4,148, and 4,222 blood samples from patients with laboratory-confirmed malaria, respectively, were extracted from these publications and uploaded onto the WorldWide Antimalarial Resistance Network molecular surveyors. These data were fed into hierarchical geostatistical models to produce maps with a predicted prevalence of the PfK13 and PfDHPS markers, and of the associated uncertainty. Zones with a predicted PfDHPS 540E prevalence of >15% were identified in central, eastern, and northeastern India. The predicted prevalence of PfK13 mutants was nonzero at only a few locations, but were within or adjacent to the zones with >15% prevalence of PfDHPS 540E. There may be a greater probability of artesunate-sulfadoxine-pyrimethamine failures in these regions, but these predictions need confirmation. This work can be applied in India and elsewhere to help identify the treatments most likely to be effective for malaria elimination.

Spatio-temporal spread of artemisinin resistance in Southeast Asia

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia.

Mathematical models of Plasmodium vivax transmission: A scoping review

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world, primarily found across South-East Asia, Latin America, and parts of Africa. One of the significant characteristics of the P. vivax parasite is its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). Mathematical modelling approaches have been widely applied to understand P. vivax dynamics and predict the impact of intervention outcomes. Models that capture P. vivax dynamics differ from those that capture P. falciparum dynamics, as they must account for relapses caused by the activation of hypnozoites. In this article, we provide a scoping review of mathematical models that capture P. vivax transmission dynamics published between January 1988 and May 2023. The primary objective of this work is to provide a comprehensive summary of the mathematical models and techniques used to model P. vivax dynamics. In doing so, we aim to assist researchers working on mathematical epidemiology, disease transmission, and other aspects of P. vivax malaria by highlighting best practices in currently published models and highlighting where further model development is required. We categorise P. vivax models according to whether a deterministic or agent-based approach was used. We provide an overview of the different strategies used to incorporate the parasite's biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites' complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for P. vivax encompasses various aspects of the parasite's dynamics. We recommend that future research should focus on refining how key aspects of P. vivax dynamics are modelled, including spatial heterogeneity in exposure risk and heterogeneity in susceptibility to infection, the accumulation of hypnozoite variation, the interaction between P. falciparum and P. vivax, acquisition of immunity, and recovery under superinfection.

A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia

Disease surveillance aims to collect data at different times or locations, to assist public health authorities to respond appropriately. Surveillance of the simian malaria parasite, Plasmodium knowlesi, is sparse in some endemic areas and the spatial extent of transmission is uncertain. Zoonotic transmission of Plasmodium knowlesi has been demonstrated throughout Southeast Asia and represents a major hurdle to regional malaria elimination efforts. Given an arbitrary spatial prediction of relative disease risk, we develop a flexible framework for surveillance site selection, drawing on principles from multi-criteria decision-making. To demonstrate the utility of our framework, we apply it to the case study of Plasmodium knowlesi malaria surveillance site selection in western Indonesia. We demonstrate how statistical predictions of relative disease risk can be quantitatively incorporated into public health decision-making, with specific application to active human surveillance of zoonotic malaria. This approach can be used in other contexts to extend the utility of modelling outputs.

A spatio-temporal model of multi-marker antimalarial resistance

The emergence and spread of drug-resistant Plasmodium falciparum parasites have hindered efforts to eliminate malaria. Monitoring the spread of drug resistance is vital, as drug resistance can lead to widespread treatment failure. We develop a Bayesian model to produce spatio-temporal maps that depict the spread of drug resistance, and apply our methods for the antimalarial sulfadoxine-pyrimethamine. We infer from genetic count data the prevalences over space and time of various malaria parasite haplotypes associated with drug resistance. Previous work has focused on inferring the prevalence of individual molecular markers. In reality, combinations of mutations at multiple markers confer varying degrees of drug resistance to the parasite, indicating that multiple markers should be modelled together. However, the reporting of genetic count data is often inconsistent as some studies report haplotype counts, whereas some studies report mutation counts of individual markers separately. In response, we introduce a latent multinomial Gaussian process model to handle partially reported spatio-temporal count data. As drug-resistant mutations are often used as a proxy for treatment efficacy, point estimates from our spatio-temporal maps can help inform antimalarial drug policies, whereas the uncertainties from our maps can help with optimizing sampling strategies for future monitoring of drug resistance.

Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia

BACKGROUND

Plasmodium knowlesi is a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of the Anopheles Leucosphyrus Group. The risk of human P. knowlesi infection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available on P. knowlesi occurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk of P. knowlesi malaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.

METHODS & RESULTS

We extend and apply an environmental niche modelling framework as implemented by a previous mapping study of P. knowlesi transmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records of P. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation on P. knowlesi transmission.

DISCUSSION

Our map of P. knowlesi relative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority for P. knowlesi surveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.

Mathematical assessment of the role of intervention programs for malaria control

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.

Comparison of new computational methods for spatial modelling of malaria

BACKGROUND

Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases.

METHODS

This work presents an applied comparison of four proposed 'fast' computational methods for spatial modelling and the software provided to implement them-Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales-country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation.

RESULTS

Two of these methods-SpRF and GPBoost-do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods-INLA and FRK-do scale well computationally, however the resulting model fits are very sensitive to the user's modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution.

CONCLUSIONS

INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters.

Free and Interfacial Boundaries in Individual-Based Models of Multicellular Biological systems

Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.

A model for malaria treatment evaluation in the presence of multiple species

Plasmodium falciparum and P. vivax are the two most common causes of malaria. While the majority of deaths and severe morbidity are due to P. falciparum, P. vivax poses a greater challenge to eliminating malaria outside of Africa due to its ability to form latent liver stage parasites (hypnozoites), which can cause relapsing episodes within an individual patient. In areas where P. falciparum and P. vivax are co-endemic, individuals can carry parasites of both species simultaneously. These mixed infections complicate dynamics in several ways: treatment of mixed infections will simultaneously affect both species, P. falciparum can mask the detection of P. vivax, and it has been hypothesised that clearing P. falciparum may trigger a relapse of dormant P. vivax. When mixed infections are treated for only blood-stage parasites, patients are at risk of relapse infections due to P. vivax hypnozoites. We present a stochastic mathematical model that captures interactions between P. falciparum and P. vivax, and incorporates both standard schizonticidal treatment (which targets blood-stage parasites) and radical cure treatment (which additionally targets liver-stage parasites). We apply this model via a hypothetical simulation study to assess the implications of different treatment coverages of radical cure for mixed and P. vivax infections and a "unified radical cure" treatment strategy where P. falciparum, P. vivax, and mixed infections all receive radical cure after screening glucose-6-phosphate dehydrogenase (G6PD) normal. In addition, we investigated the impact of mass drug administration (MDA) of blood-stage treatment. We find that a unified radical cure strategy leads to a substantially lower incidence of malaria cases and deaths overall. MDA with schizonticidal treatment was found to decrease P. falciparum with little effect on P. vivax. We perform a univariate sensitivity analysis to highlight important model parameters.

Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia

Background

Plasmodium knowlesi is a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of the Anopheles Leucosphyrus Group. The risk of human P. knowlesi infection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available on P. knowlesi occurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk of P. knowlesi malaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.

Methods & Results

We extend and apply an environmental niche modelling framework as implemented by a previous mapping study of P. knowlesi transmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records of P. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation on P. knowlesi transmission.

Discussion

Our map of P. knowlesi relative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority for P. knowlesi surveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.

Mathematical models of developmental vascular remodelling: A review

Over the past 40 years, there has been a strong focus on the development of mathematical models of angiogenesis, while developmental remodelling has received little such attention from the mathematical community. Sprouting angiogenesis can be seen as a very crude way of laying out a primitive vessel network (the raw material), while remodelling (understood as pruning of redundant vessels, diameter control, and the establishment of vessel identity and hierarchy) is the key to turning that primitive network into a functional network. This multiscale problem is of prime importance in the development of a functional vasculature. In addition, defective remodelling (either during developmental remodelling or due to a reactivation of the remodelling programme caused by an injury) is associated with a significant number of diseases. In this review, we discuss existing mathematical models of developmental remodelling and explore the important contributions that these models have made to the field of vascular development. These mathematical models are effectively used to investigate and predict vascular development and are able to reproduce experimentally observable results. Moreover, these models provide a useful means of hypothesis generation and can explain the underlying mechanisms driving the observed structural and functional network development. However, developmental vascular remodelling is still a relatively new area in mathematical biology, and many biological questions remain unanswered. In this review, we present the existing modelling paradigms and define the key challenges for the field.

Optimal Interruption of P. vivax Malaria Transmission Using Mass Drug Administration

Plasmodium vivax is the most geographically widespread malaria-causing parasite resulting in significant associated global morbidity and mortality. One of the factors driving this widespread phenomenon is the ability of the parasites to remain dormant in the liver. Known as 'hypnozoites', they reside in the liver following an initial exposure, before activating later to cause further infections, referred to as 'relapses'. As around 79-96% of infections are attributed to relapses from activating hypnozoites, we expect it will be highly impactful to apply treatment to target the hypnozoite reservoir (i.e. the collection of dormant parasites) to eliminate P. vivax. Treatment with radical cure, for example tafenoquine or primaquine, to target the hypnozoite reservoir is a potential tool to control and/or eliminate P. vivax. We have developed a deterministic multiscale mathematical model as a system of integro-differential equations that captures the complex dynamics of P. vivax hypnozoites and the effect of hypnozoite relapse on disease transmission. Here, we use our multiscale model to study the anticipated effect of radical cure treatment administered via a mass drug administration (MDA) program. We implement multiple rounds of MDA with a fixed interval between rounds, starting from different steady-state disease prevalences. We then construct an optimisation model with three different objective functions motivated on a public health basis to obtain the optimal MDA interval. We also incorporate mosquito seasonality in our model to study its effect on the optimal treatment regime. We find that the effect of MDA interventions is temporary and depends on the pre-intervention disease prevalence (and choice of model parameters) as well as the number of MDA rounds under consideration. The optimal interval between MDA rounds also depends on the objective (combinations of expected intervention outcomes). We find radical cure alone may not be enough to lead to P. vivax elimination under our mathematical model (and choice of model parameters) since the prevalence of infection eventually returns to pre-MDA levels.

Population heterogeneity in Plasmodium vivax relapse risk

A key characteristic of Plasmodium vivax parasites is their ability to adopt a latent liver-stage form called hypnozoites, able to cause relapse of infection months or years after a primary infection. Relapses of infection through hypnozoite activation are a major contributor to blood-stage infections in P vivax endemic regions and are thought to be influenced by factors such as febrile infections which may cause temporary changes in hypnozoite activation leading to 'temporal heterogeneity' in reactivation risk. In addition, immunity and variation in exposure to infection may be longer-term characteristics of individuals that lead to 'population heterogeneity' in hypnozoite activation. We analyze data on risk of P vivax in two previously published data sets from Papua New Guinea and the Thailand-Myanmar border region. Modeling different mechanisms of reactivation risk, we find strong evidence for population heterogeneity, with 30% of patients having almost 70% of all P vivax infections. Model fitting and data analysis indicates that individual variation in relapse risk is a primary source of heterogeneity of P vivax risk of recurrences. Trial Registration: ClinicalTrials.gov NCT01640574, NCT01074905, NCT02143934.

How quickly does a wound heal? Bayesian calibration of a mathematical model of venous leg ulcer healing

Chronic wounds, such as venous leg ulcers, are difficult to treat and can reduce the quality of life for patients. Clinical trials have been conducted to identify the most effective venous leg ulcer treatments and the clinical factors that may indicate whether a wound will successfully heal. More recently, mathematical modelling has been used to gain insight into biological factors that may affect treatment success but are difficult to measure clinically, such as the rate of oxygen flow into wounded tissue. In this work, we calibrate an existing mathematical model using a Bayesian approach with clinical data for individual patients to explore which clinical factors may impact the rate of wound healing for individuals. Although the model describes group-level behaviour well, it is not able to capture individual-level responses in all cases. From the individual-level analysis, we propose distributions for coefficients of clinical factors in a linear regression model, but ultimately find that it is difficult to draw conclusions about which factors lead to faster wound healing based on the existing model and data. This work highlights the challenges of using Bayesian methods to calibrate partial differential equation models to individual patient clinical data. However, the methods used in this work may be modified and extended to calibrate spatiotemporal mathematical models to multiple data sets, such as clinical trials with several patients, to extract additional information from the model and answer outstanding biological questions.

COVID-19 morbidity in Afghanistan: a nationwide, population-based seroepidemiological study

OBJECTIVE

The primary objectives were to determine the magnitude of COVID-19 infections in the general population and age-specific cumulative incidence, as determined by seropositivity and clinical symptoms of COVID-19, and to determine the magnitude of asymptomatic or subclinical infections.

DESIGN, SETTING AND PARTICIPANTS

We describe a population-based, cross-sectional, age-stratified seroepidemiological study conducted throughout Afghanistan during June/July 2020. Participants were interviewed to complete a questionnaire, and rapid diagnostic tests were used to test for SARS-CoV-2 antibodies. This national study was conducted in eight regions of Afghanistan plus Kabul province, considered a separate region. The total sample size was 9514, and the number of participants required in each region was estimated proportionally to the population size of each region. For each region, 31-44 enumeration areas (EAs) were randomly selected, and a total of 360 clusters and 16 households per EA were selected using random sampling. To adjust the seroprevalence for test sensitivity and specificity, and seroreversion, Bernoulli's model methodology was used to infer the population exposure in Afghanistan.

OUTCOME MEASURES

The main outcome was to determine the prevalence of current or past COVID-19 infection.

RESULTS

The survey revealed that, to July 2020, around 10 million people in Afghanistan (31.5% of the population) had either current or previous COVID-19 infection. By age group, COVID-19 seroprevalence was reported to be 35.1% and 25.3% among participants aged ≥18 and 5-17 years, respectively. This implies that most of the population remained at risk of infection. However, a large proportion of the population had been infected in some localities, for example, Kabul province, where more than half of the population had been infected with COVID-19.

CONCLUSION

As most of the population remained at risk of infection at the time of the study, any lifting of public health and social measures needed to be considered gradually.

A Multiscale Mathematical Model of Plasmodium Vivax Transmission

Malaria is caused by Plasmodium parasites which are transmitted to humans by the bite of an infected Anopheles mosquito. Plasmodium vivax is distinct from other malaria species in its ability to remain dormant in the liver (as hypnozoites) and activate later to cause further infections (referred to as relapses). Mathematical models to describe the transmission dynamics of P. vivax have been developed, but most of them fail to capture realistic dynamics of hypnozoites. Models that do capture the complexity tend to involve many governing equations, making them difficult to extend to incorporate other important factors for P. vivax, such as treatment status, age and pregnancy. In this paper, we have developed a multiscale model (a system of integro-differential equations) that involves a minimal set of equations at the population scale, with an embedded within-host model that can capture the dynamics of the hypnozoite reservoir. In this way, we can gain key insights into dynamics of P. vivax transmission with a minimum number of equations at the population scale, making this framework readily scalable to incorporate more complexity. We performed a sensitivity analysis of our multiscale model over key parameters and found that prevalence of P. vivax blood-stage infection increases with both bite rate and number of mosquitoes but decreases with hypnozoite death rate. Since our mathematical model captures the complex dynamics of P. vivax and the hypnozoite reservoir, it has the potential to become a key tool to inform elimination strategies for P. vivax.

Detection and identification of cis-regulatory elements using change-point and classification algorithms

BACKGROUND

Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation.

RESULTS

We extend an alignment-based method, changept, and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite.

CONCLUSIONS

The analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.

Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys

Accurate knowledge of prior population exposure has critical ramifications for preparedness plans for future SARS-CoV-2 epidemic waves and vaccine prioritization strategies. Serological studies can be used to estimate levels of past exposure and thus position populations in their epidemic timeline. To circumvent biases introduced by the decay in antibody titers over time, methods for estimating population exposure should account for seroreversion, to reflect that changes in seroprevalence measures over time are the net effect of increases due to recent transmission and decreases due to antibody waning. Here, we present a new method that combines multiple datasets (serology, mortality, and virus positivity ratios) to estimate seroreversion time and infection fatality ratios (IFR) and simultaneously infer population exposure levels. The results indicate that the average time to seroreversion is around six months, IFR is 0.54% to 1.3%, and true exposure may be more than double the current seroprevalence levels reported for several regions of England.

Revisiting the Natural History of Pulmonary Tuberculosis: A Bayesian Estimation of Natural Recovery and Mortality Rates

BACKGROUND

Tuberculosis (TB) natural history remains poorly characterized, and new investigations are impossible as it would be unethical to follow up TB patients without treatment.

METHODS

We considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB).

RESULTS

We included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. The median estimates of the TB-specific mortality rates were 0.389 year-1 (95% credible interval [CrI], .335-.449) and 0.025 year-1 (95% CrI, .017-.035) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.231 year-1 (95% CrI, .177-.288) and 0.130 year-1 (95% CrI, .073-.209) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (95% CrI, 1.37-1.81) and 5.35 years (95% CrI, 3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a non-TB-related mortality rate of 0.014 year-1 (ie, a 70-year life expectancy).

CONCLUSIONS

TB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modeling studies, raising concerns about the accuracy of the associated predictions. Despite being less infectious, SN-TB may be responsible for equivalent numbers of secondary infections as SP-TB due to its much longer duration.

Mathematical Modeling Can Advance Wound Healing Research

Significance: For over 30 years, there has been sustained interest in the development of mathematical models for investigating the complex mechanisms underlying each stage of the wound healing process. Despite the immense associated challenges, such models have helped usher in a paradigm shift in wound healing research. Recent Advances: In this article, we review contributions in the field that span epidermal, dermal, and corneal wound healing, and treatments of nonhealing wounds. The recent influence of mathematical models on biological experiments is detailed, with a focus on wound healing assays and fibroblast-populated collagen lattices. Critical Issues: We provide an overview of the field of mathematical modeling of wound healing, highlighting key advances made in recent decades, and discuss how such models have contributed to the development of improved treatment strategies and/or an enhanced understanding of the tightly regulated steps that comprise the healing process. Future Directions: We detail some of the open problems in the field that could be addressed through a combination of theoretical and/or experimental approaches. To move the field forward, we need to have a common language between scientists to facilitate cross-collaboration, which we hope this review can support by highlighting progress to date.

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to two ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.

A mathematical model of the use of supplemental oxygen to combat surgical site infection

Infections are a common complication of any surgery, often requiring a recovery period in hospital. Supplemental oxygen therapy administered during and immediately after surgery is thought to enhance the immune response to bacterial contamination. However, aerobic bacteria thrive in oxygen-rich environments, and so it is unclear whether oxygen has a net positive effect on recovery. Here, we develop a mathematical model of post-surgery infection to investigate the efficacy of supplemental oxygen therapy on surgical-site infections. A 4-species, coupled, set of non-linear partial differential equations that describes the space-time dependence of neutrophils, bacteria, chemoattractant and oxygen is developed and analysed to determine its underlying properties. Through numerical solutions, we quantify the efficacy of different supplemental oxygen regimes on the treatment of surgical site infections in wounds of different initial bacterial load. A sensitivity analysis is performed to investigate the robustness of the predictions to changes in the model parameters. The numerical results are in good agreement with analyses of the associated well-mixed model. Our model findings provide insight into how the nature of the contaminant and its initial density influence bacterial infection dynamics in the surgical wound.

Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

Background

Emerging resistance to anti-malarial drugs has led malaria researchers to investigate what covariates (parasite and host factors) are associated with resistance. In this regard, investigation of how covariates impact malaria parasites clearance is often performed using a two-stage approach in which the WWARN Parasite Clearance Estimator or PCE is used to estimate parasite clearance rates and then the estimated parasite clearance is regressed on the covariates. However, the recently developed Bayesian Clearance Estimator instead leads to more accurate results for hierarchial regression modelling which motivated the authors to implement the method as an R package, called “bhrcr”.

Methods

Given malaria parasite clearance profiles of a set of patients, the “bhrcr” package performs Bayesian hierarchical regression to estimate malaria parasite clearance rates along with the effect of covariates on them in the presence of “lag” and “tail” phases. In particular, the model performs a linear regression of the log clearance rates on covariates to estimate the effects within a Bayesian hierarchical framework. All posterior inferences are obtained by a “Markov Chain Monte Carlo” based sampling scheme which forms the core of the package.

Results

The “bhrcr” package can be utilized to study malaria parasite clearance data, and specifically, how covariates affect parasite clearance rates. In addition to estimating the clearance rates and the impact of covariates on them, the “bhrcr” package provides tools to calculate the WWARN PCE estimates of the parasite clearance rates as well. The fitted Bayesian model to the clearance profile of each individual, as well as the WWARN PCE estimates, can also be plotted by this package.

Conclusions

This paper explains the Bayesian Clearance Estimator for malaria researchers including describing the freely available software, thus making these methods accessible and practical for modelling covariates’ effects on parasite clearance rates.

Agent-based models of malaria transmission: a systematic review

BACKGROUND

Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations.

METHODS

A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field.

RESULTS

The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques.

CONCLUSION

Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts.

Cyclin A2 modulates kinetochore-microtubule attachment in meiosis II

Cyclin A2 is a crucial mitotic Cdk regulatory partner that coordinates entry into mitosis and is then destroyed in prometaphase within minutes of nuclear envelope breakdown. The role of cyclin A2 in female meiosis and its dynamics during the transition from meiosis I (MI) to meiosis II (MII) remain unclear. We found that cyclin A2 decreases in prometaphase I but recovers after the first meiotic division and persists, uniquely for metaphase, in MII-arrested oocytes. Conditional deletion of cyclin A2 from mouse oocytes has no discernible effect on MI but leads to disrupted MII spindles and increased merotelic attachments. On stimulation of exit from MII, there is a dramatic increase in lagging chromosomes and an inhibition of cytokinesis. These defects are associated with an increase in microtubule stability in MII spindles, suggesting that cyclin A2 mediates the fidelity of MII by maintaining microtubule dynamics during the rapid formation of the MII spindle.

Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine Exert Inverse Selective Pressure on Plasmodium Falciparum Drug Sensitivity-Associated Haplotypes in Uganda

BACKGROUND

Altered sensitivity to multiple antimalarial drugs is mediated by polymorphisms in pfmdr1, which encodes the Plasmodium falciparum multidrug resistance transporter. In Africa the N86Y and D1246Y polymorphisms have been shown to be selected by treatment, with artemether-lumefantrine (AL) and dihydroartemisinin-piperaquine (DP) selecting for wild-type and mutant alleles, respectively. However, there has been little study of pfmdr1 haplotypes, in part because haplotype analyses are complicated by multiclonal infections.

METHODS

We fit a haplotype frequency estimation model, which accounts for multiclonal infections, to the polymorphic pfmdr1 N86Y, Y184F, and D1246Y alleles in samples from a longitudinal trial comparing AL and DP to treat uncomplicated P falciparum malaria in Tororo, Uganda from 2007 to 2012. We regressed estimates onto covariates of trial arm and selective drug pressure.

RESULTS

Yearly trends showed increasing frequency estimates for haplotypes with wild type pfmdr1 N86 and D1246 alleles and decreasing frequency estimates for haplotypes with the mutant pfmdr1 86Y allele. Considering days since prior therapy, we saw evidence suggestive of selection by AL for haplotypes with N86 combined with 184F, D1246, or both, and against all haplotypes with 86Y, and evidence suggestive of selection by DP for 86Y only when combined with Y184 and 1246Y (haplotype YYY) and against haplotypes NFD and NYY.

CONCLUSIONS

Based on our model, AL selected several haplotypes containing N86, whereas DP selection was haplotype specific, demonstrating the importance of haplotype analyses. Inverse selective pressure of AL and DP on the complementary haplotypes NFD and YYY suggests that rotating artemisinin-based antimalarial combination regimens may be the best treatment option to prevent resistance selection.

Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia

BACKGROUND

Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently.

METHODS

The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling.

RESULTS

The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion.

CONCLUSION

This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.

On the mathematical modeling of wound healing angiogenesis in skin as a reaction-transport process

Over the last 30 years, numerous research groups have attempted to provide mathematical descriptions of the skin wound healing process. The development of theoretical models of the interlinked processes that underlie the healing mechanism has yielded considerable insight into aspects of this critical phenomenon that remain difficult to investigate empirically. In particular, the mathematical modeling of angiogenesis, i.e., capillary sprout growth, has offered new paradigms for the understanding of this highly complex and crucial step in the healing pathway. With the recent advances in imaging and cell tracking, the time is now ripe for an appraisal of the utility and importance of mathematical modeling in wound healing angiogenesis research. The purpose of this review is to pedagogically elucidate the conceptual principles that have underpinned the development of mathematical descriptions of wound healing angiogenesis, specifically those that have utilized a continuum reaction-transport framework, and highlight the contribution that such models have made toward the advancement of research in this field. We aim to draw attention to the common assumptions made when developing models of this nature, thereby bringing into focus the advantages and limitations of this approach. A deeper integration of mathematical modeling techniques into the practice of wound healing angiogenesis research promises new perspectives for advancing our knowledge in this area. To this end we detail several open problems related to the understanding of wound healing angiogenesis, and outline how these issues could be addressed through closer cross-disciplinary collaboration.

Baseline data of parasite clearance in patients with falciparum malaria treated with an artemisinin derivative: an individual patient data meta-analysis

Background

Artemisinin resistance in Plasmodium falciparum manifests as slow parasite clearance but this measure is also influenced by host immunity, initial parasite biomass and partner drug efficacy. This study collated data from clinical trials of artemisinin derivatives in falciparum malaria with frequent parasite counts to provide reference parasite clearance estimates stratified by location, treatment and time, to examine host factors affecting parasite clearance, and to assess the relationships between parasite clearance and risk of recrudescence during follow-up.

Methods

Data from 24 studies, conducted from 1996 to 2013, with frequent parasite counts were pooled. Parasite clearance half-life (PC_1/2) was estimated using the WWARN Parasite Clearance Estimator. Random effects regression models accounting for study and site heterogeneity were used to explore factors affecting PC_1/2 and risk of recrudescence within areas with reported delayed parasite clearance (western Cambodia, western Thailand after 2000, southern Vietnam, southern Myanmar) and in all other areas where parasite populations are artemisinin sensitive.

Results

PC_1/2 was estimated in 6975 patients, 3288 of whom also had treatment outcomes evaluate d during 28–63 days follow-up, with 93 (2.8 %) PCR-confirmed recrudescences. In areas with artemisinin-sensitive parasites, the median PC_1/2 following three-day artesunate treatment (4 mg/kg/day) ranged from 1.8 to 3.0 h and the proportion of patients with PC_1/2 >5 h from 0 to 10 %. Artesunate doses of 4 mg/kg/day decreased PC_1/2 by 8.1 % (95 % CI 3.2–12.6) compared to 2 mg/kg/day, except in populations with delayed parasite clearance. PC_1/2 was longer in children and in patients with fever or anaemia at enrolment. Long PC_1/2 (HR = 2.91, 95 % CI 1.95–4.34 for twofold increase, p < 0.001) and high initial parasitaemia (HR = 2.23, 95 % CI 1.44–3.45 for tenfold increase, p < 0.001) were associated independently with an increased risk of recrudescence. In western Cambodia, the region with the highest prevalence of artemisinin resistance, there was no evidence for increasing PC_1/2 since 2007.

Conclusions

Several factors affect PC_1/2. As substantial heterogeneity in parasite clearance exists between locations, early detection of artemisinin resistance requires reference PC_1/2 data. Studies with frequent parasite count measurements to characterize PC_1/2 should be encouraged. In western Cambodia, where PC_1/2 values are longest, there is no evidence for recent emergence of higher levels of artemisinin resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0874-1) contains supplementary material, which is available to authorized users.

Bayesian hierarchical regression on clearance rates in the presence of "lag" and "tail" phases with an application to malaria parasites

We present a principled technique for estimating the effect of covariates on malaria parasite clearance rates in the presence of "lag" and "tail" phases through the use of a Bayesian hierarchical linear model. The hierarchical approach enables us to appropriately incorporate the uncertainty in both estimating clearance rates in patients and assessing the potential impact of covariates on these rates into the posterior intervals generated for the parameters associated with each covariate. Furthermore, it permits us to incorporate information about individuals for whom there exists only one observation time before censoring, which alleviates a systematic bias affecting inference when these individuals are excluded. We use a changepoint model to account for both lag and tail phases, and hence base our estimation of the parasite clearance rate only on observations within the decay phase. The Bayesian approach allows us to treat the delineation between lag, decay, and tail phases within an individual's clearance profile as themselves being random variables, thus taking into account the additional uncertainty of boundaries between phases. We compare our method to existing methodology used in the antimalarial research community through a simulation study and show that it possesses desirable frequentist properties for conducting inference. We use our methodology to measure the impact of several covariates on Plasmodium falciparum clearance rate data collected in 2009 and 2010. Though our method was developed with this application in mind, it can be easily applied to any biological system exhibiting these hindrances to estimation.

Defining the in vivo phenotype of artemisinin-resistant falciparum malaria: a modelling approach

BACKGROUND

Artemisinin-resistant falciparum malaria has emerged in Southeast Asia, posing a major threat to malaria control. It is characterised by delayed asexual-stage parasite clearance, which is the reference comparator for the molecular marker 'Kelch 13' and in vitro sensitivity tests. However, current cut-off values denoting slow clearance based on the proportion of individuals remaining parasitaemic on the third day of treatment ('day-3'), or on peripheral blood parasite half-life, are not well supported. We here explore the parasite clearance distributions in an area of artemisinin resistance with the aim refining the in vivo phenotypic definitions.

METHODS AND FINDINGS

Data from 1,518 patients on the Thai-Myanmar and Thai-Cambodian borders with parasite half-life assessments after artesunate treatment were analysed. Half-lives followed a bimodal distribution. A statistical approach was developed to infer the characteristics of the component distributions and their relative contribution to the composite mixture. A model representing two parasite subpopulations with geometric mean (IQR) parasite half-lives of 3.0 (2.4-3.9) hours and 6.50 (5.7-7.4) hours was consistent with the data. For individual patients, the parasite half-life provided a predicted likelihood of an artemisinin-resistant infection which depends on the population prevalence of resistance in that area. Consequently, a half-life where the probability is 0.5 varied between 3.5 and 5.5 hours. Using this model, the current 'day-3' cut-off value of 10% predicts the potential presence of artemisinin-resistant infections in most but not all scenarios. These findings are relevant to the low-transmission setting of Southeast Asia. Generalisation to a high transmission setting as in regions of Sub-Saharan Africa will need additional evaluation.

CONCLUSIONS

Characterisation of overlapping distributions of parasite half-lives provides quantitative insight into the relationship between parasite clearance and artemisinin resistance, as well as the predictive value of the 10% cut-off in 'day-3' parasitaemia. The findings are important for the interpretation of in vitro sensitivity tests and molecular markers for artemisinin resistance and for contextualising the 'day 3' threshold to account for initial parasitaemia and sample size.

The effect of dosing strategies on the therapeutic efficacy of artesunate-amodiaquine for uncomplicated malaria: a meta-analysis of individual patient data

BACKGROUND

Artesunate-amodiaquine (AS-AQ) is one of the most widely used artemisinin-based combination therapies (ACTs) to treat uncomplicated Plasmodium falciparum malaria in Africa. We investigated the impact of different dosing strategies on the efficacy of this combination for the treatment of falciparum malaria.

METHODS

Individual patient data from AS-AQ clinical trials were pooled using the WorldWide Antimalarial Resistance Network (WWARN) standardised methodology. Risk factors for treatment failure were identified using a Cox regression model with shared frailty across study sites.

RESULTS

Forty-three studies representing 9,106 treatments from 1999-2012 were included in the analysis; 4,138 (45.4%) treatments were with a fixed dose combination with an AQ target dose of 30 mg/kg (FDC), 1,293 (14.2%) with a non-fixed dose combination with an AQ target dose of 25 mg/kg (loose NFDC-25), 2,418 (26.6%) with a non-fixed dose combination with an AQ target dose of 30 mg/kg (loose NFDC-30), and the remaining 1,257 (13.8%) with a co-blistered non-fixed dose combination with an AQ target dose of 30 mg/kg (co-blistered NFDC). The median dose of AQ administered was 32.1 mg/kg [IQR: 25.9-38.2], the highest dose being administered to patients treated with co-blistered NFDC (median = 35.3 mg/kg [IQR: 30.6-43.7]) and the lowest to those treated with loose NFDC-25 (median = 25.0 mg/kg [IQR: 22.7-25.0]). Patients treated with FDC received a median dose of 32.4 mg/kg [IQR: 27-39.0]. After adjusting for reinfections, the corrected antimalarial efficacy on day 28 after treatment was similar for co-blistered NFDC (97.9% [95% confidence interval (CI): 97.0-98.8%]) and FDC (98.1% [95% CI: 97.6%-98.5%]; P = 0.799), but significantly lower for the loose NFDC-25 (93.4% [95% CI: 91.9%-94.9%]), and loose NFDC-30 (95.0% [95% CI: 94.1%-95.9%]) (P < 0.001 for all comparisons). After controlling for age, AQ dose, baseline parasitemia and region; treatment with loose NFDC-25 was associated with a 3.5-fold greater risk of recrudescence by day 28 (adjusted hazard ratio, AHR = 3.51 [95% CI: 2.02-6.12], P < 0.001) compared to FDC, and treatment with loose NFDC-30 was associated with a higher risk of recrudescence at only three sites.

CONCLUSIONS

There was substantial variation in the total dose of amodiaquine administered in different AS-AQ combination regimens. Fixed dose AS-AQ combinations ensure optimal dosing and provide higher antimalarial treatment efficacy than the loose individual tablets in all age categories.

Spread of artemisinin-resistant Plasmodium falciparum in Myanmar: a cross-sectional survey of the K13 molecular marker

BACKGROUND

Emergence of artemisinin resistance in southeast Asia poses a serious threat to the global control of Plasmodium falciparum malaria. Discovery of the K13 marker has transformed approaches to the monitoring of artemisinin resistance, allowing introduction of molecular surveillance in remote areas through analysis of DNA. We aimed to assess the spread of artemisinin-resistant P falciparum in Myanmar by determining the relative prevalence of P falciparum parasites carrying K13-propeller mutations.

METHODS

We did this cross-sectional survey at malaria treatment centres at 55 sites in ten administrative regions in Myanmar, and in relevant border regions in Thailand and Bangladesh, between January, 2013, and September, 2014. K13 sequences from P falciparum infections were obtained mainly by passive case detection. We entered data into two geostatistical models to produce predictive maps of the estimated prevalence of mutations of the K13 propeller region across Myanmar.

FINDINGS

Overall, 371 (39%) of 940 samples carried a K13-propeller mutation. We recorded 26 different mutations, including nine mutations not described previously in southeast Asia. In seven (70%) of the ten administrative regions of Myanmar, the combined K13-mutation prevalence was more than 20%. Geospatial mapping showed that the overall prevalence of K13 mutations exceeded 10% in much of the east and north of the country. In Homalin, Sagaing Region, 25 km from the Indian border, 21 (47%) of 45 parasite samples carried K13-propeller mutations.

INTERPRETATION

Artemisinin resistance extends across much of Myanmar. We recorded P falciparum parasites carrying K13-propeller mutations at high prevalence next to the northwestern border with India. Appropriate therapeutic regimens should be tested urgently and implemented comprehensively if spread of artemisinin resistance to other regions is to be avoided.

FUNDING

Wellcome Trust-Mahidol University-Oxford Tropical Medicine Research Programme and the Bill & Melinda Gates Foundation.

Spread of artemisinin resistance in Plasmodium falciparum malaria

BACKGROUND

Artemisinin resistance in Plasmodium falciparum has emerged in Southeast Asia and now poses a threat to the control and elimination of malaria. Mapping the geographic extent of resistance is essential for planning containment and elimination strategies.

METHODS

Between May 2011 and April 2013, we enrolled 1241 adults and children with acute, uncomplicated falciparum malaria in an open-label trial at 15 sites in 10 countries (7 in Asia and 3 in Africa). Patients received artesunate, administered orally at a daily dose of either 2 mg per kilogram of body weight per day or 4 mg per kilogram, for 3 days, followed by a standard 3-day course of artemisinin-based combination therapy. Parasite counts in peripheral-blood samples were measured every 6 hours, and the parasite clearance half-lives were determined.

RESULTS

The median parasite clearance half-lives ranged from 1.9 hours in the Democratic Republic of Congo to 7.0 hours at the Thailand-Cambodia border. Slowly clearing infections (parasite clearance half-life >5 hours), strongly associated with single point mutations in the "propeller" region of the P. falciparum kelch protein gene on chromosome 13 (kelch13), were detected throughout mainland Southeast Asia from southern Vietnam to central Myanmar. The incidence of pretreatment and post-treatment gametocytemia was higher among patients with slow parasite clearance, suggesting greater potential for transmission. In western Cambodia, where artemisinin-based combination therapies are failing, the 6-day course of antimalarial therapy was associated with a cure rate of 97.7% (95% confidence interval, 90.9 to 99.4) at 42 days.

CONCLUSIONS

Artemisinin resistance to P. falciparum, which is now prevalent across mainland Southeast Asia, is associated with mutations in kelch13. Prolonged courses of artemisinin-based combination therapies are currently efficacious in areas where standard 3-day treatments are failing. (Funded by the U.K. Department of International Development and others; ClinicalTrials.gov number, NCT01350856.).

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

Background

Reliable measures of anti-malarial resistance are crucial for malaria control. Resistance is typically a complex trait: multiple mutations in a single parasite (a haplotype or genotype) are necessary for elaboration of the resistant phenotype. The frequency of a genetic motif (proportion of parasite clones in the parasite population that carry a given allele, haplotype or genotype) is a useful measure of resistance. In areas of high endemicity, malaria patients generally harbour multiple parasite clones; they have multiplicities of infection (MOIs) greater than one. However, most standard experimental procedures only allow measurement of marker prevalence (proportion of patient blood samples that test positive for a given mutation or combination of mutations), not frequency. It is misleading to compare marker prevalence between sites that have different mean MOIs; frequencies are required instead.

Methods

A Bayesian statistical model was developed to estimate Plasmodium falciparum genetic motif frequencies from prevalence data collected in the field. To assess model performance and computational speed, a detailed simulation study was implemented. Application of the model was tested using datasets from five sites in Uganda. The datasets included prevalence data on markers of resistance to sulphadoxine-pyrimethamine and an average MOI estimate for each study site.

Results

The simulation study revealed that the genetic motif frequencies that were estimated using the model were more accurate and precise than conventional estimates based on direct counting. Importantly, the model did not require measurements of the MOI in each patient; it used the average MOI in the patient population. Furthermore, if a dataset included partially genotyped patient blood samples, the model imputed the data that were missing. Using the model and the Ugandan data, genotype frequencies were estimated and four biologically relevant genotypes were identified.

Conclusions

The model allows fast, accurate, reliable estimation of the frequency of genetic motifs associated with resistance to anti-malarials using prevalence data collected from malaria patients. The model does not require per-patient MOI measurements and can easily analyse data from five markers. The model will be a valuable tool for monitoring markers of anti-malarial drug resistance, including markers of resistance to artemisinin derivatives and partner drugs.

Optimal sampling designs for estimation of Plasmodium falciparum clearance rates in patients treated with artemisinin derivatives

BACKGROUND

The emergence of Plasmodium falciparum resistance to artemisinins in Southeast Asia threatens the control of malaria worldwide. The pharmacodynamic hallmark of artemisinin derivatives is rapid parasite clearance (a short parasite half-life), therefore, the in vivo phenotype of slow clearance defines the reduced susceptibility to the drug. Measurement of parasite counts every six hours during the first three days after treatment have been recommended to measure the parasite clearance half-life, but it remains unclear whether simpler sampling intervals and frequencies might also be sufficient to reliably estimate this parameter.

METHODS

A total of 2,746 parasite density-time profiles were selected from 13 clinical trials in Thailand, Cambodia, Mali, Vietnam, and Kenya. In these studies, parasite densities were measured every six hours until negative after treatment with an artemisinin derivative (alone or in combination with a partner drug). The WWARN Parasite Clearance Estimator (PCE) tool was used to estimate "reference" half-lives from these six-hourly measurements. The effect of four alternative sampling schedules on half-life estimation was investigated, and compared to the reference half-life (time zero, 6, 12, 24 (A1); zero, 6, 18, 24 (A2); zero, 12, 18, 24 (A3) or zero, 12, 24 (A4) hours and then every 12 hours). Statistical bootstrap methods were used to estimate the sampling distribution of half-lives for parasite populations with different geometric mean half-lives. A simulation study was performed to investigate a suite of 16 potential alternative schedules and half-life estimates generated by each of the schedules were compared to the "true" half-life. The candidate schedules in the simulation study included (among others) six-hourly sampling, schedule A1, schedule A4, and a convenience sampling schedule at six, seven, 24, 25, 48 and 49 hours.

RESULTS

The median (range) parasite half-life for all clinical studies combined was 3.1 (0.7-12.9) hours. Schedule A1 consistently performed the best, and schedule A4 the worst, both for the individual patient estimates and for the populations generated with the bootstrapping algorithm. In both cases, the differences between the reference and alternative schedules decreased as half-life increased. In the simulation study, 24-hourly sampling performed the worst, and six-hourly sampling the best. The simulation study confirmed that more dense parasite sampling schedules are required to accurately estimate half-life for profiles with short half-life (≤ three hours) and/or low initial parasite density (≤ 10,000 per μL). Among schedules in the simulation study with six or fewer measurements in the first 48 hours, a schedule with measurements at times (time windows) of 0 (0-2), 6 (4-8), 12 (10-14), 24 (22-26), 36 (34-36) and 48 (46-50) hours, or at times 6, 7 (two samples in time window 5-8), 24, 25 (two samples during time 23-26), and 48, 49 (two samples during time 47-50) hours, until negative most accurately estimated the "true" half-life. For a given schedule, continuing sampling after two days had little effect on the estimation of half-life, provided that adequate sampling was performed in the first two days and the half-life was less than three hours. If the measured parasitaemia at two days exceeded 1,000 per μL, continued sampling for at least once a day was needed for accurate half-life estimates.

CONCLUSIONS

This study has revealed important insights on sampling schedules for accurate and reliable estimation of Plasmodium falciparum half-life following treatment with an artemisinin derivative (alone or in combination with a partner drug). Accurate measurement of short half-lives (rapid clearance) requires more dense sampling schedules (with more than twice daily sampling). A more intensive sampling schedule is, therefore, recommended in locations where P. falciparum susceptibility to artemisinins is not known and the necessary resources are available. Counting parasite density at six hours is important, and less frequent sampling is satisfactory for estimating long parasite half-lives in areas where artemisinin resistance is present.

Surveillance of travellers: an additional tool for tracking antimalarial drug resistance in endemic countries

INTRODUCTION

There are growing concerns about the emergence of resistance to artemisinin-based combination therapies (ACTs). Since the widespread adoption of ACTs, there has been a decrease in the systematic surveillance of antimalarial drug resistance in many malaria-endemic countries. The aim of this work was to test whether data on travellers returning from Africa with malaria could serve as an additional surveillance system of local information sources for the emergence of drug resistance in endemic-countries.

METHODOLOGY

Data were collected from travellers with symptomatic Plasmodium falciparum malaria returning from Senegal (n = 1,993), Mali (n = 2,372), Cote d'Ivoire (n = 4,778) or Cameroon (n = 3,272) and recorded in the French Malaria Reference Centre during the period 1996-2011. Temporal trends of the proportion of parasite isolates that carried the mutant genotype, pfcrt 76T, a marker of resistance to chloroquine (CQ) and pfdhfr 108N, a marker of resistance to pyrimethamine, were compared for travellers and within-country surveys that were identified through a literature review in PubMed. The in vitro response to CQ was also compared between these two groups for parasites from Senegal.

RESULTS

The trends in the proportion of parasites that carried pfcrt 76T, and pfdhfr 108N, were compared for parasites from travellers and patients within-country using the slopes of the curves over time; no significant differences in the trends were found for any of the 4 countries. These results were supported by in vitro analysis of parasites from the field in Senegal and travellers returning to France, where the trends were also not significantly different.

CONCLUSION

The results have not shown different trends in resistance between parasites derived from travellers or from parasites within-country. This work highlights the value of an international database of drug responses in travellers as an additional tool to assess the emergence of drug resistance in endemic areas where information is limited.

Trends in antimalarial drug use in Africa

Resistance to chloroquine (CQ) and sulphadoxine-pyrimethamine (SP) led the World Health Organization (WHO) to recommend changes in national drug policies. The time between policy changes and their implementation profoundly affects program impact. We developed a model based on data on antimalarial treatments, extracted from household surveys and national antimalarial policy information from the literature. Drug use in each country during the time period 1999–2011 and the trend in reduction of CQ use after policy change were estimated. The SP use estimates were correlated with the prevalence of a molecular marker associated with SP resistance. There was no spatial pattern in the country-level rate of reduction of CQ use, after policy change. In East Africa SP drug use was strongly correlated to resistance. If artemisinin resistance spreads to, or emerges in, Africa this methodology will be a valuable tool to estimate actual drug use and its impact on changes in drug efficacy.

Longitudinal study assessing the return of chloroquine susceptibility of Plasmodium falciparum in isolates from travellers returning from West and Central Africa, 2000-2011

Background

Chloroquine (CQ) was the main malaria therapy worldwide from the 1940s until the 1990s. Following the emergence of CQ-resistant Plasmodium falciparum, most African countries discontinued the use of CQ, and now promote artemisinin-based combination therapy as the first-line treatment. This change was generally initiated during the last decade in West and Central Africa. The aim of this study is to describe the changes in CQ susceptibility in this African region, using travellers returning from this region as a sentinel system.

Methods

The study was conducted by the Malaria National Reference Centre, France. The database collated the pfcrtK76T molecular marker for CQ susceptibility and the in vitro response to CQ of parasites from travellers’ isolates returning from Senegal, Mali, Ivory Coast or Cameroon. As a proxy of drug pressure, data regarding CQ intake in febrile children were collated for the study period. Logistic regression models were used to detect trends in the proportions of CQ resistant isolates.

Results

A total of 2874 parasite isolates were genotyped between 2000–2011. The prevalence of the pfcrt76T mutant genotype significantly decreased for Senegal (from 78% to 47%), Ivory Coast (from 63% to 37%), Cameroon (from 90% to 59%) and remained stable for Mali. The geometric mean of the 50% inhibitory concentration (IC₅₀) of CQ in vitro susceptibility and the proportion of resistant isolates (defining resistance as an IC50 value > 100 nM) significantly decreased for Senegal (from 86 nM (59%) to 39 nM (25%)), Mali (from 84 nM (50%) to 51 nM (31%)), Ivory Coast (from 75 nM (59%) to 29 nM (16%)) and Cameroon (from 181 nM (75%) to 51 nM (37%)). Both analyses (molecular and in vitro susceptibility) were performed for the 2004–2011 period, after the four countries had officially discontinued CQ and showed an accelerated decline of the resistant isolates for the four countries. Meanwhile, CQ use among children significantly deceased in this region (fixed effects slope = −0.3, p < 10^-3).

Conclusions

An increase in CQ susceptibility following official withdrawal of the drug was observed in travellers returning from West and Central African countries. The same trends were observed for molecular and in vitro analysis between 2004-2011and they correlated to the decrease of the drug pressure.

Modelling the interaction of keratinocytes and fibroblasts during normal and abnormal wound healing processes

The crosstalk between fibroblasts and keratinocytes is a vital component of the wound healing process, and involves the activity of a number of growth factors and cytokines. In this work, we develop a mathematical model of this crosstalk in order to elucidate the effects of these interactions on the regeneration of collagen in a wound that heals by second intention. We consider the role of four components that strongly affect this process: transforming growth factor-β, platelet-derived growth factor, interleukin-1 and keratinocyte growth factor. The impact of this network of interactions on the degradation of an initial fibrin clot, as well as its subsequent replacement by a matrix that is mainly composed of collagen, is described through an eight-component system of nonlinear partial differential equations. Numerical results, obtained in a two-dimensional domain, highlight key aspects of this multifarious process, such as re-epithelialization. The model is shown to reproduce many of the important features of normal wound healing. In addition, we use the model to simulate the treatment of two pathological cases: chronic hypoxia, which can lead to chronic wounds; and prolonged inflammation, which has been shown to lead to hypertrophic scarring. We find that our model predictions are qualitatively in agreement with previously reported observations and provide an alternative pathway for gaining insight into this complex biological process.

Wound healing angiogenesis: the clinical implications of a simple mathematical model

Nonhealing wounds are a major burden for health care systems worldwide. In addition, a patient who suffers from this type of wound usually has a reduced quality of life. While the wound healing process is undoubtedly complex, in this paper we develop a deterministic mathematical model, formulated as a system of partial differential equations, that focusses on an important aspect of successful healing: oxygen supply to the wound bed by a combination of diffusion from the surrounding unwounded tissue and delivery from newly formed blood vessels. While the model equations can be solved numerically, the emphasis here is on the use of asymptotic methods to establish conditions under which new blood vessel growth can be initiated and wound-bed angiogenesis can progress. These conditions are given in terms of key model parameters including the rate of oxygen supply and its rate of consumption in the wound. We use our model to discuss the clinical use of treatments such as hyperbaric oxygen therapy, wound bed debridement, and revascularisation therapy that have the potential to initiate healing in chronic, stalled wounds.

Standardizing the measurement of parasite clearance in falciparum malaria: the parasite clearance estimator

Background

A significant reduction in parasite clearance rates following artesunate treatment of falciparum malaria, and increased failure rates following artemisinin combination treatments (ACT), signaled emergent artemisinin resistance in Western Cambodia. Accurate measurement of parasite clearance is therefore essential to assess the spread of artemisinin resistance in Plasmodium falciparum. The slope of the log-parasitaemia versus time relationship is considered to be the most robust measure of anti-malarial effect. However, an initial lag phase of numerical instability often precedes a steady exponential decline in the parasite count after the start of anti-malarial treatment. This lag complicates the clearance estimation, introduces observer subjectivity, and may influence the accuracy and consistency of reported results.

Methods

To address this problem, a new approach to modelling clearance of malaria parasites from parasitaemia-time profiles has been explored and validated. The methodology detects when a lag phase is present, selects the most appropriate model (linear, quadratic or cubic) to fit log-transformed parasite data, and calculates estimates of parasite clearance adjusted for this lag phase. Departing from previous approaches, parasite counts below the level of detection are accounted for and not excluded from the calculation.

Results

Data from large clinical studies with frequent parasite counts were examined. The effect of a lag phase on parasite clearance rate estimates is discussed, using individual patient data examples. As part of the World Wide Antimalarial Resistance Network's (WWARN) efforts to make innovative approaches available to the malaria community, an automated informatics tool: the parasite clearance estimator has been developed.

Conclusions

The parasite clearance estimator provides a consistent, reliable and accurate method to estimate the lag phase and malaria parasite clearance rate. It could be used to detect early signs of emerging resistance to artemisinin derivatives and other compounds which affect ring-stage clearance.