coiaf: Directly estimating complexity of infection with allele frequencies.

In malaria, individuals are often infected with different parasite strains. The complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current approaches in the literature. Through a sensitivity analysis, we characterize how the distribution of parasite densities, the assumed sequencing depth, and the number of sampled loci impact the bias and accuracy of our two methods. Using our developed methods, we further estimate the COI globally from Plasmodium falciparum sequencing data and compare the results against the literature. We show significant differences in the estimated COI globally between continents and a weak relationship between malaria prevalence and COI.

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions.

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.

Malaria Transmission Dynamics in a High-Transmission Setting of Western Kenya and the Inadequate Treatment Response to Artemether-Lumefantrine in an Asymptomatic Population.

BACKGROUND: Assessing the infectious reservoir is critical in malaria control and elimination strategies. We conducted a longitudinal epidemiological study in a high-malaria-burden region in Kenya to characterize transmission in an asymptomatic population. METHODS: 488 study participants encompassing all ages in 120 households within 30 clusters were followed for 1 year with monthly sampling. Malaria was diagnosed by microscopy and molecular methods. Transmission potential in gametocytemic participants was assessed using direct skin and/or membrane mosquito feeding assays, then treated with artemether-lumefantrine. Study variables were assessed using mixed-effects generalized linear models. RESULTS: Asexual and sexual parasite data were collected from 3792 participant visits, with 903 linked with feeding assays. Univariate analysis revealed that the 6-11-year-old age group was at higher risk of harboring asexual and sexual infections than those <6 years old (odds ratio [OR] 1.68, P < .001; and OR 1.81, P < .001), respectively. Participants with submicroscopic parasitemia were at a lower risk of gametocytemia compared with microscopic parasitemia (OR 0.04, P < .001), but they transmitted at a significantly higher rate (OR 2.00, P = .002). A large proportion of the study population who were infected at least once remained infected (despite treatment) with asexual (71.7%, 291/406) or sexual (37.4%, 152/406) parasites. 88.6% (365/412) of feeding assays conducted in individuals who failed treatment the previous month resulted in transmissions. CONCLUSIONS: Individuals with asymptomatic infection sustain the transmission cycle, with the 6-11-year age group serving as an important reservoir. The high rates of artemether-lumefantrine treatment failures suggest surveillance programs using molecular methods need to be expanded for accurate monitoring and evaluation of treatment outcomes.

Long-term vaccination strategies to mitigate the impact of SARS-CoV-2 transmission: A modelling study.

BACKGROUND: Vaccines have reduced severe disease and death from Coronavirus Disease 2019 (COVID-19). However, with evidence of waning efficacy coupled with continued evolution of the virus, health programmes need to evaluate the requirement for regular booster doses, considering their impact and cost-effectiveness in the face of ongoing transmission and substantial infection-induced immunity. METHODS AND FINDINGS: We developed a combined immunological-transmission model parameterised with data on transmissibility, severity, and vaccine effectiveness. We simulated Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission and vaccine rollout in characteristic global settings with different population age-structures, contact patterns, health system capacities, prior transmission, and vaccine uptake. We quantified the impact of future vaccine booster dose strategies with both ancestral and variant-adapted vaccine products, while considering the potential future emergence of new variants with modified transmission, immune escape, and severity properties. We found that regular boosting of the oldest age group (75+) is an efficient strategy, although large numbers of hospitalisations and deaths could be averted by extending vaccination to younger age groups. In countries with low vaccine coverage and high infection-derived immunity, boosting older at-risk groups was more effective than continuing primary vaccination into younger ages in our model. Our study is limited by uncertainty in key parameters, including the long-term durability of vaccine and infection-induced immunity as well as uncertainty in the future evolution of the virus. CONCLUSIONS: Our modelling suggests that regular boosting of the high-risk population remains an important tool to reduce morbidity and mortality from current and future SARS-CoV-2 variants. Our results suggest that focusing vaccination in the highest-risk cohorts will be the most efficient (and hence cost-effective) strategy to reduce morbidity and mortality.

Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis.

BACKGROUND: The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. METHODS: Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. RESULTS: The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics. CONCLUSIONS: Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India.

India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.

A semi-supervised learning framework for quantitative structure-activity regression modelling.

MOTIVATION: Quantitative structure-activity relationship (QSAR) methods are increasingly used in assisting the process of preclinical, small molecule drug discovery. Regression models are trained on data consisting of a finite-dimensional representation of molecular structures and their corresponding target-specific activities. These supervised learning models can then be used to predict the activity of previously unmeasured novel compounds. RESULTS: This work provides methods that solve three problems in QSAR modelling: (i) a method for comparing the information content between finite-dimensional representations of molecular structures (fingerprints) with respect to the target of interest, (ii) a method that quantifies how the accuracy of the model prediction degrades as a function of the distance between the testing and training data and (iii) a method to adjust for screening dependent selection bias inherent in many training datasets. For example, in the most extreme cases, only compounds which pass an activity-dependent screening threshold are reported. A semi-supervised learning framework combines (ii) and (iii) and can make predictions, which take into account the similarity of the testing compounds to those in the training data and adjust for the reporting selection bias. We illustrate the three methods using publicly available structure-activity data for a large set of compounds reported by GlaxoSmithKline (the Tres Cantos AntiMalarial Set, TCAMS) to inhibit asexual in vitro Plasmodium falciparum growth. AVAILABILITYAND IMPLEMENTATION: https://github.com/owatson/PenalizedPrediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using syndromic measures of mortality to capture the dynamics of COVID-19 in Java, Indonesia, in the context of vaccination rollout.

BACKGROUND: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island. METHODS: We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine rollout. RESULTS: C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign. CONCLUSIONS: Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.

Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes.

High-throughput Plasmodium genomic data is increasingly useful in assessing prevalence of clinically important mutations and malaria transmission patterns. Understanding parasite diversity is important for identification of specific human or parasite populations that can be targeted by control programmes, and to monitor the spread of mutations associated with drug resistance. An up-to-date understanding of regional parasite population dynamics is also critical to monitor the impact of control efforts. However, this data is largely absent from high-burden nations in Africa, and to date, no such analysis has been conducted for malaria parasites in Tanzania countrywide. To this end, over 1,000 P. falciparum clinical isolates were collected in 2017 from 13 sites in seven administrative regions across Tanzania, and parasites were genotyped at 1,800 variable positions genome-wide using molecular inversion probes. Population structure was detectable among Tanzanian P. falciparum parasites, approximately separating parasites from the northern and southern districts and identifying genetically admixed populations in the north. Isolates from nearby districts were more likely to be genetically related compared to parasites sampled from more distant districts. Known drug resistance mutations were seen at increased frequency in northern districts (including two infections carrying pfk13-R561H), and additional variants with undetermined significance for antimalarial resistance also varied by geography. Malaria Indicator Survey (2017) data corresponded with genetic findings, including average region-level complexity-of-infection and malaria prevalence estimates. The parasite populations identified here provide important information on extant spatial patterns of genetic diversity of Tanzanian parasites, to which future surveys of genetic relatedness can be compared.

Transmission of Artemisinin-Resistant Malaria Parasites to Mosquitoes under Antimalarial Drug Pressure.

Resistance to artemisinin-based combination therapy (ACT) in the Plasmodium falciparum parasite is threatening to reverse recent gains in reducing global deaths from malaria. While resistance manifests as delayed parasite clearance in patients, the phenotype can only spread geographically via the sexual stages and mosquito transmission. In addition to their asexual killing properties, artemisinin and its derivatives sterilize sexual male gametocytes. Whether resistant parasites overcome this sterilizing effect has not, however, been fully tested. Here, we analyzed P. falciparum clinical isolates from the Greater Mekong Subregion, each demonstrating delayed clinical clearance and known resistance-associated polymorphisms in the Kelch13 (PfK13var) gene. As well as demonstrating reduced asexual sensitivity to drug, certain PfK13var isolates demonstrated a marked reduction in sensitivity to artemisinin in an in vitro male gamete formation assay. Importantly, this same reduction in sensitivity was observed when the most resistant isolate was tested directly in mosquito feeds. These results indicate that, under artemisinin drug pressure, while sensitive parasites are blocked, resistant parasites continue transmission. This selective advantage for resistance transmission could favor acquisition of additional host-specificity or polymorphisms affecting partner drug sensitivity in mixed infections. Favored resistance transmission under ACT coverage could have profound implications for the spread of multidrug-resistant malaria beyond Southeast Asia.

Response to COVID-19 in South Korea and implications for lifting stringent interventions.

BACKGROUND: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere. METHODS: We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. RESULTS: We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. CONCLUSIONS: Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.

A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery.

MOTIVATION: Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. RESULTS: The quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and 'memorize' the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. AVAILABILITY AND IMPLEMENTATION: All software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasmodium falciparum genetic variation of var2csa in the Democratic Republic of the Congo.

BACKGROUND: The Democratic Republic of the Congo (DRC) bears a high burden of malaria, which is exacerbated in pregnant women. The VAR2CSA protein plays a crucial role in pregnancy-associated malaria (PAM), and hence quantifying diversity at the var2csa locus in the DRC is important in understanding the basic epidemiology of PAM, and in developing a robust vaccine against PAM. METHODS: Samples were taken from the 2013-14 Demographic and Health Survey conducted in the DRC, focusing on children under 5 years of age. A short subregion of the var2csa gene was sequenced in 115 spatial clusters, giving country-wide estimates of sequence polymorphism and spatial population structure. RESULTS: Results indicate that var2csa is highly polymorphic, and that diversity is being maintained through balancing selection, however, there is no clear signal of phylogenetic or geographic structure to this diversity. Linear modelling demonstrates that the number of var2csa variants in a cluster correlates directly with cluster prevalence, but not with other epidemiological factors such as urbanicity. CONCLUSIONS: Results suggest that the DRC fits within the global pattern of high var2csa diversity and little genetic differentiation between regions. A broad multivalent VAR2CSA vaccine candidate could benefit from targeting stable regions and common variants to address the substantial genetic diversity.

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.

Loss of function of parathyroid hormone receptor 1 induces Notch-dependent aortic defects during zebrafish vascular development.

OBJECTIVE: Coarctation of the aorta is rarely associated with known gene defects. Blomstrand chondrodysplasia, caused by mutations in the parathyroid hormone receptor 1 (PTHR1) is associated with coarctation of the aorta in some cases, although it is unclear whether PTHR1 deficiency causes coarctation of the aorta directly. The zebrafish allows the study of vascular development using approaches not possible in other models. We therefore examined the effect of loss of function of PTHR1 or its ligand parathyroid hormone-related peptide (PTHrP) on aortic formation in zebrafish. APPROACH AND RESULTS: Morpholino antisense oligonucleotide knockdown of either PTHR1 or PTHrP led to a localized occlusion of the mid-aorta in developing zebrafish. Confocal imaging of transgenic embryos showed that these defects were caused by loss of endothelium, rather than failure to lumenize. Using a Notch reporter transgenic ([CSL:Venus]qmc61), we found both PTHR1 and PTHrP knockdown-induced defective Notch signaling in the hypochord at the site of the aortic defect before onset of circulation, and the aortic occlusion was rescued by inducible Notch upregulation. CONCLUSIONS: Loss of function of either PTHR1 or PTHrP leads to a localized aortic defect that is Notch dependent. These findings may underlie the aortic defect seen in Blomstrand chondrodysplasia, and reveal a link between parathyroid hormone and Notch signaling during aortic development.

Stability of the cationic oxidation states in Pr(0.50)Sr(0.50)CoO3 across the magnetostructural transition by X-ray absorption spectroscopy.

The possible hybridization between Pr 4f and O 2p states in Pr(0.50)Sr(0.50)CoO3 at low temperatures was investigated by different techniques. First, using neutron diffraction we observed a strong contraction of some Pr-O bonds across the magnetostructural transition at T(S) ∼ 120 K. In contrast to the Pr-O bond contraction in Pr(0.50)Sr(0.50)CoO3, this transition is not accompanied by the appearance of Pr(4+) at low temperatures, as revealed by X-ray absorption spectroscopy at Pr edges. Despite the fact that a Pr valence change is not the mechanism that drives this transition, we point out an active participation of Pr ions across T(S). Moreover, Co L(2,3)-edge and O K edge X-ray absorption spectra did not reveal any spin-state variation and showed the stability of the average formal valence of cobalt ions. The large density of empty t(2g) symmetry states in the studied thermal range does not suggest the occurrence of Co(3+) in a pure low-spin state. The overall metallic behavior agrees with our findings. We propose a mixture of Co(3+) ions in the intermediate-spin or high-spin configuration together with Co(4+) ions in a low- or intermediate-spin state.