A common approach to quantifying DNA involves repeated cycles of DNA amplification. This approach, employed by the polymerase chain reaction (PCR), produces outputs that are corrupted by amplification noise, making it challenging to accurately back-calculate the amount of input DNA. Standard mathematical solutions to this back-calculation problem do not take adequate account of such noise and are error-prone. Here, we develop a parsimonious mathematical model of the stochastic mapping of input DNA onto experimental outputs that accounts, in a natural way, for amplification noise. We use the model to derive the probability density of the quantification cycle, a frequently reported experimental output, which can be fit to data to estimate input DNA. Strikingly, the model predicts that a sample with only one input DNA molecule has a $<$4% chance of testing positive, which is $>$25-fold lower than assumed by a standard method of interpreting PCR data. We provide formulae for calculating both the limit of detection and the limit of quantification, two important operating characteristics of DNA quantification methods that are frequently assessed by using ad-hoc mathematical techniques. Our results provide a mathematical foundation for the rigorous analysis of DNA quantification.

La pandémie de COVID-19 ayant touché l'ensemble des pays en même temps a posé de sérieux problèmes d'approvisionnement en moyens et fournitures de lutte : masques, gels, kits de dépistage, oxygène, etc. Dans les pays en développement, une contrainte financière s'est rajoutée du fait d'un manque de ressources pour le secteur de la santé. De nombreux pays se sont alors adaptés à cette situation. Au Rwanda, les autorités ont décidé d'opter pour un dépistage regroupé des échantillons (Pooling) pour limiter l'utilisation des réactifs très coûteux et sous tension d'approvisionnement. L'étude a été basée sur un prélèvement exhaustif réalisé chez tous les voyageurs entrés au Rwanda par l'aéroport de Kigali durant l'année 2021 et testés au COVID-19, puis par une analyse groupée en pool de 10 échantillons. Des données précises sur le nombre de voyageurs testés, le nombre de pools constitués et le ratio des pools positifs ont été utilisés. L'analyse économique est basée sur un modèle décisionnel comparant le coût du dépistage en Pooling au coût d'un scénario contrefactuel d'un dépistage individuel des voyageurs. Ce modèle a pris en compte les facteurs d'incertitude liés à la prévalence du virus parmi les voyageurs et la valeur diagnostique du test utilisé. Des analyses de sensibilité déterministes ont été menées pour simuler une fluctuation de la prévalence de l'épidémie ou un changement de la taille du pool ou du coût d'achat des réactifs. Parmi les 16 308 pools testés correspondant à 163 088 voyageurs, 636 pools se sont révélés positifs soit 3,9 % (IC 95% 3,6-4,2) des pools testés. En prenant en compte les paramètres de sensibilité et de spécificité, le taux de vrais pools positifs a été estimé à 1,9 % (IC95% 1,6-2,2), soit une prévalence de la contamination au niveau individuel de 0,19 % (IC95% 0,16-0,22). Le coût total du dépistage individuel a été estimé à 7,17 millions d'euros. Le calcul du coût total du dépistage en situation de recours au Pooling était de 1,01 million d'euros, soit une économie de coût de 86 %, sans pour autant augmenter le risque de faux négatif. Toutefois, à mesure que la prévalence était élevée, la stratégie du Pooling devenait moins attractive Dans un contexte marqué par une faible prévalence des contaminations et une bonne valeur diagnostique du test, le Pooling a permis à la fois d'augmenter la capacité analytique (nombre et fréquence des tests) et de diminuer drastiquement la consommation des réactifs et des coûts afférents au Rwanda. Pooling, Dépistage, COVID-19, Impact économique Les auteurs n'ont pas précisé leurs éventuels liens d'intérêts.

The changes occurring in the T cell repertoire during clinical malaria infection in children remain unknown. In this study, we undertook the first detailed comparative study of the T cell repertoire in African children with and without clinical malaria to test the hypothesis that clonotypic expansions that occur during P. falciparum infection will contribute to the generation of a T cell repertoire that is unique to each disease state. We profiled the complementarity-determining region 3 (CDR3) of the TCRβ chain sequences from children with Plasmodium falciparum infections (asymptomatic, uncomplicated and severe malaria) and compared these with sequences from healthy children. Interestingly, we discovered that children with symptomatic malaria have a lower TCR diversity and frequency of shared (or "public") TCR sequences compared to asymptomatic children. Also, TCR diversity was inversely associated with parasitemia. Furthermore, by clustering TCR sequences based on their predicted antigen specificities, we identified a specificity cluster, with a 4-mer amino acid motif, that is overrepresented in the asymptomatic group compared to the diseased groups. Further investigations into this finding may help in delineating important antigenic targets for vaccine and therapeutic development. The results show that the T cell repertoire in children is altered during malaria, suggesting that exposure to P. falciparum antigens disrupts the adaptive immune response, which is an underlying feature of the disease.

The adaptive immune system responds to foreign invaders by activating T cells. However, this response is perilous if T cells are activated by the wrong signal or if they remain activated unduly long after the threat has been eliminated. It is therefore important that T cells get activated only by the right kind of signals and for the right duration. The dominant theory in immunology over recent decades has been that a T cell must receive at least two signals before it can become activated. It is, however, unclear whether and how this two-signal requirement ensures that T cell activation is provoked only by the right signals and that the response is just long enough. Here, we propose that the two signal requirement induces a novel coherent feedforward motif whose properties align with those preferred for an ideal immune response. Further consideration of the interaction between helper and regulatory T cells induces a composite feedforward-feedback motif, analysis of which generates a condition for a healthy concentration of activated T helper cells.

Long-term effects of the growing population of HIV-treated people in Southern Africa on individuals and the public health sector at large are not yet understood. This study proposes a novel 'ratio' model that relates CD4+ T-cell counts of HIV-infected individuals to the CD4+ count reference values from healthy populations. We use mixed-effects regression to fit the model to data from 1616 children (median age 4.3 years at ART initiation) and 14,542 adults (median age 36 years at ART initiation). We found that the scaled carrying capacity, maximum CD4+ count relative to an HIV-negative individual of similar age, and baseline scaled CD4+ counts were closer to healthy values in children than in adults. Post-ART initiation, CD4+ growth rate was inversely correlated with baseline CD4+ T-cell counts, and consequently higher in adults than children. Our results highlight the impacts of age on dynamics of the immune system of healthy and HIV-infected individuals.

Background: Mathematical models have been used throughout the COVID-19 pandemic to inform policymaking decisions. The COVID-19 Multi-Model Comparison Collaboration (CMCC) was established to provide country governments, particularly low- and middle-income countries (LMICs), and other model users with an overview of the aims, capabilities and limits of the main multi-country COVID-19 models to optimise their usefulness in the COVID-19 response. Methods: Seven models were identified that satisfied the inclusion criteria for the model comparison and had creators that were willing to participate in this analysis. A questionnaire, extraction tables and interview structure were developed to be used for each model, these tools had the aim of capturing the model characteristics deemed of greatest importance based on discussions with the Policy Group. The questionnaires were first completed by the CMCC Technical group using publicly available information, before further clarification and verification was obtained during interviews with the model developers. The fitness-for-purpose flow chart for assessing the appropriateness for use of different COVID-19 models was developed jointly by the CMCC Technical Group and Policy Group. Results: A flow chart of key questions to assess the fitness-for-purpose of commonly used COVID-19 epidemiological models was developed, with focus placed on their use in LMICs. Furthermore, each model was summarised with a description of the main characteristics, as well as the level of engagement and expertise required to use or adapt these models to LMIC settings. Conclusions: This work formalises a process for engagement with models, which is often done on an ad-hoc basis, with recommendations for both policymakers and model developers and should improve modelling use in policy decision making.

COVID-19 transmission rates are often linked to locally circulating strains of SARS-CoV-2. Here we describe 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in Rwanda from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the B.1.1.7 and B.1.351 variants of concern among incoming travelers tested at Kigali International Airport. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences were available. Our results highlight the importance of systematic genomic surveillance and regional collaborations for a durable response towards combating COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 19 (COVID-19), is a single-stranded positive-sense ribonucleic acid (RNA) virus that typically undergoes one to two single nucleotide mutations per month. COVID-19 continues to spread globally, with case fatality and test positivity rates often linked to locally circulating strains of SARS-CoV-2. Furthermore, mutations in this virus, in particular those occurring in the spike protein (involved in the virus binding to the host epithelial cells) have potential implications in current vaccination efforts. In Rwanda, more than twenty thousand cases have been confirmed as of March 14th 2021, with a case fatality rate of 1.4% and test positivity rate of 2.3% while the recovery rate is at 91.9%. Rwanda started its genomic surveillance efforts, taking advantage of pre-existing research projects and partnerships, to ensure early detection of SARS-CoV-2 variants and to potentially contain the spread of variants of concern (VOC). As a result of this initiative, we here present 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in the country from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the newly emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the VOCs, B.1.1.7 and B.1.351, among incoming travelers tested at Kigali International Airport. We also discuss the potential impact of COVID-19 control measures established in the country to control the spread of the virus. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations during the time frame of interest. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences are currently available or that no longer report positive cases. Our results point to the importance of systematically screening all incoming travelers, regardless of the origin of their travels as well as regional considerations for durable response to COVID-19.

The rapid identification and isolation of infected individuals remains a key strategy for controlling the spread of SARS-CoV-2. Frequent testing of populations to detect infection early in asymptomatic or presymptomatic individuals can be a powerful tool for intercepting transmission, especially when the viral prevalence is low. However, RT-PCR testing-the gold standard of SARS-CoV-2 diagnosis-is expensive, making regular testing of every individual unfeasible. Sample pooling is one approach to lowering costs. By combining samples and testing them in groups the number of tests required is reduced, substantially lowering costs. Here we report on the implementation of pooling strategies using 3-d and 4-d hypercubes to test a professional sports team in South Africa. We have shown that infected samples can be reliably detected in groups of 27 and 81, with minimal loss of assay sensitivity for samples with individual Ct values of up to 32. We report on the automation of sample pooling, using a liquid-handling robot and an automated web interface to identify positive samples. We conclude that hypercube pooling allows for the reliable RT-PCR detection of SARS-CoV-2 infection, at significantly lower costs than lateral flow antigen (LFA) tests.

Suppressing infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will probably require the rapid identification and isolation of individuals infected with the virus on an ongoing basis. Reverse-transcription polymerase chain reaction (RT–PCR) tests are accurate but costly, which makes the regular testing of every individual expensive. These costs are a challenge for all countries around the world, but particularly for low-to-middle-income countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups 1 – 7 . A balance must be struck between increasing the group size and retaining test sensitivity, as sample dilution increases the likelihood of false-negative test results for individuals with a low viral load in the sampled region at the time of the test 8 . Similarly, minimizing the number of tests to reduce costs must be balanced against minimizing the time that testing takes, to reduce the spread of the infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof-of-concept experiments in which a positive subsample was detected even when diluted 100-fold with negative subsamples (compared with 30–48-fold dilutions described in previous studies 9 – 11 ). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by the frequent re-testing of groups, for example. With the use of these methods, the cost of mass testing could be reduced by a large factor. At low prevalence, the costs decrease in rough proportion to the prevalence. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to monitor infection rates closely and continually in a population, along with the rapid and effective isolation of people with SARS-CoV-2 infections, provides a promising pathway towards the long-term control of coronavirus disease 2019 (COVID-19).

Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals, on an ongoing basis. RT-PCR (reverse transcription polymerase chain reaction) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by combining samples and testing them in groups. We propose an algorithm for grouping subsamples, prior to testing, based on the geometry of a hypercube. At low prevalence, this testing procedure uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, parallel searches are preferred. We report proof of concept experiments in which a positive sample was detected even when diluted a hundred-fold with negative samples. Using these methods, the costs of mass testing could be reduced by a factor of ten to a hundred or more. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the costs of regularly testing everyone. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2. Field trials of our approach are now under way in Rwanda and initial data from these are reported here.

T cells play significant roles during Plasmodium falciparum infections. Their regulation of the immune response in symptomatic children with malaria has been deemed necessary to prevent immune associated pathology. In this study, we phenotypically characterized the expression of T cell inhibitory(PD-1, CTLA-4) and senescent markers (CD28(-), CD57) from children with symptomatic malaria, asymptomatic malaria and healthy controls using flow cytometry. We observed increased expression of T cell exhaustion and senescence markers in the symptomatic children compared to the asymptomatic and healthy controls. T cell senescence markers were more highly expressed on CD8 T cells than on CD4 T cells. Asymptomatically infected children had comparable levels of these markers with healthy controls except for CD8+ PD-1+ T cells which were significantly elevated in the asymptomatic children. Also, using multivariate regression analysis, CTLA-4 was the only marker that could predict parasitaemia level. The results suggest that the upregulation of immune exhaustion and senescence markers during symptomatic malaria may affect the effector function of T cells leading to inefficient clearance of parasites, hence the inability to develop sterile immunity to malaria.

Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model’s parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.

The quest for a licensed effective vaccine against malaria remains a global priority. Even though classical vaccine design strategies have been successful for some viral and bacterial pathogens, little success has been achieved for Plasmodium falciparum, which causes the deadliest form of malaria due to its diversity and ability to evade host immune responses. Nevertheless, recent advances in vaccinology through high throughput discovery of immune correlates of protection, lymphocyte repertoire sequencing and structural design of immunogens, provide a comprehensive approach to identifying and designing a highly efficacious vaccine for malaria. In this review, we discuss novel vaccine approaches that can be employed in malaria vaccine design.

The adaptive immune system (AIS) acquires significant deficiency during chronological ageing, making older individuals more susceptible to infections and less responsive to vaccines compared to younger individuals. At the cellular level, one of the most striking features of this ageing-related immune deficiency is the dramatic loss of T-cell diversity that occurs in elderly humans. After the age of 70 years, there is a sharp decline in the diversity of naïve T cells, including a >10-fold decrease in the CD4+ compartment and a >100-fold decrease in the CD8+ compartment. Such changes are detrimental because the AIS relies on a diverse naïve T-cell pool to respond to novel pathogens. Recent work suggests that this collapse of naïve T-cell diversity results from T cells reaching the Hayflick limit and being eliminated through both antigen-dependent and -independent pathways. The progressive attrition of telomeres is the molecular mechanism that underlies this Hayflick limit. Therefore, we propose that by measuring the telomere lengths of T cells with high resolution, it is possible to develop a unique biomarker of immune deficiency, potentially much better correlated with individual susceptibility to diseases compared to chronological age alone.

Parameter estimates (credible interval) of non-antigenic variables

Parameter estimates (credible interval) of affinities of Abs

Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.