Biology subjects

Tsoungui Obama, H. C. J.

Publications and source records attributed to Tsoungui Obama, H. C. J..

3 recordsLinked to original sources

bioRxiv2025

Molecular surveillance of multiplicity of infection, haplotype frequencies, and prevalence in infectious diseases

BackgroundThe presence of multiple different pathogen variants within the same infection, referred to as multiplicity of infection (MOI), confounds molecular disease surveillance in diseases such as malaria. Specifically, if molecular/genetic assays yield unphased data, MOI causes ambiguity concerning pathogen haplotypes. Hence, statistical models are required to infer haplotype frequencies and MOI from ambiguous data. Such methods must apply to a general genetic architecture, when aiming to condition secondary analyses, e.g., population genetic measures such as heterozygosity or linkage disequilibrium, on the background of variants of interest, e.g., drug-resistance associated haplotypes. Methods and FindingsHere, a statistical method to estimate MOI and pathogen haplotype frequencies, assuming a general genetic architecture, is introduced. The statistical model is formulated and the relation between haplotype frequency, prevalence and MOI is explained. Because no closed solution exists for the maximum-likelihood estimate, the expectation-maximization (EM) algorithm is used to derive the maximum-likelihood estimate. The asymptotic variance of the estimator (inverse Fisher information) is derived. This yields a lower bound for the variance of the estimated model parameters (Cramer-Rao lower bound; CRLB). By numerical simulations, it is shown that the bias of the estimator decrease with sample size, and that its covariance is well approximated by the inverse Fisher information, suggesting that the estimator is asymptotically unbiased and efficient. Application of the method is exemplified by analyzing an empirical dataset from Cameroon concerning anti-malarial drug resistance. It is shown how the method can be utilized to derive population genetic measures associated with haplotypes of interest. ConclusionThe proposed method has desirable statistical properties and is adequate for handling molecular consisting of moderate number of multiallelic molecular markers. The EM-algorithm provides a stable iteration to numerically calculate the maximum-likelihood estimates. An efficient implementation of the algorithm alongside a detailed documentation is provided as supplementary material. Author summaryMalaria annually causes 263 million infections and 596,000 deaths. Control efforts are challenged by factors like spreading drug resistance. Monitoring pathogen variants at the genetic level (molecular surveillance), especially those linked to drug resistance, is a public health priority. A major challenge is the presence of multiple, genetically distinct pathogen variants (characterized by several genetic markers) within infections (multiplicity of infection). Because genetic assays do not provide phased information in this context, ambiguity in reconstructing the actual variants present in an infection arises. This challenge is not limited to malaria. Probabilistic methods are required to phase genetic data, i.e., to reconstruct the pathogen variants present in infections. As such, we introduce a statistical method to estimate the distribution of pathogen variants at the population level from unphased molecular data obtained from disease-positive specimens. This is a combinatorially difficult task, as the number of possible genetic variants grows exponentially with the amount of genetic information included. Although the method applies to data with an arbitrary genetic architecture, its application is constrained by computational limitations. The methods adequacy is explored and used to analyze a malaria dataset from Cameroon to guide applications. A stable numerical implementation is provided.

genetics↗

bioRxiv2023

Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance

Molecular/genetic methods are becoming increasingly important for surveillance of diseases like malaria. Such methods allow to monitor routes of disease transmission or the origin and spread of variants associated with drug resistance. A confounding factor in molecular disease surveillance is the presence of multiple distinct variants in the same infection (multiplicity of infection - MOI), which leads to ambiguity when reconstructing which pathogenic variants are present in an infection. Heuristic approaches often ignore ambiguous infections, which leads to biased results. To avoid such bias, we introduce a statistical framework to estimate haplotype frequencies alongside MOI from a pair of multi-allelic molecular markers. Estimates are based on maximum-likelihood using the expectation-maximization (EM)-algorithm. The estimates can be used as plug-ins to construct pairwise linkage disequilibrium (LD) maps. The finite-sample properties of the proposed method are studied by systematic numerical simulations. These reveal that the EM-algorithm is a numerically stable method in our case and that the proposed method is accurate (little bias) and precise (small variance) for a reasonable sample size. In fact, the results suggest that the estimator is asymptotically unbiased. Furthermore, the method is appropriate to estimate LD (by D', r2, Q*, or conditional asymmetric LD). Furthermore, as an illustration, we apply the new method to a previously-published dataset from Cameroon concerning sulfadoxine-pyrimethamine (SP) resistance. The results are in accordance with the SP drug pressure at the time and the observed spread of resistance in the country, yielding further evidence for the adequacy of the proposed method. The method is particularly useful for deriving LD maps from data with many ambiguous observations due to MOI. Importantly, the method per se is not restricted to malaria, but applicable to any disease with a similar transmission pattern. The method and several extensions are implemented in an easy-to-use R script. Author summaryAdvances in genetics render molecular disease surveillance increasingly popular. Unlike traditional incidence-based epidemiological data, genetic information provides fine-grained resolution, which allows monitoring and reconstructing routes of transmission, the spread of drug resistance, etc. Molecular surveillance is particularly popular in highly relevant diseases such as malaria. The presence of multiple distinct pathogenic variants within one infection, i.e., multiplicity of infection (MOI), is a confounding factor hampering the analysis of molecular data in the context of disease surveillance. Namely, due to MOI ambiguity concerning the pathogenic variants being present in mixed-clone infections arise. These are often disregarded by heuristic approaches to molecular disease surveillance and lead to biased results. To avoid such bias we introduce a method to estimate the distribution of MOI and frequencies of pathogenic variants based on a concise probabilistic model. The method is designed for two multi-allelic genetic markers, which is the appropriate genetic architecture to derive pairwise linkage-disequilibrium maps, which are informative on population structure or evolutionary processes, such as the spread of drug resistance. We validate the appropriateness of our method by numerical simulations and apply it to a malaria dataset from Cameroon, concerning sulfadoxine-pyrimethamine resistance, the drug used for intermittent preventive treatment during pregnancy.

genetics↗

bioRxiv2021

The impact of COVID-19 vaccination campaigns accounting for antibody-dependent enhancement

BackgroundCOVID-19 vaccines are approved, vaccination campaigns are launched, and worldwide return to normality seems within close reach. Nevertheless, concerns about the safety of COVID-19 vaccines arose, due to their fast emergency approval. In fact, the problem of antibody-dependent enhancement was raised in the context of COVID-19 vaccines. Methods and findingsWe introduce a complex extension of the model underlying the pandemic preparedness tool CovidSim 1.1 (http://covidsim.eu/) to optimize vaccination strategies with regard to the onset of campaigns, vaccination coverage, vaccination schedules, vaccination rates, and efficiency of vaccines. Vaccines are not assumed to immunize perfectly. Some individuals fail to immunize, some reach only partial immunity, and - importantly - some develop antibody-dependent enhancement, which increases the likelihood of developing symptomatic and severe episodes (associated with higher case fatality) upon infection. Only a fraction of the population will be vaccinated, reflecting vaccination hesitancy or contraindications. The model is intended to facilitate decision making by exploring ranges of parameters rather than to be fitted by empirical data. We parameterized the model to reflect the situation in Germany and predict increasing incidence (and prevalence) in early 2021 followed by a decline by summer. Assuming contact reductions (curfews, social distancing, etc.) to be lifted in summer, disease incidence will peak again. Fast vaccine deployment contributes to reduce disease incidence in the first quarter of 2021, and delay the epidemic outbreak after the summer season. Higher vaccination coverage results in a delayed and reduced epidemic peak. A coverage of 75% - 80% is necessary to prevent an epidemic peak without further drastic contact reductions. ConclusionsWith the vaccine becoming available, compliance with contact reductions is likely to fade. To prevent further economic damage from COVID-19, high levels of immunization need to be reached before next years flu season, and vaccination strategies and disease management need to be flexibly adjusted. The predictive model can serve as a refined decision support tool for COVID-19 management.

immunology↗