SARS-CoV-2 within-host diversity of human hosts and its implications for viral immune evasion.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving, bringing great challenges to the control of the virus. In the present study, we investigated the characteristics of SARS-CoV-2 within-host diversity of human hosts and its implications for immune evasion using about 2,00,000 high-depth next-generation genome sequencing data of SARS-CoV-2. A total of 44% of the samples showed within-host variations (iSNVs), and the average number of iSNVs in the samples with iSNV was 1.90. C-to-U is the dominant substitution pattern for iSNVs. C-to-U/G-to-A and A-to-G/U-to-C preferentially occur in 5'-CG-3' and 5'-AU-3' motifs, respectively. In addition, we found that SARS-CoV-2 within-host variations are under negative selection. About 15.6% iSNVs had an impact on the content of the CpG dinucleotide (CpG) in SARS-CoV-2 genomes. We detected signatures of faster loss of CpG-gaining iSNVs, possibly resulting from zinc-finger antiviral protein-mediated antiviral activities targeting CpG, which could be the major reason for CpG depletion in SARS-CoV-2 consensus genomes. The non-synonymous iSNVs in the S gene can largely alter the S protein's antigenic features, and many of these iSNVs are distributed in the amino-terminal domain (NTD) and receptor-binding domain (RBD). These results suggest that SARS-CoV-2 interacts actively with human hosts and attempts to take different evolutionary strategies to escape human innate and adaptive immunity. These new findings further deepen and widen our understanding of the within-host evolutionary features of SARS-CoV-2.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of the coronavirus disease 2019, has evolved rapidly since it was discovered. Recent studies have pointed out that some mutations in the SARS-CoV-2 S protein could confer SARS-CoV-2 the ability to evade the human adaptive immune system. In addition, it is observed that the content of the CpG dinucleotide in SARS-CoV-2 genome sequences has decreased over time, reflecting the adaptation to the human host. The significance of our research is revealing the characteristics of SARS-CoV-2 within-host diversity of human hosts, identifying the causes of CpG depletion in SARS-CoV-2 consensus genomes, and exploring the potential impacts of non-synonymous within-host variations in the S gene on immune escape, which could further deepen and widen our understanding of the evolutionary features of SARS-CoV-2.

Within-host modeling to measure dynamics of antibody responses after natural infection or vaccination: A systematic review.

BACKGROUND: Within-host models describe the dynamics of immune cells when encountering a pathogen, and how these dynamics can lead to an individual-specific immune response. This systematic review aims to summarize which within-host methodology has been used to study and quantify antibody kinetics after infection or vaccination. In particular, we focus on data-driven and theory-driven mechanistic models. MATERIALS: PubMed and Web of Science databases were used to identify eligible papers published until May 2022. Eligible publications included those studying mathematical models that measure antibody kinetics as the primary outcome (ranging from phenomenological to mechanistic models). RESULTS: We identified 78 eligible publications, of which 8 relied on an Ordinary Differential Equations (ODEs)-based modelling approach to describe antibody kinetics after vaccination, and 12 studies used such models in the context of humoral immunity induced by natural infection. Mechanistic modeling studies were summarized in terms of type of study, sample size, measurements collected, antibody half-life, compartments and parameters included, inferential or analytical method, and model selection. CONCLUSIONS: Despite the importance of investigating antibody kinetics and underlying mechanisms of (waning of) the humoral immunity, few publications explicitly account for this in a mathematical model. In particular, most research focuses on phenomenological rather than mechanistic models. The limited information on the age groups or other risk factors that might impact antibody kinetics, as well as a lack of experimental or observational data remain important concerns regarding the interpretation of mathematical modeling results. We reviewed the similarities between the kinetics following vaccination and infection, emphasising that it may be worth translating some features from one setting to another. However, we also stress that some biological mechanisms need to be distinguished. We found that data-driven mechanistic models tend to be more simplistic, and theory-driven approaches lack representative data to validate model results.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search.

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.

Within-Host Evolution of SARS-CoV-2 in a B-Cell Depleted Patient With Successful Treatment.

Prolonged viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an immunocompromised host is a challenge as the treatment and infection control for chronic coronavirus disease 2019 infection is not well established and there is a potential risk of new variants emerging. A 48-year-old woman who underwent chemotherapy, including rituximab and steroid, had reactivation of SARS-CoV-2 68 days after the virus was first detected. She successfully recovered after receiving convalescent plasma and intravenous immunoglobulin. Genomic analysis demonstrated that viruses collected from the nasopharyngeal specimens at day 0 and day 68 had 18 different nucleotide mutations, implying within-host evolution after in-depth epidemiologic investigation.

Simplified within-host and Dose-response Models of SARS-CoV-2.

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis.

Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.

Challenges in Harnessing Shared Within-Host Severe Acute Respiratory Syndrome Coronavirus 2 Variation for Transmission Inference.

Background: The limited variation observed among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consensus sequences makes it difficult to reconstruct transmission linkages in outbreak settings. Previous studies have recovered variation within individual SARS-CoV-2 infections but have not yet measured the informativeness of within-host variation for transmission inference. Methods: We performed tiled amplicon sequencing on 307 SARS-CoV-2 samples, including 130 samples from 32 individuals in 14 households and 47 longitudinally sampled individuals, from 4 prospective studies with household membership data, a proxy for transmission linkage. Results: Consensus sequences from households had limited diversity (mean pairwise distance, 3.06 single-nucleotide polymorphisms [SNPs]; range, 0-40). Most (83.1%, 255 of 307) samples harbored at least 1 intrahost single-nucleotide variant ([iSNV] median, 117; interquartile range [IQR], 17-208), above a minor allele frequency threshold of 0.2%. Pairs in the same household shared significantly more iSNVs (mean, 1.20 iSNVs; 95% confidence interval [CI], 1.02-1.39) than did pairs in different households infected with the same viral clade (mean, 0.31 iSNVs; 95% CI, .28-.34), a signal that decreases with increasingly stringent minor allele frequency thresholds. The number of shared iSNVs was significantly associated with an increased odds of household membership (adjusted odds ratio, 1.35; 95% CI, 1.23-1.49). However, the poor concordance of iSNVs detected across sequencing replicates (24.8% and 35.0% above a 0.2% and 1% threshold) confirms technical concerns that current sequencing and bioinformatic workflows do not consistently recover low-frequency within-host variants. Conclusions: Shared within-host variation may augment the information in consensus sequences for predicting transmission linkages. Improving sensitivity and specificity of within-host variant identification will improve the informativeness of within-host variation.

A Multiscale Model of COVID-19 Dynamics.

COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

Assessing the hidden diversity underlying consensus sequences of SARS-CoV-2 using VICOS, a novel bioinformatic pipeline for identification of mixed viral populations.

INTRODUCTION: Coinfection with two SARS-CoV-2 viruses is still a very understudied phenomenon. Although next generation sequencing methods are very sensitive to detect heterogeneous viral populations in a sample, there is no standardized method for their characterization, so their clinical and epidemiological importance is unknown. MATERIAL AND METHODS: We developed VICOS (Viral COinfection Surveillance), a new bioinformatic algorithm for variant calling, filtering and statistical analysis to identify samples suspected of being mixed SARS-CoV-2 populations from a large dataset in the framework of a community genomic surveillance. VICOS was used to detect SARS-CoV-2 coinfections in a dataset of 1,097 complete genomes collected between March 2020 and August 2021 in Argentina. RESULTS: We detected 23 cases (2%) of SARS-CoV-2 coinfections. Detailed study of VICOS's results together with additional phylogenetic analysis revealed 3 cases of coinfections by two viruses of the same lineage, 2 cases by viruses of different genetic lineages, 13 were compatible with both coinfection and intra-host evolution, and 5 cases were likely a product of laboratory contamination. DISCUSSION: Intra-sample viral diversity provides important information to understand the transmission dynamics of SARS-CoV-2. Advanced bioinformatics tools, such as VICOS, are a necessary resource to help unveil the hidden diversity of SARS-CoV-2.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Gastrointestinal helminths increase Bordetella bronchiseptica shedding and host variation in supershedding.

Co-infected hosts, individuals that carry more than one infectious agent at any one time, have been suggested to facilitate pathogen transmission, including the emergence of supershedding events. However, how the host immune response mediates the interactions between co-infecting pathogens and how these affect the dynamics of shedding remains largely unclear. We used laboratory experiments and a modeling approach to examine temporal changes in the shedding of the respiratory bacterium Bordetella bronchiseptica in rabbits with one or two gastrointestinal helminth species. Experimental data showed that rabbits co-infected with one or both helminths shed significantly more B. bronchiseptica, by direct contact with an agar petri dish, than rabbits with bacteria alone. Co-infected hosts generated supershedding events of higher intensity and more frequently than hosts with no helminths. To explain this variation in shedding an infection-immune model was developed and fitted to rabbits of each group. Simulations suggested that differences in the magnitude and duration of shedding could be explained by the effect of the two helminths on the relative contribution of neutrophils and specific IgA and IgG to B. bronchiseptica neutralization in the respiratory tract. However, the interactions between infection and immune response at the scale of analysis that we used could not capture the rapid variation in the intensity of shedding of every rabbit. We suggest that fast and local changes at the level of respiratory tissue probably played a more important role. This study indicates that co-infected hosts are important source of variation in shedding, and provides a quantitative explanation into the role of helminths to the dynamics of respiratory bacterial infections.

Rapid evolution of SARS-CoV-2 in domestic cats.

SARS-CoV-2 (SARS2) infection of a novel permissive host species can result in rapid viral evolution. Data suggest that felids are highly susceptible to SARS2 infection, and species-specific adaptation following human-to-felid transmission may occur. We employed experimental infection and analysis of publicly available SARS2 sequences to observe variant emergence and selection in domestic cats. Three cohorts of cats (N = 23) were inoculated with SARS-CoV-2 USA-WA1/2020 or infected via cat-to-cat contact transmission. Full viral genomes were recovered from RNA obtained from nasal washes 1-3 days post-infection and analyzed for within-host viral variants. We detected 118 unique variants at ≥3 per cent allele frequency in two technical replicates. Seventy of these (59 per cent) were nonsynonymous single nucleotide variants (SNVs); the remainder were synonymous SNVs or structural variants. On average, we observed twelve variants per cat, nearly 10-fold higher than what is commonly reported in human patients. We observed signatures of positive selection in the spike protein and the emergence of eleven within-host variants located at the same genomic positions as mutations in SARS2 variant lineages that have emerged during the pandemic. Fewer variants were noted in cats infected from contact with other cats and in cats exposed to lower doses of cultured inoculum. An analysis of ninety-three publicly available SARS2 consensus genomes recovered from naturally infected domestic cats reflected variant lineages circulating in the local human population at the time of sampling, illustrating that cats are susceptible to SARS2 variants that have emerged in humans, and suggesting human-to-felid transmission occurring in domestic settings is typically unidirectional. These experimental results underscore the rapidity of SARS2 adaptation in felid hosts, representing a theoretical potential origin for variant lineages in human populations. Further, cats should be considered susceptible hosts capable of shedding virus during infections occurring within households.

A mathematical model of the within-host kinetics of SARS-CoV-2 neutralizing antibodies following COVID-19 vaccination.

Compelling evidence continues to build to support the idea that SARS-CoV-2 Neutralizing Antibody (NAb) levels in an individual can serve as an important indicator of the strength of protective immunity against infection. It is not well understood why NAb levels in some individuals remain high over time, while in others levels decline rapidly. In this work, we present a two-state mathematical model of within-host NAb dynamics in response to vaccination. By fitting only four host-specific parameters, the model is able to capture individual-specific NAb levels over time as measured by the AditxtScore™ for NAbs. The model can serve as a foundation for predicting NAb levels in the long-term, understanding connections between NAb levels, protective immunity, and breakthrough infections, and potentially guiding decisions about whether and when a booster vaccination may be warranted.

The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK.

The first SARS-CoV-2 variant of concern (VOC) to be designated was lineage B.1.1.7, later labelled by the World Health Organization as Alpha. Originating in early autumn but discovered in December 2020, it spread rapidly and caused large waves of infections worldwide. The Alpha variant is notable for being defined by a long ancestral phylogenetic branch with an increased evolutionary rate, along which only two sequences have been sampled. Alpha genomes comprise a well-supported monophyletic clade within which the evolutionary rate is typical of SARS-CoV-2. The Alpha epidemic continued to grow despite the continued restrictions on social mixing across the UK and the imposition of new restrictions, in particular, the English national lockdown in November 2020. While these interventions succeeded in reducing the absolute number of cases, the impact of these non-pharmaceutical interventions was predominantly to drive the decline of the SARS-CoV-2 lineages that preceded Alpha. We investigate the only two sampled sequences that fall on the branch ancestral to Alpha. We find that one is likely to be a true intermediate sequence, providing information about the order of mutational events that led to Alpha. We explore alternate hypotheses that can explain how Alpha acquired a large number of mutations yet remained largely unobserved in a region of high genomic surveillance: an under-sampled geographical location, a non-human animal population, or a chronically infected individual. We conclude that the latter provides the best explanation of the observed behaviour and dynamics of the variant, although the individual need not be immunocompromised, as persistently infected immunocompetent hosts also display a higher within-host rate of evolution. Finally, we compare the ancestral branches and mutation profiles of other VOCs and find that Delta appears to be an outlier both in terms of the genomic locations of its defining mutations and a lack of the rapid evolutionary rate on its ancestral branch. As new variants, such as Omicron, continue to evolve (potentially through similar mechanisms), it remains important to investigate the origins of other variants to identify ways to potentially disrupt their evolution and emergence.

Modeling the stochastic within-host dynamics SARS-CoV-2 infection with discrete delay.

In this paper, a new mathematical model that describes the dynamics of the within-host COVID-19 epidemic is formulated. We show the stochastic dynamics of Target-Latent-Infected-Virus free within the human body with discrete delay and noise. Positivity and uniqueness of the solutions are established. Our study shows the extinction and persistence of the disease inside the human body through the stability analysis of the disease-free equilibrium [Formula: see text] and the endemic equilibrium [Formula: see text], respectively. Moreover, we show the impact of delay tactics and noise on the extinction of the disease. The most interesting result is even if the deterministic system is inevitably pandemic at a specific point, extinction will become possible in the stochastic version of our model.

SARS-CoV-2 Within-Host and in vitro Genomic Variability and Sub-Genomic RNA Levels Indicate Differences in Viral Expression Between Clinical Cohorts and in vitro Culture.

Background: Low frequency intrahost single nucleotide variants (iSNVs) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have been increasingly recognised as predictive indicators of positive selection. Particularly as growing numbers of SARS-CoV-2 variants of interest (VOI) and concern (VOC) emerge. However, the dynamics of subgenomic RNA (sgRNA) expression and its impact on genomic diversity and infection outcome remain poorly understood. This study aims to investigate and quantify iSNVs and sgRNA expression in single and longitudinally sampled cohorts over the course of mild and severe SARS-CoV-2 infection, benchmarked against an in vitro infection model. Methods: Two clinical cohorts of SARS-CoV-2 positive cases in New South Wales, Australia collected between March 2020 and August 2021 were sequenced. Longitudinal samples from cases hospitalised due to SARS-CoV-2 infection (severe) (n = 16) were analysed and compared with cases that presented with SARS-CoV-2 symptoms but were not hospitalised (mild) (n = 23). SARS-CoV-2 genomic diversity profiles were also examined from daily sampling of culture experiments for three SARS-CoV-2 variants (Lineage A, B.1.351, and B.1.617.2) cultured in VeroE6 C1008 cells (n = 33). Results: Intrahost single nucleotide variants were detected in 83% (19/23) of the mild cohort cases and 100% (16/16) of the severe cohort cases. SNP profiles remained relatively fixed over time, with an average of 1.66 SNPs gained or lost, and an average of 4.2 and 5.9 low frequency variants per patient were detected in severe and mild infection, respectively. sgRNA was detected in 100% (25/25) of the mild genomes and 92% (24/26) of the severe genomes. Total sgRNA expressed across all genes in the mild cohort was significantly higher than that of the severe cohort. Significantly higher expression levels were detected in the spike and the nucleocapsid genes. There was significantly less sgRNA detected in the culture dilutions than the clinical cohorts. Discussion and Conclusion: The positions and frequencies of iSNVs in the severe and mild infection cohorts were dynamic overtime, highlighting the importance of continual monitoring, particularly during community outbreaks where multiple SARS-CoV-2 variants may co-circulate. sgRNA levels can vary across patients and the overall level of sgRNA reads compared to genomic RNA can be less than 1%. The relative contribution of sgRNA to the severity of illness warrants further investigation given the level of variation between genomes. Further monitoring of sgRNAs will improve the understanding of SARS-CoV-2 evolution and the effectiveness of therapeutic and public health containment measures during the pandemic.

Modelling: Understanding pandemics and how to control them.

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Spatial dynamics for an SIRE epidemic model with diffusion and prevention in contaminated environments

In this paper, a reaction-diffusion SIRE epidemic model in contaminated environments is proposed, in which the effect of protection for susceptible individuals is included by the nonlinear incidence functions b(S)E$b(S)E$ and g(S)I$g(S)I$. When the space is heterogeneous, the basic reproduction number R0$\mathcal {R}_{0}$ is derived, by which we find that if R0 <= 1$\mathcal {R}_{0}\le 1$, the disease-free steady state is globally asymptotically stable, while R0>1$\mathcal {R}_{0}>1$, the disease is uniform persistent. Furthermore, when R0>1$\mathcal {R}_{0}>1$ and additional conditions hold, the global asymptotic stability of special endemic steady state is obtained in homogeneous space. Finally, the theoretical results are validated by numerical simulations, some open questions are illustrated.

Variant-specific SARS-CoV-2 within-host kinetics.

Since early 2021, SARS-CoV-2 variants of concern (VOCs) have been causing epidemic rebounds in many countries. Their properties are well characterized at the epidemiological level but the potential underlying within-host determinants remain poorly understood. We analyze a longitudinal cohort of 6944 individuals with 14 304 cycle threshold (Ct) values of reverse-transcription quantitative polymerase chain reaction (RT-qPCR) VOC screening tests performed in the general population and hospitals in France between February 6 and August 21, 2021. To convert Ct values into numbers of virus copies, we performed an additional analysis using droplet digital PCR (ddPCR). We find that the number of viral genome copies reaches a higher peak value and has a slower decay rate in infections caused by Alpha variant compared to that caused by historical lineages. Following the evidence that viral genome copies in upper respiratory tract swabs are informative on contagiousness, we show that the kinetics of the Alpha variant translate into significantly higher transmission potentials, especially in older populations. Finally, comparing infections caused by the Alpha and Delta variants, we find no significant difference in the peak viral copy number. These results highlight that some of the differences between variants may be detected in virus load variations.

Mathematical models for dengue fever epidemiology: A 10-year systematic review.

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.