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Abstract Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data. We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections. This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data. We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections. This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements. [ABSTRACT FROM AUTHOR]

Assessing the genomic evolution of Staphylococcus aureus can help us understand how the bacteria adapt to its environment. In this study, we aimed to assess the mutation rate within 144 methicillin-resistant Staphylococcus aureus (MRSA) carriers with a carriage time from 4 to 11 years, including some carriers who belonged to the same households. We found that 23 of the 144 individuals had completely different MRSA types over time and were therefore not long-term carriers of the same MRSA. From the remaining 121 individuals, we performed whole-genome sequencing (WGS) on 424 isolates and then compared these pairwise using core genome multilocus sequence typing (cgMLST) and single-nucleotide polymorphism (SNP) analyses. We found a median within-host mutation rate in long-term MRSA carriers of 4.9 (3.4–6.9) SNPs/genome/year and 2.7 (1.8–4.2) allelic differences/genome/year, when excluding presumed recombination. Furthermore, we stratified the cohort into subgroups and found no significant difference between the median mutation rate of members of households, individuals with presumed continued exposure, e.g., from travel and persons without known continued exposure. Finally, we found that SNPs occurred at random within the genes in our cohort. Key points: • Median mutation rate within long-term MRSA carriers of 4.9 (3.4–6.9) SNPs/genome/year • Similar median mutation rates in subgroups (households, travelers) • No hotspots for SNPs within the genome [ABSTRACT FROM AUTHOR]

Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.

In this paper, we investigate two within-host mathematical models that are based on differential equations. These mathematical models include healthy cells, tachyzoites, and bradyzoites. The first model is based on ordinary differential equations and the second one includes a discrete time delay. We found the models' steady states and computed the basic reproduction number R 0 . Two equilibrium points exist in both models: the first is the disease-free equilibrium point and the second one is the endemic equilibrium point. We found that the initial quantity of uninfected cells has an impact on the basic reproduction number R 0 . This threshold parameter also depends on the contact rate between tachyzoites and uninfected cells, the contact rate between encysted bradyzoite and the uninfected cells, the conversion rate from tachyzoites to bradyzoites, and the death rate of the bradyzoites- and tachyzoites-infected cells. We investigated the local and global stability of the two equilibrium points for the within-host models that are based on differential equations. We perform numerical simulations to validate our analytical findings. We also demonstrated that the disease-free equilibrium point cannot lose stability regardless of the value of the time delay. The numerical simulations corroborated our analytical results. [ABSTRACT FROM AUTHOR]

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. 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). 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. 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. [ABSTRACT FROM AUTHOR]

In this paper, we investigate two within-host mathematical models that are based on differential equations. These mathematical models include healthy cells, tachyzoites, and bradyzoites. The first model is based on ordinary differential equations and the second one includes a discrete time delay. We found the models’ steady states and computed the basic reproduction number R0. Two equilibrium points exist in both models: the first is the disease-free equilibrium point and the second one is the endemic equilibrium point. We found that the initial quantity of uninfected cells has an impact on the basic reproduction number R0. This threshold parameter also depends on the contact rate between tachyzoites and uninfected cells, the contact rate between encysted bradyzoite and the uninfected cells, the conversion rate from tachyzoites to bradyzoites, and the death rate of the bradyzoites- and tachyzoites-infected cells. We investigated the local and global stability of the two equilibrium points for the within-host models that are based on differential equations. We perform numerical simulations to validate our analytical findings. We also demonstrated that the disease-free equilibrium point cannot lose stability regardless of the value of the time delay. The numerical simulations corroborated our analytical results.

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. [ABSTRACT FROM AUTHOR]

Abstract Background The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions. Results Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models. Conclusion Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention.

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

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. • 10-year systematic review on mathematical models for dengue fever epidemiology. • Detailed review of multi-strain frameworks describing host-to-host dengue transmission models. • Detailed review of vector-host dengue transmission. • Detailed review of within-host dengue models. • Comprehensive description of dengue and COVID-19 co-infection modeling. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in more than 65.5 million infections and 15,14,695 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced, thereby decreasing the mortality. Also population level control strategies can reduce the spread of the COVID-19 substantially. Motivated by these observations, in this work we propose and study a multi scale model linking both within-host and between-host dynamics of COVID-19. Initially the natural history dealing with the disease dynamics is studied. Later comparative effectiveness is performed to understand the efficacy of both the within-host and population level interventions. Findings of this study suggest that a combined strategy involving treatment with drugs such as Arbidol, remdesivir, Lopinavir/Ritonavir that inhibits viral replication and immunotherapies like monoclonal antibodies, along with environmental hygiene and generalized social distancing proved to be the best and optimal in reducing the basic reproduction number and environmental spread of the virus at the population level.

Tuberculosis (TB) is among the 10 top causes of deaths worldwide, and one-quarter of the world population hosts latent TB pathogens. Therefore, avoiding the emergence of drug-resistant strains has become a central issue in TB control. In this work, we propose a nested model for TB transmission and control, wherein both within-host and between-host dynamics are modeled. We use the model to compare the effects of three types of antibiotic treatment protocols and combinations thereof in an in silico population. For a fixed value of antibiotics clearance rate and relative efficacy against resistant strains, the oscillating intermittent protocol, pure or combined, is the most effective against the sensitive strains. However, this protocol also creates a selective advantage for the resistant strains, returning the worst result in comparison to the other protocols. We suggest that nested models should be further developed, since they might be able to inform decision-makers regarding the optimal TB control protocols to be applied under the specific parameters and other epidemiological factors in different populations. [ABSTRACT FROM AUTHOR]

The population dynamics of infectious disease is a mature field in terms of theory and to some extent, application. However for microparasites, the theory and application of models of the dynamics within a single infected host is still an open field. Further, connecting across the scales--from cellular to host level, to population level--has potential to vastly improve our understanding of pathogen dynamics and evolution. Here, we highlight seven challenges in the following areas: transmission bottlenecks, heterogeneity within host, dynamic fitness landscapes within hosts, making use of next-generation sequencing data, capturing superinfection and when and how to model more than two scales.

We present a differential equation model of the innate immune response to SARS-CoV-2 within the alveolar epithelium. Critical determinants of the viral dynamics and host response, including type I and type II alveolar epithelial cells, interferons, chemokines, toxins and innate immune cells, are included. We estimate model parameters, compute the within-host basic reproductive number, and study the impacts of therapies, prophylactics, and host/pathogen variability on the course of the infection. Model simulations indicate that the innate immune response suppresses the infection and enables the alveolar epithelium to partially recover. While very robust antiviral therapy controls the infection and enables the epithelium to heal, moderate therapy is of limited benefit. Meanwhile interferon therapy is predicted to reduce viral load but exacerbate tissue damage. The deleterious effects of interferon therapy are especially apparent late in the infection. Individual variation in ACE2 expression, epithelial cell interferon production, and SARS-CoV-2 spike protein binding affinity are predicted to significantly impact prognosis.

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95.1% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within- and between-host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

Background: The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions. Results: Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models. Conclusion: Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC. [ABSTRACT FROM AUTHOR]

Human activities alter elemental nutrient cycling, which can have profound impacts on agriculture, grasslands, lakes, and other systems. It is becoming increasingly clear that enhanced nitrogen and phosphorus levels can affect disease dynamics across a range of taxa. However, there are few mathematical models that explicitly incorporate nutrients into host-pathogen interactions. Using viral load and plant mass data from an experiment with cereal yellow dwarf virus and its host plant, Avena sativa, we propose and compare two models describing the overall infection dynamics. However, the first model considers nutrient-limited virus production while the other considers a nutrient-induced viral production delay. A virus reproduction number is derived for this nutrient model, which depends on environmental and physiological attributes. Results suggest that including nutrient mediated viral production mechanisms can give rise to robust models that can be used to untangle how nutrients impact pathogen dynamics.

Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research. [ABSTRACT FROM AUTHOR]

Whole-genome sequencing has enabled detailed studies on bacterial evolution during infection, but there is limited knowledge on intraclonal variation. In this study, we sought to provide a snapshot of the intraclonal diversity of Escherichia coli as both commensal in the faecal environment and pathogen during urinary tract infection, respectively. This was performed by whole-genome sequencing and analyses of single nucleotide polymorphisms (SNPs) and gene-content variation in ten isolates belonging to the same clone and isolated from rectal swabs or urine samples. We identified only one clone in eight of the nine urines sampled (89 %). In both the commensal and pathogenic state, the within-host diversity was limited with intraclonal SNP diversity of 0–2 non-synonymous SNPs for each clone. The genetic diversity showed variation in gene content in a range of 2–15 genes in total for all clones, including genes positioned on plasmids, and in the K- and O-antigen cluster. The observed SNP- and gene variation shows that sampling of one colony would be enough for surveillance, outbreak investigations and clonal evolution. However, for studies of adaptation during or between colonization and infection, this variation is relevant to consider.

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19. [ABSTRACT FROM AUTHOR]

Co-infections of hosts by multiple pathogen species are ubiquitous, but predicting their impact on disease remains challenging. Interactions between co-infecting pathogens within hosts can alter pathogen transmission, with the impact on transmission typically dependent on the relative arrival order of pathogens within hosts (within-host priority effects). However, it is unclear how these within-host priority effects influence multi-pathogen epidemics, particularly when the arrival order of pathogens at the host-population scale varies. Here, we combined models and experiments with zooplankton and their naturally co-occurring fungal and bacterial pathogens to examine how within-host priority effects influence multi-pathogen epidemics. Epidemiological models parametrized with within-host priority effects measured at the single-host scale predicted that advancing the start date of bacterial epidemics relative to fungal epidemics would decrease the mean bacterial prevalence in a multi-pathogen setting, while models without within-host priority effects predicted the opposite effect. We tested these predictions with experimental multi-pathogen epidemics. Empirical dynamics matched predictions from the model including within-host priority effects, providing evidence that within-host priority effects influenced epidemic dynamics. Overall, within-host priority effects may be a key element of predicting multi-pathogen epidemic dynamics in the future, particularly as shifting disease phenology alters the order of infection within hosts. [ABSTRACT FROM AUTHOR]

Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases. • We systematically review the literature on multiscale infectious disease modelling. • We divide previously published models into five methodological categories. • We explain the benefits and limitations of each category. • We provide a primer on developing multiscale models of infectious diseases. [Display omitted] [ABSTRACT FROM AUTHOR]

The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.

The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction. [ABSTRACT FROM AUTHOR]

Over a billion people on earth are infected with helminth parasites and show remarkable variation in parasite burden and chronicity. These parasite distributions are captured well by classic statistics, such as the negative binomial distribution. But the within-host processes underlying this variation are not well understood. In this study, we explain variation in macroparasite infection outcomes on the basis of resource flows within hosts. Resource flows realize the interactions between parasites and host immunity and metabolism. When host metabolism is modulated by parasites, we find a positive feedback of parasites on their own resources. While this positive feedback results in parasites improving their resource availability at high burdens, giving rise to chronic infections, it also results in a threshold biomass required for parasites to establish in the host, giving rise to acute infections when biomass fails to clear the threshold. Our finding of chronic and acute outcomes in bistability contrasts with classic theory, yet is congruent with the variation in helminth burdens observed in human and wildlife populations. [ABSTRACT FROM AUTHOR]

• A novel multi-scale coupled malaria transmission model is developed. • The model couples within-human and -mosquito dynamics to population level dynamics. • Feedback functions using the parasite life-history stages uniquely couples the levels. • Human host immune response leads to damping oscillations with increased biting rate. • Oscillations in the within-human model amplifies the oscillations within mosquitoes. In this paper, we developed a novel deterministic coupled model tying together the effects of within-host and population level dynamics on malaria transmission dynamics. We develop within-host and within-vector dynamic models, population level between-hosts models, and a nested coupled model combining these levels. The unique feature of this work is the way the coupling and feedback for the model use the various life stages of the malaria parasite both in the human host and the mosquito vector. Analysis of the coupled and the within-human host models indicate the existence of locally asymptotically stable infection- and parasite-free equilibria when the associated reproduction numbers are less than one. The population-level model, on the other hand, exhibits backward bifurcation, where the stable disease-free equilibrium co-exists with a stable endemic equilibrium. A global sensitivity analysis was carried out to measure the effects of the sensitivity and uncertainty in the various model parameters estimates. The results indicate that the most important parameters driving the pathogen level within an infected human are the production rate of the red blood cells from the bone marrow, the infection rate, the immunogenicity of the infected red blood cells, merozoites and gametocytes, and the immunosensitivity of the merozoites and gametocytes. The key parameters identified at the population level are the human recovery rate, the death rate of the mosquitoes, the recruitment rate of susceptible humans into the population, the mosquito biting rate, the transmission probabilities per contact in mosquitoes and in humans, and the parasite production and clearance rates in the mosquitoes. Defining the feedback functions as a linear function of the mosquito biting rate, numerical exploration of the coupled model reveals oscillations in the parasite populations within a human host in the presence of the host immune response. These oscillations dampen as the mosquito biting rate increases. We also observed that the oscillation and damping effect seen in the within-human host dynamics fed back into the population level dynamics; this in turn amplifies the oscillations in the parasite population within the mosquito-host. [ABSTRACT FROM AUTHOR]

The standard approach to modeling survival times, or more generally, time to event data, is often based on parametric assumptions that may not fit the data collected well. One of the goals of this article is to discuss and compare several commonly used parametric and non-parametric, as well as a Bayesian semi-parametric method for survival data. We do so in the context of the data from an experimental system where insect herbivores become infected when consuming a lethal virus along with the plant on which the virus resides. We used data collected on individual insects that were fed known doses of virus along with varying genotypes of a single plant species (soybean), to compare how the insect's diet affects its time to death. Through hazard characterization and model selection, we found that the flexible semi-parametric analysis is better at describing the time-to-death data while maintaining a relatively parsimonious form. Unlike the standard parametric and non-parametric approaches, the Bayesian semi-parametric approach better captured the rapid decline in the hazard function after a window of time where the host was most vulnerable to the virus. For our study system, being able to accurately model time to death and quantify how plant genetics affects within-insect disease processes allows us to gain a better understanding of the host-pathogen interaction at an individual level. While we show the appropriateness of the Bayesian semi-parametric approach for infection data, this method readily applies to data sets concerned with characterizing a time until any event. [ABSTRACT FROM AUTHOR]

Abstract Background Next generation sequencing (NGS) methods have significantly contributed to a paradigm shift in genomic research for nearly a decade now. These methods have been useful in studying the dynamic interactions between RNA viruses and human hosts. Scope of the review In this review, we summarise and discuss key applications of NGS in studying the host – pathogen interactions in RNA viral infections of humans with examples. Major conclusions Use of NGS to study globally relevant RNA viral infections have revolutionized our understanding of the within host and between host evolution of these viruses. These methods have also been useful in clinical decision-making and in guiding biomedical research on vaccine design. General significance NGS has been instrumental in viral genomic studies in resolving within-host viral genomic variants and the distribution of nucleotide polymorphisms along the full-length of viral genomes in a high throughput, cost effective manner. In the future, novel advances such as long read, single molecule sequencing of viral genomes and simultaneous sequencing of host and pathogens may become the standard of practice in research and clinical settings. This will also bring on new challenges in big data analysis. Highlights • NGS methods have revolutionized our understanding of RNA virus evolutionover the last decade • The advances in this field led to new discoveries such as transmitted founder variants and better vaccine candidates • The key challenge for the next decade of NGS is the use of this technology in clinical decision making [ABSTRACT FROM AUTHOR]

The population dynamics of infectious disease is a mature field in terms of theory and to some extent, application. However for microparasites, the theory and application of models of the dynamics within a single infected host is still an open field. Further, connecting across the scales – from cellular to host level, to population level – has potential to vastly improve our understanding of pathogen dynamics and evolution. Here, we highlight seven challenges in the following areas: transmission bottlenecks, heterogeneity within host, dynamic fitness landscapes within hosts, making use of next-generation sequencing data, capturing superinfection and when and how to model more than two scales.

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus’s global evolution. [ABSTRACT FROM AUTHOR]

Summary: Malaria infection continues to be a major health problem worldwide and drug resistance in the major human parasite species, Plasmodium falciparum, is increasing in South East Asia. Control measures including novel drugs and vaccines are in development, and contributions to the rational design and optimal usage of these interventions are urgently needed. Infection involves the complex interaction of parasite dynamics, host immunity, and drug effects. The long life cycle (48 hours in the common human species) and synchronized replication cycle of the parasite population present significant challenges to modeling the dynamics of Plasmodium infection. Coupled with these, variation in immune recognition and drug action at different life cycle stages leads to further complexity. We review the development and progress of “within‐host” models of Plasmodium infection, and how these have been applied to understanding and interpreting human infection and animal models of infection. [ABSTRACT FROM AUTHOR]

Phage therapy is the use of bacterial viruses (phages) to treat bacterial infections, a medical intervention long abandoned in the West but now experiencing a revival. Currently, therapeutic phages are often chosen based on limited criteria, sometimes merely an ability to plate on the pathogenic bacterium. Better treatment might result from an informed choice of phages. Here we consider whether phages used to treat the bacterial infection in a patient may specifically evolve to improve treatment on that patient or benefit subsequent patients. With mathematical and computational models, we explore in vivo evolution for four phage properties expected to influence therapeutic success: generalized phage growth, phage decay rate, excreted enzymes to degrade protective bacterial layers, and growth on resistant bacteria. Within-host phage evolution is strongly aligned with treatment success for phage decay rate but only partially aligned for phage growth rate and growth on resistant bacteria. Excreted enzymes are mostly not selected for treatment success. Even when evolution and treatment success are aligned, evolution may not be rapid enough to keep pace with bacterial evolution for maximum benefit. An informed use of phages is invariably superior to naive reliance on within-host evolution.

• The new informatic pipeline VICOS revealed 2% of SARS-CoV-2 coinfections during the first COVID-19 wave in Argentina. • VICOS output together with additional metadata help to differentiate between coinfection by 2 SARS-CoV-2, intra-host evolution or sequencing contamination. • SARS-CoV-2 inter- and intra-lineage coinfections need to be monitored as the driving force of recombination and evolution. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

The population dynamics of infectious disease is a mature field in terms of theory and to some extent, application. However for microparasites, the theory and application of models of the dynamics within a single infected host is still an open field. Further, connecting across the scales – from cellular to host level, to population level – has potential to vastly improve our understanding of pathogen dynamics and evolution. Here, we highlight seven challenges in the following areas: transmission bottlenecks, heterogeneity within host, dynamic fitness landscapes within hosts, making use of next-generation sequencing data, capturing superinfection and when and how to model more than two scales. [ABSTRACT FROM AUTHOR]

Parasites that are molecular mimics express proteins which resemble host proteins. This resemblance facilitates immune evasion because the immune molecules with the specificity to react with the parasite also cross-react with the host's own proteins, and these lymphocytes are rare. Given this advantage, why are not most parasites molecular mimics? Here we explore potential factors that can select against molecular mimicry in parasites and thereby limit its occurrence. We consider two hypotheses: (1) molecular mimics are more likely to induce autoimmunity in their hosts, and hosts with autoimmunity generate fewer new infections (the 'costly autoimmunity hypothesis'); and (2) molecular mimicry compromises protein functioning, lowering the within-host replication rate and leading to fewer new infections (the 'mimicry trade-off hypothesis'). Our analysis shows that although both hypotheses may select against molecular mimicry in parasites, unique hallmarks of protein expression identify whether selection is due to the costly autoimmunity hypothesis or the mimicry trade-off hypothesis. We show that understanding the relevant selective forces is necessary to predict how different medical interventions will affect the proportion of hosts that experience the different infection types, and that if parasite evolution is ignored, interventions aimed at reducing infection-induced autoimmunity may ultimately fail. [ABSTRACT FROM AUTHOR]