Heterogeneity In Infectiousness Research Articles

Assessing infectiousness and the impact of effective treatment to guide isolation recommendations for people with pulmonary tuberculosis.

Determining the extent and duration of infectiousness of individuals with pulmonary tuberculosis (TB) is critical for various aspects of TB care, including decisions regarding isolation. Studies suggest considerable heterogeneity in infectiousness of people with pulmonary TB. Pre-treatment, measures of bacillary burden including sputum smear microscopy, culture time-to-positivity, and Xpert MTB/RIF cycle threshold (Ct) value, predict the risk of transmission to contacts. Index patients with smear negative disease pose lower infectious risk than those who have smear-positive disease, and household contact infection is more likely with index patients who have lower Xpert Ct values. Newer tools that enable detection of Mycobacterium tuberculosis complex (Mtb complex) from cough aerosol sampling and face mask sampling may be better predictors of contact infection risk. Clinical factors such as cough strength and frequency, and presence of cavitation on chest imaging, may also assist with risk prediction. Post-treatment, smear and culture status are poor predictors of infectiousness. While the exact duration of infectiousness post treatment initiation remains uncertain, data from human-to-guinea pig transmission studies and clinical studies suggest effective treatment results in a rapid decline in infectiousness, irrespective of smear or culture conversion. This is largely supported by early bactericidal activity and transcriptomic studies, and cough aerosol sampling studies, although a subset of patients may have persistent cough aerosol positivity. These findings can enable a more nuanced approach to isolation decision making, while further research studies are awaited.

Infectious Disease in the Workplace: Quantifying Uncertainty in Transmission

Understanding disease transmission in the workplace is essential for protecting workers. To model disease outbreaks, the small populations in many workplaces require that stochastic effects are considered, which results in higher uncertainty. The aim of this study was to quantify and interpret the uncertainty inherent in such circumstances. We assessed how uncertainty of an outbreak in workplaces depends on i) the infection dynamics in the community, ii) the workforce size, iii) spatial structure in the workplace, iv) heterogeneity in susceptibility of workers, and v) heterogeneity in infectiousness of workers. To address these questions, we developed a multiscale model: A deterministic model to predict community transmission, and a stochastic model to predict workplace transmission. We extended this basic workplace model to allow for spatial structure, and heterogeneity in susceptibility and infectiousness in workers. We found a non-monotonic relationship between the workplace transmission rate and the coefficient of variation (CV), which we use as a measure of uncertainty. Increasing community transmission, workforce size and heterogeneity in susceptibility decreased the CV. Conversely, increasing the level of spatial structure and heterogeneity in infectiousness increased the CV. However, when the model predicts bimodal distributions, for example when community transmission is low and workplace transmission is high, the CV fails to capture this uncertainty. Overall, our work informs modellers and policy makers on how model complexity impacts outbreak uncertainty. In particular: workforce size, community and workplace transmission, spatial structure and individual heterogeneity contribute in a specific and individual manner to the predicted workplace outbreak size distribution.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. “Superspreading,” or “over-dispersion,” is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2.

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

Daily longitudinal sampling of SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness.

The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimated viral expansion and clearance rates, and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to superspreading. Viral genome loads often peaked days earlier in saliva than in nasal swabs, indicating strong tissue compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of Alpha (B.1.1.7) and previously circulating non-variant of concern viruses were mostly indistinguishable, indicating that the enhanced transmissibility of this variant cannot be simply explained by higher viral loads or delayed clearance. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.

Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand

Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modeling approach. Our stochastic model incorporated the effects of individual heterogeneity in infectiousness on disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3091 (95%CI: 2782–3400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, the effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.

Heterogeneity and superspreading effect on herd immunity

We model and calculate the fraction of infected population necessary to reach herd immunity, taking into account the heterogeneity in infectiousness and susceptibility, as well as the correlation between those two parameters. We show that these cause the effective reproduction number to decrease more rapidly, and consequently have a drastic effect on the estimate of the necessary percentage of the population that has to contract the disease for herd immunity to be reached. We quantify the difference between the size of the infected population when the effective reproduction number decreases below 1 vs the ultimate fraction of population that had contracted the disease. This sheds light on an important distinction between herd immunity and the end of the disease and highlights the importance of limiting the spread of the disease even if we plan to naturally reach herd immunity. We analyze the effect of various lock-down scenarios on the resulting final fraction of infected population. We discuss implications to COVID-19 and other pandemics and compare our theoretical results to population-based simulations. We consider the dependence of the disease spread on the architecture of the infectiousness graph and analyze different graph architectures and the limitations of the graph models.

Heterogeneous infectiousness in mathematical models of tuberculosis: A systematic review

TB mathematical models employ various assumptions and approaches in dealing with the heterogeneous infectiousness of persons with active TB. We reviewed existing approaches and considered the relationship between them and existing epidemiological evidence.We searched the following electronic bibliographic databases from inception to 9 October 2018: MEDLINE, EMBASE, Biosis, Global Health and Scopus. Two investigators extracted data using a standardised data extraction tool. We included in the review any transmission dynamic model of M. tuberculosis transmission explicitly simulating heterogeneous infectiousness of person with active TB. We extracted information including: study objective, model structure, number of active TB compartments, factors used to stratify the active TB compartment, relative infectiousness of each active TB compartment and any intervention evaluated in the model.Our search returned 1899 unique references, of which the full text of 454 records were assessed for eligibility, and 99 studies met the inclusion criteria. Of these, 89 used compartmental models implemented with ordinary differential equations, while the most common approach to stratification of the active TB compartment was to incorporate two levels of infectiousness. However, various clinical characteristics were used to stratify the active TB compartments, and models differed as to whether they permitted transition between these states. Thirty-four models stratified the infectious compartment according to sputum smear status or pulmonary involvement, while 18 models stratified based on health care-related factors. Variation in infectiousness associated with drug-resistant M. tuberculosis was the rationale for stratifying active TB in 33 models, with these models consistently assuming that drug-resistant active TB cases were less infectious.Given the evidence of extensive heterogeneity in infectiousness of individuals with active TB, an argument exists for incorporating heterogeneous infectiousness, although this should be considered in light of the objectives of the study and the research question.PROSPERO Registration: CRD42019111936.

Understanding the Importance of Contact Heterogeneity and Variable Infectiousness in the Dynamics of a Large Norovirus Outbreak.

Large norovirus (NoV) outbreaks are explosive in nature and vary widely in final size and duration, suggesting that superspreading combined with heterogeneous contact may explain these dynamics. Modeling tools that can capture heterogeneity in infectiousness and contact are important for NoV outbreak prevention and control, yet they remain limited. Data from a large NoV outbreak at a Dutch scout jamboree, which resulted in illness among 326 (of 4500 total) individuals from 7 separate camps, were used to examine the contributions of individual variation in infectiousness and clustered contact patterns to the transmission dynamics. A Bayesian hierarchical model of heterogeneous, clustered outbreak transmission was applied to represent (1) between-individual heterogeneity in infectiousness and (2) heterogeneous patterns of contact. We found wide heterogeneity in infectiousness across individuals, suggestive of superspreading. Nearly 50% of individual infectiousness was concentrated in the individual's subcamp of residence, with the remainder distributed over other subcamps. This suggests a source-and-sink dynamic in which subcamps with greater average infectiousness fed cases to those with a lower transmission rate. Although the per capita transmission rate within camps was significantly greater than that between camps, the large pool of susceptible individuals across camps enabled similar numbers of secondary cases generated between versus within camps. The consideration of clustered transmission and heterogeneous infectiousness is important for understanding NoV transmission dynamics. Models including these mechanisms may be useful for providing early warning and guiding outbreak response.

The role of super-spreading events in Mycobacterium tuberculosis transmission: evidence from contact tracing

BackgroundIn current epidemiology of tuberculosis (TB), heterogeneity in infectiousness among TB patients is a challenge, which is not well studied. We aimed to quantify this heterogeneity and the presence of “super-spreading” events that can assist in designing optimal public health interventions.MethodsTB epidemiologic investigation data notified between 1 January 2005 and 31 December 2015 from Victoria, Australia were used to quantify TB patients’ heterogeneity in infectiousness and super-spreading events. We fitted a negative binomial offspring distribution (NBD) for the number of secondary infections and secondary active TB disease each TB patient produced. The dispersion parameter, k, of the NBD measures the level of heterogeneity, where low values of k (e.g. k < 1) indicate over-dispersion. Super-spreading was defined as patients causing as many or more secondary infections as the 99th centile of an equivalent homogeneous distribution. Contact infection was determined based on a tuberculin skin test (TST) result of ≥10 mm. A NBD model was fitted to identify index characteristics that were associated with the number of contacts infected and risk ratios (RRs) were used to quantify the strength of this association.ResultsThere were 4190 (2312 pulmonary and 1878 extrapulmonary) index TB patients and 18,030 contacts. A total of 15,522 contacts were tested with TST, of whom 3213 had a result of ≥10 mm. The dispersion parameter, k for secondary infections was estimated at 0.16 (95%CI 0.14–0.17) and there were 414 (9.9%) super-spreading events. From the 3213 secondary infections, 2415 (75.2%) were due to super-spreading events. There were 226 contacts who developed active TB disease and a higher level of heterogeneity was found for this outcome than for secondary infection, with k estimated at 0.036 (95%CI 0.025–0.046). In regression analyses, we found that infectiousness was greater among index patients found by clinical presentation and those with bacteriological confirmation.ConclusionTB transmission is highly over dispersed and super-spreading events are responsible for a substantial majority of secondary infections. Heterogeneity of transmission and super-spreading are critical issues to consider in the design of interventions and models of TB transmission dynamics.

Stochastic modelling and inference in electronic hospital databases for the spread of infections: Clostridium difficile transmission in Oxfordshire hospitals 2007-2010.

The combination of genetic information with electronic patient records promises to provide a powerful new resource for understanding human disease and its treatment. Here we develop and apply a novel stochastic compartmental model to a large dataset on Clostridium difficile infection (CDI) in three Oxfordshire hospitals over a 2.5 year period which combines genetic information on 858 confirmed cases of CDI with a database of 750,000 patient records. C. difficile is a major cause of healthcare-associated diarrhoea and is responsible for substantial mortality and morbidity, with relatively little known about its biology or its transmission epidemiology. Bayesian analysis of our model, via Markov chain Monte Carlo, provides new information about the biology of CDI, including genetic heterogeneity in infectiousness across different sequence types, and evidence for ward contamination as a significant mode of transmission, and allows inferences about the contribution of particular individuals, wards, or hospitals to transmission of the bacterium, and assessment of changes in these over time following changes in hospital practice. Our work demonstrates the value of using statistical modelling and computational inference on large-scale hospital patient databases and genetic data.

Heterogeneous infectiousness in guinea pigs experimentally infected with Trypanosoma cruzi

Guinea pigs are important reservoirs of Trypanosoma cruzi, the causative parasite of Chagas disease, and in the Southern Cone of South America, transmission is mediated mainly by the vector Triatoma infestans. Interestingly, colonies of Triatoma infestans captured from guinea pig corrals sporadically have infection prevalence rates above 80%. Such high values are not consistent with the relatively short 7-8 week parasitemic period that has been reported for guinea pigs in the literature. We experimentally measured the infectious periods of a group of T. cruzi-infected guinea pigs by performing xenodiagnosis and direct microscopy each week for one year. Another group of infected guinea pigs received only direct microscopy to control for the effect that inoculation by triatomine saliva may have on parasitemia in the host. We observed infectious periods longer than those previously reported in a number of guinea pigs from both the xenodiagnosis and control groups. While some guinea pigs were infectious for a short time, other "super-shedders" were parasitemic up to 22 weeks after infection, and/or positive by xenodiagnosis for a year after infection. This heterogeneity in infectiousness has strong implications for T. cruzi transmission dynamics and control, as super-shedder guinea pigs may play a disproportionate role in pathogen spread.

Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts

BackgroundThe infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.Methods and FindingsWe estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (d acute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own.From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79–57) and d acute = 1.7 mo (95% CrI: 0.55–6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × d acute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3–9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design.Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.ConclusionsPrevious EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses

Modelling tree shape and structure in viral phylodynamics

Epidemiological models have highlighted the importance of population structure in the transmission dynamics of infectious diseases. Using HIV-1 as an example of a model evolutionary system, we consider how population structure affects the shape and the structure of a viral phylogeny in the absence of strong selection at the population level. For structured populations, the number of lineages as a function of time is insufficient to describe the shape of the phylogeny. We develop deterministic approximations for the dynamics of tips of the phylogeny over evolutionary time, the number of ‘cherries’, tips that share a direct common ancestor, and Sackin's index, a commonly used measure of phylogenetic imbalance or asymmetry. We employ cherries both as a measure of asymmetry of the tree as well as a measure of the association between sequences from different groups. We consider heterogeneity in infectiousness associated with different stages of HIV infection, and in contact rates between groups of individuals. In the absence of selection, we find that population structure may have relatively little impact on the overall asymmetry of a tree, especially when only a small fraction of infected individuals is sampled, but may have marked effects on how sequences from different subpopulations cluster and co-cluster.

Spread of infectious disease through clustered populations

Networks of person-to-person contacts form the substrate along which infectious diseases spread. Most network-based studies of this spread focus on the impact of variations in degree (the number of contacts an individual has). However, other effects such as clustering, variations in infectiousness or susceptibility, or variations in closeness of contacts may play a significant role. We develop analytic techniques to predict how these effects alter the growth rate, probability and size of epidemics, and validate the predictions with a realistic social network. We find that (for a given degree distribution and average transmissibility) clustering is the dominant factor controlling the growth rate, heterogeneity in infectiousness is the dominant factor controlling the probability of an epidemic and heterogeneity in susceptibility is the dominant factor controlling the size of an epidemic. Edge weights (measuring closeness or duration of contacts) have impact only if correlations exist between different edges. Combined, these effects can play a minor role in reinforcing one another, with the impact of clustering the largest when the population is maximally heterogeneous or if the closer contacts are also strongly clustered. Our most significant contribution is a systematic way to address clustering in infectious disease models, and our results have a number of implications for the design of interventions.

Antiviral therapy and the transmission dynamics of HIV-1.

The factors influencing the transmission dynamics of HIV are discussed in the context of the community wide use of antiviral therapy. A mathematical framework is constructed to examine the impact of chemotherapy on transmission, based on a description of the progressive decline of CD4 counts in infected patients. Treatment which acts to increase the incubation period before the onset of serious immunodeficiency, but has no impact on the infectiousness of a patient, is obviously beneficial to the individual. However it can act to either reduce or increase net morbidity and mortality when used on a community wide basis. When treatment prolongs the incubation period and reduces infectiousness, community based chemotherapy is invariably beneficial to the individual and the community. Heterogeneity in infectiousness over the incubation period of AIDS, with peaks in early and late stages of infection, has an important influence on the effect of treatment on the net transmission within the community. In these circumstances community wide benefit is influenced by the timing of treatment within the incubation period of AIDS. The many heterogeneities arising in the transmission dynamics of HIV argue for careful analysis of the net impact of antiviral therapy on mortality at a community wide level as well as at the level of the individual patient.