Infectious disease transmission as a forensic problem: who infected whom?

Factors driving norovirus transmission in long-term care facilities: A case-level analysis of 107 outbreaks

Norovirus is the most common cause of gastroenteritis outbreaks in long-term care facilities (LTCFs) in the United States, causing a high burden of disease in both residents and staff. Understanding how case symptoms and characteristics contribute to norovirus transmission can lead to more informed outbreak control measures in LTCFs. We examined line lists for 107 norovirus outbreaks that took place in LTCFs in five U.S. states from 2015 to 2019. We estimated the individual effective reproduction number, R_i, to quantify individual case infectiousness and examined the contribution of vomiting, diarrhea, and being a resident (vs. staff) to case infectiousness. The associations between case characteristics and R_i were estimated using a multivariable, log-linear mixed model with inverse variance weighting. We found that cases with vomiting infected 1.28 (95 % CI: 1.11, 1.48) times the number of secondary cases compared to cases without vomiting, and LTCF residents infected 1.31 (95 % CI: 1.15, 1.50) times the number of secondary cases compared to staff. There was no difference in infectiousness between cases with and without diarrhea (1.07; 95 % CI: 0.90, 1.29). This suggests that vomiting, particularly by LTCF residents, was a primary driver of norovirus transmission. These results support control measures that limit exposure to vomitus during norovirus outbreaks in LTCFs.

TransFlow: a Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data

MOTIVATION

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.

RESULTS

Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment.

AVAILABILITY AND IMPLEMENTATION

TransFlow is free available at https://github.com/cvn001/transflow.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An open-access database of infectious disease transmission trees to explore superspreader epidemiology

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders.

Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in Georgia, USA, February 1-July 13, 2020

The serial interval and effective reproduction number for coronavirus disease (COVID-19) are heterogenous, varying by demographic characteristics, region, and period. During February 1–July 13, 2020, we identified 4,080 transmission pairs in Georgia, USA, by using contact tracing information from COVID-19 cases reported to the Georgia Department of Public Health. We examined how various transmission characteristics were affected by symptoms, demographics, and period (during shelter-in-place and after subsequent reopening) and estimated the time course of reproduction numbers for all 159 Georgia counties. Transmission varied by time and place but also by persons’ sex and race. The mean serial interval decreased from 5.97 days in February–April to 4.40 days in June–July. Younger adults (20–50 years of age) were involved in most transmission events occurring during or after reopening. The shelter-in-place period was not long enough to prevent sustained virus transmission in densely populated urban areas connected by major transportation links.

Recursive contact tracing in Reed-Frost epidemic models

We introduce a Reed-Frost epidemic model with recursive contact tracing and asymptomatic transmission. This generalizes the branching-process model introduced by the authors in a previous work (Bulchandani et al 2021Phys. Biol.18045004) to finite populations and general contact networks. We simulate the model numerically for two representative examples, the complete graph and the square lattice. On both networks, we observe clear signatures of a contact-tracing phase transition from an 'epidemic phase' to an 'immune phase' as contact-network coverage is increased. We verify that away from the singular line of perfect tracing, the finite-size scaling of the contact-tracing phase transition on each network lies in the corresponding percolation universality class. Finally, we use the model to quantify the efficacy of recursive contact-tracing in regimes where epidemic spread is not contained.

Estimating the relative probability of direct transmission between infectious disease patients

BACKGROUND

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.

METHODS

Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.

RESULTS

In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.

CONCLUSIONS

Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Strongly Heterogeneous Transmission of COVID-19 in Mainland China: Local and Regional Variation

Background: The outbreak of novel coronavirus disease 2019 (COVID-19) started in the city of Wuhan, China, with a period of rapid initial spread. Transmission on a regional and then national scale was promoted by intense travel during the holiday period of the Chinese New Year. We studied the variation in transmission of COVID-19, locally in Wuhan, as well as on a larger spatial scale, among different cities and even among provinces in mainland China.

Methods: In addition to reported numbers of new cases, we have been able to assemble detailed contact data for some of the initial clusters of COVID-19. This enabled estimation of the serial interval for clinical cases, as well as reproduction numbers for small and large regions.

Findings: We estimated the average serial interval was 4.8 days. For early transmission in Wuhan, any infectious case produced as many as four new cases, transmission outside Wuhan was less intense, with reproduction numbers below two. During the rapid growth phase of the outbreak the region of Wuhan city acted as a hot spot, generating new cases upon contact, while locally, in other provinces, transmission was low.

Interpretation: COVID-19 is capable of spreading very rapidly. The sizes of outbreak in provinces of mainland China mainly depended on the numbers of cases imported from Wuhan as the local reproduction numbers were low. The COVID-19 epidemic should be controllable with appropriate interventions (suspension of public transportation, cancellation of mass gatherings, implementation of surveillance and testing, and promotion of personal hygiene and face mask use).

outbreaker2: a modular platform for outbreak reconstruction

BACKGROUND

Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort.

RESULTS

Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort.

CONCLUSIONS

outbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis.

General methodology for inferring failure-spreading dynamics in networks

A generic modeling framework to infer the failure-spreading process based on failure times of individual nodes is proposed and tested in four simulation studies: one for cascading failures in interdependent power and transportation networks, one for influenza epidemics, one benchmark test case for congestion cascade in a transportation network, and one benchmark test case for cascading power outages. Four general failure-spreading mechanisms-external, temporal, spatial, and functional-are quantified to capture what drives the spreading of failures. With the failure time of each node given, the proposed methodology demonstrates remarkable capability of inferring the underlying general failure-spreading mechanisms and accurately reconstructing the failure-spreading process in all four simulation studies. The analysis of the two benchmark test cases also reveals the robustness of the proposed methodology: It is shown that a failure-spreading process embedded by specific failure-spreading mechanisms such as flow redistribution can be captured with low uncertainty by our model. The proposed methodology thereby presents a promising channel for providing a generally applicable framework for modeling, understanding, and controlling failure spreading in a variety of systems.

Genomics and Foodborne Viral Infections

Foodborne viral illness, resulting from the consumption of contaminated food or water containing pathogenic viruses, remains a major public health problem globally with substantial economic impact. Major challenges regarding recognizing, detecting, characterizing, and effectively responding to foodborne viral threats to health exist. Adequate health crisis management is largely dependent on early detection of potential public health threats, which is hampered by changing trends in disease outbreaks, from localized clusters of disease in confined populations to dispersed outbreaks with excellent opportunity for further transmission. In addition, no precise and consistent global baseline syndrome and diagnostic surveillance information exists. An integrated multidisciplinary approach with a combination of sustained pathogen syndrome and diagnostic surveillance, genomics-based, and standardized global analytical networks gathering clinical, epidemiological and genetic data alike would be required to understand the dynamics of foodborne viral infection and to mitigate potential effects of future threats. A huge global effort in virus syndrome and diagnostic surveillance may be justified in the light of global health impact in general, and timely with the development of new metagenomics tools that hold the promise of not only identifying viral pathogens, but possibly the complete microbiome in a single assay.

Network inference from multimodal data: A review of approaches from infectious disease transmission

Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.

Norovirus in healthcare settings

Purpose of review

To provide an overview of the burden of norovirus disease in healthcare settings and the factors responsible for outbreaks in these institutions; to assess progress on interventions aimed at reducing the burden of norovirus disease.

Recent findings

Norovirus outbreaks in healthcare settings are driven by confluence of viral diversity, the built environment, and host factors. Some of these characteristics may be modifiable and the target of successful interventions.

Summary

Most norovirus outbreaks in hospital and residential care institutions are associated with a particular genotype, known as GII.4. The persistence of norovirus is associated with strain diversity, which is driven by immune evasion and viral adaptation to interaction with a variety of human histo-blood group antigens. The healthcare environment presents serious challenges for control, both because of the physical structure of the built space and the high levels of contact among patient populations who may have compromised hygiene. Increased vulnerability among the populations in healthcare institutions is likely to be multifactorial and may include the following: nutritional status, immunodeficiency or senescence, chronic inflammation, and microbiome alterations. Current control measures are based on general infection control principles, and treatment is mainly supportive and nonspecific. Vaccines and antiviral agents are being developed with promising results, but none are currently available.

Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration

Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).

A central goal of phylogenetic analysis is to estimate evolutionary relationships and the dynamical parameters underlying the evolutionary branching process (e.g. macroevolutionary or epidemiological parameters) from molecular data. The statistical methods used in these analyses require that the underlying tree branching process is specified. Standard models for the branching process which were originally designed to describe the evolutionary past of present day species do not allow one sampled taxon to be the ancestor of another. However the probability of sampling a direct ancestor is not negligible for many types of data. For example, when fossil and living species are analysed together to infer species divergence times, fossil species may or may not be direct ancestors of living species. In epidemiology, a sampled individual (a host from which a pathogen sequence was obtained) can infect other individuals after sampling, which then go on to be sampled themselves. The models that account for direct ancestors produce phylogenetic trees with a different structure from classic phylogenetic trees and so using these models in inference requires new computational methods. Here we developed a method for phylogenetic analysis that accounts for the possibility of direct ancestors.

Inferring disease transmission networks at a metapopulation level

Background

To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.

Results

A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.

Conclusions

This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.

Shedding of norovirus in symptomatic and asymptomatic infections

Norovirus is the most frequent cause of acute infectious gastroenteritis and it is difficult to control in crowded environments like hospitals and nursing homes. Transmission depends on oral intake of virus deposited in the environment by infectious subjects. Data from volunteer studies indicate that virus concentrations in stool are highly variable, but systematic studies of the time-course of shedding and its individual variation are lacking. This paper quantifies norovirus shedding in a large population of 102 subjects, including asymptomatic shedders, and uses a longitudinal model to generalize shedding patterns. Enhanced surveillance for studies of transmission of norovirus in hospital outbreaks has yielded a considerable number of faecal samples from symptomatic and asymptomatic shedders, both from patients and staff. Norovirus concentrations were determined by real-time PCR. A quantitative dynamic model was fitted to the shedding data, in a multilevel Bayesian framework, to study the time-course of shedding and its variation. The results indicate that shedding in asymptomatic subjects is similar to that in symptomatic infections, both showing considerable variation in peak levels (average 105-109 /g faeces) as well as duration of virus shedding (average 8-60 days). Patients appear to shed higher numbers of virus than staff, for slightly longer durations, but the differences are too small to be significant. Given equal shedding, the greater contribution of symptomatic cases to transmission must be caused by their higher efficiency in spreading these viruses. The results of this study will be helpful for risk studies that need to quantify the deposition of virus in the environment.

OutbreakTools: a new platform for disease outbreak analysis using the R software

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.

Estimation of the serial interval of pertussis in Dutch households

Increasing incidence has led to the re-appearance of pertussis as a public health problem in developed countries. Pertussis infection is usually mild in vaccinated children and adults, but it can be fatal in infants who are too young for effective vaccination (≤3 months). Tailoring of control strategies to prevent infection of the infant hinges on the availability of estimates of key epidemiological quantities. Here we estimate the serial interval of pertussis, i.e., the time between symptoms onset in a case and its infector, using data from a household-based study carried out in the Netherlands in 2007-2009. We use statistical methodology to tie infected persons to probable infector persons, and obtain statistically supported stratifications of the data by person-type (infant, mother, father, sibling). The analyses show that the mean serial interval is 20 days (95% CI: 16-23 days) when the mother is the infector of the infant, and 28 days (95% CI: 23-33 days) when the infector is the father or a sibling. These time frames offer opportunities for early mitigation of the consequences of infection of an infant once a case has been detected in a household. If preventive measures such as social distancing or antimicrobial treatment are taken promptly they could decrease the probability of infection of the infant.

Fitting outbreak models to data from many small norovirus outbreaks

Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.

Does spatial proximity drive norovirus transmission during outbreaks in hospitals?

OBJECTIVE

To assess the role of spatial proximity, defined as patients sharing bays, in the spread of norovirus during outbreaks in hospitals.

DESIGN

Enhanced surveillance of norovirus outbreaks between November 2009 and November 2011.

METHODS

Data were gathered during 149 outbreaks of norovirus in hospital wards from five hospitals in two major cities in England serving a population of two million. We used the time between the first two cases of each outbreak to estimate the serial interval for norovirus in this setting. This distribution and dates of illness onset were used to calculate epidemic trees for each outbreak. We then used a permutation test to assess whether proximity, for all outbreaks, was more extreme than would be expected by chance under the null hypothesis that proximity was not associated with transmission risk.

RESULTS

65 outbreaks contained complete data on both onset dates and ward position. We estimated the serial interval to be 1.86 days (95% CI 1.6 to 2.2 days), and with this value found strong evidence to reject the null hypothesis that proximity was not significant (p<0.001). Sensitivity analysis using different values of the serial interval showed that there was evidence to reject the null hypothesis provided the assumed serial interval was less than 2.5 days.

CONCLUSIONS

Our results provide evidence that patients occupying the same bay as patients with symptomatic norovirus infection are at an increased risk of becoming infected by these patients compared with patients elsewhere in the same ward.