Estimating the relative probability of direct transmission between infectious disease patients

Transmissibility and control of tuberculosis in school outbreaks: a modeling study based on four outbreaks in China

BACKGROUND

The elevated incidence of tuberculosis (TB) outbreaks in schools poses a significant challenge to prevention and control efforts in China. The commonality among most outbreaks is the failure to isolate patients at an early stage. Early isolation of TB cases is crucial for reducing the spread of TB within schools. This study aims to quantify the impact of different isolation proportions and durations on the attack rate of TB in schools. It explored the intervention effects of isolation measures in preventing and controlling TB in school settings. The goal is to provide insights that can serve as a reference for reducing the occurrence of TB outbreaks in schools.

METHODS

We collected data from 4 school TB outbreaks. Susceptible-Exposed-Infected-Recovered (SEIR) model was used to fit the collected data and calculate transmissibility. Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) model was employed to evaluate the effect of isolation. Effective reproduction numbers and cumulative incidence were used to quantify the transmissibility of TB.

RESULTS

In the 4 outbreaks, the majority of student cases were distributed in high grades of high school and universities, with a widespread occurrence of significant intervention delays. The median ascending reproduction value for the 4 outbreaks was 18.44 [interquartile range: 15.40-20.11]. Isolating 100% of the patients at the first month could reduce the number of cases by 99.47%, 87.99%, 96.48%, and 99.16%, respectively.

CONCLUSIONS

This study suggests that high schools and universities may represent significant high-risk environments for TB outbreaks. Early detection and isolation of cases are important strategies that can help reduce the risk of TB outbreaks, with observed case reductions of up to 99.47% when implemented promptly.

Estimation of local time-varying reproduction numbers in noisy surveillance data

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Estimation of the generation interval using pairwise relative transmission probabilities

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

Estimation of local time-varying reproduction numbers in noisy surveillance data

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.

Transmission Of Tuberculosis Among illicit drug use Linkages (TOTAL): A cross-sectional observational study protocol using respondent driven sampling

People who use illicit drugs (PWUDs) have been identified as a key at-risk group for tuberculosis (TB). Examination of illicit drug use networks has potential to assess the risk of TB exposure and disease progression. Research also is needed to assess mechanisms for accelerated TB transmission in this population. This study aims to 1) assess the rate of TB exposure, risk of disease progression, and disease burden among PWUD; 2) estimate the proportion of active TB cases resulting from recent transmission within this network; and 3) evaluate whether PWUD with TB disease have physiologic characteristics associated with more efficient TB transmission. Our cross-sectional, observational study aims to assess TB transmission through illicit drug use networks, focusing on methamphetamine and Mandrax (methaqualone) use, in a high TB burden setting and identify mechanisms underlying accelerated transmission. We will recruit and enroll 750 PWUD (living with and without HIV) through respondent driven sampling in Worcester, South Africa. Drug use will be measured through self-report and biological measures, with sputum specimens collected to identify TB disease by Xpert Ultra (Cepheid) and mycobacterial culture. We will co-enroll those with microbiologic evidence of TB disease in Aim 2 for molecular and social network study. Whole genome sequencing of Mycobacteria tuberculosis (Mtb) specimens and social contact surveys will be done for those diagnosed with TB. For Aim 3, aerosolized Mtb will be compared in individuals with newly diagnosed TB who do and do not smoke illicit drug. Knowledge from this study will provide the basis for a strategy to interrupt TB transmission in PWUD and provide insight into how this fuels overall community transmission. Results have potential for informing interventions to reduce TB spread applicable to high TB and HIV burden settings. Trial registration: Clinicaltrials.gov Registration Number: NCT041515602. Date of Registration: 5 November 2019.

What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

BACKGROUND

To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission.

METHODS

We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission.

RESULTS

Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes.

CONCLUSIONS

We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.

Leveraging Pathogen Sequence and Contact Tracing Data to Enhance Vaccine Trials in Emerging Epidemics

INTRODUCTION

Advance planning of vaccine trials conducted during outbreaks increases our ability to rapidly define the efficacy and potential impact of a vaccine. Vaccine efficacy against infectiousness (VEI) is an important measure for understanding a vaccine's full impact, yet it is currently not identifiable in many trial designs because it requires knowledge of infectors' vaccination status. Recent advances in genomics have improved our ability to reconstruct transmission networks. We aim to assess if augmenting trials with pathogen sequence and contact tracing data can permit them to estimate VEI.

METHODS

We develop a transmission model with a vaccine trial in an outbreak setting, incorporate pathogen sequence data and contact tracing data, and assign probabilities to likely infectors. We then propose and evaluate the performance of an estimator of VEI.

RESULTS

We find that under perfect knowledge of infector-infectee pairs, we are able to accurately estimate VEI. Use of sequence data results in imperfect reconstruction of transmission networks, biasing estimates of VEI towards the null, with approaches using deep sequence data performing better than approaches using consensus sequence data. Inclusion of contact tracing data reduces the bias.

CONCLUSION

Pathogen genomics enhance identifiability of VEI, but imperfect transmission network reconstruction biases estimate toward the null and limits our ability to detect VEI. Given the consistent direction of the bias, estimates obtained from trials using these methods will provide lower bounds on the true VEI. A combination of sequence and epidemiologic data results in the most accurate estimates, underscoring the importance of contact tracing.

Statistical Estimation of the Reproductive Number From Case Notification Data

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.