Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Alberto Aleta, David Martín-Corral, Michiel A. Bakker, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini Jr., Alex Pentland, Alessandro Vespignani, Yamir Moreno, Esteban Moro

medRxiv

December 26, 2021

ABSTRACT

Detailed characterization of SARS-CoV-2 transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic’s first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered super-spreading events (SSEs). Although mass-gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

Markov Genealogy Processes

AARON A. KING, QIANYING LIN, EDWARD L. IONIDES

Theoretical Population Biology

December 9, 2021

Abstract

We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave

Jessica T. Davis, Matteo Chinazzi, Nicola Perra, Kunpeng Mu, Ana Pastore y Piontti, Marco Ajelli, Natalie E. Dean, Corrado Gioannini, Maria Litvinova, Stefano Merler, Luca Rossi, Kaiyuan Sun, Xinyue Xiong, Ira M. Longini Jr, M. Elizabeth Halloran, Cécile Viboud & Alessandro Vespignani

Nature

October 25, 2021

ABSTRACT

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of SARS-CoV-2 globally1–7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 20208,9, the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections, and the temporal windows of the introduction and onset of SARS-CoV-2 local transmission in Europe and the United States. We find that community transmission of SARS-CoV-2 was likely in several areas of Europe and the United States by January 2020, and estimate that by early March, only 1 to 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2 with possible introductions and transmission events as early as December 2019–January 2020. We find a heterogeneous, geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78%–15.2% across US states and 0.19%–13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

Challenges of evaluating and modelling vaccination in emerging infectious diseases

Zachary J Madewell, Natalie E Dean, Jesse A Berlin, Paul M Coplan, Kourtney J Davis, Claudio J Struchiner, M Elizabeth Halloran

Epidemics

October 5, 2021

ABSTRACT

Outbreaks of emerging pathogens pose unique methodological and practical challenges for the design, implementation, and evaluation of vaccine efficacy trials. Lessons learned from COVID-19 highlight the need for innovative and flexible study design and application to quickly identify promising candidate vaccines. Trial design strategies should be tailored to the dynamics of the specific pathogen, location of the outbreak, and vaccine prototypes, within the regional socioeconomic constraints. Mathematical and statistical models can assist investigators in designing infectious disease clinical trials. We introduce key challenges for planning, evaluating, and modelling vaccine efficacy trials for emerging pathogens.

Transition to endemicity: Understanding COVID-19

Rustom Antia, M. Elizabeth Halloran

Immunity

September 23, 2021

ABSTRACT

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, coronavirus disease 2019 (COVID-19), has caused a devastating pandemic worldwide. Here, we explain basic concepts underlying the transition from an epidemic to an endemic state, where a pathogen is stably maintained in a population. We discuss how the number of infections and the severity of disease change in the transition from the epidemic to the endemic phase and consider the implications of this transition in the context of COVID-19.

Durability of protection after 5 doses of acellular pertussis vaccine among 5–9 year old children in King County, Washington

Madhura S. Rane, Pejman Rohani, M. Elizabeth Halloran

Vaccine

September 4, 2021

ABSTRACT

Purpose
Waning of immunity after vaccination with the acellular Pertussis (aP) vaccine has been proposed as one of the main reasons for pertussis resurgence in the US. In this study, we estimated time-varying vaccine effectiveness after 5 doses of aP vaccine.

Methods
We conducted a retrospective cohort study among children 5–9 years old (born between 2008 and 2012) living in King County, Washington, USA, who participated in the Washington State Immunization Information System. We estimated time-varying vaccine effectiveness after 5 doses of aP using smoothed scaled Schoenfeld residuals obtained from fitting Cox proportional hazards models to the data as well as piecewise constant Poisson regression.

Results
There were 55 pertussis cases in this cohort, of whom 22 (40%) were fully-vaccinated and 33 (60%) were under-vaccinated. Vaccine effectiveness (VE) remained high for up to 42 months after the fifth dose (VE(t) = 89%; 95% CI: 64%, 97%) as estimated using survival analysis methods and up to 4 years (VE(t) = 93%; 95% CI: 67%, 98%) as estimated using Poisson regression.

Conclusion
We did not find evidence for waning of vaccine effectiveness for up to four years after 5 doses of aP among 5 –9 years old children in King County, WA.

Association of Diphtheria-Tetanus–Acellular Pertussis Vaccine Timeliness and Number of Doses With Age-Specific Pertussis Risk in Infants and Young Children

Madhura S. Rane, Pejman Rohani, M. Elizabeth Halloran

JAMA
August 10, 2021

ABSTRACT

Importance In most countries, the diphtheria-tetanus–acellular pertussis (DTaP) vaccine is administered as a 3-dose infant series followed by additional booster doses in the first 5 years of life. Short-term immunity from the DTaP vaccine can depend on the number, timing, and interval between doses. Not receiving doses in a timely manner might be associated with a higher pertussis risk.

Objective To examine the association between number and timeliness of vaccine doses and age-specific pertussis risk.

Design, Setting, and Participants This population-based, retrospective cohort study used Washington State Immunization Information System data and pertussis surveillance data from Public Health Seattle and King County, Washington. Included participants were children aged 3 months to 9 years born or living in King County, Washington, between January 1, 2008, and December 31, 2017. Data were analyzed from June 30 to December 1, 2019.

Exposures Being undervaccinated (receiving fewer than recommended doses at a given age) or delayed vaccination (not receiving doses within time frames recommended by Centers for Disease Control and Prevention).

Main Outcomes and Measures Suspected, probable, and confirmed pertussis diagnosis.

Results A total of 316 404 children (median age, 65.2 months [interquartile range, 35.3-94.1 months]; 162 025 boys [51.2%]) as of December 31, 2017, with 17.4 million person-months of follow-up were included in the analysis. A total of 19 943 children (6.3%) had no vaccines recorded in the Immunization Information System, 116 193 (36.7%) received a vaccine with a delay, and 180 268 (56.9%) were fully vaccinated with no delay. Delayed vaccination and undervaccination rates were higher for older children (17.6% delayed or undervaccinated at age 2 months for dose 1 at 3 months vs 41.6% at age 5 years for dose 5) but improved for successive birth cohorts (52.2% for 2008 birth cohort vs 32.3% for 2017 birth cohort). Undervaccination was significantly associated with higher risk of pertussis for the 3-dose primary series (adjusted relative risk [aRR], 4.8; 95% CI, 3.1-7.6), the first booster (aRR, 3.2; 95% CI, 2.3-4.5), and the second booster (aRR, 4.6; 95% CI, 2.6-8.2). However, delay in vaccination among children who received the recommended number of vaccine doses was not associated with pertussis risk.

Conclusions and Relevance The results of this cohort study suggest that undervaccination is associated with higher pertussis risk. Short delays in vaccine receipt may be less important if the age-appropriate number of doses is administered, but delaying doses is not recommended. Ensuring that children receive all doses of pertussis vaccine, even if there is some delay, is important.

Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic

Zachary J Madewell, Ana Pastore Y Piontti, Qian Zhang, Nathan Burton, Yang Yang, Ira M Longini, M Elizabeth Halloran, Alessandro Vespignani, Natalie E Dean

Clinical Trials

July 3, 2021

ABSTRACT

Background:
Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites.

Methods:
We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015–2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data.

Results:
When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites.

Conclusion:
Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.

Modeling the impacts of clinical influenza testing on influenza vaccine effectiveness estimates

Leora R Feldstein, Jill M Ferdinands, Wesley H Self, Adrienne G Randolph, Michael Aboodi, Adrienne H Baughman, Samuel M Brown, Matthew C Exline, D Clark Files, Kevin Gibbs, Adit A Ginde, Michelle N Gong, Carlos G Grijalva, Natasha Halasa, Akram Khan, Christopher J Lindsell, Margaret Newhams, Ithan D Peltan, Matthew E Prekker, Todd W Rice, Nathan I Shapiro, Jay Steingrub, H Keipp Talbot, M Elizabeth Halloran, Manish Patel

Journal of Infectious Diseases

ABSTRACT

Background

Test-negative design studies for evaluating influenza vaccine effectiveness (VE) enroll patients with acute respiratory infection. Enrollment typically occurs before influenza status is determined, resulting in over-enrollment of influenza-negative patients. With availability of rapid and accurate molecular clinical testing, influenza status could be ascertained prior to enrollment, thus improving study efficiency. We estimate potential biases in VE when using clinical testing.

Methods

We simulate data assuming 60% vaccinated, 25% of those vaccinated are influenza positive, and VE of 50%. We show the effect on VE in five scenarios.

Results

VE is affected only when clinical testing preferentially targets patients based on both vaccination and influenza status. VE is overestimated by 10% if non-testing occurs in 39% of vaccinated influenza-positive patients and 24% of others; and if non-testing occurs in 8% of unvaccinated influenza-positive patients and 27% of others. VE is underestimated by 10% if non-testing occurs in 32% of unvaccinated influenza-negative patients and 18% of others.

Conclusions

Although differential clinical testing by vaccine receipt and influenza positivity may produce errors in estimated VE, bias in testing would have to be substantial and overall proportion of patients tested would have to be small to result in a meaningful difference in VE.

Estimating population-level effects of the acellular pertussis vaccine using routinely collected immunization data

Madhura S Rane, M Elizabeth Halloran

Clinical Infectious Diseases

April 21, 2021

ABSTRACT

Background
Measuring and reporting the different population-level effects of the acellular pertussis vaccine on pertussis disease in addition to direct effects can increase the cost-effectiveness of a vaccine.

Methods
We conducted a retrospective cohort study of children born between January 1, 2008, and December 31, 2017, in King County, Washington, who were enrolled in the Washington State Immunization Information System. Diphtheria-Tetanus-acellular-Pertussis (DTaP) vaccination data from WA-IIS was linked with pertussis case data from Public Health Seattle and King County. Census-level vaccination coverage was estimated as proportion of age-appropriately vaccinated children residing in it. Direct vaccine effectiveness was estimated by comparing pertussis risk in fully-vaccinated and under-vaccinated children. Population-level vaccine effects were estimated by comparing pertussis risk in census tracts in the highest vaccination coverage quartile to that in the lowest vaccination coverage quartile.

Results
For direct protection, estimated vaccine effectiveness was 76% (95% CI: 63% - 84%) in low vaccination coverage clusters and it decreased to 47% (95% CI: 13% - 68%) in high vaccination coverage clusters, after adjusting for potential confounders. The estimated indirect effect was 45.0% (95% CI: 1%, 70%), total effect was 93.9% (95% CI: 91%, 96%), and overall effect was 42.2% (95% CI: 19%, 60%).

Conclusion
Our findings suggest that DTaP vaccination provided direct as well as indirect protection in the highly immunized King County, WA. Routine DTaP vaccination programs may have the potential to provide not only protection for vaccinated individuals but also for the under-vaccinated individuals living in the same area.

Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring

Sujatro Chakladar, Samuel Rosin, Michael G. Hudgens, M. Elizabeth Halloran, John D. Clemens, Mohammad Ali, Michael E. Emch

Biometrics

March 25, 2021

ABSTRACT

Estimating population-level effects of a vaccine is challenging because there may be interference, that is, the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in 2 Europe and the United States

Jessica T Davis, Matteo Chinazzi, Nicola Perra, Kunpeng Mu, Ana Pastore Y Piontti, Marco Ajelli, Natalie E Dean, Corrado Gioannini, Maria Litvinova, Stefano Merler, Luca Rossi, Kaiyuan Sun, Xinyue Xiong, M Elizabeth Halloran, Ira M Longini, Cécile Viboud, Alessandro Vespignani

medRxiv

March 25, 2021

ABSTRACT

Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1 - 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.

Estimating and interpreting secondary attack risk: Binomial considered biased

Yushuf Sharker, Eben Kenah

PLOS Computational Biology

January 20, 2021

ABSTRACT

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.

Inferring high-resolution human mixing patterns for disease modeling

Dina Mistry, Maria Litvinova, Ana Pastore y Piontti, Matteo Chinazzi, Laura Fumanelli, Marcelo F. C. Gomes, Syed A. Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M. Elizabeth Halloran, Ira M. Longini Jr., Stefano Merler, Marco Ajelli, Alessandro Vespignani

Nature Communications

January 12, 2021

ABSTRACT

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administrative regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Cost-effectiveness of live-attenuated influenza vaccination among school-age children

Natasha S.Wenzel, Katherine E.Atkins, Edwin van Leeuwen, M. Elizabeth Halloran, Marc Baguelin

Vaccine

January 8, 2021

ABSTRACT

The current pediatric vaccination program in England and Wales administers Live-Attenuated Influenza Vaccine (LAIV) to children ages 2–16 years old. Annual administration of LAIV to this age group is costly and poses substantial logistical issues. This study aims to evaluate the cost-effectiveness of prioritizing vaccination to age groups within the 2–16 year old age range to mitigate the operational and resource challenges of the current strategy. We performed economic evaluations comparing the influenza vaccination program from 1995–2013 to seven alternative strategies targeted at low risk individuals along the school age divisions Preschool (2–4 years old), Primary school (5–11 years old), and Secondary school (12–16 years old). These extensions are evaluated incrementally on the status quo scenario (vaccinating subgroups at high risk of influenza-related complications and individuals 65+ years old). Impact of vaccination was assessed using a transmission model from a previously published study and updated with new data. At all levels of coverage, all strategies had a 100% probability of being cost-effective at the current National Health Service threshold, £20,000/QALY gained. The incremental analysis demonstrated vaccinating Primary School children was the most cost-efficient strategy compared incrementally against others with an Incremental Cost-Effectiveness Ratio of £639 spent per QALY gained (Net Benefit: 404 M£ [155, 795]). When coverage was varied between 30%, 55%, and 70% strategies which included Primary school children had a higher probability of being cost-effective at lower willingness-to-pay levels. Although children were the vaccine target the majority of QALY gains occurred in the 25–44 years old and 65+ age groups. Influenza strain A/H3N2 incurred the greatest costs and QALYs lost regardless of which strategy was used. Improvement could be made to the current LAIV pediatric vaccination strategy by eliminating vaccination of 2–4 year olds and focusing on school-based delivery to Primary and Secondary school children in tandem.