Infection-Induced Immunity Is Associated With Protection Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Decreased Infectivity.

BACKGROUND: The impact of infection-induced immunity on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has not been well established. Here we estimate the effects of prior infection induced immunity in adults and children on SARS-CoV-2 transmission in households. METHODS: We conducted a household cohort study from March 2020-November 2022 in Managua, Nicaragua; following a housheold SARS-CoV-2 infection, household members are closely monitored for infection. We estimate the association of time period, age, symptoms, and prior infection with secondary attack risk. RESULTS: Overall, transmission occurred in 70.2% of households, 40.9% of household contacts were infected, and the secondary attack risk ranged from 8.1% to 13.9% depending on the time period. Symptomatic infected individuals were more infectious (rate ratio [RR] 21.2, 95% confidence interval [CI]: 7.4-60.7) and participants with a prior infection were half as likely to be infected compared to naïve individuals (RR 0.52, 95% CI:.38-.70). In models stratified by age, prior infection was associated with decreased infectivity in adults and adolescents (secondary attack risk [SAR] 12.3, 95% CI: 10.3, 14.8 vs 17.5, 95% CI: 14.8, 20.7). However, although young children were less likely to transmit, neither prior infection nor symptom presentation was associated with infectivity. During the Omicron era, infection-induced immunity remained protective against infection. CONCLUSIONS: Infection-induced immunity is associated with decreased infectivity for adults and adolescents. Although young children are less infectious, prior infection and asymptomatic presentation did not reduce their infectivity as was seen in adults. As SARS-CoV-2 transitions to endemicity, children may become more important in transmission dynamics.

A Potential Outcomes Approach to Selection Bias.

We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). This approach is nonparametric, and selection bias under the approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks.

We prove that it is possible to obtain the exact closure of SIR pairwise epidemic equations on a configuration model network if and only if the degree distribution follows a Poisson, binomial, or negative binomial distribution. The proof relies on establishing the equivalence, for these specific degree distributions, between the closed pairwise model and a dynamical survival analysis (DSA) model that was previously shown to be exact. Specifically, we demonstrate that the DSA model is equivalent to the well-known edge-based Volz model. Using this result, we also provide reductions of the closed pairwise and Volz models to a single equation that involves only susceptibles. This equation has a useful statistical interpretation in terms of times to infection. We provide some numerical examples to illustrate our results.

Shortening Duration of Swine Exhibitions to Reduce Risk for Zoonotic Transmission of Influenza A Virus.

Reducing zoonotic influenza A virus (IAV) risk in the United States necessitates mitigation of IAV in exhibition swine. We evaluated the effectiveness of shortening swine exhibitions to <72 hours to reduce IAV risk. We longitudinally sampled every pig daily for the full duration of 16 county fairs during 2014-2015 (39,768 nasal wipes from 6,768 pigs). In addition, we estimated IAV prevalence at 195 fairs during 2018-2019 to test the hypothesis that <72-hour swine exhibitions would have lower IAV prevalence. In both studies, we found that shortening duration drastically reduces IAV prevalence in exhibition swine at county fairs. Reduction of viral load in the barn within a county fair is critical to reduce the risk for interspecies IAV transmission and pandemic potential. Therefore, we encourage fair organizers to shorten swine shows to protect the health of both animals and humans.

Estimating and interpreting secondary attack risk: Binomial considered biased.

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.

COVID-19 Vaccinations in EMS Professionals: Prevalence and Predictors.

Background: Immunizations for emergency medical services (EMS) professionals during pandemics are an important tool to increase the safety of the workforce as well as their patients. The purpose of this study was to better understand EMS professionals' decisions to receive or decline a COVID-19 vaccine.Methods: We conducted a cross-sectional analysis of nationally certified EMS professionals (18-85 years) in April 2021. Participants received an electronic survey asking whether they received a vaccine, why or why not, and their associated beliefs using three validated scales: perceived risk of COVID-19, medical mistrust, and confidence in the COVID-19 vaccine. Data were merged with National Registry dataset demographics. Analyses included descriptive analysis and multivariable logistic regression (OR, 95% CI). Multivariate imputation by chained equations was used for missingness.Results: A total of 2,584 respondents satisfied inclusion criteria (response rate = 14%). Overall, 70% of EMS professionals were vaccinated. Common reasons for vaccination among vaccinated respondents were to protect oneself (76%) and others (73%). Common reasons for non-vaccination among non-vaccinated respondents included concerns about vaccine safety (53%) and beliefs that vaccination was not necessary (39%). Most who had not received the vaccine did not plan to get it in the future (84%). Hesitation was most frequently related to wanting to see how the vaccine was working for others (55%). Odds of COVID-19 vaccination were associated with demographics including age (referent <28 years; 39-50 years: 1.56, 1.17-2.08; >51 years: 2.22, 1.64-3.01), male sex (1.26, 1.01-1.58), residing in an urban/suburban area (referent rural; 1.36, 1.08-1.70), advanced education (referent GED/high school and below; bachelor's and above: 1.72, 1.19-2.47), and working at a hospital (referent fire-based agency; 1.53, 1.04-2.24). Additionally, vaccination odds were significantly higher with greater perceived risk of COVID-19 (2.05, 1.68-2.50), and higher vaccine confidence (2.84, 2.40-3.36). Odds of vaccination were significantly lower with higher medical mistrust (0.54, 0.46-0.63).Conclusion: Despite vaccine availability, not all EMS professionals had been vaccinated. The decision to receive a COVID-19 vaccine was associated with demographics, beliefs regarding COVID-19 and the vaccine, and medical mistrust. Efforts to increase COVID-19 vaccination rates should emphasize the safety and efficacy of vaccines.

Incorporating age and delay into models for biophysical systems.

In many biological systems, chemical reactions or changes in a physical state are assumed to occur instantaneously. For describing the dynamics of those systems, Markov models that require exponentially distributed inter-event times have been used widely. However, some biophysical processes such as gene transcription and translation are known to have a significant gap between the initiation and the completion of the processes, which renders the usual assumption of exponential distribution untenable. In this paper, we consider relaxing this assumption by incorporating age-dependent random time delays (distributed according to a given probability distribution) into the system dynamics. We do so by constructing a measure-valued Markov process on a more abstract state space, which allows us to keep track of the 'ages' of molecules participating in a chemical reaction. We study the large-volume limit of such age-structured systems. We show that, when appropriately scaled, the stochastic system can be approximated by a system of partial differential equations (PDEs) in the large-volume limit, as opposed to ordinary differential equations (ODEs) in the classical theory. We show how the limiting PDE system can be used for the purpose of further model reductions and for devising efficient simulation algorithms. In order to describe the ideas, we use a simple transcription process as a running example. We, however, note that the methods developed in this paper apply to a wide class of biophysical systems.

The role of benzathine penicillin G in predicting and preventing all-cause acute respiratory disease in military recruits: 1991-2017.

The adenovirus vaccine and benzathine penicillin G (BPG) have been used by the US military to prevent acute respiratory diseases (ARD) in trainees, though these interventions have had documented manufacturing problems. We fit Poisson regression and random forest models (RF) to 26 years of weekly ARD incidence data to explore the impact of the adenovirus vaccine and BPG prophylaxis on respiratory disease burden. Adenovirus vaccine availability was among the most important predictors of ARD in the RF, while BPG was the ninth most important. BPG was a significant protective factor against ARD (incidence rate ratio (IRR) = 0.68; 95% confidence interval (CI) 0.67-0.70), but less so than either the old or new adenovirus vaccine (IRR = 0.39, 95% CI 0.38-0.39 and IRR = 0.11, 95% CI 0.11-0.11), respectively. These results suggest that BPG is moderately predictive of, and significantly protective against ARD, though to a lesser extent than either the old or new adenovirus vaccine.

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees.

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology.

The epidemiology and transmissibility of Zika virus in Girardot and San Andres island, Colombia, September 2015 to January 2016.

Transmission of Zika virus (ZIKV) was first detected in Colombia in September 2015. As of April 2016, Colombia had reported over 65,000 cases of Zika virus disease (ZVD). We analysed daily surveillance data of ZVD cases reported to the health authorities of San Andres and Girardot, Colombia, between September 2015 and January 2016. ZVD was laboratory-confirmed by reverse transcription-polymerase chain reaction (RT-PCR) in the serum of acute cases within five days of symptom onset. We use daily incidence data to estimate the basic reproductive number (R0) in each population. We identified 928 and 1,936 reported ZVD cases from San Andres and Girardot, respectively. The overall attack rate for reported ZVD was 12.13 cases per 1,000 residents of San Andres and 18.43 cases per 1,000 residents of Girardot. Attack rates were significantly higher in females in both municipalities (p < 0.001). Cases occurred in all age groups with highest rates in 20 to 49 year-olds. The estimated R0 for the Zika outbreak was 1.41 (95% confidence interval (CI): 1.15-1.74) in San Andres and 4.61 (95% CI: 4.11-5.16) in Girardot. Transmission of ZIKV is ongoing in the Americas. The estimated R0 from Colombia supports the observed rapid spread.

A pathway EM-algorithm for estimating vaccine efficacy with a non-monotone validation set.

Here, we consider time-to-event data where individuals can experience two or more types of events that are not distinguishable from one another without further confirmation, perhaps by laboratory test. The event type of primary interest can occur only once. The other types of events can recur. If the type of a portion of the events is identified, this forms a validation set. However, even if a random sample of events are tested, confirmations can be missing nonmonotonically, creating uncertainty about whether an individual is still at risk for the event of interest. For example, in a study to estimate efficacy of an influenza vaccine, an individual may experience a sequence of symptomatic respiratory illnesses caused by various pathogens over the season. Often only a limited number of these episodes are confirmed in the laboratory to be influenza-related or not. We propose two novel methods to estimate covariate effects in this survival setting, and subsequently vaccine efficacy. The first is a pathway expectation-maximization (EM) algorithm that takes into account all pathways of event types in an individual compatible with that individual's test outcomes. The pathway EM iteratively estimates baseline hazards that are used to weight possible event types. The second method is a non-iterative pathway piecewise validation method that does not estimate the baseline hazards. These methods are compared with a previous simpler method. Simulation studies suggest mean squared error is lower in the efficacy estimates when the baseline hazards are estimated, especially at higher hazard rates. We use the pathway EM-algorithm to reevaluate the efficacy of a trivalent live-attenuated influenza vaccine during the 2003-2004 influenza season in Temple-Belton, Texas, and compare our results with a previously published analysis.

Rothman diagrams: the geometry of causal inference in epidemiology.

Here, we explain and illustrate a geometric perspective on causal inference in cohort studies that can help epidemiologists understand the role of standardization in causal inference as well as the distinctions between confounding, effect modification, and noncollapsibility. For simplicity, we focus on a binary exposure X, a binary outcome D, and a binary confounder C that is not causally affected by X. Rothman diagrams plot risk in the unexposed on the x-axis and risk in the exposed on the y-axis. The crude risks define one point in the unit square, and the stratum-specific risks define two other points in the unit square. These three points can be used to identify confounding and effect modification, and we show briefly how these concepts generalize to confounders with more than two levels. We propose a simplified but equivalent definition of collapsibility in terms of standardization, and we show that a measure of association is collapsible if and only if all of its contour lines are straight. We illustrate these ideas using data from a study conducted in Newcastle upon Tyne, United Kingdom, where the causal effect of smoking on 20-year mortality was confounded by age. We conclude that causal inference should be taught using geometry before using regression models.

Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus.

Objective: Over the 2018-2019 flu season we conducted a randomized controlled trial examining the efficacy of a Twitter campaign on vaccination rates. Concurrently we investigated potential interactions between digital social network structure and vaccination status. Participants: Undergratuates at a large midwestern public university were randomly assigned to an intervention (n = 353) or control (n = 349) group. Methods: Vaccination data were collected via monthly surveys. Participant Twitter data were collected through the public-facing Twitter API. Intervention impact was assessed with logistic regression. Standard network science tools examined vaccination coverage over online social networks. Results: The campaign had no effect on vaccination outcome. Receiving a flu shot the prior year had a positive impact on participant vaccination. Evidence of an interaction between digital social network structure and vaccination status was detected. Conclusions: Social media campaigns may not be sufficient for increasing vaccination rates. There may be potential for social media campaigns that leverage network structure.

COVID-19 dynamics in an Ohio prison.

Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number >14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons.

Identifying beliefs driving COVID-19 vaccination: Lessons for effective messaging.

Increasing vaccination acceptance has been essential during the COVID-19 pandemic and in preparation for future public health emergencies. This study aimed to identify messaging strategies to encourage vaccine uptake by measuring the drivers of COVID-19 vaccination among the general public. A survey to assess COVID-19 vaccination acceptance and hesitancy was advertised on Facebook in February-April 2022. The survey included items asking about COVID-19 vaccination status and participant demographics, and three scales assessing medical mistrust, perceived COVID-19 risk, and COVID-19 vaccine confidence (adapted from the Oxford COVID-19 vaccine confidence and complacency scale). The main outcome was vaccination, predicted by patient demographics and survey scale scores. Of 1,915 survey responses, 1,450 (75.7%) were included, with 1,048 (72.3%) respondents reporting they had been vaccinated. In a multivariable regression model, the COVID-19 vaccine confidence scale was the strongest predictor of vaccination, along with education level and perceived COVID-19 risk. Among the items on this scale, not all were equally important in predicting COVID-19 vaccination. The items that best predicted vaccination, at a given score on the COVID-19 vaccine confidence scale, included confidence that vaccine side effects are minimal, that the vaccine will work, that the vaccine will help the community, and that the vaccine provides freedom to move on with life. This study improved our understanding of perceptions most strongly associated with vaccine acceptance, allowing us to consider how to develop messages that may be particularly effective in encouraging vaccination among the general public for both the COVID-19 pandemic and future public health emergencies.

Contact intervals, survival analysis of epidemic data, and estimation of R(0).

We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.

Dynamic survival analysis for non-Markovian epidemic models.

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.

Analysis of individual-level data from 2018-2020 Ebola outbreak in Democratic Republic of the Congo.

The 2018-2020 Ebola virus disease epidemic in Democratic Republic of the Congo (DRC) resulted in 3481 cases (probable and confirmed) and 2299 deaths. In this paper, we use a novel statistical method to analyze the individual-level incidence and hospitalization data on DRC Ebola victims. Our analysis suggests that an increase in the rate of quarantine and isolation that has shortened the infectiousness period by approximately one day during the epidemic's third and final wave was likely responsible for the eventual containment of the outbreak. The analysis further reveals that the total effective population size or the average number of individuals at risk for the disease exposure in three epidemic waves over the period of 24 months was around 16,000-a much smaller number than previously estimated and likely an evidence of at least partial protection of the population at risk through ring vaccination and contact tracing as well as adherence to strict quarantine and isolation policies.

The COVID-19 vaccine concerns scale: Development and validation of a new measure.

Reasons for COVID-19 hesitancy are multi-faceted and tend to differ from those for general vaccine hesitancy. We developed the COVID-19 Vaccine Concerns Scale (CVCS), a self-report measure intended to better understand individuals' concerns about COVID-19 vaccines. We validated the scale using data from a convenience sample of 2,281 emergency medical services providers, a group of professionals with high occupational COVID-19 risk. Measures included the CVCS items, an adapted Oxford COVID-19 vaccine hesitancy scale, a general vaccine hesitancy scale, demographics, and self-reported COVID-19 vaccination status. The CVCS had high internal consistency reliability (α = .89). A one-factor structure was determined by exploratory and confirmatory factor analyses (EFA and CFA), resulting in a seven-item scale. The model had good fit (X2[14] = 189.26, p < .001; CFI = .95, RMSEA = .11 [.09, .12], NNFI = .93, SRMR = .03). Moderate Pearson correlations with validated scales of general vaccine hesitancy (r = .71 , p < .001; n = 2144) and COVID-19 vaccine hesitancy (r = .82; p < .001; n = 2279) indicated construct validity. The CVCS predicted COVID-19 vaccination status (B = -2.21, Exp(B) = .11 [95% CI = .09, .13], Nagelkerke R2 = .55), indicating criterion-related validity. In sum, the 7-item CVCS is a reliable and valid self-report measure to examine fears and concerns about COVID-19 vaccines. The scale predicts COVID-19 vaccination status and can be used to inform efforts to reduce COVID-19 vaccine hesitancy.