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.

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.

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".

Commentary on the use of the reproduction number R during the COVID-19 pandemic.

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

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.

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.

Closing the Gap on COVID-19 Vaccinations in First Responders and Beyond: Increasing Trust.

Although COVID-19 vaccines are widely available in the U.S. and much of the world, many have chosen to forgo this vaccination. Emergency medical services (EMS) professionals, despite their role on the frontlines and interactions with COVID-positive patients, are not immune to vaccine hesitancy. Via a survey conducted in April 2021, we investigated the extent to which first responders in the U.S. trusted various information sources to provide reliable information about COVID-19 vaccines. Those vaccinated generally trusted healthcare providers as a source of information, but unvaccinated first responders had fairly low trust in this information source-a group to which they, themselves, belong. Additionally, regardless of vaccination status, trust in all levels of government, employers, and their community as sources of information was low. Free-response explanations provided some context to these findings, such as preference for other COVID-19 management options, including drugs proven ineffective. A trusted source of COVID-19 vaccination information is not readily apparent. Individuals expressed a strong desire for the autonomy to make vaccination decisions for themselves, as opposed to mandates. Potential reasons for low trust, possible solutions to address them, generalizability to the broader public, and implications of low trust in official institutions are discussed.

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.

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.

Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study.

BACKGROUND: As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. METHODS: In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period. FINDINGS: Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8-15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3-21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio [OR] 0·23 [95% CI 0·11-0·46]) and among adults aged 20-59 years (OR 0·64 [95% CI 0·43-0·97]). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 [95% CI 0·27-1·38]). The estimated local reproductive number (R) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41-0·62) in Guangzhou. The projected local R, had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49-0·74) when household was defined on the basis of close relatives. INTERPRETATION: SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contacts should be implemented to prevent onward transmission during the viral incubation period. FUNDING: US National Institutes of Health, Science and Technology Plan Project of Guangzhou, Project for Key Medicine Discipline Construction of Guangzhou Municipality, Key Research and Development Program of China.