Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial

The coming Omicron waves and factors affecting its spread after China reopening borders

The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model [Formula: see text], and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified [Formula: see text] model. The results demonstrate that the second and third waves will occur in July-September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders.

Assessing intervention effects in a randomized trial within a social network

Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.

Changes in Immune Function during Initial Military Training

PURPOSE

Initial military training (IMT) is a transitionary period wherein immune function may be suppressed and infection risk heightened due to physical and psychological stress, communal living, and sleep deprivation. This study characterized changes in biomarkers of innate and adaptive immune function, and potential modulators of those changes, in military recruits during IMT.

METHODS

Peripheral leukocyte distribution and mitogen-stimulated cytokine profiles were measured in fasted blood samples, Epstein-Barr (EBV), varicella zoster (VZV), and herpes simplex 1 (HSV1) DNA was measured in saliva by quantitative polymerase chain reaction as an indicator of latent herpesvirus reactivation, and diet quality was determined using the healthy eating index measured by food frequency questionnaire in 61 US Army recruits (97% male) at the beginning (PRE) and end (POST) of 22-wk IMT.

RESULTS

Lymphocytes and terminally differentiated cluster of differentiation (CD)4+ and CD8+ T cells increased PRE to POST, whereas granulocytes, monocytes, effector memory CD4+ and CD8+ T cells, and central memory CD8+ T cells decreased ( P ≤ 0.02). Cytokine responses to anti-CD3/CD28 stimulation were higher POST compared with PRE, whereas cytokine responses to lipopolysaccharide stimulation were generally blunted ( P < 0.05). Prevalence of EBV reactivation was higher at POST ( P = 0.04), but neither VZV nor HSV1 reactivation was observed. Diet quality improvements were correlated with CD8+ cell maturation and blunted proinflammatory cytokine responses to anti-CD3/CD28 stimulation.

CONCLUSIONS

Lymphocytosis, maturation of T-cell subsets, and increased T-cell reactivity were evident POST compared with PRE IMT. Although EBV reactivation was more prevalent at POST, no evidence of VZV or HSV1 reactivation, which are more common during severe stress, was observed. Findings suggest increases in the incidence of EBV reactivation were likely appropriately controlled by recruits and immune-competence was not compromised at the end of IMT.

A mixed methods analysis of participation in a social contact survey

BACKGROUND

Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data.

METHODS

We conducted focus groups with university students who had (n = 13) and had never (n = 14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data.

RESULTS

The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation.

CONCLUSIONS

Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy.

Targeted maximum likelihood estimation of causal effects with interference: A simulation study

Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided.

Randomization for the susceptibility effect of an infectious disease intervention

Randomized trials of infectious disease interventions, such as vaccines, often focus on groups of connected or potentially interacting individuals. When the pathogen of interest is transmissible between study subjects, interference may occur: individual infection outcomes may depend on treatments received by others. Epidemiologists have defined the primary parameter of interest-called the "susceptibility effect"-as a contrast in infection risk under treatment versus no treatment, while holding exposure to infectiousness constant. A related quantity-the "direct effect"-is defined as an unconditional contrast between the infection risk under treatment versus no treatment. The purpose of this paper is to show that under a widely recommended randomization design, the direct effect may fail to recover the sign of the true susceptibility effect of the intervention in a randomized trial when outcomes are contagious. The analytical approach uses structural features of infectious disease transmission to define the susceptibility effect. A new probabilistic coupling argument reveals stochastic dominance relations between potential infection outcomes under different treatment allocations. The results suggest that estimating the direct effect under randomization may provide misleading conclusions about the effect of an intervention-such as a vaccine-when outcomes are contagious. Investigators who estimate the direct effect may wrongly conclude an intervention that protects treated individuals from infection is harmful, or that a harmful treatment is beneficial.

Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination

Assortativity is the tendency of individuals connected in a network to share traits and behaviors. Through simulations, we demonstrated the potential for bias resulting from assortativity by vaccination, where vaccinated individuals are more likely to be connected with other vaccinated individuals. We simulated outbreaks of a hypothetical infectious disease and vaccine in a randomly generated network and a contact network of university students living on campus. We varied protection of the vaccine to the individual, transmission potential of vaccinated-but-infected individuals, and assortativity by vaccination. We compared a traditional approach, which ignores the structural features of a network, with simple approaches which summarized information from the network. The traditional approach resulted in biased estimates of the unit-treatment effect when there was assortativity by vaccination. Several different approaches that included summary measures from the network reduced bias and improved confidence interval coverage. Through simulations, we showed the pitfalls of ignoring assortativity by vaccination. While our example is described in terms of vaccines, our results apply more widely to exposures for contagious outcomes. Assortativity should be considered when evaluating exposures for contagious outcomes.

SARS-CoV-2 Infection in Health Care Personnel and Their Household Contacts at a Tertiary Academic Medical Center: Protocol for a Longitudinal Cohort Study

BACKGROUND

Health care personnel (HCP) are at high risk for exposure to the SARS-CoV-2 virus. While personal protective equipment (PPE) may mitigate this risk, prospective data collection on its use and other risk factors for seroconversion in this population is needed.

OBJECTIVE

The primary objectives of this study are to (1) determine the incidence of, and risk factors for, SARS-CoV-2 infection among HCP at a tertiary care medical center and (2) actively monitor PPE use, interactions between study participants via electronic sensors, secondary cases in households, and participant mental health and well-being.

METHODS

To achieve these objectives, we designed a prospective, observational study of SARS-CoV-2 infection among HCP and their household contacts at an academic tertiary care medical center in North Carolina, USA. Enrolled HCP completed frequent surveys on symptoms and work activities and provided serum and nasal samples for SARS-CoV-2 testing every 2 weeks. Additionally, interactions between participants and their movement within the clinical environment were captured with a smartphone app and Bluetooth sensors. Finally, a subset of participants' households was randomly selected every 2 weeks for further investigation, and enrolled households provided serum and nasal samples via at-home collection kits.

RESULTS

As of December 31, 2020, 211 HCP and 53 household participants have been enrolled. Recruitment and follow-up are ongoing and expected to continue through September 2021.

CONCLUSIONS

Much remains to be learned regarding the risk of SARS-CoV-2 infection among HCP and their household contacts. Through the use of a multifaceted prospective study design and a well-characterized cohort, we will collect critical information regarding SARS-CoV-2 transmission risks in the health care setting and its linkage to the community.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/25410.

Age groups that sustain resurging COVID-19 epidemics in the United States

After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions-including transmission-blocking vaccines-to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.

Study of COVID-19 pandemic in London (UK) from urban context

COVID-19 transmission in London city was discussed in this work from an urban context. The association between COVID-19 cases and climate indicators in London, UK were analysed statistically employing published data from national health services, UK and Time and Date AS based weather data. The climatic indicators included in the study were the daily averages of maximum and minimum temperatures, humidity, and wind speed. Pearson, Kendall, and Spearman rank correlation tests were selected for data analysis. The data was considered up to two different dates to study the climatic effect (10th May in the first study and then updated up to 16th of July in the next study when the rest of the data was available). The results were contradictory in the two studies and it can be concluded that climatic parameters cannot solely determine the changes in the number of cases in the pandemic. Distance from London to four other cities (Birmingham, Leeds, Manchester, and Sheffield) showed that as the distance from the epicentre of the UK (London) increases, the number of COVID-19 cases decrease. What should be the necessary measure to be taken to control the transmission in cities have been discussed.

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.

A Guide for Choosing Community Detection Algorithms in Social Network Studies: The Question Alignment Approach

INTRODUCTION

Community detection, the process of identifying subgroups of highly connected individuals within a network, is an aspect of social network analysis that is relevant but potentially underutilized in prevention research. Guidance on using community detection methods stresses aligning methods with specific research questions but lacks clear operationalization. The Question Alignment approach was developed to help address this gap and promote the high-quality use of community detection methods.

METHODS

A total of 6 community detection methods are discussed: Walktrap, Edge-Betweenness, Infomap, Louvain, Label Propagation, and Spinglass. The Question Alignment approach is described and demonstrated using real-world data collected in 2013. This hypothetical case study was conducted in 2019 and focused on targeting a hand hygiene intervention to high-risk communities to prevent influenza transmission.

RESULTS

Community detection using the Walktrap method best fit the hypothetical case study. The communities derived using the Walktrap method were quite different from communities derived through the other 5 methods in both the number of communities and individuals within communities. There was evidence to support that the Question Alignment approach can help researchers produce more useful community detection results. Compared to other methods of selecting high-risk groups, the Walktrap produced the most communities that met the hypothetical intervention requirements.

CONCLUSIONS

As prevention research incorporating social networks increases, researchers can use the Question Alignment approach to produce more theoretically meaningful results and potentially more useful results for practice. Future research should focus on assessing whether the Question Alignment approach translates into improved intervention results.

A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections

BACKGROUND

Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published.

METHODS

We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible.

RESULTS

In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies.

CONCLUSIONS

We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.

Transmission of viral pathogens in a social network of university students: the eX-FLU study

Previous research on respiratory infection transmission among university students has primarily focused on influenza. In this study, we explore potential transmission events for multiple respiratory pathogens in a social contact network of university students. University students residing in on-campus housing (n = 590) were followed for the development of influenza-like illness for 10-weeks during the 2012-13 influenza season. A contact network was built using weekly self-reported contacts, class schedules, and housing information. We considered a transmission event to have occurred if students were positive for the same pathogen and had a network connection within a 14-day period. Transmitters were individuals who had onset date prior to their infected social contact. Throat and nasal samples were analysed for multiple viruses by RT-PCR. Five viruses were involved in 18 transmission events (influenza A, parainfluenza virus 3, rhinovirus, coronavirus NL63, respiratory syncytial virus). Transmitters had higher numbers of co-infections (67%). Identified transmission events had contacts reported in small classes (33%), dormitory common areas (22%) and dormitory rooms (17%). These results suggest that targeting person-to-person interactions, through measures such as isolation and quarantine, could reduce transmission of respiratory infections on campus.

Emerging study on the transmission of the Novel Coronavirus (COVID-19) from urban perspective: Evidence from China

This study presents an in-depth investigation on the transmission of the novel coronavirus (COVID-19) from the urban perspective. It focuses on the "aftermath" of the outbreak and the spread of the infection among cities. Especially, this study provides insights of the fundamentals of the factors that may affect the spread of the infection in cities, where the marginal effects of some most influential factors to the virus transmission are estimated. It reveals that the distance to epicenter is a very strong influential factor, and is negatively linked with the spread of COVID-19. In addition, subway, wastewater and residential garbage are positively connected with the virus transmission. Moreover, both urban area and population density are negatively associated with the spread of COVID-19 at the early stage of the epidemic. Furthermore, this study also provides high precision estimation of the number of COVID-19 infection in Wuhan city, which is the epicenter of the outbreak in China. Based on the real-world data of cities outside Wuhan on March 2, 2020, the estimated number is 56,944.866 (mean value), which is very close to the officially reported number. The methodology and main conclusions shown in this paper are of general interest, and they can be applied to other countries to help understand the local transmission of COVID-19 as well.

Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities

Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to ten-node graphs. We find that combinations of two or more centralities are predictive (R2 scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centrality (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.

Quantifying heterogeneous contact patterns in Japan: a social contact survey

BACKGROUND

Social contact surveys can greatly help in quantifying the heterogeneous patterns of infectious disease transmission. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza.

METHODS

An internet-based questionnaire survey was conducted, covering all 47 prefectures in Japan and including a total of 1476 households. The social contact matrix was quantified assuming reciprocity and using the maximum likelihood method. By imposing several parametric assumptions for the next-generation matrix, the empirical seroepidemiological data of influenza A (H1N1) 2009 was analysed and we estimated the basic reproduction number, R0.

RESULTS

In total, the reported number of contacts on weekdays was 10,682 whereas that on weekend days was 8867. Strong age-dependent assortativity was identified. Forty percent of weekday contacts took place at schools or workplaces, but that declined to 14% on weekends. Accounting for the age-dependent heterogeneity with the known social contact matrix, the minimum value of the Akaike information criterion was obtained and R0 was estimated at 1.45 (95% confidence interval: 1.42, 1.49).

CONCLUSIONS

Survey datasets will be useful for parameterizing the heterogeneous transmission model of various directly transmitted infectious diseases in Japan. Age-dependent assortativity, especially among children, along with numerous contacts in school settings on weekdays implies the potential effectiveness of school closure.

Use of social network analysis in the development, dissemination, implementation, and sustainability of health behavior interventions for adults: A systematic review

Interest in conceptualizing, measuring, and applying social network analysis (SNA) in public health has grown tremendously in recent years. While these studies have broadened our understanding of the role that social networks play in health, there has been less research that has investigated the application of SNA to inform health-related interventions. This systematic review aimed to capture the current applied use of SNA in the development, dissemination, implementation, and sustainability of health behavior interventions for adults. We identified 52 articles published between 2004 and 2016. A wide variety of study settings were identified, most commonly in the US context and most often related to sexual health and HIV prevention. We found that 38% of articles explicitly applied SNA to inform some aspect of interventions. Use of SNA to inform intervention development (as opposed to dissemination, implementation, or sustainability) was most common. The majority of articles represented in this review (n = 39) were quantitative studies, and 13 articles included a qualitative component. Partial networks were most represented across articles, and over 100 different networks measures were assessed. The most commonly described measures were network density, size, and degree centrality. Finally, very few articles defined SNA and not all articles using SNA were theoretically-informed. Given the nascent and heterogeneous state of the literature in this area, this is an important time for the field to coalesce on terminology, measures, and theoretical frameworks. We highlight areas for researchers to advance work on the application of SNA in the design, dissemination, implementation and sustainability of behavioral interventions.

Dynamics and Determinants of HPV Infection: The Michigan HPV and Oropharyngeal Cancer (M-HOC) Study

INTRODUCTION

Human papillomavirus (HPV) is the primary cause of cervical and other anogenital cancers and is also associated with head and neck cancers. Incidence of HPV-related oropharyngeal squamous cell cancers (OPSCCs) is increasing, and HPV-related OPSCCs have surpassed cervical cancer as the most common HPV-related cancer in the USA. Given the multisite nature of HPV, there is strong interest in collecting data from both genital and oral sites, as well as associated data on social and sexual behaviours. The overarching goal of this study is to evaluate patterns of oral HPV infection incidence, clearance and persistence and their relationship to sexual behaviour history.

METHODS AND ANALYSIS

Participants are recruited from two populations: college students at a large public university and general population from the surrounding area. At the first study visit, participants complete a detailed sexual history, health and behaviour questionnaire. Follow-up visits occur every 3-4 months over 3 years, when participants complete an abbreviated questionnaire. All participants provide a saliva sample at each visit, and eligible participants may provide a cervicovaginal self-swab. Genetic material isolated from specimens is tested for 15 high-risk and 3 low-risk HPV types. Statistical analyses will examine outcome variables including HPV prevalence, incidence, persistence and clearance. Logistic regression models will be used to estimate odds ratios and 95% confidence intervals for associations between the outcomes of interest and demographic/behavioural variables collected in the questionnaires. The longitudinal HPV infection data and detailed sexual history data collected in the questionnaires will allow us to develop individual-based network models of HPV transmission and will be used to parameterise multiscale models of HPV-related OPSC carcinogenesis.

ETHICS AND DISSEMINATION

This study has been approved by the University of Michigan Institutional Review Board. All participants are consented in person by trained study staff. Study results will be disseminated through peer-reviewed publications.

Human coronaviruses and other respiratory infections in young adults on a university campus: Prevalence, symptoms, and shedding

BACKGROUND

The prevalence, symptom course, and shedding in persons infected with the 4 most common human coronaviruses (HCoV)-229E, HKU1, NL63, and OC43 are poorly described.

OBJECTIVES

We estimate their prevalence and associated symptoms among college students identified via a social network study design.

PATIENTS/METHODS

We collected 1-3 samples (n = 250 specimens) from 176 participants between October 2012 and January 17, 2013: participants with acute respiratory infection (ARI; cough and body aches or chills or fever/feverishness) and their social contacts. Virus was detected using RT-PCR.

RESULTS

30.4% (76/250) of specimens tested positive for any virus tested, and 4.8% (12/250) were positive for 2 or more viruses. Human coronaviruses (HCoVs [22.0%; 55/250]), rhinovirus (7.6%; 19/250), and influenza A (6.4%; 16/250) were most prevalent. Symptoms changed significantly over time among ARI participants with HCoV: the prevalence of cough and chills decreased over 6 days (P = .04, and P = .01, respectively), while runny nose increased over the same period (P = .02). HCoV-NL63 was the most frequent virus detected 6 days following symptom onset (8.9%), followed by rhinovirus (6.7%).

CONCLUSIONS

During a 3-month period covering a single season, HCoVs were common, even among social contacts without respiratory symptoms; specific symptoms may change over the course of HCoV-associated illness and were similar to symptoms from influenza and rhinovirus.

Big Data in Public Health: Terminology, Machine Learning, and Privacy

The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

Invited Commentary: Evolution of Social Networks, Health, and the Role of Epidemiology

Almost 40 years ago, Berkman and Syme demonstrated that social networks were related to the risk of early mortality (Am J Epidemiol. 1979;109(2):186-204). Their study was highly innovative because they directly measured and quantified social networks in a large prospective population-based survey with mortality follow-up. The results of the study showed robust network gradients, whereby those with fewer networks and weaker social ties had significantly higher mortality rates. The important influence of social networks that Berkman and Syme noted many years ago is likely to heighten in the future, as demographic characteristics shift and individuals become more inclined to socialize through online platforms instead of real-world interactions. Berkman and Syme's research in 1979 continues to play a key role in shaping recent efforts to uncover the influence of social networks on health. Looking back on their findings may help epidemiologists better understand the importance of both online and offline networks for population health today.