Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study

Using contact tracing from interlocking diaries to map mood contagion along network chains

Both viruses and moods are transmitted through interpersonal contacts, but it has been extremely difficult to track each unique chain of contacts through which particular moods diffuse. By analyzing 56,060 contact records from 113 interlocking, yearlong diaries collected through a web-based platform in Taiwan, we traced mood states before and after each specific contact along a triplet of persons where B contacts C and subsequently contacts A. Multilevel analyses show that both positive and negative emotions are contagious, but the two paths diverge markedly in how the diffusion stops. Positive contact between C and B (which leads to improved mood for B) spreads to A through B’s contact with A, making A feel better afterward, regardless of whether B’s mood deteriorated between the two interactions. Negative contact between C and B (which leads to worsened mood for B) also spreads to A, making A feel worse after the contact with B. However, the spread of a negative mood discontinues if B’s mood improved between the two contacts. The different patterns of diffusion suggest that a negative mood is harder to disperse, probably because people generally make efforts to keep their negative emotions from spreading to others.

The relationship between duration and quality of sleep and upper respiratory tract infections: a systematic review

BACKGROUND

Upper respiratory tract infections (URTIs) are common, mostly self-limiting, but result in inappropriate antibiotic prescriptions. Poor sleep is cited as a factor predisposing to URTIs, but the evidence is unclear.

OBJECTIVE

To systematically review whether sleep duration and quality influence the frequency and duration of URTIs.

METHODS

Three databases and bibliographies of included papers were searched for studies assessing associations between sleep duration or quality and URTIs. We performed dual title and abstract selection, discussed full-text exclusion decisions and completed 50% of data extraction in duplicate. The Newcastle-Ottawa Quality Assessment Scale assessed study quality and we estimated odds ratios (ORs) using random effects meta-analysis.

RESULTS

Searches identified 5146 papers. Eleven met inclusion criteria, with nine included in meta-analyses: four good, two fair and five poor for risk of bias. Compared to study defined 'normal' sleep duration, shorter sleep was associated with increased URTIs (OR: 1.30, 95% confidence interval [CI]: 1.19-1.42, I2: 11%, P < 0.001) and longer sleep was not significantly associated (OR: 1.11 95% CI: 0.99-1.23, I2: 0%, P = 0.070). Sensitivity analyses using a 7- to 9-hour baseline found that sleeping shorter than 7-9 hours was associated with increased URTIs (OR: 1.31, 95% CI: 1.22-1.41, I2: 0%, P < 0.001). Sleeping longer than 7-9 hours was non-significantly associated with increased URTIs (OR: 1.15, 95% CI: 1.00-1.33, I2: 0%, P = 0.050, respectively). We were unable to pool sleep quality studies. No studies reported on sleep duration and URTI severity or duration.

CONCLUSIONS

Reduced sleep, particularly shorter than 7-9 hours, is associated with increased URTIs. Strategies improving sleep should be explored to prevent URTIs.

Public activities preceding the onset of acute respiratory infection syndromes in adults in England - implications for the use of social distancing to control pandemic respiratory infections

Background: Social distancing measures may reduce the spread of emerging respiratory infections however, there is little empirical data on how exposure to crowded places affects risk of acute respiratory infection. Methods: We used a case-crossover design nested in a community cohort to compare self-reported measures of activities during the week before infection onset and baseline periods. The design eliminates the effect of non-time-varying confounders. Time-varying confounders were addressed by exclusion of illnesses around the Christmas period and seasonal adjustment.  Results: 626 participants had paired data from the week before 1005 illnesses and the week before baseline. Each additional day of undertaking the following activities in the prior week was associated with illness onset: Spending more than five minutes in a room with someone (other than a household member) who has a cold (Seasonally adjusted OR 1·15, p=0·003); use of underground trains (1·31, p=0·036); use of supermarkets (1·32, p<0·001); attending a theatre, cinema or concert (1·26, p=0·032); eating out at a café, restaurant or canteen (1·25, p=0·003); and attending parties (1·47, p<0·001). Undertaking the following activities at least once in the previous week was associated with illness onset: using a bus, (aOR 1.48, p=0.049), shopping at small shops (1.9, p<0.002) attending a place of worship (1.81, p=0.005).    Conclusions: Exposure to potentially crowded places, public transport and to individuals with a cold increases risk of acquiring circulating acute respiratory infections. This suggests social distancing measures can have an important impact on slowing transmission of emerging respiratory infections.

Assessing effects of personal behaviors and environmental exposure on asthma episodes: a diary-based approach

Background

Quantifying the effects of personal health behaviors and environmental exposure on asthma flare-ups is a challenge. Most studies have focused on monitoring the symptoms and drug usage for relieving symptoms. In this study, we emphasize the need to understand how personal and environmental conditions are related to the occurrence of asthma symptoms.

Methods

We designed an online health diary platform to collect personal health behaviors from children, their parents and other adults with any allergic diseases including asthma, allergic rhinitis, atopic dermatitis and allergic conjunctivitis. The participants used mobile devices or computers to record their daily health-related activities such as sleep, exercise, diet, perception of air quality and temperature, and asthma symptoms. The participants also recorded secondhand smoke exposure and the time of activities, which were combined with ambient air quality measurements for calculating personal air pollution exposure. A generalized linear mixed model was used to estimate the effects of the factors.

Results

During the study period (January 2017–June 2017, and October 2017–September 2018), 132 participants provided 25,016 diary entries, and 84 participants had experienced asthma symptoms in 1458 diary entries. The results showed some different risk factors for the minors and adults. For minors, high-intensity exercise, contact with persons with influenza-like illness (ILI) and the perception of hot temperature and bad indoor air quality were associated with the occurrence of asthma episodes. The identified risk factors for the adult participants included having dehumidifiers at home, exposure to secondhand smoke, having bad sleep quality, contact with persons with ILI, not eating fruit and seafood, perceiving cold temperature, bad quality of indoor and outdoor air, and exposure to high concentration of ozone.

Conclusions

The revealed personal risk factors and perceptions of air quality and temperature may provide guidance on behavioral change for people susceptible to asthma to help control acute onset and severe exacerbation of asthma flare-ups.

The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study

Background

Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease.

Objective

This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities.

Methods

Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC).

Results

During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC.

Conclusions

The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.