Objective: The possible effect of obesity in the outcome of treated children with abdominal pain-related functional gastrointestinal disorders (FGIDs) has not yet been studied. We hypothesized that obesity is associated with a poor long-term prognosis in children with FGIDs. Study design: Prospective cohort study in an outpatient clinic-based sample of patients diagnosed with abdominal pain-related FGIDs. Principal outcome measured was persistence of pain at long-term follow-up (12-15 months). Frequency of pain, intensity of pain, school absenteeism and disruption of daily activities were compared between obese and non-obese subjects. Results: The group mean age was 13.27 ± 3.84 years, distribution of diagnosis was 32% (functional abdominal pain), 42.5% (irritable bowel syndrome) and 25.5% (functional dyspepsia). Overall, 20.2% of patients were obese. A total of 116 patients (61.7%) reported abdominal pain and 72 (38.3%) were asymptomatic at long-term follow-up. Obese patients were more likely to have abdominal pain (Po0.0001), higher intensity of pain (P ¼ 0.0002), higher frequency of pain (P ¼ 0.0032), school absenteeism (Po0.0001) and disruption of daily activities (Po0.0001) at follow-up than non-obese patients. Conclusion: Obesity is associated with poor outcome and disability at long-term follow-up in children with abdominal pain-related FGIDs. Our novel findings could have important implications in the prognosis and management of FGIDs.

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Here we propose an effective non-pharmacological intervention method of detecting the asymptomatic spreaders in contact-tracing networks, and validated it on the empirical COVID-19 spreading network in Singapore. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy. Specifically, based on the unique characteristics of COVID-19 spreading dynamics, we propose a computational framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. Our simulation results indicate that a screening method using our prediction outperforms machine learning algorithms, e.g. graph neural networks, that are designed as baselines in this work, as well as random screening of infection's closest contacts widely used by China in its early outbreak. Furthermore, our method provides high precision even with incomplete information of the contract-tracing networks. Our work can be of critical importance to the non-pharmacological interventions of COVID-19, especially with increasing adoptions of contact tracing measures using various new technologies. Beyond COVID-19, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.