No abstract

Background: The role of respiratory viruses as the major cause of acute lower respiratory tract infections (ALRTIs) in children is becoming increasingly evident due to the use of sensitive molecular detection methods. The aim of this study was to use conventional and molecular detection methods to assess the epidemiology of respiratory viral infections in children less than five years of age that were hospitalized with ALRTIs. Methods: The cross-sectional study was designed to investigate the occurrence of respiratory viruses including respiratory syncytisl virus (RSV), human metapneumovirus (HMPV), influenza virus A and B (IFV-A and B), parainfluenzavirus 1, 2, 3 and 4 (PIV 1, 2, 3 and 4), human rhinoviruses (HRV), human enterovirus (HEV), human coronaviruses (HCoV) 229E and OC43, human bocavirus (HBoV) and human adenovirus (HAdV) in hospitalized children with ALRTIs, at Hospital Serdang, Malaysia, from June 16 to December 21, 2009. The study was also designed in part to assess the performance of the conventional methods against molecular methods.Results: Viral pathogens were detected in 158 (95.8%) of the patients. Single virus infections were detected in 114 (67.9%) patients; 46 (27.9%) were co-infected with different viruses including double-virus infections in 37 (22.4%) and triple-virus infections in 9 (5.5%) cases. Approximately 70% of samples were found to be positive using conventional methods compared with 96% using molecular methods. A wide range of respiratory viruses were detected in the study. There was a high prevalence of RSV (50.3%) infections, particularly group B viruses. Other etiological agents including HAdV, HMPV, IFV-A, PIV 1-3, HBoV, HCoV-OC43 and HEV were detected in 14. 5, 9.6, 9.1, 4.8, 3.6, 2.4 and 1.8 percent of the samples, respectively. Conclusion: Our results demonstrated the increased sensitivity of molecular detection methods compared with conventional methods for the diagnosis of ARTIs in hospitalized children. This is the first report of HMPV infections in Malaysia.

The epidemic threshold of a social system is the ratio of infection and recovery rate above which a disease spreading in it becomes an epidemic. In the absence of pharmaceutical interventions (i.e. vaccines), the only way to control a given disease is to move this threshold by non-pharmaceutical interventions like social distancing, past the epidemic threshold corresponding to the disease, thereby tipping the system from epidemic into a non-epidemic regime. Modeling the disease as a spreading process on a social graph, social distancing can be modeled by removing some of the graphs links. It has been conjectured that the largest eigenvalue of the adjacency matrix of the resulting graph corresponds to the systems epidemic threshold. Here we use a Markov chain Monte Carlo (MCMC) method to study those link removals that do well at reducing the largest eigenvalue of the adjacency matrix. The MCMC method generates samples from the relative canonical network ensemble with a defined expectation value of $$\lambda _{\text {max}}$$ λ max . We call this the “well-controlling network ensemble” (WCNE) and compare its structure to randomly thinned networks with the same link density. We observe that networks in the WCNE tend to be more homogeneous in the degree distribution and use this insight to define two ad-hoc removal strategies, which also substantially reduce the largest eigenvalue. A targeted removal of 80% of links can be as effective as a random removal of 90%, leaving individuals with twice as many contacts. Finally, by simulating epidemic spreading via either an SIS or an SIR model on network ensembles created with different link removal strategies (random, WCNE, or degree-homogenizing), we show that tipping from an epidemic to a non-epidemic state happens at a larger critical ratio between infection rate and recovery rate for WCNE and degree-homogenized networks than for those obtained by random removals.