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Evolution and emergence of infectious diseases in theoretical and real-world networks.

ABSTRACT: One of the most important advancements in theoretical epidemiology has been the development of methods that account for realistic host population structure. The central finding is that heterogeneity in contact networks, such as the presence of 'superspreaders', accelerates infectious disease spread in real epidemics. Disease control is also complicated by the continuous evolution of pathogens in response to changing environments and medical interventions. It remains unclear, however, how population structure influences these adaptive processes. Here we examine the evolution of infectious disease in empirical and theoretical networks. We show that the heterogeneity in contact structure, which facilitates the spread of a single disease, surprisingly renders a resident strain more resilient to invasion by new variants. Our results suggest that many host contact structures suppress invasion of new strains and may slow disease adaptation. These findings are important to the natural history of disease evolution and the spread of drug-resistant strains.

SUBMITTER: Leventhal GE

PROVIDER: S-EPMC4335509 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Evolution and emergence of infectious diseases in theoretical and real-world networks.

Leventhal Gabriel E GE Hill Alison L AL Nowak Martin A MA Bonhoeffer Sebastian S

Nature communications 20150116

One of the most important advancements in theoretical epidemiology has been the development of methods that account for realistic host population structure. The central finding is that heterogeneity in contact networks, such as the presence of 'superspreaders', accelerates infectious disease spread in real epidemics. Disease control is also complicated by the continuous evolution of pathogens in response to changing environments and medical interventions. It remains unclear, however, how populat ...[more]

PMID: 25592476

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Project description:BackgroundRespiratory infections are common in the pediatric population. Preschoolers, especially those in kindergarten and 3-6 years old, are highly vulnerable to various respiratory infections.ObjectiveTo investigate the efficacy of indoor mask-wearing in mitigating respiratory infections in preschoolers in a real-world campus setting.MethodsThe study was conducted over a 115-day period in a kindergarten. Eligible children were assigned into study and control groups. The study group wore masks indoors but not outdoors, and the control group did not wear masks in either setting. We used a questionnaire to collect participant information, including age, height, weight, monthly dietary living expenses, family annual income, parent education level, primary caregiver, number of family members, and number of children under 6 years of age in the household. Incidences of clinical respiratory infections were recorded. We calculated the relative risk and analyzed the relationship between mask-wearing and respiratory infections by inter-group comparison, logistic regression, and Cox regression analyses.ResultsA total of 135 preschoolers were included, with 35 and 100 preschoolers in the study and control groups, respectively. Baseline comparisons showed a significant difference in the number of children under 6 years old in the household between the two groups. Mask-wearing did not significantly reduce the risk of respiratory infections (RR = 1.086, 95% CI: 0.713, 1.435). Logistic and Cox regression analyses also showed no significant relationship between mask-wearing and occurrence of respiratory infections after controlling for potential confounders (OR = 0.816, 95% CI: 0.364, 1.826, and HR = 0.845, 95% CI: 0.495, 1.444).ConclusionIndoor mask-wearing did not reduce the incidence of respiratory infections in preschoolers in a real-world campus setting. However, this study included a small number of preschoolers and observed them for a short period of time. Preschoolers were instructed to wear masks only when indoors. These factors could lead to bias and limit the generalizability of the study results.

Dataset Information

Evolution and emergence of infectious diseases in theoretical and real-world networks.

Publications

Evolution and emergence of infectious diseases in theoretical and real-world networks.

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