An incidence curve of acute respiratory infections in Moscow has three picks between September and April and reaches its maximum in January- February. The emergence of new strains of influenza A could account for only one pick a year. The most cases of common cold are caused by ubiquitous low pathogenic viruses. In order to simulate weekly fluctuation of incidence rate of acute respiratory illnesses we developed an agent-based model. It contains 10 millions agents with such attributes as sex, age, social status, levels of specific immune memory and lists of contacts. Each agent can contact with members of its household, colleagues or classmates. Through such contacts susceptible agent can be infected with one of seven circulating respiratory viruses. Viruses differ in their immunologic properties and assume to present influenza A virus, influenza B virus, parainfluenza, adenovirus, coronavirus, rhinovirus and respiratory syncytial virus. The rate of transmission depends on duration of contact, vulnerability of susceptible agent, infectivity of infected agent and air temperature. Proposed network of social interactions proved to be sufficiently detailed as it provided good fitting for observed incidence rate including periods of school holidays and winter public holidays. Additionally, the estimates of basic reproductive rate for the viruses confirm that all these viruses except new strains of influenza A are relatively harmless and unable to cause significant growth of acute respiratory infections morbidity.
A B S T R A C TObjectives: To find residential areas with high incidence rate of tuberculosis in Moscow using spatiotemporal analysis of incidence data. Methods: We analyzed the spatial patterns of residence locations of smear or culture positive patients with pulmonary tuberculosis in Moscow. To identify clusters with high local incidence rates, the neighborhoods of detected cases were studied. We assessed the spatial and temporal stability of clusters. Results: For 19 033 cases diagnosed with smear or culture positive pulmonary tuberculosis among residents of Moscow in 2000-2015 we identified 18 small-scale clusters of increased incidence rate responsible for 3% of all registered cases identified on a territory inhabited by only 1% of the population. Locations of clusters were sufficiently stable in space throughout the whole period. The local incidence rate inside clusters was significantly (3-4 times) higher than the city average during the whole observation period. The presence of clusters was associated with the incidence rate in the surrounding area. Socio-demographic characteristics of patients in clusters were not significantly different from the average characteristics of patients in the city. Conclusions: The detected small-scale clusters of increased incidence may be used to target active case finding for tuberculosis. The causes and mechanisms of cluster formation and stability need further study.
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