Identification of growing tuberculosis incidence clusters in a region with a decrease in tuberculosis prevalence in Moscow (2000-2019).

Background: The control of tuberculosis (TB) may benefit from a prospective identification of areas where the incidence may increase in addition to the traditionally identified foci of high incidence. We aimed to identify residential areas with growing tuberculosis incidence rates and assess their significance and stability. Methods: We analysed the changes in TB incidence rates using case data georeferenced with spatial granularity to apartment buildings in the territory of Moscow from 2000 to 2019. We identified sparsely distributed areas with significant increases in the incidence rate inside residential areas. We tested the stability of found growth areas to case underreporting via stochastic modelling. Results: For 21 350 cases with smear- or culture-positive pulmonary TB among residents from 2000 to 2019, we identified 52 small-scale clusters of growing incidence rate responsible for 1% of all registered cases. We tested clusters of disease growth for underreporting and found them to be relatively unstable to resampling with case drop-out, but their spatial displacement was small. Territories with a stable increase in TB incidence rate were identified and compared to the rest of the city, which is characterised by a significant decrease in incidence. Conclusions: Identified areas with a tendency for an increase in the TB incidence rate may be important targets for disease control services.

Small-scale stable clusters of elevated tuberculosis incidence in Moscow, 2000-2015: Discovery and spatiotemporal analysis.

OBJECTIVES: To find residential areas with high incidence rate of tuberculosis in Moscow using spatio-temporal 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 19033 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.

Energy cost of infection burden: an approach to understanding the dynamics of host-pathogen interactions.

A mathematical model of long-term immune defense against infection was used to estimate the energy involved in the principal processes of immune resistance during periods of health and infection. From these values, an optimal level of energy was determined for immune response depending on infection burden. The present findings suggest that weak but prevalent pathogens lead to latent or chronic infection, whereas more virulent but less prevalent pathogens result in acute infection. This energy-based approach offers insight into the mechanisms of immune system adaptation leading to the development of chronic infectious diseases and immune deficiencies.

Age related changes in population of peripheral T cells: towards a model of immunosenescence.

In this paper, we presented the results of analysis of experimental evidence for the decline of the human immune system functioning with age using mathematical model of immunosenescence. The most prominent changes in this system are related to the decline in the T-cellular immunity. These include the decline in the nai;ve T cells generation rate, shrinkage of the volume of the peripheral lymphoid tissue, decline of absolute and relative concentrations of nai;ve T cells in the blood, shortening of the average telomere length of T cells. These alterations in the immune system are responsible for sharp increase of morbidity and mortality caused by infectious agents at old ages. Analysis shows that concentrations of memory and nai;ve T cells in peripheral lymphoid tissue are the key variables in this process. Simulation experiments with our model show that the average life span of memory T cells must grow with age, and that decreasing of antigenic load led to considerable increase in organism's resistance in middle ages, but only to slight increase in old ages. Restriction in the rate of thymus involution resulted in an increase of organism's resistance to infections in old ages. However, this growth is accompanied by the decline of concentration of memory T cells and shortening of their life span. The proposed model describes the trade-off between concentrations of nai;ve and memory T cells and their potential to proliferate in human organism.