Mean field game for modeling of COVID-19 spread.

The paper presents one of the possible approaches to pandemic spread modeling. The proposed model is based on the mean-field control inside separate groups of population, namely, suspectable (S), infected (I), removed (R) and cross-immune (C) ones. The numerical algorithm to solve this problem ensures conservation of the total population mass during timeline. The numerical experiments demonstrate modeling results for COVID-19 spread in Novosibirsk (Russia) for two 100-day periods.

Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm.

This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

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.