TY - JOUR

T1 - BUILDING A SEIR-MODEL FOR PREDICTING THE HIV/TUBERCULOSIS COINFECTION EPIDEMIC FOR RUSSIAN TERRITORIES WITH LOW TB BURDEN

AU - Guseva, V.

AU - Doktorova, N.

AU - Krivorotko, O.

AU - Otpushchennikova, O.

AU - Parolina, L.

AU - Vasilyeva, I.

AU - Sosnovskaya, M.

AU - Nemerov, A.

N1 - 11 regions with low morbidity and mortality from tuberculosis were identified. To build a model in each region, the population was divided into the following groups: S – susceptible non-immunized population; L – patients with latent TB (LTB); I – patients with active TB; T – population cured from TB; J1 – HIV infected individuals; J2 – infected with HIV and LTB; J3 – infected with HIV and active TB; A – AIDS patients. Graphs of the population groups vs. Time dependence for each territory were plotted. The results obtained in the SEIR model graphs of the number of HIV/TB infected and TB patients are similar to real-world data.

PY - 2023/8

Y1 - 2023/8

N2 - Intro: Study goal. To build a SEIR-model for predicting the HIV/tuberculosis coinfection (HIV/TB) epidemic for Russian territories with low TB burden Study goal. Predicting the HIV/TB epidemic for Russian territories with low TB burden Methods: A quantile ranking of territories (at 0.33 and 0.66 levels) was made for regions Russia (incidence and mortality from TB). Regions at the bottom 30% indicators over the entire observation period (2010-2020) were assigned a low incidence and mortality rate. The description of the dynamics of TB and HIV coinfection was based on the SEIR model, which is characterized by a system of ordinary differential equations. Incidence data for 2010-2017 were used to build the model, data for 2018-2020 were used to test it. The model is implemented in the Python programming language. Finding(s): 11 regions with low morbidity and mortality from tuberculosis were identified. To build a model in each region, the population was divided into the following groups: S - susceptible non-immunized population; L - patients with latent TB (LTB); I - patients with active TB; T - population cured from TB; J1 - HIV infected individuals; J2 - infected with HIV and LTB; J3 - infected with HIV and active TB; A - AIDS patients. Graphs of the population groups vs. Time dependence for each territory were plotted. The results obtained in the SEIR model graphs of the number of HIV/TB infected and TB patients are similar to real-world data. Discussion(s): The study results will help predict the direction of the epidemic process HIV/TB, the number of diseased and recovered individuals in the next 3-5 years and allows estimating workload on the health system in each time period. Conclusion(s): The study yielded the SEIR model that can be used for short-term prediction of the epidemic of HIV/TB in the Russian territories with low TB burden.Copyright © 2023

AB - Intro: Study goal. To build a SEIR-model for predicting the HIV/tuberculosis coinfection (HIV/TB) epidemic for Russian territories with low TB burden Study goal. Predicting the HIV/TB epidemic for Russian territories with low TB burden Methods: A quantile ranking of territories (at 0.33 and 0.66 levels) was made for regions Russia (incidence and mortality from TB). Regions at the bottom 30% indicators over the entire observation period (2010-2020) were assigned a low incidence and mortality rate. The description of the dynamics of TB and HIV coinfection was based on the SEIR model, which is characterized by a system of ordinary differential equations. Incidence data for 2010-2017 were used to build the model, data for 2018-2020 were used to test it. The model is implemented in the Python programming language. Finding(s): 11 regions with low morbidity and mortality from tuberculosis were identified. To build a model in each region, the population was divided into the following groups: S - susceptible non-immunized population; L - patients with latent TB (LTB); I - patients with active TB; T - population cured from TB; J1 - HIV infected individuals; J2 - infected with HIV and LTB; J3 - infected with HIV and active TB; A - AIDS patients. Graphs of the population groups vs. Time dependence for each territory were plotted. The results obtained in the SEIR model graphs of the number of HIV/TB infected and TB patients are similar to real-world data. Discussion(s): The study results will help predict the direction of the epidemic process HIV/TB, the number of diseased and recovered individuals in the next 3-5 years and allows estimating workload on the health system in each time period. Conclusion(s): The study yielded the SEIR model that can be used for short-term prediction of the epidemic of HIV/TB in the Russian territories with low TB burden.Copyright © 2023

UR - https://www.mendeley.com/catalogue/9925fc9d-71c7-34da-b26e-ff4f6fd6fc8a/

U2 - 10.1016/j.ijid.2023.05.028

DO - 10.1016/j.ijid.2023.05.028

M3 - Article

VL - 134

SP - S4-S5

JO - International Journal of Infectious Diseases

JF - International Journal of Infectious Diseases

SN - 1201-9712

ER -