Modelling of a drug resistant tuberculosis for the contribution of resistance and relapse in Xinjiang, China

Modelling the preventive treatment under media impact on tuberculosis: A comparison in four regions of China

“…The data came from the four regions of China, that is, Hubei Province, Henan Province, Jiangxi Province and Xinjiang Uygur Autonomous Region, which were available from the Data-Center of China Public Health Science ( The Data-center of China Public Health Science, 2023 ). The actual number of newly reported cases almost all comes from confirmed patients in hospital ( Li et al., 2022 ; Wang et al., 2023 ), so according to our model (1), the number of newly reported TB cases can be expressed as where the time step is year. And (12) will be used to fit the data of newly reported TB cases each year.…”

“… According to the results in Li et al. (2022) and Wang et al. (2023) , we appropriately set the range of β be [10 −8 , 10 −6 ] per year and the range of θ be [0.001, 1], respectively.…”

“…(2020) constructed a TB model with media impact on transmission rate. There are certainly more considerations in the mathematical modeling of TB, including vaccination ( Bhunu, Garira, Mukandavire, & Magombedze, 2008 ; Okuonghae & Omosigho, 2011 ), treatment and incomplete treatment ( Castillo-Chavez & Feng, 1997 ; Dye & Williams, 2000 ; Lemmer et al., 2014 ), fast and slow progression ( Aparicio et al., 2002 ; Cai et al., 2021 ; Gomes et al., 2007 ), relapse ( Ozcaglar et al., 2012 ; Ren, 2017 ), reinfection ( Aparicio et al., 2002 ; Chinnathambi et al., 2021 ), confection with HIV ( Bhunu et al., 2009 ), drug-resistant strains ( Dye & Williams, 2000 ; Trauer et al., 2014 ; Wang et al., 2023 ) and so on.…”

Unveiling measles transmission dynamics: Insights from a stochastic model with nonlinear incidence

“…[4][5][6] In the landscape of infectious disease modeling, the incorporation of environmental noise, particularly in the study of diseases such as measles, assumes paramount importance. [7][8][9] Environmental noise introduces a stochastic dimension that reflects the inherent unpredictability observed in real-world epidemiological systems. The exploration of infectious disease dynamics under the influence of environmental noise provides a nuanced understanding of the intricate interplay between stochastic factors and disease transmission dynamics.…”

“…In the landscape of infectious disease modeling, the incorporation of environmental noise, particularly in the study of diseases such as measles, assumes paramount importance 7–9 . Environmental noise introduces a stochastic dimension that reflects the inherent unpredictability observed in real‐world epidemiological systems.…”