Modelling the effects of the contaminated environments on tuberculosis in Jiangsu, China.
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TLDR
This study formulate a novel TB epidemic model accounting for the effects of the contaminated environments on the TB transmission dynamics, and proves that the annual average of TB cases in Jiangsu province, China can be used to govern the threshold dynamics of the model.About:
This article is published in Journal of Theoretical Biology.The article was published on 2021-01-07 and is currently open access. It has received 21 citations till now.read more
Citations
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Modeling the effects of the contaminated environments on COVID-19 transmission in India.
TL;DR: In this paper, a nonlinear mathematical model of the COVID-19 epidemic is proposed and analyzed under the effects of the environmental virus on the transmission patterns, and the sensitivity analysis is performed for the proposed model that determines the relative importance of the disease transmission parameters.
Journal ArticleDOI
Dynamic analysis and optimal control of a class of SISP respiratory diseases
Lei Shi,Long Xing Qi +1 more
TL;DR: Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission P0 of PM2.5 and the SISP respiratory disease model.
Journal ArticleDOI
Global analysis of tuberculosis dynamical model and optimal control strategies based on case data in the United States
TL;DR: In this article , an SVEITR dynamical model with vaccination, fast and slow progression, incomplete treatment, and relapse was proposed and a Markov chain Monte-Carlo method was used to acquire the model's optimal parameters.
Journal ArticleDOI
Study on the Associations between Meteorological Factors and the Incidence of Pulmonary Tuberculosis in Xinjiang, China
TL;DR: In this article , a penalized distributed lag nonlinear model was applied to explore the influence of meteorological factors (such as air temperature, relative humidity and wind speed) on the PTB incidence in Xinjiang from 2004 to 2019.
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Modelling the effects of the vaccination on seasonal influenza in gansu, china
TL;DR: In this article , a new influenza model with vaccination and periodic transmission rate is introduced, and the basic reproduction number R 0 is derived, and formulate that R., sub> 0, is an important indicator to measure whether seasonal influenza can spread in the population.
References
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Potential Public Health Impact of New Tuberculosis Vaccines
TL;DR: Widely deployed and highly effective pre- or postexposure vaccines would reduce the number of TB cases by only one third.
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Modeling the spread of Middle East respiratory syndrome coronavirus in Saudi Arabia.
TL;DR: The estimated loss-of-immunity rate and prevalence of Middle East respiratory syndrome coronavirus infections among camels largely matched with previous serological or virological studies, shedding light on this issue.
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Long-term exposure to ambient air pollution and mortality in a Chinese tuberculosis cohort
TL;DR: Long-term exposure to PM2.5 increases the risk of death from TB and other diseases among TB patients, and the control of ambient air pollution may help decreasing the mortality caused by TB.
Posted ContentDOI
Toilets dominate environmental detection of SARS-CoV-2 virus in a hospital
Zhen Ding,Hua Qian,Bin Xu,Ying Huang,Te Miao,Hui-Ling Yen,Shenglan Xiao,Lunbiao Cui,Xiaosong Wu,Wei Shao,Yan Song,Li Sha,Lian Zhou,Yan Xu,Baoli Zhu,Baoli Zhu,Yuguo Li +16 more
TL;DR: The faecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 virus in the hospital, highlighting the importance of surface and hand hygiene for intervention.
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Simple framework for real-time forecast in a data-limited situation: the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example
TL;DR: The authors' simple models fitted the epidemic reasonably well and thus revealed the spatial heterogeneity in the epidemiological features across Brazilian states, and the real-time estimation framework is able to yield a reliable prediction of the final epidemic size.