Addressing the tuberculosis (TB) epidemic is crucial to reduce morbidity, mortality, and health-care burden. Cases of confirmed drug-resistant TB have almost doubled over the past decade. Drug resistance represents one of leading causes of death among TB patients and is responsible for approximately one-third of TB-related deaths worldwide, which is associated with TB recurrence and transmission. In recent years, the emerging molecular epidemiology methods have attributed to better TB management. For example, in many settings, studies using molecular epidemiology methods have demonstrated the recent transmission of Mycobacterium tuberculosis (M. TB) strains. However, the transmission patterns of drug-resistant TB in most low- and middle- income developing countries remain unclear. Traditional transmission models based on clinical and epidemiological parameters were not sufficient to identify potential risk factors. By using molecular epidemiology analysis, further insights can be gained into the association between M. TB isolates and epidemic populations. This makes molecular epidemiology studies effective measures to establish phylogenetic relationships with clinical patients, identify clusters and track TB transmission. Studies investigating clinical and molecular epidemiology will provide theoretical basis and scientific strategy for TB prevention and control.
We hope to address the TB-associated health-care burden, and the current insufficient or imprecise control strategies for TB management. More importantly, we would like to discuss more efficient approaches in this field with the development of molecular epidemiology method and share insights to reduce TB burden.
In the purpose of this research topic, we hope to shed light on studies of the following, including: updated clinical epidemiology studies investigating on TB-related incidence, prevalence, mortality, recurrence and transmission; screening, prevention, management of coexisting diseases in TB; cost-effective analysis; updated molecular epidemiology studies using new approaches to reveal molecular mechanisms, potential targets or pathways for the transmission, diagnosis, treatment, genetic evolution, and drug resistance for TB. Moreover, studies investigating on TB transmission models based on model algorithm, artificial intelligence and machine learning are welcomed. The subtopics of interests include but not limited to:
• Recent updates on TB-related health-care burden
• Recent updates on screening, prevention, diagnosis, treatment, recurrence and transmission, and management of coexisting diseases in TB/drug-resistant TB.
• Recent updates on molecular epidemiology approaches to reveal potential molecular mechanisms, targets or pathways for the transmission, diagnosis, treatment, genetic evolution, and TB/drug-resistant TB.
• Recent updates on TB/drug-resistant TB transmission based on statistics models
Addressing the tuberculosis (TB) epidemic is crucial to reduce morbidity, mortality, and health-care burden. Cases of confirmed drug-resistant TB have almost doubled over the past decade. Drug resistance represents one of leading causes of death among TB patients and is responsible for approximately one-third of TB-related deaths worldwide, which is associated with TB recurrence and transmission. In recent years, the emerging molecular epidemiology methods have attributed to better TB management. For example, in many settings, studies using molecular epidemiology methods have demonstrated the recent transmission of Mycobacterium tuberculosis (M. TB) strains. However, the transmission patterns of drug-resistant TB in most low- and middle- income developing countries remain unclear. Traditional transmission models based on clinical and epidemiological parameters were not sufficient to identify potential risk factors. By using molecular epidemiology analysis, further insights can be gained into the association between M. TB isolates and epidemic populations. This makes molecular epidemiology studies effective measures to establish phylogenetic relationships with clinical patients, identify clusters and track TB transmission. Studies investigating clinical and molecular epidemiology will provide theoretical basis and scientific strategy for TB prevention and control.
We hope to address the TB-associated health-care burden, and the current insufficient or imprecise control strategies for TB management. More importantly, we would like to discuss more efficient approaches in this field with the development of molecular epidemiology method and share insights to reduce TB burden.
In the purpose of this research topic, we hope to shed light on studies of the following, including: updated clinical epidemiology studies investigating on TB-related incidence, prevalence, mortality, recurrence and transmission; screening, prevention, management of coexisting diseases in TB; cost-effective analysis; updated molecular epidemiology studies using new approaches to reveal molecular mechanisms, potential targets or pathways for the transmission, diagnosis, treatment, genetic evolution, and drug resistance for TB. Moreover, studies investigating on TB transmission models based on model algorithm, artificial intelligence and machine learning are welcomed. The subtopics of interests include but not limited to:
• Recent updates on TB-related health-care burden
• Recent updates on screening, prevention, diagnosis, treatment, recurrence and transmission, and management of coexisting diseases in TB/drug-resistant TB.
• Recent updates on molecular epidemiology approaches to reveal potential molecular mechanisms, targets or pathways for the transmission, diagnosis, treatment, genetic evolution, and TB/drug-resistant TB.
• Recent updates on TB/drug-resistant TB transmission based on statistics models
