ABSTRACT
IntroductionThe COVID-19 pandemic has affected every domain of human life globally and has presented an unprecedented challenge to public health, the food system, work, education, and social life. The healthcare services were prioritized and thus many ongoing important Healthcare services were indirectly affected by that. The current study assumed the perception of Healthcare functionaries regarding the impact of COVID-19 on the health-seeking behavior of presumptive TB patients in tri-city (Chandigarh, Panchkula, and Mohali).MethodsA concurrent mixed method study design was conducted among 100 randomly selected healthcare functionaries from public health facilities in Tri-city (Chandigarh, Panchkula, and Mohali). The data was collected using a self-structured and validated questionnaire. A one-to-one method was adopted for collecting data and analysis was done with SPSS V24 both qualitative (frequencies, Chi-square, odd ratio) and quantitative (themes) approaches.ResultAmong 100 respondents 62% participants responded that there was a disruption of the normal functioning of testing and treatment of the National Tuberculosis Elimination Program (NTEP). The logistics and manpower were shifted to COVID-19 management and these were not available routinely for the proper functioning of NTEP. Testing of PTB patients was mostly affected during the COVID-19 pandemic. The in-depth interview found that factors like social stigma, downplaying of TB disease, and the knowledge about TB were the reasons behind the disruption of PTB services. The health functionaries also give suggestions for the betterment of PTB services if these kinds of pandemics arise in future.ConclusionHaving National Programs such as NTEP should be not kept back foot while dealing with the pandemic, as Tuberculosis is considered to be one of the greatest challenge for Health System and human beings.
ABSTRACT
Since the beginning of COVID-19 (corona virus disease 2019), the Indian government implemented several policies and restrictions to curtail its spread. The timely decisions taken by the government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread. Future predictions about the spread can be helpful for future policy-making, i.e., to plan and control the COVID-19 spread. Further, it is observed throughout the world that asymptomatic corona cases play a major role in the spread of the disease. This motivated us to include such cases for accurate trend prediction. India was chosen for the study as the population and population density is very high for India, resulting in the spread of the disease at high speed. In this paper, the modified SEIRD (susceptible-exposed-infected-recovered-deceased) model is proposed for predicting the trend and peak of COVID-19 in India and its four worst-affected states. The modified SEIRD model is based on the SEIRD model, which also uses an asymptomatic exposed population that is asymptomatic but infectious for the predictions. Further, a deep learning-based long short-term memory (LSTM) model is also used for trend prediction in this paper. Predictions of LSTM are compared with the predictions obtained from the proposed modified SEIRD model for the next 30 days. The epidemiological data up to 6th September 2020 have been used for carrying out predictions in this paper. Different lockdowns imposed by the Indian government have also been used in modeling and analyzing the proposed modified SEIRD model.