Dynamics and control of COVID-19 pandemic with nonlinear incidence rates.

World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible-exposed-infectious-removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy.

A deep learning based encoder-decoder model for speed planning of autonomous electric truck platoons.

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Modification of proportion sensitivity testing method for ethionamide.

Standardized methodology for drug susceptibility testing of second line drugs is vital for treatment of multi/extensively drug resistant tuberculosis. Discrepancy between laboratory methods and clinical interpretation is well established for bacteriostatic drugs such as ethionamide. Optimization of the standard proportion sensitivity testing (PST) method for ethionamide was under taken in 235 Mycobacterium tuberculosis isolates from new and previously treated pulmonary tuberculosis patients. An additional higher concentration of 57 µg/ml was evaluated against at the standard 40 µg/ml concentration in PST method. Performance parameters and agreement between the two drug concentrations was higher indicating the efficiency of PST method at its present format at 40 µg/ml and additional higher concentration of 57 µg/ml as an alternative when required.

Spoligotype Diversity of Mycobacterium tuberculosis over Two Decades from Tiruvallur, South India.

Geographically, most tuberculosis (TB) cases in 2018 were reported from India. This TB burden is compounded by MDR-TB and XDR-TB. The strategies for the management and control of TB in the community depend on an understanding of the mode of spread of the different strains of TB isolates in the community. To determine the distribution and trends of M. tb strains over the time period in the community due to treatment, we carried out the present study on changes over two decades. Design/Methods. A total of 1218 M. tb isolates (year: 2001-2018) from Tiruvallur, India, were genotyped by spoligotyping after DNA extraction and subjected to anti-TB drug susceptibility testing for the first-line anti-TB drugs. Results. On analysis with the SpolDB4 database, majority (2001-2003: 53.32% and 2015-2018: 46.3%) of the isolates belonged to East African Indian (EAI) lineage, and the orphans designated in comparison to SpolDB4 stood 33% among 2001-2003 strain collection and 46.3% among 2015-2018 strain collection. 10.2% (2001-2003) and 9.26% (2015 to 2018) of isolates were monoresistant to isoniazid (H). MDR strains were less common among EAI strains (3.2%) compared to non-EAI strains (10.32%). Conclusions. EAI is the most predominant lineage in Tiruvallur, despite the presence of highly transmissible lineages like Beijing for the last two decades. The prevalence of MDR-TB is below the national average of 2-3% among the new TB cases in the last two decades. The reason can be attributed to the well-established nature of the locally circulating strains in this region which are not associated with drug resistance.

Combined drug medium with isoniazid and rifampicin for identification of multi-drug resistant Mycobacterium tuberculosis.

A low-cost method of detecting multi-drug resistant Mycobacterium tuberculosis (MDR-TB) with the possibility of quick adoption in a resource limited setting is urgently required. We conducted a study combining isoniazid and rifampicin in a single LJ medium, to detect MDR-TB strains. Combined and individual drug media showed 100% concordance for the detection of MDR-TB and susceptible strains by proportion method. Considering the results, combined isoniazid and rifampicin containing medium could be considered for use in settings where the sole detection of MDR-TB strains is justified.