Impact of COVID-19 pandemic on road safety in Tamil Nadu, India

Tuberculosis Prevalence Trends from a Predictive Modelling Study - 10 High-Burden Countries, 1980-2035

What is already known about this topic?

Given the challenges presented by drug-resistant strains of tuberculosis (TB) and the rising mobility of the population, achieving the objective of eradicating TB appears uncertain.

What is added by this report?

The examination of TB incidence trends in 10 high-burden countries (HBCs) indicated a steady rise in cases, with India and China jointly accounting for nearly 70% of the burden. Projections for the future show diverse trajectories in these countries, with potential difficulties in reaching the TB elimination target, especially in Nigeria, Congo, and South Africa.

What are the implications for public health practice?

The number of TB cases is on the rise. It is crucial to learn from successful strategies to improve TB prevention and control worldwide through collaborative efforts.

Threshold behavior and exponential ergodicity of an sir epidemic model: the impact of random jamming and hospital capacity

This article uses hospital capacity to determine the treatment rate for an infectious disease. To examine the impact of random jamming and hospital capacity on the spread of the disease, we propose a stochastic SIR model with nonlinear treatment rate and degenerate diffusion. Our findings demonstrate that the disease's persistence or eradication depends on the basic reproduction number [Formula: see text]. If [Formula: see text], the disease is eradicated with a probability of 1, while [Formula: see text] results in the disease being almost surely strongly stochastically permanent. We also demonstrate that if [Formula: see text], the Markov process has a unique stationary distribution and is exponentially ergodic. Additionally, we identify a critical capacity which determines the minimum hospital capacity required.

Relationship between mobility and road traffic injuries during COVID-19 pandemic-The role of attendant factors

This study investigates the important role of attendant factors, such as road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels-Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave's partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury-Grievous as against Road Traffic Injury-Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents.

Impact of COVID-19 spread on road safety indices of Turkey

Little is known about the effect of the COVID-19 on road safety indicators (RSIs) in developing countries, and conducted studies provide limited information regarding this impact. These prompted the author to evaluate the impact of COVID-19 on RSIs in Turkey. RSIs and related indices of Turkey between 2016 and 2020 were collected. For evaluating the impact, RSIs whose 2020 measures differed significantly from the pre-COVID era were identified using the outlier detection technique and Regression analysis. K-means clustering was used to group RSIs according to their variation patterns in the study period. Results show that COVID-19 led to significant decreases in 26 RSIs, especially ones related to non-fatal road traffic injuries. COVID-19 resulted in a significant drop in road traffic crashes and related indices. Also, considerable changes in monthly and daily fatalities and injuries in 2020 were observed. Clustering results revealed that COVID-19 significantly impacts variation patterns of studied RSIs, especially ones related to non-fatal injuries. Clustering aided in identifying affected RSIs by COVID-19, which other used methods were unable to detect. COVID-19 led to significant changes in road safety indices in Turkey. Road authorities and researchers should be aware of these significant fluctuations in road safety data.