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Introduction
Several diseases have arrived in different time periods shaking the world in an unusual manner affecting the life style of individuals, health and economy, etc. COVID-19 is one among the several infectious diseases, which spread widely affecting millions of lives. While diseases come and go, some remain and turn into endemic. One of the major challenges faced is lack of preparedness on an epidemic and negligence, which the other existing infectious diseases face on arrival of a new disease. This leads to coinfections causing major threat to human life. In addition to this, existing ailments in individuals and age can lead to comorbidities putting them at higher risk of contracting the disease. Though these are some of the concerning issues, antimicrobial resistance is one such concern, which needs immediate attention, as negligence toward it can lead to antimicrobial resistance becoming a soon to be pandemic. Antimicrobial resistance can lead to rise in medical expenditure in addition to increased morbidity and mortality. In [1], a detailed review is presented highlighting the significance of addressing antimicrobial resistance due to the changes caused as a result of the pandemic. The authors have analyzed in detail the impact on health care systems, prevention of accelerating infections, usage of antimicrobial and burden of the same due to COVID-19 changes. In the similar context, the authors in [2] bring out the parallel and interacting health emergencies concerning COVID-19 and antimicrobial resistance.
In [3], the authors have worked on a COVID-19 mathematical model considering age factor by classifying population into high-risk and low-risk individuals. In the study, they have included an parameter associating it with non-pharmaceutical interventions and provided a detailed analysis of the same by showing its impact on number of infected cases and basic reproduction number for the countries Brazil and South Africa. It was concluded that with planned implementation of mitigation strategies, contribution in control of disease spread was significant through low-risk groups. The authors in [4] discussed regarding the culmination of the COVID-19 pandemic. This study utilized the SIR and fractal interpolation models to predict the number of positive cases and investigate the duration of the second and third waves in India, focusing on specific states like Delhi, Karnataka, Tamil Nadu, Kerala and Maharashtra. In [5], the...