COVID-19 and tuberculosis: A mathematical model based forecasting in Delhi, India

Management of tuberculosis patients and the role of forensic medicine in COVID-19 pandemic

Tuberculosis and Coronaviral disease-19 had a global impact in 2020 and still predominating, both infectious diseases similar to the lethal pandemics spread in one route, likely airborne transmission, the infected person could spread to healthy people. However, tuberculosis slightly varies from COVID-19. Though the primordial disease of the tuberculosis epidemic has had a vast impact on this society, besides the COVID-19 pandemic with other co-morbidities, conditions faced numerous complications. This review exemplified the impact of two lethal diseases in changing patient care, diagnostic issues, and forensic sciences roles. The diagnosis of tuberculosis with a massive concern due to standard testing methods, leading to inaccuracy, sensitivity, and prolonged time consumption. In addition, unavailability of testing kits, equipment failure, over-crowd in hospitals and fewer healthcare workers, a prolonged testing period, and finally, anxiety about COVID-19. Also, the contribution of forensic sciences in the autopsy of the exact cause of infectious diseases is crucial. Likewise, during this pandemic, there has been a drastic reduction in tuberculosis incidence in high-burden countries and a synergistic effect of both diseases. So, this review summarized the overall burden of tuberculosis management during COVID-19 and followed the guidelines of various nations' healthcare authorities to mitigate the consequences of tuberculosis diagnosis and prognosis during the pandemic.

Fractal fractional model for tuberculosis: existence and numerical solutions

This paper deals with the mathematical analysis of Tuberculosis by using fractal fractional operator. Mycobacterium TB is the bacteria that causes tuberculosis. This airborne illness mostly impacts the lungs but may extend to other body organs. When the infected individual coughs, sneezes or speaks, the bacterium gets released into the air and travels from one person to another. Five classes have been formulated to study the dynamics of this disease: susceptible class, infected of DS, infected of MDR, isolated class, and recovered class. To study the suggested fractal fractional model's wellposedness associated with existence results, and boundedness of solutions. Further, the invariant region of the considered model, positive solutions, equilibrium point, and reproduction number. One would typically employ a fractional calculus approach to obtain numerical solutions for the fractional order Tuberculosis model using the Adams-Bashforth-Moulton method. The fractional order derivatives in the model can be approximated using appropriate numerical schemes designed for fractional order differential equations.

Association of air pollution and COVID-19 in India

The COVID-19 pandemic has affected the world, leading to significant morbidity and mortality. Various meteorological parameters are considered essential for the viability and transmission of the virus. Multiple reports from various parts of the world suggest a correlation between the disease spread and air pollution severity. This study was carried out to identify the relationship between meteorological parameters, air pollution, and COVID-19 in New Delhi, one of the worst-affected states in India. We studied air pollution and meteorological parameters in New Delhi, India. We obtained data about COVID-19 occurrence, meteorological parameters, and air pollution indicators from various sources from April 1, 2020, until November 12, 2020. We performed correlational analysis and employed autoregressive distributed lag models to identify the relationship between COVID-19 cases, air pollution and meteorological parameters. We found a significant impact of particulate matter (PM) 2.5, PM10, and meteorological parameters on COVID-19. There was a significant positive correlation between daily COVID-19 cases and COVID-19-related deaths with PM2.5 and PM10 levels. Increasing temperature and wind speed were associated with a reduction in the number of cases, while increasing humidity was associated with increased cases. This study demonstrated a significant association between PM2.5 and PM10 and daily COVID-19 cases and COVID-19-related mortality. This knowledge will likely help us prepare well for the future and implement air pollution control measures for other airborne disease epidemics.

Transition dynamics between a novel coinfection model of fractional-order for COVID-19 and tuberculosis via a treatment mechanism

In this paper, a fractional-order coinfection model for the transmission dynamics of COVID-19 and tuberculosis is presented. The positivity and boundedness of the proposed coinfection model are derived. The equilibria and basic reproduction number of the COVID-19 sub-model, Tuberculosis sub-model, and COVID-19 and Tuberculosis coinfection model are derived. The local and global stability of both the COVID-19 and Tuberculosis sub-models are discussed. The equilibria of the coinfection model are locally asymptotically stable under certain conditions. Later, the impact of COVID-19 on TB and TB on COVID-19 is analyzed. Finally, the numerical simulation is carried out to assess the effect of various biological parameters in the transmission dynamics of COVID-19 and Tuberculosis coinfection.

A mathematical model for the co-dynamics of COVID-19 and tuberculosis

In this study, we formulated and analyzed a deterministic mathematical model for the co-infection of COVID-19 and tuberculosis, to study the co-dynamics and impact of each disease in a given population. Using each disease's corresponding reproduction number, the existence and stability of the disease-free equilibrium were established. When the respective threshold quantitiesR C , andR T are below unity, the COVID-19 and TB-free equilibrium are said to be locally asymptotically stable. The impact of vaccine (i.e., efficacy and vaccinated proportion) and the condition required for COVID-19 eradication was examined. Furthermore, the presence of the endemic equilibria of the sub-models is analyzed and the criteria for the phenomenon of backward bifurcation of the COVID-19 sub-model are presented. To better understand how each disease condition impacts the dynamics behavior of the other, we investigate the invasion criterion of each disease by computing the threshold quantity known as the invasion reproduction number. We perform a numerical simulation to investigate the impact of threshold quantities ( R C , R T ) with respect to their invasion reproduction number, co-infection transmission rate ( β c t ) , and each disease transmission rate ( β c , β t ) on disease dynamics. The outcomes established the necessity for the coexistence or elimination of both diseases from the communities. Overall, our findings imply that while COVID-19 incidence decreases with co-infection prevalence, the burden of tuberculosis on the human population increases.

Mathematical modelling and analysis of COVID-19 and tuberculosis transmission dynamics

In this paper, a mathematical model for assessing the impact of COVID-19 on tuberculosis disease is proposed and analysed. There are pieces of evidence that patients with Tuberculosis (TB) have more chances of developing the SARS-CoV-2 infection. The mathematical model is qualitatively and quantitatively analysed by using the theory of stability analysis. The dynamic system shows endemic equilibrium point which is stable when R 0 < 1 and unstable when R 0 > 1 . The global stability of the endemic point is analysed by constructing the Lyapunov function. The dynamic stability also exhibits bifurcation behaviour. The optimal control theory is used to find an optimal solution to the problem in the mathematical model. The sensitivity analysis is performed to clarify the effective parameters which affect the reproduction number the most. Numerical simulation is carried out to assess the effect of various biological parameters in the dynamic of both tuberculosis and COVID-19 classes. Our simulation results show that the COVID-19 and TB infections can be mitigated by controlling the transmission rate γ .

A noninteger order SEITR dynamical model for TB

This research paper designs the noninteger order SEITR dynamical model in the Caputo sense for tuberculosis. The authors of the article have classified the infection compartment into four different compartments such as newly infected unrecognized individuals, diagnosed patients, highly infected patients, and patients with delays in treatment which provide better detail of the TB infection dynamic. We estimate the model parameters using the least square curve fitting and demonstrate that the proposed model provides a good fit to tuberculosis confirmed cases of India from the year 2000 to 2020. Further, we compute the basic reproduction number as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\Re _{0} \approx 1.73$\end{document}ℜ0≈1.73 of the model using the next-generation matrix method and the model equilibria. The existence and uniqueness of the approximate solution for the SEITR model is validated using the generalized Adams–Bashforth–Moulton method. The graphical representation of the fractional order model is given to validate the result using the numerical simulation. We conclude that the fractional order model is more realistic than the classical integer order model and provide more detailed information about the real data of the TB disease dynamics.

Mathematical modeling and analysis for the co-infection of COVID-19 and tuberculosis

We developed a TB-COVID-19 co-infection epidemic model using a non-linear dynamical system by subdividing the human population into seven compartments. The biological well-posedness of the formulated mathematical model was studied via proving properties like boundedness of solutions, no-negativity, and the solution's dependence on the initial data. We then computed the reproduction numbers separately for TB and COVID-19 sub-models. The criterion for stability conditions for stationary points was examined. The basic reproduction number of sub-models used to suggest the mitigation and persistence of the diseases. Qualitative analysis of the sub-models revealed that the disease-free stationary points are both locally and globally stable provided the respective reproduction numbers are smaller than unit. The endemic stationary points for each sub-models were globally stable if their respective basic reproduction numbers are greater than unit. In each sub-model, we performed an analysis of sensitive parameters concerning the corresponding reproduction numbers. Results from sensitivity indices of the parameters revealed that deceasing contact rate and increasing the transferring rates from the latent stage to an infected class of individuals leads to mitigating the two diseases and their co-infections. We have also studied the analytical behavior of the full co-infection model by deriving the equilibrium points and investigating the conditions of their stability. The numerical experiments of the proposed co-infection model agree with the findings in the analytical results.

The COVID-19 and TB syndemic: the way forward

Together, SARS-CoV-2 and M. tuberculosis have killed approximately 5.7 million people worldwide over the past 2 years. The COVID-19 pandemic, and the non-pharmaceutical interventions to mitigate COVID-19 transmission (including social distancing regulations, partial lockdowns and quarantines), have disrupted healthcare services and led to a reallocation of resources to COVID-19 care. There has also been a tragic loss of healthcare workers who succumbed to the disease. This has had consequences for TB services, and the fear of contracting COVID-19 may also have contributed to reduced access to TB services. Altogether, this is projected to have resulted in a 5-year setback in terms of mortality from TB and a 9-year setback in terms of TB detection. In addition, past and present TB disease has been reported to increase both COVID-19 fatality and incidence. Similarly, COVID-19 may adversely affect TB outcomes. From a more positive perspective, the pandemic has also created opportunities to improve TB care. In this review, we highlight similarities and differences between these two infectious diseases, describe gaps in our knowledge and discuss solutions and priorities for future research.

Mathematical modelling of COVID-19 transmission dynamics in a partially comorbid community

A deterministic S , E m , E c , I m , I c , H , R epidemic model that describes the spreading of SARS-COV-2 within a community with comorbidities is formulated. Size dependent area is incorporated into the model to quantify the effect of social distancing and the results indicate that the risk of community transmission is optimally minimised when the occupancy area is increased. The reproduction number is shown to have a positive relationship with the infection rate, the proportion of individuals with comorbidities and the proportion of susceptible individuals adhering to standard operating procedures. The model exhibits a unique endemic equilibrium whose stability largely depends on the rate of hospitalisation of individuals with underlying health conditions (ω m ) as compared to those without these conditions (ω c ), such that stability is guaranteed if ω m < ω c . Furthermore, if individuals with comorbidities effectively report for treatment and hospitalisation at a rate of 0.5 per day, the epidemic curve peaks 3-fold higher among people with comorbidities. The infection peaks are delayed if the area occupied by community is increased. In conclusion, we observed that community infections increase significantly with decreasing detection rates for both individuals with or without comorbidities.

A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India

World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

COVID-19 Global Humanitarian Response Plan: An optimal distribution model for high-priority countries

BACKGROUND

The 2019 novel coronavirus disease (COVID-19) has spread rapidly worldwide, and the outbreak of the disease was designated a global pandemic by the World Health Organization. Such outbreaks would certainly be catastrophic for some of the best-ranked health systems and would be more catastrophic in countries with more fragile health systems. Accordingly, the World Health Organization and other organizations have been appealing to donor countries to support a rapid response plan. The primary objectives of this response plan are to appeal for funds from donors and to distribute these funds to the most affected countries based on the requirements.

METHODS

In this study, we developed a mathematical model to provide initial insights into the efficient and equitable distribution of urgent funds to high-priority countries. Three phases were proposed for the construction of this mathematical model. In the first phase, the final epidemic sizes in all the target countries were predicted by using three epidemiological models. In the second phase, the urgent requirements for each country were estimated in parallel with the estimates issued by the humanitarian response plan, based on the size of the epidemic and several other factors. In the third and final phase, a multi-objective optimization model was proposed. The first objective was to maximize the funds from donors to cover all the requirements. The second objective was to minimize the unmet demands by ensuring a fair distribution of the urgent funds based on the requirements of the target countries.

RESULTS

Predictions of the basic reproduction numbers and the final epidemic sizes were calculated for all target countries. The urgent requirements were estimated, and the requirements issued by the humanitarian response plan for all target countries were also considered. Moreover, a proposed response plan for the distribution network was demonstrated. Donors must provide urgent funds exceeding US$ 2,608,084,209 to cover at least 40 % of each target country's requirements. Overall, results demonstrate the importance of an urgent and fair distribution of funds to the target countries to overcome the outbreak of COVID-19.

CONCLUSIONS

Rapid responses by donor countries to humanitarian appeals will facilitate the immediate and fair distribution of relief supplies to the poorest countries. This distribution may help to support health systems, restrain the spread of COVID-19, and prevent an unlimited catastrophe.

COVID-19 and Chikungunya: an optimal control model with consideration of social and environmental factors

Chikungunya is one of the Aedes aegypti diseases that mosquito transmits to humans and that are common in tropical countries like Yemen. In this work, we formulated a novel dynamic mathematical model framework, which integrates COVID-19 and Chikungunya outbreaks. The proposed model is governed by a system of dynamic ordinary differential equations (ODEs). Particle swarm optimization was employed to solve the parameters estimation problem of the outbreaks of COVID-19 and Chikungunya in Yemen (March 1, 2020, to May 30, 2020). Besides, a bi-objective optimal control model was formulated, which minimizes the number of affected individuals and minimizes the total cost associated with the intervention strategies. The bi-objective optimal control was also solved using PSO. Five preventive measures were considered to curb the environmental and social factors that trigger the emergence of these viruses. Several strategies were simulated to evaluate the best possible strategy under the conditions and available resources in Yemen. The results obtained confirm that the strategy, which provides resources to prevent the transmission of Chikungunya and provides sufficient resources for testing, applying average social distancing, and quarrying the affected individuals, has a significant effect on flattening the epidemic curves and is the most suitable strategy in Yemen.

COVID-19 pandemic and antimicrobial resistance in developing countries

A wide range of antimicrobial agents were touted as potential remedies during the COVID-19 pandemic. While both developed and developing countries have recorded an increase in the use of antimicrobial drugs, use and misuse have occurred to a far greater degree in developing countries. This can have deleterious consequences on antimicrobial resistance, especially when various developing countries have already reported the emergence of various drug-resistant organisms even before the pandemic. Telemedicine services, societal and cultural pressures, and bacterial co-infections can predispose to overwhelming antimicrobial prescriptions. The emergence of new multidrug resistance species is a major concern for the developing world especially since health services are already overburdened and lack the diagnostic capabilities and basic amenities for infection prevention and control. This can lead to outbreaks and the rampant spread of such microorganisms. Improper waste management and disposal from hospitals and communities establish freshwater runoffs as hubs of various microorganisms that can predispose to the rise of multidrug-resistant species. Microplastics' ability to act as vectors for antibiotic-resistant organisms is also particularly concerning for lower-middle-income countries. In this review, we aim to study the impact of antimicrobial use during the COVID-19 pandemic and antimicrobial resistance in lower middle-income countries, by understanding various determinants of resistance unique to the developing world and exploring solutions to combat the problem.

Modelling and analysis of a SEIQR model on COVID-19 pandemic with delay

This paper deals with mathematical modelling and analysis of a SEIQR model to study the dynamics of COVID-19 considering delay in conversion of exposed population to the infected population. The model is analysed for local and global stability using Lyapunov method of stability followed by Hopf bifurcation analysis. Basic reproduction number is determined, and it is observed that local and global stability conditions are dependent on the number of secondary infections due to exposed as well as infected population. Our study reveals that asymptomatic cases due to exposed population play a vital role in increasing the COVID-19 infection among the population.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Review of potential risk groups for coronavirus disease 2019 (COVID-19)

The current pandemic of coronavirus disease 19 (COVID-19) is a global issue caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Studies have revealed that this virus results in poorer consequences and a higher rate of mortality in older adults and those with comorbidities such as cardiovascular disease, hypertension, diabetes and prolonged respiratory illness. In this review, we discuss in detail the potential groups at risk of COVID-19 and outline future recommendations to mitigate community transmission of COVID-19. The rate of COVID-19 was high in healthcare workers, smokers, older adults, travellers and pregnant women. Furthermore, patients with severe medical complications such as heart disease, hypertension, respiratory illness, diabetes mellitus and cancer are at higher risk of disease severity and mortality. Therefore, special effort and devotion are needed to diminish the threat of SARS-CoV-2 infection. Proper vaccination, use of sanitizers for handwashing and complete lockdown are recommended to mitigate the chain of COVID-19 transmission.

Geospatial Spread of Antimicrobial Resistance, Bacterial and Fungal Threats to Coronavirus Infectious Disease 2019 (COVID-19) Survival, and Point-of-Care Solutions

CONTEXT.—

Point-of-care testing (POCT) is inherently spatial, that is, performed where needed, and intrinsically temporal, because it accelerates decision-making. POCT efficiency and effectiveness have the potential to facilitate antimicrobial resistance (AMR) detection, decrease risks of coinfections for critically ill patients with coronavirus infectious disease 2019 (COVID-19), and improve the cost-effectiveness of health care.

OBJECTIVES.—

To assess AMR identification by using POCT, describe the United States AMR Diagnostic Challenge, and improve global standards of care for infectious diseases.

DATA SOURCES.—

PubMed, World Wide Web, and other sources were searched for papers focusing on AMR and POCT. EndNote X9.1 (Clarivate Analytics) consolidated abstracts, URLs, and PDFs representing approximately 500 articles were assessed for relevance. Panelist insights at Tri•Con 2020 in San Francisco and finalist POC technologies competing for a US $20,000,000 AMR prize are summarized.

CONCLUSIONS.—

Coinfections represent high risks for COVID-19 patients. POCT potentially will help target specific pathogens, refine choices for antimicrobial drugs, and prevent excess morbidity and mortality. POC assays that identify patterns of pathogen resistance can help tell us how infected individuals spread AMR, where geospatial hotspots are located, when delays cause death, and how to deploy preventative resources. Shared AMR data "clouds" could help reduce critical care burden during pandemics and optimize therapeutic options, similar to use of antibiograms in individual hospitals. Multidisciplinary health care personnel should learn the principles and practice of POCT, so they can meet needs with rapid diagnostic testing. The stakes are high. Antimicrobial resistance is projected to cause millions of deaths annually and cumulative financial loses in the trillions by 2050.

A dynamic optimal control model for COVID-19 and cholera co-infection in Yemen

In this work, we propose a new dynamic mathematical model framework governed by a system of differential equations that integrates both COVID-19 and cholera outbreaks. The estimations of the model parameters are based on the outbreaks of COVID-19 and cholera in Yemen from January 1, 2020 to May 30, 2020. Moreover, we present an optimal control model for minimizing both the number of infected people and the cost associated with each control. Four preventive measures are to be taken to control the outbreaks: social distancing, lockdown, the number of tests, and the number of chlorine water tablets (CWTs). Under the current conditions and resources available in Yemen, various policies are simulated to evaluate the optimal policy. The results obtained confirm that the policy of providing resources for the distribution of CWTs, providing sufficient resources for testing with an average social distancing, and quarantining of infected individuals has significant effects on flattening the epidemic curves.

Coronavirus disease 2019 and extra-pulmonary tuberculosis co-infection - A case report and review of literature

The Coronavirus disease 2019 (COVID-19) pandemic continues to cause significant global morbidity and mortality, leading to the need to study the course of the disease in different clinical circumstances and patient populations. While co-infection between COVID-19 and many pathogens has been reported, there has been limited published research regarding co-infection with Mycobacterium tuberculosis. We describe a case of co-infection involving COVID-19 and extra-pulmonary tuberculosis in a patient with cirrhosis, and review the current literature regarding COVID-19 and tuberculosis co-infection. In spite of several co-morbidities that have been shown to portend a poor prognosis in patients with COVID-19 infection, our patient fully recovered.

A SIQ mathematical model on COVID-19 investigating the lockdown effect

This research paper aims at studying the impact of lockdown on the dynamics of novel Corona Virus Disease (COVID-19) emerged in Wuhan city of China in December 2019. Perceiving the pandemic situation throughout the world, Government of India restricted international passenger traffic through land check post (Liang, 2020) and imposed complete lockdown in the country on 24 March 2020. To study the impact of lockdown on disease dynamics we consider a three-dimensional mathematical model using nonlinear ordinary differential equations. The proposed model has been studied using stability theory of nonlinear ordinary differential equations. Basic reproduction ratio is computed and significant parameters responsible to keep basic reproduction ratio less than one are identified. The study reveals that disease vanishes from the system only if complete lockdown is imposed otherwise disease will always persist in the population. However, disease can be kept under control by implementing contact tracing and quarantine measures as well along with lockdown if lockdown is imposed partially.

Tuberculosis and Coronavirus Disease 2019 (COVID-19) from A Clinical Perspective: A Systematic Review

The aim of this review is to examine the effects of COVID-19 on Tuberculosis (TB) management and to highlight evidence of the extent of TB and COVID-19 co-infection. Current findings on TB and COVID-19 have been identified using six databases: Pubmed, Science Direct, Pubmed Central, MedXRiv, Wiley, and Google Scholar. This search in literature was conducted up to 8 May 2020. We included five studies that met the selection criteria. These selected studies have been performed in regions having various demographic characteristics including developed and developing countries, mainly China. The total number of participants in each study ranged from 24 to 203. The case fatality rate of patients with TB and COVID-19 co-infection was found to be high (6/49; 12.3 percent) while a combined diagnosis of TB and COVID-19 was found in 9/49 patients. This condition is linked to several complications, manifested as the need for ex novo oxygen supply, pneumothorax, and extreme hypoxia. Researches on BCG vaccination have shown that countries without vaccination policy are more likely to be seriously affected than those with BCG vaccination programs. COVID-19 infection in patients with TB or the lack of sufficient BCG vaccination may be associated with higher detrimental consequences, including mortality.

Tuberculosis in the era of COVID-19 in India

BACKGROUND AND AIMS

Tuberculosis (TB) still continues to be endemic in various regions of the world, including in India and needs surveillance, clinical assessment, testing, contact tracing, confirmation of diagnosis with supervised or in-supervised treatment regimens for an effective eradication. We assess the challenges due to COVID- 19 pandemic on management of Tuberculosis and current strategies adopted to mitigate them.

METHODS

We carried out a comprehensive review of the literature using suitable keywords such as 'COVID-19', 'Pandemics', 'Tuberculosis' and 'India' on the search engines of PubMed, Scopus, Google Scholar and Research Gate in the month of May 2020 during the current COVID-19 pandemic to assess the impact of COVID-19 on management of Tuberculosis.

RESULTS

We found considerable disruption in Tuberculosis service provisions both in the primary care and hospital settings. Lockdown, social distancing, isolation strategies and public health guidelines to prevent viral transmission impacted the delivery of all aspects of Tuberculosis care.

CONCLUSIONS

COVID-19 pandemic has had a significant impact in the delivery of various tuberculosis prevention, surveillance, and treatment programmes. Lockdown and public health guidelines have resulted in tough challenges in traditional management of tuberculosis and has required reconfiguration of methods to support patients including wider use of remote consultations.

Coronaviruses pathogenesis, comorbidities and multi-organ damage - A review

Human coronaviruses, especially COVID-19, is an emerging pandemic infectious disease with high morbidity and mortality. Coronaviruses are associated with comorbidities, along with the symptoms of it. SARS-CoV-2 is one of the highly pathogenic coronaviruses that causes a high death rate compared to the SARS-CoV and MERS. In this review, we focused on the mechanism of coronavirus with comorbidities and impairment in multi-organ function. The main dysfunction upon coronavirus infection is damage to alveolar and acute respiratory failure. It is associated with the other organ damage such as cardiovascular risk via an increased level of hypertension through ACE2, gastrointestinal dysfunction, chronic kidney disease, diabetes mellitus, liver dysfunction, lung injury, CNS risk, ocular risks such as chemosis, conjunctivitis, and conjunctival hyperemia, cancer risk, venous thromboembolism, tuberculosis, aging, and cardiovascular dysfunction and reproductive risk. Along with this, we have discussed the immunopathology and coronaviruses at a molecular level and therapeutic approaches for the coronavirus infection. The comorbidities and multi-organ failure of COVID-19 have been explained at a molecular level along with the base of the SARS-CoV and MERS-CoV. This review would help us to understand the comorbidities associated with the coronaviruses with multi-organ damage.

How India is dealing with COVID-19 pandemic

India, which has the second-largest population in the world is suffering severely from COVID-19 disease. By May 18th, India investigated ∼1 lakh (0.1million) infected cases from COVID-19, and as of 11th July the cases equalled 8 lakhs. Social distancing and lockdown rules were employed in India, which however had an additional impact on the economy, human living, and environment. Where a negative impact was observed for the economy and human life, the environment got a positive one. How India dealt and can potentially deal with these three factors during and post COVID-19 situation has been discussed here.