Optimal control for co-infection with COVID-19-Associated Pulmonary Aspergillosis in ICU patients with environmental contamination.

In this paper, we propose a mathematical model for COVID-19-Associated Pulmonary Aspergillosis (CAPA) co-infection, that enables the study of relationship between prevention and treatment. The next generation matrix is employed to find the reproduction number. We enhanced the co-infection model by incorporating time-dependent controls as interventions based on Pontryagin's maximum principle in obtaining the necessary conditions for optimal control. Finally, we perform numerical experiments with different control groups to assess the elimination of infection. In numerical results, transmission prevention control, treatment controls, and environmental disinfection control provide the best chance of preventing the spread of diseases more rapidly than any other combination of controls.

Dynamics of a fractional COVID-19 model with immunity using harmonic incidence mean-type.

The transmission dynamics of COVID-19 is investigated through the prism of the Atangana-Baleanu fractional model with acquired immunity. Harmonic incidence mean-type aims to drive exposed and infected populations towards extinction in a finite time frame. The reproduction number is calculated based on the next-generation matrix. A disease-free equilibrium point can be achieved globally using the Castillo-Chavez approach. Using the additive compound matrix approach, the global stability of endemic equilibrium can be demonstrated. Utilizing Pontryagin's maximum principle, we introduce three control variables to obtain the optimal control strategies. Laplace transform allows simulating the fractional-order derivatives analytically. Analysis of the graphical results led to a better understanding of the transmission dynamics.

Does Iron Deficiency Anaemia and its Severity Influence HbA1C Level in Non Diabetics? An Analysis of 150 Cases.

INTRODUCTION: Anaemia has a high prevalence having great impact worldwide and potentially contributing to the pathogenesis of various chronic diseases. Approximately 1/3rd of patients with anaemia have iron deficiency. American Diabetes Association (ADA) has affirmed Glycated Haemoglobin (HbA1C) ≥ 6.5% as a diagnostic criterion for Diabetes Mellitus (DM). Variation of HbA1C in Iron Deficiency Anaemia (IDA) has clashing results. AIM: To decide the impact of IDA on HbA1C levels among non diabetics. To assess and analyse the variation of HbA1C according to the degree of anaemia (mild, moderate and severe). MATERIALS AND METHODS: This cross-sectional study was carried out in SRM Medical College Hospital and Research Centre, Chennai, Tamil Nadu from February 2016 to October 2016 and approved by our Institutional Ethical Committee. Totally 150 non diabetics (75 with IDA and 75 without IDA) were included in this study. Medical history was recorded and HbA1C, Haemoglobin (Hb), Haematocrit (Hct), red cell indices, serum iron, ferritin and Fasting Plasma Glucose (FPG) were tested. RESULTS: The IDA patients in this study had a mean HbA1C (6.84±0.07%) which was higher than the non anaemic group (5.12±0.04%) and this difference was statistically significant (p< 0.05). HbA1C level was increased when severity of anaemia worsened. Also, noteworthy statistical significance was observed between no anaemia, mild, moderate and severe anaemia (p< 0.05). CONCLUSION: In this study, we observed a positive correlation between IDA and elevated HbA1C level in non-diabetic population.