Nutritional Immunity, Zinc Sufficiency, and COVID-19 Mortality in Socially Similar European Populations.

The impact of zinc (Zn) sufficiency/supplementation on COVID-19-associated mortality and incidence (SARS-CoV-2 infections) remains unknown. During an infection, the levels of free Zn are reduced as part of "nutritional immunity" to limit the growth and replication of pathogen and the ensuing inflammatory damage. Considering its key role in immune competency and frequently recorded deficiency in large sections of different populations, Zn has been prescribed for both prophylactic and therapeutic purposes in COVID-19 without any corroborating evidence for its protective role. Multiple trials are underway evaluating the effect of Zn supplementation on COVID-19 outcome in patients getting standard of care treatment. However, the trial designs presumably lack the power to identify negative effects of Zn supplementation, especially in the vulnerable groups of elderly and patients with comorbidities (contributing 9 out of 10 deaths; up to >8,000-fold higher mortality). In this study, we have analyzed COVID-19 mortality and incidence (case) data from 23 socially similar European populations with comparable confounders (population: 522.47 million; experiencing up to >150-fold difference in death rates) and at the matching stage of the pandemic (March 12 to June 26, 2020; first wave of COVID-19 incidence and mortality). Our results suggest a positive correlation between populations' Zn-sufficiency status and COVID-19 mortality [r (23): 0.7893-0.6849, p-value < 0.0003] as well as incidence [r (23):0.8084-0.5658; p-value < 0.005]. The observed association is contrary to what would be expected if Zn sufficiency was protective in COVID-19. Thus, controlled trials or retrospective analyses of the adverse event patients' data should be undertaken to correctly guide the practice of Zn supplementation in COVID-19.

Modeling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlook

A very special type of pneumonic disease that generated the COVID-19 was first identified in Wuhan, China in December 2019 and is spreading all over the world. The ongoing outbreak presents a challenge for data scientists to model COVID-19, when the epidemiological characteristics of the COVID-19 are yet to be fully explained. The uncertainty around the COVID-19 with no vaccine and effective medicine available till today create additional pressure on the epidemiologists and policy makers. In such a crucial situation, it is very important to predict infected cases to support prevention of the disease and aid in the preparation of healthcare service. India is fighting efficiently against COVID-19 and facing greater challenges because of its large population and high population density. Though the government of India is taking all needful steps to prevent its spread but it is not enough to control and stop spread of the disease so far, perhaps due to defiant nature of people living in India. Effective measure to control this disease, medical professionals needs to know the estimated size of this pandemic and pace. In this study, an attempt has been made to understand the spreading capability of COVID-19 in India through some simple models. Findings suggest that the lockdown strategies implemented in India are not successfully reducing the pace of the pandemic significantly after first lockdown.

A Data Driven Model for Prediction of COVID-19 Outbreak in Bangladesh

COVID-19 disease due to Corona virus of type SARS-CoV-2 has emerged from Wuhan, China in December 2019 and now it has expanded through infection all over the world. Now-a-days, this is a disaster pandemic for almost every nation in the earth. Such novel Corona virus impacts on every country in the world without regarding country's race, nationality and economic status. In this study, an attempt has been made to propose a data driven model to know the present and future Corona virus situation of Bangladesh. It also predicts the cumulative infected people, disease propagation, doubling time and new cases of COVID-19 patients. The result shows that more than two lakh fifty thousand people will be infected by Corona virus and its effect will start to decrease by the mid of July and decline to disappear by the end of August. This study will help policy makers to take plan for healthcare system and management of various aspects related to the control and prevention of Corona virus outspread in Bangladesh.

Modeling tempo of COVID-19 pandemic in India and significance of lockdown.

A very special type of pneumonic disease that generated the COVID-19 pandemic was first identified in Wuhan, China in December 2019 and is spreading all over the world. The ongoing outbreak presents a challenge for data scientists to model COVID-19, when the epidemiological characteristics of the COVID-19 are yet to be fully explained. The uncertainty around the COVID-19 with no vaccine and effective medicine available until today create additional pressure on the epidemiologists and policy makers. In such a crucial situation, it is very important to predict infected cases to support prevention of the disease and aid in the preparation of healthcare service. In this paper, we have tried to understand the spreading capability of COVID-19 in India taking into account of the lockdown period. The numbers of confirmed cases are increased in India and states in the past few weeks. A differential equation based simple model has been used to understand the pattern of COVID-19 in India and some states. Our findings suggest that the physical distancing and lockdown strategies implemented in India are successfully reducing the spread and that the tempo of pandemic growth has slowed in recent days.