COVID-19 in Italy: Is the Mortality Analysis a Way to Estimate How the Epidemic Lasts?

When an epidemic breaks out, many health, economic, social, and political problems arise that require a prompt and effective solution. It would be useful to obtain all information about the virus, including epidemiological ones, as soon as possible. In a previous study of our group, the analysis of the positive-alive was proposed to estimate the epidemic duration. It was stated that every epidemic ends when the number of positive-alive (=infected-healed-dead) glides toward zero. In fact, if with the contagion everyone can enter the epidemic phenomenon, only by healing or dying can they get out of it. In this work, a different biomathematical model is proposed. A necessary condition for the epidemic to be resolved is that the mortality reaches the asymptotic value, from there, remains stable. At that time, the number of positive-alive must also be close to zero. This model seems to allow us to interpret the entire development of the epidemic and highlight its phases. It is also more appropriate than the previous one, especially when the spread of the infection is so rapid that the increase in live positives is staggering.

"Five Keys to Safer Food" and COVID-19.

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) and, up to 18:37 a.m. on 9 December 2021, it has produced 268,440,530 cases and 5,299,511 deaths. This disease, in some patients, included pneumonia and shortness of breath, being transmitted through droplets and aerosols. To date, there is no scientific literature to justify transmission directly from foods. In this review, we applied the precautionary principle for the home and the food industry using the known "Five Keys to Safer Food" manual developed by the World Health Organization (WHO) and extended punctually in its core information from five keys, in the light of new COVID-19 evidence, to guarantee a possible food safety tool.

COVID Obesity: A One-Year Narrative Review.

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). This study focuses on a narrative review about the illness during the first year of the pandemic in relation to obesity. Databases were used to search studies published up to 8 December 2020. In total, 4430 articles and other scientific literature were found, and 24 articles were included in this one-year narrative review. The mean BMI value of severe COVID-19 patients ranged from 24.5 to 33.4 kg/m2, versus <18.5 to 24.3 kg/m2 for non-severe patients. Articles using the terms obesity or overweight without indicating the BMI value in these patients were common, but this is not useful, as the anthropometric parameters, when not defined by this index, are confusing due to the classification being different in the West compared to among Asian and Korean criteria-based adults. We proposed a new term, called COVID obesity, to define the importance of this anthropometric parameter, among others, in relation with this pandemic.

Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19?

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). As of 12.44 GMT on 15 January 2021, it has produced 93,640,296 cases and 2,004,984 deaths. The use of mathematical modelling was applied in Italy, Spain, and UK to help in the prediction of this pandemic. We used equations from general and reduced logistic models to describe the epidemic development phenomenon and the trend over time. We extracted this information from the Italian Ministry of Health, the Spanish Ministry of Health, Consumer Affairs, and Social Welfare, and the UK Statistics Authority from 3 February to 30 April 2020. We estimated that, from the seriousness of the phenomenon, the consequent pathology, and the lethal outcomes, the COVID-19 trend relate to the same classic laws that govern epidemics and their evolution. The curve d(t) helps to obtain information on the duration of the epidemic phenomenon, as its evolution is related to the efficiency and timeliness of the system, control, diagnosis, and treatment. In fact, the analysis of this curve, after acquiring the data of the first three weeks, also favors the advantage to formulate forecast hypotheses on the progress of the epidemic.