The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called ‘first wave' of COVID-19 epidemic (21 February–10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures. Copyright © 2022 Inderscience Enterprises Ltd.

The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called 'first wave' of COVID-19 epidemic (21 February-10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures.

Analysis of Infected Population Threshold Exceedance in an SIR Epidemiological Model

Since the beginning of the COVID-19 outbreak, much attention has been given to the idea of flattening the curve of cases to reduce the harmful effects of an over-loaded medical system. In this context, it is relevant to determine conditions to ensure that the health care threshold capacity will not be exceeded. If such a situa-tion is unavoidable, it would be useful to effectively quantify the potential negative impact produced. In this paper, we consider an epidemiological SIR model and a positive threshold M. Using a parametric expression for the solution curve of the SIR model and the properties of the Lambert W function, we establish necessary and sufficient conditions on the basic reproduction number R0 to ensure that the infected population I does not exceed M. We also introduce and numerically ana-lyze, five different quantities to measure the impact caused by a possible threshold exceedance. © 2021, Intercollegiate Biomathematics Alliance. All rights reserved.

Forecasting COVID-19 new cases using deep learning methods.

After nearly two years since the first identification of SARS-CoV-2 virus, the surge in cases because of virus mutations is a cause of grave public health concern across the globe. As a result of this health crisis, predicting the transmission pattern of the virus is one of the most vital tasks for preparing and controlling the pandemic. In addition to mathematical models, machine learning tools, especially deep learning models have been developed for forecasting the trend of the number of patients affected by SARS-CoV-2 with great success. In this paper, three deep learning models, including CNN, LSTM, and the CNN-LSTM have been developed to predict the number of COVID-19 cases for Brazil, India and Russia. We also compare the performance of our models with the previously developed deep learning models and notice significant improvements in prediction performance. Although our models have been used only for forecasting cases in these three countries, the models can be easily applied to datasets of other countries. Among the models developed in this work, the LSTM model has the highest performance when forecasting and shows an improvement in the forecasting accuracy compared with some existing models. The research will enable accurate forecasting of the COVID-19 cases and support the global fight against the pandemic.

One-Year Recording of Cardiac Arrhythmias in a Non-Infected Population with Cardiac Implantable Devices During the COVID-19 Pandemic.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) was associated with a higher risk of arrhythmia in infected patients. However, there are no reports about the effect of the ongoing pandemic on arrhythmias in the non-infected population. We measured the arrhythmia burden in a non-infected population with cardiac implantable devices. METHODS: The arrhythmia burden during the COVID-19 pandemic was compared to a 6-month interval in the pre-COVID-19 period. The COVID-19 pandemic was divided into high-risk (17 January 2020 to 16 March 2020) and low-risk periods (17 March 2020 to 17 July 2020) according to whether there were locally infected patients. Arrhythmia burdens were compared among the pre-COVID-19, high-risk, and low-risk periods. RESULTS: A total of 219 patients with 1859 episodes were included. We observed a larger proportion of patients with atrial fibrillation (AF) during the COVID-19 pandemic (38.36% vs 26.03%, p = 0.006). There was not significantly more ventricular arrhythmia during the COVID period than the pre-COVID-19 period (p > 0.05). During the high-risk period, daily frequency of non-sustained ventricular tachycardia (NSVT) (0.0172, 0.0475 vs 0.0109, 0.0164, p < 0.05), atrial tachycardia (AT) (0.0345, 0.0518 vs 0.0164, 0.0219 p < 0.05) and AF (0.0345, 0.0432 vs 0.0164, 0.0186, p < 0.05) and daily duration of NSVT (0.1982, 0.2845 vs 0.0538, 0.1640 p < 0.05) were higher and longer than those in the pre-COVID-19 period. Regression modeling showed that the impact of COVID-19 pandemic lead to an increased onset of AF (odds ratio 2.465; p < 0.01). Patients with paroxysmal AF who had undergone a previous radiofrequency ablation had a lower burden of AF (incidence 21.43% vs 55.00%, P = 0.049, daily frequency 0.0000, 0.0027 vs 0.0000, 241.7978, P = 0.020) during the pandemic. CONCLUSION: The COVID-19 pandemic contributed to a higher burden of arrhythmias in non-infected patients. Patients would experience a lower burden of AF following radiofrequency ablation treatment, and this effect persisted during the pandemic.

Dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection.

Most of the widely populated countries across the globe have been observing vicious spread and detrimental effects of pandemic COVID-19 since its inception on December 19. Therefore to restrict the spreading of pandemic COVID-19, various researches are going on in both medical and administrative sectors. The focus has been given in this research keeping an administrative point of view in mind. In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection has been proposed. Few factors related to intra zone mobilization; inter zone mobilization and rate of detection are the key points in the proposed model. Various remedial steps are taken into consideration in the form of operating procedures. Further such operating procedures are applied over the model in standalone or hybridized mode and responses are reported in this paper in a case-studies manner. Further zone-wise increase in infected population due to the spreading of pandemic COVID-19 has been studied and reported in this paper. Also the proposed model has been applied over the real world data considering three states of India and the predicted responses are compared with real data and reported with bar chart representation in this paper.

Dynamics of an SIS network model with a periodic infection rate.

Seasonal forcing and contact patterns are two key features of many disease dynamics that generate periodic patterns. Both features have not been ascertained deeply in the previous works. In this work, we develop and analyze a non-autonomous degree-based mean field network model within a Susceptible-Infected-Susceptible (SIS) framework. We assume that the disease transmission rate being periodic to study synergistic impacts of the periodic transmission and the heterogeneity of the contact network on the infection threshold and dynamics for seasonal diseases. We demonstrate both analytically and numerically that (1) the disease free equilibrium point is globally asymptotically stable if the basic reproduction number is less than one; and (2) there exists a unique global periodic solution that both susceptible and infected individuals coexist if the basic reproduction number is larger than one. We apply our framework to Scale-free contact networks for the simulation. Our results show that heterogeneity in the contact networks plays an important role in accelerating disease spreading and increasing the amplitude of the periodic steady state solution. These results confirm the need to address factors that create periodic patterns and contact patterns in seasonal disease when making policies to control an outbreak.