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This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge–Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics. [ABSTRACT FROM AUTHOR]

Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future. [ABSTRACT FROM AUTHOR]

Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future. [ABSTRACT FROM AUTHOR]

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus. [ABSTRACT FROM AUTHOR]