相关论文: Predicting COVID-19 Spread from Large-Scale Mobili…

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean and devoid of noise? Common sense implies the negative answer. While we cannot evaluate the…

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of…

COVID-19 has created a pandemic around the world, paused the path of building the future, and still ongoing without having any long-term solution shortly. The time taken in vaccine distribution is too slow compared to the spread of…

In this paper we propose an epidemiological model for the spread of COVID-19. The dynamics of the spread is based on four fundamental categories of people in a population: Tested and infected, Non-Tested but infected, Tested but not…

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence ,…

Millions of people have died all across the world because of the COVID-19 outbreak. Researchers worldwide are working together and facing many challenges to bring out the proper vaccines to prevent this infectious virus. Therefore, in this…

We propose a forecasting method for predicting epidemiological health series on a two-week horizon at the regional and interregional resolution. The approach is based on model order reduction of parametric compartmental models, and is…

In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of the COVID-19 virus in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics,…

Over the past two decades there has been a number of global outbreaks of viral diseases. This has accelerated the efforts to model and forecast the disease spreading, in order to find ways to confine the spreading regionally and between…

Comparing how different populations have suffered under COVID-19 is a core part of ongoing investigations into how public policy and social inequalities influence the number of and severity of COVID-19 cases. But COVID-19 incidence can vary…

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT…

In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian public and private life came to a sudden halt. Here we assess…

Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this work we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread through the…

The relationship between pandemic and human mobility has received considerable attention from scholars, as it can provide an indication of how mobility patterns change in response to a public health crisis or whether reduced mobility…

The emergence of novel COVID-19 causing an overload in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to…

We study the epidemic spreading on spatial networks where the probability that two nodes are connected decays with their distance as a power law. As the exponent of the distance dependence grows, model networks smoothly transition from the…

Understanding where and when human mobility is associated with disease infection is crucial for implementing location-based health care policy and interventions. Previous studies on COVID-19 have revealed the correlation between human…

We introduce a Bayesian sequential data assimilation method for COVID-19 forecasting. It is assumed that suitable transmission, epidemic and observation models are available and previously validated and the transmission and epidemic models…

We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic in order to inform decision-makers of future demand and assess the societal value of possible interventions. For broad applicability, we…

The ongoing Coronavirus disease 2019 (COVID-19) is a major crisis that has significantly affected the healthcare sector and global economies, which made it the main subject of various fields in scientific and technical research. To properly…