Faculty Opinions recommendation of Host-induced epidemic spread of the cholera bacterium.

2002 ◽  
Author(s):  
Victor DiRita
Keyword(s):  
Epidemic Spread

scholarly journals The Italian national survey on Coronavirus disease 2019 epidemic spread in nursing homes

2021 ◽  
Author(s):  
Flavia L. Lombardo ◽  
Ilaria Bacigalupo ◽  
Emanuela Salvi ◽  
Eleonora Lacorte ◽  
Paola Piscopo ◽  
...  
Keyword(s):  
Nursing Homes ◽  
National Survey ◽  
Epidemic Spread

scholarly journals Unresolved advantages of multipartitism in spatially structured environments

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mark P Zwart ◽  
Stéphane Blanc ◽  
Marcelle Johnson ◽  
Susanna Manrubia ◽  
Yannis Michalakis ◽  
...  
Keyword(s):  
Genome Organization ◽  
Latent Infection ◽  
Virus Transmission ◽  
Plant Viruses ◽  
Epidemic Spread ◽  
Virus Particles ◽  
Distinct Virus ◽  
Network Epidemiology ◽  
Short Commentary ◽  
Animal Hosts

Abstract Multipartite viruses have segmented genomes and package each of their genome segments individually into distinct virus particles. Multipartitism is common among plant viruses, but why this apparently costly genome organization and packaging has evolved remains unclear. Recently Zhang and colleagues developed network epidemiology models to study the epidemic spread of multipartite viruses and their distribution over plant and animal hosts (Phys. Rev. Lett. 2019, 123, 138101). In this short commentary, we call into question the relevance of these results because of key model assumptions. First, the model of plant hosts assumes virus transmission only occurs between adjacent plants. This assumption overlooks the basic but imperative fact that most multipartite viruses are transmitted over variable distances by mobile animal vectors, rendering the model results irrelevant to differences between plant and animal hosts. Second, when not all genome segments of a multipartite virus are transmitted to a host, the model assumes an incessant latent infection occurs. This is a bold assumption for which there is no evidence to date, making the relevance of these results to understanding multipartitism questionable.

scholarly journals Complex Network Modelling of Origin–Destination Commuting Flows for the COVID-19 Epidemic Spread Analysis in Italian Lombardy Region

2021 ◽  
Vol 11 (10) ◽  
pp. 4381
Author(s):  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Alfonso Monaco ◽  
Sabina Tangaro ◽  
Roberto Bellotti
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Critical Role ◽  
Potential Contribution ◽  
Epidemic Spread ◽  
Network Modelling ◽  
Lombardy Region ◽  
Commuting Flows ◽  
Dynamic Growth ◽  
Spread Analysis

Currently the whole world is affected by the COVID-19 disease. Italy was the first country to be seriously affected in Europe, where the first COVID-19 outbreak was localized in the Lombardy region. The further spreading of the cases led to the lockdown of the most affected regions in northern Italy and then the entire country. In this work we investigated an epidemic spread scenario in the Lombardy region by using the origin–destination matrix with information about the commuting flows among 1450 urban areas within the region. We performed a large-scale simulation-based modeling of the epidemic spread over the networks related to three main motivations, i.e., work, study and occasional transfers to quantify the potential contribution of each category of travellers to the spread of the epidemic process. Our findings outline that the three networks are characterised by different weight dynamic growth rates and that the network “work” has a critical role in the diffusion phenomenon showing the greatest contribution to the epidemic spread.

scholarly journals Exploring optimal control of epidemic spread using reinforcement learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abu Quwsar Ohi ◽  
M. F. Mridha ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid
Keyword(s):  
Reinforcement Learning ◽  
Economic Crisis ◽  
Transmission Rate ◽  
Optimal Solution ◽  
Short Length ◽  
Reproduction Rate ◽  
Economic Factors ◽  
Epidemic Spread ◽  
The Impact ◽  
Individual Human

AbstractPandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.

scholarly journals Epidemic spread of recombinant noroviruses with four capsid types in Hungary

2006 ◽  
Vol 35 (1) ◽  
pp. 84-88 ◽  
Author(s):  
G REUTER ◽  
H VENNEMA ◽  
M KOOPMANS ◽  
G SZUCS
Keyword(s):  
Epidemic Spread
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