scholarly journals Understanding the Epidemic Course in Order to Improve Epidemic Forecasting

GeoHealth ◽  
2020 ◽  
Vol 4 (10) ◽  
Author(s):  
Peng Jia
Keyword(s):  
Epidemic Forecasting

Altmetrics of COVID-19 research: An analysis of study dissemination by Twitter compared to citation rates (Preprint)

2020 ◽  
Author(s):  
Nicholas Fabiano ◽  
Zachary Hallgrimson ◽  
Sakib Kazi ◽  
Jean-Paul Salameh ◽  
Stanley Wong ◽  
...  
Keyword(s):  
Social Media ◽  
Case Report ◽  
Open Access ◽  
South America ◽  
Negative Binomial ◽  
Citation Rate ◽  
Sign Test ◽  
Study Characteristics ◽  
Journal Impact ◽  
Epidemic Forecasting

BACKGROUND The COVID-19 pandemic has resulted in over 1,000,000 cases across 181 countries worldwide. The global impact of COVID-19 has resulted in a surge of related research. Researchers have turned to social media platforms, namely Twitter, to disseminate their studies. The online database Altmetric is a tool which tracks the social media metrics of articles and is complementary to traditional, citation-based metrics. Citation-based metrics may fail to portray dissemination accurately, due to the lengthy publication process. Altmetrics are not subject to this time-lag, suggesting that they may be an effective marker of research dissemination during the COVID-19 pandemic. OBJECTIVE To assess the dissemination of COVID-19 research articles as measured by Twitter dissemination, compared to traditional citation-based metrics, and determine study characteristics associated with tweet rates. METHODS COVID-19 studies obtained from LitCovid published between January 1st to March 18th, 2020 were screened for inclusion. The following study characteristics were extracted independently, in single: Topic (General Info, Mechanism, Diagnosis, Transmission, Treatment, Prevention, Case Report, and Epidemic Forecasting), open access status (open access and subscription-based), continent of corresponding author (Asia, Australia, Africa, North America, South America, and Europe), tweets, and citations. A sign test was used to compare the tweet rate and citation rate per day. A negative binomial regression analysis was conducted to evaluate the association between tweet rate and study characteristics of interest. RESULTS 1328 studies were included in the analysis. Tweet rates were found to be significantly higher than citation rates for COVID-19 studies, with a median tweet rate of 1.09 (SD 156.95) tweets per day and median citation rate of 0.00 (SD 3.02) citations per day, resulting in a median of differences of 1.09 (95% CI 0.86-1.33, P < .001). 2018 journal impact factors were positively correlated with tweet rate (P < .001). The topics Diagnosis (P = .01), Transmission (P < .001), Treatment (P = .01), and Epidemic Forecasting (P < 0.001) were positively correlated with tweet rate, relative to Case Report. The following continents of the corresponding author were negatively correlated with tweet rate, Africa (P <.001), Australia (P = .03), and South America (P < .001), relative to Asia. Open access journals were negatively correlated with tweet rate, relative to subscription-based journals (P < .001). CONCLUSIONS COVID-19 studies had significantly higher tweets rates compared to citation rates. This study further identified study characteristics that are correlated with the dissemination of studies on Twitter, such as 2018 journal impact factor, continent of the corresponding author, topic of study, and open access status. This highlights the importance of altmetrics in periods of rapidly expanding research, such as the COVID-19 pandemic to localize highly disseminated articles.

scholarly journals Numerical study of discretization algorithms for stable estimation of disease parameters and epidemic forecasting

2019 ◽  
Vol 16 (5) ◽  
pp. 3674-3693
Author(s):  
Aurelie Akossi ◽  
◽  
Gerardo Chowell-Puente ◽  
Alexandra Smirnova ◽  
Keyword(s):  
Numerical Study ◽  
Stable Estimation ◽  
Epidemic Forecasting ◽  
Disease Parameters

Epidemic forecasting with a new fuzzy regression equation

2012 ◽  
Vol 47 (6) ◽  
pp. 3411-3422 ◽  
Author(s):  
Wen-Yeh Hsieh ◽  
Ruey-Chyn Tsaur
Keyword(s):  
Regression Equation ◽  
Fuzzy Regression ◽  
Epidemic Forecasting

scholarly journals Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180274 ◽  
Author(s):  
R. N. Thompson ◽  
C. P. Thompson ◽  
O. Pelerman ◽  
S. Gupta ◽  
U. Obolski
Keyword(s):  
Infectious Disease ◽  
Influenza Pandemic ◽  
Disease Outbreaks ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Global Pandemic ◽  
Cross Immunity ◽  
1918 Influenza ◽  
And Control ◽  
Epidemic Forecasting

The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains—a low virulence (LV) strain outbreak followed by a high virulence (HV) strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the HV strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the HV strain, driven by exposure to the LV strain. As a result, major epidemics of the HV strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Environmental Information Systems in Malaria Risk Mapping and Epidemic Forecasting

Disasters ◽  
1998 ◽  
Vol 22 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Stephen J. Connor ◽  
Madeleine C. Thomson ◽  
Stephane P. Flasse ◽  
Anita H. Perryman
Keyword(s):  
Information Systems ◽  
Environmental Information ◽  
Malaria Risk ◽  
Risk Mapping ◽  
Environmental Information Systems ◽  
Epidemic Forecasting

scholarly journals Epidemic Forecasting Framework Combining Agent-Based Models and Smart Beam Particle Filtering

2017 ◽  
Author(s):  
Farzaneh S. Tabataba ◽  
Bryan Lewis ◽  
Milad Hosseinipour ◽  
Foroogh S. Tabataba ◽  
Srinivasan Venkatramanan ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Agent Based Models ◽  
Beam Particle ◽  
Agent Based ◽  
Smart Beam ◽  
Epidemic Forecasting

scholarly journals Corrigendum to ‘Beyond R 0 : heterogeneity in secondary infections and probabilistic epidemic forecasting’

2021 ◽  
Vol 18 (176) ◽  
Author(s):  
Laurent Hébert-Dufresne ◽  
Benjamin M. Althouse ◽  
Samuel V. Scarpino ◽  
Antoine Allard
Keyword(s):  
Secondary Infections ◽  
Epidemic Forecasting

Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting

2021 ◽  
Author(s):  
Honglu Zhang ◽  
Yonghui Xu ◽  
Lei Liu ◽  
Xudong Lu ◽  
Xijie Lin ◽  
...  
Keyword(s):  
Information Fusion ◽  
Epidemic Forecasting

scholarly journals DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information

2019 ◽  
Vol 33 ◽  
pp. 9607-9612 ◽  
Author(s):  
Lijing Wang ◽  
Jiangzhuo Chen ◽  
Madhav Marathe
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
State Of The Art ◽  
Synthetic Data ◽  
State Level ◽  
The Other ◽  
County Level ◽  
Short Term ◽  
Neural Network Structure ◽  
Epidemic Forecasting

Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.

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