6 research outputs found
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
Epidemics like Covid-19 and Ebola have impacted people\u27s lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc. is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic
Under the Spotlight: Web Tracking in Indian Partisan News Websites
India is experiencing intense political partisanship and sectarian divisions.
The paper performs, to the best of our knowledge, the first comprehensive
analysis on the Indian online news media with respect to tracking and
partisanship. We build a dataset of 103 online, mostly mainstream news
websites. With the help of two experts, alongside data from the Media Ownership
Monitor of the Reporters without Borders, we label these websites according to
their partisanship (Left, Right, or Centre). We study and compare user tracking
on these sites with different metrics: numbers of cookies, cookie
synchronizations, device fingerprinting, and invisible pixel-based tracking. We
find that Left and Centre websites serve more cookies than Right-leaning
websites. However, through cookie synchronization, more user IDs are
synchronized in Left websites than Right or Centre. Canvas fingerprinting is
used similarly by Left and Right, and less by Centre. Invisible pixel-based
tracking is 50% more intense in Centre-leaning websites than Right, and 25%
more than Left. Desktop versions of news websites deliver more cookies than
their mobile counterparts. A handful of third-parties are tracking users in
most websites in this study. This paper, by demonstrating intense web tracking,
has implications for research on overall privacy of users visiting partisan
news websites in India
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
Epidemics like Covid-19 and Ebola have impacted people's lives significantly.
The impact of mobility of people across the countries or states in the spread
of epidemics has been significant. The spread of disease due to factors local
to the population under consideration is termed the endogenous spread. The
spread due to external factors like migration, mobility, etc. is called the
exogenous spread. In this paper, we introduce the Exo-SIR model, an extension
of the popular SIR model and a few variants of the model. The novelty in our
model is that it captures both the exogenous and endogenous spread of the
virus. First, we present an analytical study. Second, we simulate the Exo-SIR
model with and without assuming contact network for the population. Third, we
implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We
found that endogenous infection is influenced by exogenous infection.
Furthermore, we found that the Exo-SIR model predicts the peak time better than
the SIR model. Hence, the Exo-SIR model would be helpful for governments to
plan policy interventions at the time of a pandemic.Comment: To appear in Springer Nature Journal of Data Science and Analytics.
arXiv admin note: substantial text overlap with arXiv:2008.0633