Filtering by
- Resource Type: Text
![130400-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130400-Thumbnail%20Image.png?versionId=fITKeInJnFNHlXQPlJfW7Wnq5Imby1wy&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T084542Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=0883a773c48a901b038e5fe418a1fb71181e307baec8b049ce4e95a8dd928052&itok=eHIl7ATb)
![130341-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130341-Thumbnail%20Image.png?versionId=bF8BZIx4RfDu9ZRlY7PWr1NZwJwHNR.X&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240614/us-west-2/s3/aws4_request&X-Amz-Date=20240614T103230Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=5de80cbcf46c6fd1aa4ef6cc694ea50a11b5567f7dc0aed19da32b5e886e76bb&itok=ReSqnDuP)
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
![130348-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130348-Thumbnail%20Image.png?versionId=Jn0PQLruNFzlZ3_5ABxiHyqf_1hG_sXQ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T065842Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=75f10bcd70ec04332c67365bd6e56c081b4742ad23ad5f69a02f9084b0457b8b&itok=Fcw8TZQc)
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
![130349-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/130349-Thumbnail%20Image.png?versionId=z0IKM23tTCArYZKS3R8zEDc7.Dn9X1oC&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240614/us-west-2/s3/aws4_request&X-Amz-Date=20240614T103230Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=8e39cfe5bbef3c5c163619cac11393e9ac244f2fc58cb235d653869f49e5b5c3&itok=5ilSY_Nv)
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Methods
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
Conclusions
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
![131287-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/131287-Thumbnail%20Image.png?versionId=T6jJwczZvIGRrtdooFJZ.tdRkJS3TIz0&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T231202Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=772284f7a02a88b8ee80f82dc7798b41d2d16c406047c5bf9102786df42f5a75&itok=LTN3WMp9)
![132294-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/132294-Thumbnail%20Image.png?versionId=R9WEQArUxfugsXj8W3raohvWrq90rEyN&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T161821Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=a176afe680f8644379516540efb460afaf977f10eee7828b145db9eef5f6334d&itok=kcGmUUKT)
![131736-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/131736-Thumbnail%20Image.png?versionId=s_QyI5fJ.6rJncGdVovGAAkzXcOJkW.c&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T163209Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f706f172358f5f74fa8b2f6cf347434759b33505dd5ceb2352c580cd64a67590&itok=2rg56gud)
![131610-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/131610-Thumbnail%20Image.png?versionId=I7d3FFD9VuHqVV_o88k.Tltj1PbD70Wn&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T163209Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f24c6b93e0732ebacf51662a06b7f47eb84b20362cfb00db1b82931daf501a75&itok=vGEero8U)
The combined use of methamphetamine and opioids has been reported to be on the rise throughout the United States (U.S.). However, our knowledge of this phenomenon is largely based upon reported overdoses and overdose-related deaths, law enforcement seizures, and drug treatment records; data that are often slow, restricted, and only track a portion of the population participating in drug consumption activities. As an alternative, wastewater-based epidemiology (WBE) has the capability to track licit and illicit drug trends within an entire community, at a low cost and in near real-time, while providing anonymity to those contributing to the sewer shed. In this study, wastewater was collected from two Midwestern U.S. cities (2017-2019) and analyzed for the prevalence of methamphetamine and the opioids oxycodone, codeine, fentanyl, tramadol, hydrocodone, and hydromorphone. Monthly 24-hour time-weighted composite samples (n = 48) from each city were analyzed using isotope dilution liquid chromatography tandem mass spectrometry. Results showed that methamphetamine and total opioid consumption (milligram morphine equivalents) in City 1 were strongly correlated only in 2017 (Spearman rank order correlation coefficient, ρ = 0.78), the relationship driven by fentanyl, hydrocodone, and hydromorphone. For City 2, methamphetamine and total opioid consumption were strongly positively correlated during the entire study (ρ = 0.54), with the correlations driven by hydrocodone and hydromorphone. In both cities, hydrocodone and hydromorphone mass loads were highly correlated, suggesting a parent and metabolite relationship. WBE provides important insights into licit and illicit drug consumption patterns in near real-time as they evolve; important information for community stakeholders in municipalities across the U.S.
![132548-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/132548-Thumbnail%20Image.png?versionId=T8UHMMmN3s0NQTOZZxvy5ydgLWAQjIIW&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T030232Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=96b700b438faf4909ec9eeacc562f916b27cd7a31f42d1ffe3f4177400a33826&itok=LYrilozt)
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
![132915-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/132915-Thumbnail%20Image.png?versionId=P8f_vNeKQ3H0yhbu.0GUToiOnlH1L0T8&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T030232Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=5b62d6508b5c0525d24e2ae40bfa62ea4c6c235bb5055e0755d6fa32d8e60698&itok=XLZTfEGm)