Dataset Information

Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission.

ABSTRACT: We quantified measles transmissibility during a measles outbreak in Ohio in 2014 to evaluate the impact of public health responses. Case incidence and the serial interval (time between symptom onset in primary cases and secondary cases) were used to assess trends in the effective reproduction number R (the average number of secondary cases generated per case). A mathematical model was parameterized using early R values to determine the size and duration of the outbreak that would have occurred if containment measures had not been initiated, as well as the impact of vaccination. As containment started, we found a 4-fold decline in R (from approximately 4 to 1) over the course of 2 weeks and maintenance of R < 1 as control measures continued. Under a conservative scenario, the model estimated 8,472 cases (90% confidence interval (CI): 8,447, 8,489) over 195 days (90% CI: 179, 223) without control efforts and 715 cases (90% CI: 103, 1,338) over 128 days (90% CI: 117, 139) when vaccination was included; 7,757 fewer cases (90% CI: 7,130, 8,365) and 67 fewer outbreak days (90% CI: 48, 98) were attributed to vaccination. Vaccination may not account entirely for transmission reductions, suggesting that changes in community behavior (social distancing) and other control efforts (isolation, quarantining) are important. Our findings highlight the benefits of measles outbreak response and of understanding behavior change dynamics.

SUBMITTER: Gastanaduy PA

PROVIDER: S-EPMC6118071 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission.

Gastañaduy Paul A PA Funk Sebastian S Paul Prabasaj P Tatham Lilith L Fisher Nicholas N Budd Jeremy J Fowler Brian B de Fijter Sietske S DiOrio Mary M Wallace Gregory S GS Grenfell Bryan B

American journal of epidemiology 20180901 9

We quantified measles transmissibility during a measles outbreak in Ohio in 2014 to evaluate the impact of public health responses. Case incidence and the serial interval (time between symptom onset in primary cases and secondary cases) were used to assess trends in the effective reproduction number R (the average number of secondary cases generated per case). A mathematical model was parameterized using early R values to determine the size and duration of the outbreak that would have occurred i ...[more]

PMID: 29635277

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Project description:BackgroundOf the eight large (>50 cases) US postelimination outbreaks, the first and last occurred in Ohio. Ohio's vaccination registry is incomplete. Community-level immunity gaps threaten more than two decades of measles elimination in the US. We developed a statistical model, VaxEstim, to rapidly estimate the early-phase vaccination coverage and immunity gap in the exposed population during the 2022 Central Ohio outbreak.MethodsWe used reconstructed daily incidence (from publicly available data) and assumptions about the distribution of the serial interval, or the time between symptom onset in successive measles cases, to estimate the effective reproduction number (i.e., the average number of secondary infections caused by an infected individual in a partially immune population). We estimated early-phase measles vaccination coverage by comparing the effective reproduction number to the basic reproduction number (i.e., the average number of secondary infections caused by an infected individual in a fully susceptible population) while accounting for vaccine effectiveness. Finally, we estimated the early-phase immunity gap as the difference between the estimated critical vaccination threshold and vaccination coverage.FindingsVaxEstim estimated the early-phase vaccination coverage as 53% (95% credible interval, 21%-77%), the critical vaccination threshold as 93%, and the immunity gap as 42% (95% credible interval, 18%-74%).InterpretationThis study estimates a significant immunity gap in the exposed population during the early phase of the 2022 Central Ohio measles outbreak, suggesting a robust public health response is needed to identify the susceptible community and develop community-specific strategies to close the immunity gap.FundingThis work was supported in part by the National Institute of General Medical Sciences, National Institutes of Health; the UK Medical Research Council (MRC); the Foreign, Commonwealth and Development Office; the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling Methodology; Imperial College London, and the London School of Hygiene & Tropical Medicine, Community Jameel; the EDCTP2 programme, supported by the EU; and the Sergei Brin Foundation.

Project description:Importance:Internationally imported cases of measles into the United States can lead to outbreaks requiring extensive and rapid control measures. Importation of measles from an unvaccinated adolescent in 2013 led to what has been the largest outbreak of measles in New York City, New York, since 1992. Objective:To describe the epidemiology and public health burden in terms of resources and cost of the 2013 measles outbreak in New York City. Design, Setting, and Participants:This epidemiologic assessment and cost analysis conducted between August 15, 2013, and August 5, 2014, examined all outbreak-associated cases of measles among persons residing in New York City in 2013. Exposures:Measles virus. Main Outcomes and Measures:Numbers of measles cases and contacts. Total personnel time and total direct cost to the New York City Department of Health and Mental Hygiene (DOHMH), calculated as the sum of inputs (supplies and materials, equipment, and logistics) and personnel time (salary and fringe benefits). Results:Between March 13, 2013, and June 9, 2013, 58 persons in New York City with a median age of 3 years (range, 0-32 years) were identified as having measles. Among these individuals, 45 (78%) were at least 12 months old and were unvaccinated owing to parental refusal or intentional delay. Only 28 individuals (48%) visited a medical health care professional who suspected measles and reported the case to the DOHMH at the initial clinical suspicion. Many case patients were not immediately placed into airborne isolation, resulting in exposures in 11 health care facilities. In total, 3351 exposed contacts were identified. Total direct costs to the New York City DOHMH were $394?448, and a total of 10?054 hours were consumed responding to and controlling the outbreak. Conclusions and Relevance:Vaccine refusals and delays appeared to have propagated a large outbreak following importation of measles into the United States. Prompt recognition of measles along with rapid implementation of airborne isolation of individuals suspected of measles infection in health care facilities and timely reporting to public health agencies may avoid large numbers of exposures. The response and containment of measles outbreaks are resource intensive.

Project description:BACKGROUND:Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. OBJECTIVE:The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. METHODS:We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word "measles" posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. RESULTS:Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). CONCLUSIONS:The proposed scheme can successfully classify the public's opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.

Dataset Information

Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission.

Publications

Impact of Public Health Responses During a Measles Outbreak in an Amish Community in Ohio: Modeling the Dynamics of Transmission.

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