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Measles during arbovirus outbreak: a diagnostic challenge.

ABSTRACT: Introduction:Dengue fever is a major public health problem in New Caledonia, like in many Pacific Islands Countries and territories. In 2017 New Caledonia faced multiple circulations of arboviruses with a major outbreak of dengue and a co-circulating Zika virus. New Caledonia is considered as a non-endemic territory for measles since the mid 1990's. Case presentation:A 41-year-old male presented fever, headache, sinusitis and exanthematous maculopapular rash. A clinical diagnosis of arbovirus was first suspected due to the local epidemic context. A few days later the patient was admitted to the main hospital. The real time RT-PCR for dengue and Zika virus were negative on the first blood sample. A drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome and other infectious diseases including measles were then suspected. ELISA tests for measles were positive for IgM and equivocal for IgG. A throat swab was immediately shipped to a reference laboratory for measles nucleic acid testing. After a week, the patient recovered and the presence of measles RNA was confirmed. No secondary cases were reported among contacts of the patient and the source of his infection could not be ascertained. Conclusion:Diagnosis of measles during an arbovirus outbreak in a country where measles disease is rare can be a pitfall for healthcare professionals. The introduction of measles via returned travellers or tourists from areas where measles remains endemic is a real threat to countries with high vaccine coverage.

SUBMITTER: Gourinat AC

PROVIDER: S-EPMC6152402 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Measles during arbovirus outbreak: a diagnostic challenge.

Gourinat Ann-Claire AC Cazorla Cécile C Pfannstiel Anne A Tran Thomas T

JMM case reports 20180810 8

<h4>Introduction</h4>Dengue fever is a major public health problem in New Caledonia, like in many Pacific Islands Countries and territories. In 2017 New Caledonia faced multiple circulations of arboviruses with a major outbreak of dengue and a co-circulating Zika virus. New Caledonia is considered as a non-endemic territory for measles since the mid 1990's.<h4>Case presentation</h4>A 41-year-old male presented fever, headache, sinusitis and exanthematous maculopapular rash. A clinical diagnosis ...[more]

PMID: 30323934

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Project description:BackgroundTo understand operational challenges involved with responding to US measles outbreaks in 2017-19 and identify applicable lessons in order to inform preparedness and response operations for future outbreaks, particularly with respect to specific operational barriers and recommendations for outbreak responses among insular communities.MethodsFrom August 2019 to January 2020, we conducted 11 telephone interviews with 18 participants representing state and local health departments and community health centers that responded to US measles outbreaks in 2017-19, with a focus on outbreaks among insular communities. We conducted qualitative, thematic coding to identify and characterize key operational challenges and lessons identified by the interviewees.ResultsWe categorized principal insights into 5 topic areas: scale of the response, vaccination operations, exclusion policies, community engagement, and countering anti-vaccine efforts. These topics address resource-intensive aspects of these outbreak responses, including personnel demands; guidance needed to support response operations and reduce transmission, such as excluding exposed or at-risk individuals from public spaces; operational challenges and barriers to vaccination and other response activities; and effectively engaging and educating affected populations, particularly with respect to insular and vulnerable communities.ConclusionsMeasles outbreak responses are resource intensive, which can quickly overwhelm existing public health capacities. Early and effective coordination with trusted leaders and organizations in affected communities, including to provide vaccination capacity and facilitate community engagement, can promote efficient response operations. The firsthand experiences of public health and healthcare personnel who responded to measles outbreaks, including among insular communities, provide evidence-based operational lessons that can inform future preparedness and response operations for outbreaks of highly transmissible diseases.

Project description:BackgroundMeasles is one of the most contagious diseases caused by an acute viral illness called Morbillivirus that usually occurs as an outbreak in low-income countries. As of May 2016 measles suspected outbreak was reported from Sekota Zuria district. We investigated the outbreak to identify its possible sources and risk factors of acquiring the infection in the district.MethodWe conducted a 1:2 unmatched case-control study in May 2016 in Sekota Zuria district, Northern Ethiopia. Cases involved in the study were lab confirmed and epidemiologically linked. Controls were those who had no clinical signs of measles and residing in the same communities where the cases were identified. An interviewer-administered questionnaire was used to collect the data. Data were cleaned and entered to Epi-info7 and analyzed using SPSS-20. A logistic regression analysis was conducted to identify risk factors associated with measles infection at a p-value ≤0.05.Results29 cases were identified during the outbreak investigation. The probable source of an outbreak was an index case who had a travel history to a district with a measles epidemic. Five samples were collected for confirmation of the diagnosis. No measles-related deaths were reported. The median age of cases and controls was 15 years (SD ± 7.8) and 11 years (SD ± 9.8), respectively. More than 55% of the cases were in age ≥ 15 years. In the multivariable analysis, being previously vaccinated for measles reduced the risk of measles infection by 83% (AOR, 95%CI = 0.17, 0.05-0.53) and having a contact history increased the risk of measles infection by 3.44 times (AOR, 95%CI = 3.44, 1.26-9.38).ConclusionWe confirmed a measles outbreak in Sekota Zuria district. The majority of the cases were in age ≥ 15 years. Being un-vaccinated and having a contact history with confirmed or suspected cases were increased the risk of measles infection. To catch up with missed children at the time of the first dose of measles vaccine and reduce their susceptibility, supplementary immunization activities (SIAs) or immunization campaigns shall be strengthened.

Project description:BACKGROUND:Measles continues to circulate in the Democratic Republic of Congo, and the country suffered from several important outbreaks over the last 5 years. Despite a large outbreak starting in the former province of Katanga in 2010 and the resulting immunization activities, another outbreak occurred in 2015 in this same region. We conducted measles seroprevalence surveys in four health zones (HZ) in the former Katanga Province in order to assess the immunity against measles in children 6 months to 14 years after the 2015 outbreak. METHODS:We conducted multi-stage cluster surveys stratified by age group in four HZs, Kayamba, Malemba-Nkulu, Fungurume, and Manono. The age groups were 6-11 months, 12-59 months, and 5-14 years in Kayamba and Malemba-Nkulu, 6-59 months and 5-14 years in Manono and Fungurume. The serological status was measured on dried capillary blood spots collected systematically along with vaccination status (including routine Extended Program of Immunization (EPI), and supplementary immunization activities (SIAs)) and previous self-reported history of suspected measles. RESULTS:Overall seroprevalence against measles was 82.7% in Kayamba, 97.6% in Malemba-Nkulu, 83.2% in Manono, and 74.4% in Fungurume, and it increased with age in all HZs. It was 70.7 and 93.8% in children 12-59 months in Kayamba and Malemba-Nkulu, and 49.8 and 64.7% in children 6-59 months in Fungurume and Manono. The EPI coverage was low but varied across HZ. The accumulation of any type of vaccination against measles resulted in an overall vaccine coverage (VC) of at least 85% in children 12-59 months in Kayamba and Malemba-Nkulu, 86.1 and 74.8% in children 6-59 months in Fungurume and Manono. Previous measles infection in 2015-early 2016 was more frequently reported in children aged 12-59 months or 6-59 months (depending on the HZ). CONCLUSION:The measured seroprevalence was consistent with the events that occurred in these HZs over the past few years. Measles seroprevalence might prove a valuable source of information to help adjust the timing of future SIAs and prioritizing support to the EPI in this region as long as the VC does not reach a level high enough to efficiently prevent epidemic flare-ups.

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.

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Measles during arbovirus outbreak: a diagnostic challenge.

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Measles during arbovirus outbreak: a diagnostic challenge.

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