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Mpox has recently re-emerged as a global entity of concern. We report one of the first pediatric cases in the United States and provide updated recommendations relevant to infection control and prevention measures of those in close contact with mpox.

ObjectiveNorthern Syria faces a large burden of influenza-like illness (ILI) and severe acute respiratory illness (SARI). This study aimed to investigate the trends of Early Warning and Response Network (EWARN) reported ILI and SARI in northern Syria between 2016 and 2021 and the potential impact of SARS-CoV-2.MethodsWe extracted weekly EWARN data on ILI/ SARI and aggregated cases and consultations into 4-week intervals to calculate case positivity. We conducted a seasonal-trend decomposition to assess case trends in the presence of seasonal fluctuations.ResultsIt was observed that 4-week aggregates of ILI cases (n = 5,942,012), SARI cases (n = 114,939), ILI case positivity, and SARI case positivity exhibited seasonal fluctuations with peaks in the winter months. ILI and SARI cases in individuals aged ≥5 years surpassed those in individuals aged

The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.

Although the use of social media to spread misinformation and disinformation is not a new concept, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has further highlighted the dangers that misinformation can pose to public health. More than two-thirds of Americans receive their news from at least 1 social media outlet, most of which do not undergo the same review process as academic journals and some professional news organizations. Unfortunately, this can lead to inaccurate health information being conveyed as truth. The purpose of this article is to inform the infectious diseases community of the history and dangers of health misinformation and disinformation in social media, present tools for identifying and responding to misinformation, and propose other ethical considerations for social media.

Hepatitis E virus is a common cause of acute viral hepatitis. We analyzed reports of hepatitis E outbreaks among forcibly displaced populations in sub-Saharan Africa during 2010-2020. Twelve independent outbreaks occurred, and >30,000 cases were reported. Transmission was attributed to poor sanitation and overcrowding.

Antimicrobial resistance (AMR) is of increasing global concern. Human mobility is one factor that has recently been associated with AMR, though the extent of its impact has not yet been well established due to the limited availability of rigorous data. This review examines the existing literature regarding various types of human mobility including short-term travelers, forcibly displaced persons, migrant populations, and their association with global rates of AMR.

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

BackgroundThe novel COVID-19 disease has negatively impacted mortality, economic conditions, and mental health. These impacts are likely to continue after the COVID-19 pandemic ends. There are no methods for characterizing the mental health burden of the COVID-19 pandemic, and differentiating this burden from that of the prepandemic era. Accurate illness detection methods are critical for facilitating pandemic-related treatment and preventing the worsening of symptoms.ObjectiveWe aimed to identify major themes and symptom clusters in the SMS text messages that patients send to therapists. We assessed patients who were seeking treatment for pandemic-related distress on Talkspace, which is a popular telemental health platform.MethodsWe used a machine learning algorithm to identify patients' pandemic-related concerns, based on their SMS text messages in a large, digital mental health service platform (ie, Talkspace). This platform uses natural language processing methods to analyze unstructured therapy transcript data, in parallel with brief clinical assessment methods for analyzing depression and anxiety symptoms.ResultsOur results show a significant increase in the incidence of COVID-19-related intake anxiety symptoms (P

ObjectivesThe protracted and violent conflict in Syria has resulted in large-scale displacement of people and destruction of health and sanitation infrastructure. The aim of this study was to examine epidemiological trends in vector-borne disease (VBD) outbreaks before and following the onset of the Syrian conflict (2011).MethodsProMED, a digital disease surveillance tool, was queried for VBD outbreak reports affecting humans and animals in Syria and select bordering countries between 2003 and 2018. Data were normalized by dividing the number of unique VBD events by the total number of unique outbreak events reported by ProMED for each year. Suspected and confirmed case counts and deaths were manually extracted.ResultsReports on VBDs increased from a mean of 2.9/year pre-2011 to 12.8/year post-2011, a 343.5% (p < 0.05) increase. After normalization, reports increased by 485.5% (p < 0.05) over the time periods. Post-2011, the most commonly reported VBDs were leishmaniasis, Crimean-Congo hemorrhagic fever, and lumpy skin disease. Reported numbers of suspected and confirmed cases and deaths increased during the conflict period.ConclusionsVBD outbreak events in ProMED increased in Syria and select bordering countries after the onset of the Syrian conflict in 2011. Enhanced disease surveillance is critical to detect and manage outbreaks in conflict settings.

As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted "salutary sheltering" by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.

BackgroundThe United Nations Refugee Agency (UNHCR) estimates the number of forcibly displaced people increased from 22.7 million people in 1996 to 67.7 million people in 2016. Human mobility is associated with the introduction of infectious disease pathogens. The aim of this study was to describe the range of pathogens in forcibly displaced populations over time using an informal event monitoring system.MethodsWe conducted a retrospective analysis of ProMED, a digital disease monitoring system, to identify reports of outbreak events involving forcibly displaced populations between 1996 and 2016. Number of outbreak events per year was tabulated. Each record was assessed to determine outbreak location, pathogen, origin of persons implicated in the outbreak, and suspected versus confirmed case counts.ResultsOne hundred twenty-eight independent outbreak events involving forcibly displaced populations were identified. Over 840,000 confirmed or suspected cases of infectious diseases such as measles, cholera, cutaneous leishmaniasis, dengue, and others were reported in 48 destination countries/territories. The average rate of outbreak events concerning forcibly displaced persons per total number of reports published on ProMED per year increased over time. The majority of outbreak events (63%) were due to acquisition of disease in the destination country.ConclusionThis study found that reports of outbreak events involving forcibly displaced populations have increased in ProMED. The events and outbreaks detected in this retrospective review underscore the importance of capturing displaced populations in surveillance systems for rapid detection and response.

BackgroundMedia reporting on communicable diseases has been demonstrated to affect the perception of the public. Communicable disease reporting related to foreign-born persons has not yet been evaluated.ObjectiveExamine how political leaning in the media affects reporting on tuberculosis (TB) in foreign-born persons.MethodsHealthMap, a digital surveillance platform that aggregates news sources on global infectious diseases, was used. Data was queried for media reports from the U.S. between 2011-2019, containing the term "TB" or "tuberculosis" and "foreign born", "refugee (s)," or "im (migrants)." Reports were reviewed to exclude duplicates and non-human cases. Each media source was rated using two independent media bias indicators to assess political leaning. Forty-six non-tuberculosis reports were randomly sampled and evaluated as a control. Two independent reviewers performed sentiment analysis on each report.ResultsOf 891 TB-associated reports in the US, 46 referenced foreign-born individuals, and were included in this analysis. 60.9% (28) of reports were published in right-leaning news media and 6.5% (3) of reports in left-leaning media, while 39.1% (18) of the control group reports were published in left- leaning media and 10.9% (5) in right-leaning media (p < .001). 43% (20) of all study reports were posted in 2016. Sentiment analysis revealed that right-leaning reports often portrayed foreign-born persons negatively.ConclusionPreliminary data from this pilot suggest that political leaning may affect reporting on TB in US foreign-born populations. Right-leaning news organizations produced the most reports on TB, and the majority of these reports portrayed foreign-born persons negatively. In addition, the control group comprised of non-TB, non-foreign born reports on communicable diseases featured a higher percentage of left-leaning news outlets, suggesting that reporting on TB in foreign-born individuals may be of greater interest to right-leaning outlets. Further investigation both in the U.S. and globally is needed.

BackgroundThe purpose of this study was to identify global trends in Listeria monocytogenes epidemiology using ProMED reports. ProMED is a publicly available, global outbreak reporting system that uses both informal and formal sources. In the context of Listeria, ProMED reports on atypical findings such as higher than average case counts, events from unusual sources, and multinational outbreaks.MethodsKeywords "Listeria" and "listeriosis" were utilized in the ProMED search engine covering the years 1996-2018. Issue date, countries involved, source, suspected and confirmed case counts, and fatalities were extracted. Data unique to each event, including commentary by content experts, were evaluated. When multiple reports regarding the same outbreak or recall were obtained, the last report pertaining to that outbreak was utilized. Rates of Listeria events over time were compared using a normal approximation to the Poisson distribution; p

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.

ObjectiveThe Guide to Infection Control in the Hospital (Guide) is an open access resource produced by the International Society for Infectious Diseases (ISID) to assist in the prevention of infection acquisition and transmission worldwide. A survey was distributed to 8055 current Guide users to understand their needs.MethodsThe survey consisted of 48-questions regarding infection prevention and control (IPC) availability and needs. Dichotomous questions, Likert scale-type questions, and open-and closed-ended questions were used.ResultsRespondents (n=1121) from 194 countries and six WHO regions participated in the survey. 43% (488) identified as physicians. Personal protective equipment (PPE) availability, training, and antimicrobial susceptibility testing varied between regions. Only 11% of respondents from low-income countries reported consistent access to respiratory equipment, 12% to isolation gowns, 4% to negative pressure rooms or personnel trained in IPC, and 20% to antimicrobial resistance testing. This differed significantly to high and upper middle-income resource settings (p

This invited editorial introduces a special issue of Epidemiology & Infection while also discussing advances in emerging infectious diseases.

India is currently focusing on providing education for all, and special population like the gifted is being neglected. This is evident in the case of Aditya (IQ 145), a 7-year-old boy, and his parents who after discovering that their child is gifted attempt to meet his unique educational needs. Despite evidence of Aditya qualifying in examination two grades above his age level, the school authorities neither offer acceleration nor provide appropriate resources. This study attempts to delineate the importance of combined efforts of parent, school, and the community in nurturing gifted children. Finally, the researcher makes a few recommendations for parents: (i) create support groups to share parenting experiences; (ii) explore online and local learning resources to educate themselves about giftedness and nurture their children; and (iii) identify mentors in the community or other parents who can support in meeting the educational needs of these children.