In April 2009, an outbreak due to infection with the 2009 pandemic influenza A (H1N1) virus (pH1N1) was investigated in a New York City high school. We surveyed household contacts of ill students to ...characterize the extent of transmission within households, identify contact groups at highest risk for illness, and assess the potential for preventing household transmission. Influenza-like illness (ILI) was reported by 79 of 702 household contacts (11.3% attack rate). Multivariate analysis showed that older age was protective: for each increasing year of age, the risk of ILI was reduced 5%. Additional protective factors included antiviral prophylaxis and having had a household discussion about influenza. Providing care for the index case patient and watching television with the index case patient were risk factors among parents and siblings, respectively. Fifty percent of cases occurred within 3 days of onset of illness in the student. These factors have implications for mitigating the impact of pH1N1 transmission.
Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the ...2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was.
To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers.
2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies.
Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.
•School dismissals are particularly effective in delaying the epidemic peak.•Dismissals at the city or county level yield the greatest reduction in disease incidence for all but the most severe ...pandemic scenarios.•Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics.
We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4–6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5–10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented.
Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be ...assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.
Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1-G; otherwise R can be estimated by 1-F when the case detection rate is low. In more general cases, bounds on R can still be derived.
We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary.
Beginning in December 2020, the COVID-19 Scenario Modeling Hub has provided quantitative scenario-based projections for cases, hospitalizations, and deaths, aggregated across up to nine modeling ...groups. Projections spanned multiple months into the future and provided timely information on potential impacts of epidemiological uncertainties and interventions. Projections results were shared with the public, public health partners, and the Centers for Disease Control COVID-19 Response Team. The projections provided insights on situational awareness and informed decision-making to mitigate COVID-19 disease burden (e.g., vaccination strategies). By aggregating projections from multiple modeling teams, the Scenario Modeling Hub provided rapidly synthesized information in times of great uncertainty and conveyed possible trajectories in the presence of emerging variants. Here we detail several use cases of these projections in public health practice and communication, including assessments of whether modeling results directly or indirectly informed public health communication or guidance. These include multiple examples where comparisons of projected COVID-19 disease outcomes under different vaccination scenarios were used to inform Advisory Committee for Immunization Practices recommendations. We also describe challenges and lessons learned during this highly beneficial collaboration.
Background:
Antiviral chemoprophylaxis for influenza is recommended in nursing homes to prevent transmission and severe disease among residents with higher risk of severe influenza complications. ...Interim CDC guidance recommends that long-term care facilities initiate antiviral chemoprophylaxis with oral oseltamivir for all non-ill residents living in the same unit following the start of an outbreak in a facility (ie, ≥2 patients ill within 72 hours and of whom at least 1 resident has laboratory-confirmed influenza). Prophylaxis continues for a minimum of 2 weeks and for at least 7 days after the last laboratory-confirmed case. However, facilities may not strictly adhere to this guidance, with 1 study showing up to 68% of facilities were nonadherent to national guidance (Silva et al 2020). Here, we model the potential impacts of different antiviral prophylaxis strategies.
Methods:
We developed a susceptible–exposed–asymptomatic–infected–recovered (SEAIR) compartmental model of an average-sized nursing home comprising short-stay residents, long-stay residents, and healthcare personnel (HCP). Persons treated with antiviral chemoprophylaxis were less susceptible to infection, had a lower probability of symptoms if infected, a reduced viral load, and a shortened duration of infectiousness. We included influenza vaccination for residents and HCP through reduced probability of symptomatic infection. Coverage rates were estimated from CDC FluVaxView and CMS COVID-19 nursing home data. As a base case, we modeled a scenario with prophylaxis implemented according to guidance. We varied uptake by residents and HCP (from 10% to 90%), case thresholds for prophylaxis initiation (1–5 cases identified), and timing of prophylaxis cessation: either time dependent (ie, 10–14 days of prophylaxis) or case-dependent (ie, continuing prophylaxis for 1–7 days with no cases).
Results:
In the scenario based on current guidance, prophylaxis reduced resident cases by 16% and resident hospitalizations by 45%, compared to no prophylaxis (Fig. 1A). Scenarios that differed from the guidance altered case burden and timing: Time-dependent prophylaxis cessation increased resident cases and hospitalizations (Fig. 1A). Timing of prophylaxis initiation had slight effects on the timing of the epidemic and minimal effects on resident cases and hospitalizations (Fig. 1B). High resident uptake was important for reducing resident cases and hospitalizations (Fig. 1C), but increasing HCP uptake had minimal effect (Fig. 1D).
Conclusions:
Our findings support the current prophylaxis guidance. Promptly implementing prophylaxis reduces resident cases and hospitalizations. Continuing prophylaxis until cases are no longer identified reduces cases and hospitalizations.
Disclosure:
None
The United States 2017-18 influenza season (October 1, 2017-May 19, 2018) was a high severity season with high levels of outpatient clinic and emergency department visits for influenza-like illness ...(ILI), high influenza-related hospitalization rates, and elevated and geographically widespread influenza activity across the country for an extended period. Nationally, ILI activity began increasing in November, reaching an extended period of high activity during January-February, and remaining elevated through March. Influenza A(H3N2) viruses predominated through February and were predominant overall for the season; influenza B viruses predominated from March onward. This report summarizes U.S. influenza activity* during October 1, 2017-May 19, 2018.
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During August 2011, influenza A (H3N2) variant A(H3N2)v virus infection developed in a child who attended an agricultural fair in Pennsylvania, USA; the virus resulted from reassortment of a swine ...influenza virus with influenza A(H1N1)pdm09. We interviewed fair attendees and conducted a retrospective cohort study among members of an agricultural club who attended the fair. Probable and confirmed cases of A(H3N2)v virus infection were defined by serology and genomic sequencing results, respectively. We identified 82 suspected, 4 probable, and 3 confirmed case-patients who attended the fair. Among 127 cohort study members, the risk for suspected case status increased as swine exposure increased from none (4%; referent) to visiting swine exhibits (8%; relative risk 2.1; 95% CI 0.2-53.4) to touching swine (16%; relative risk 4.4; 95% CI 0.8-116.3). Fairs may be venues for zoonotic transmission of viruses with epidemic potential; thus, health officials should investigate respiratory illness outbreaks associated with agricultural events.
We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, ...including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities.
Since the introduction of pandemic influenza A (H1N1) to the USA in 2009, the Influenza Incidence Surveillance Project has monitored the burden of influenza in the outpatient setting through ...population-based surveillance.
From Oct 1, 2009, to July 31, 2013, outpatient clinics representing 13 health jurisdictions in the USA reported counts of influenza-like illness (fever including cough or sore throat) and all patient visits by age. During four years, staff at 104 unique clinics (range 35-64 per year) with a combined median population of 368,559 (IQR 352,595-428,286) attended 35,663 patients with influenza-like illness and collected 13,925 respiratory specimens. Clinical data and a respiratory specimen for influenza testing by RT-PCR were collected from the first ten patients presenting with influenza-like illness each week. We calculated the incidence of visits for influenza-like illness using the size of the patient population, and the incidence attributable to influenza was extrapolated from the proportion of patients with positive tests each week.
The site-median peak percentage of specimens positive for influenza ranged from 58.3% to 77.8%. Children aged 2 to 17 years had the highest incidence of influenza-associated visits (range 4.2-28.0 per 1000 people by year), and adults older than 65 years had the lowest (range 0.5-3.5 per 1000 population). Influenza A H3N2, pandemic H1N1, and influenza B equally co-circulated in the first post-pandemic season, whereas H3N2 predominated for the next two seasons. Of patients for whom data was available, influenza vaccination was reported in 3289 (28.7%) of 11,459 patients with influenza-like illness, and antivirals were prescribed to 1644 (13.8%) of 11,953 patients.
Influenza incidence varied with age groups and by season after the pandemic of 2009 influenza A H1N1. High levels of influenza virus circulation, especially in young children, emphasise the need for additional efforts to increase the uptake of influenza vaccines and antivirals.
US Centers for Disease Control and Prevention.
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