Modeling the critical care demand and antibiotics resources needed during the Fall 2009 wave of influenza A(H1N1) pandemic

Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

Impacts of the COVID-19 pandemic on pharmaceuticals in wastewater treated for beneficial reuse: Two case studies in central Pennsylvania

During the COVID-19 pandemic, wastewater surveillance was leveraged as a powerful tool for monitoring community-scale health. Further, the well-known persistence of some pharmaceuticals through wastewater treatment plants spurred concerns that increased usage of pharmaceuticals during the pandemic would increase the concentrations in wastewater treatment plant effluent. We collected weekly influent and effluent samples from May 2020 through May 2021 from two wastewater treatment plants in central Pennsylvania, the Penn State Water Reclamation Facility and the University Area Joint Authority, that provide effluent for beneficial reuse, including for irrigation. Samples were analyzed for severe acute respiratory syndrome coronavirus 2 (influent only), two over-the-counter medicines (acetaminophen and naproxen), five antibiotics (ampicillin, doxycycline, ofloxacin, sulfamethoxazole, and trimethoprim), two therapeutic agents (remdesivir and dexamethasone), and hydroxychloroquine. Although there were no correlations between pharmaceutical and virus concentration, remdesivir detection occurred when the number of hospitalized patients with COVID-19 increased, and dexamethasone detection co-occurred with the presence of patients with COVID-19 on ventilators. Additionally, Penn State decision-making regarding instruction modes explained the temporal variation of influent pharmaceutical concentrations, with detection occurring primarily when students were on campus. Risk quotients calculated for pharmaceuticals with known effective and lethal concentrations at which 50% of a population is affected for fish, daphnia, and algae were generally low in the effluent; however, some acute risks from sulfamethoxazole were high when students returned to campus. Remdesivir and dexamethasone persisted through the wastewater treatment plants, thereby introducing novel pharmaceuticals directly to soils and surface water. These results highlight connections between human health and water quality and further demonstrate the broad utility of wastewater surveillance.

Digitizable therapeutics for decentralized mitigation of global pandemics

When confronted with a globally spreading epidemic, we seek efficient strategies for drug dissemination, creating a competition between supply and demand at a global scale. Propagating along similar networks, e.g., air-transportation, the spreading dynamics of the supply vs. the demand are, however, fundamentally different, with the pathogens driven by contagion dynamics, and the drugs by commodity flow. We show that these different dynamics lead to intrinsically distinct spreading patterns: while viruses spread homogeneously across all destinations, creating a concurrent global demand, commodity flow unavoidably leads to a highly uneven spread, in which selected nodes are rapidly supplied, while the majority remains deprived. Consequently, even under ideal conditions of extreme production and shipping capacities, due to the inherent heterogeneity of network-based commodity flow, efficient mitigation becomes practically unattainable, as homogeneous demand is met by highly heterogeneous supply. Therefore, we propose here a decentralized mitigation strategy, based on local production and dissemination of therapeutics, that, in effect, bypasses the existing distribution networks. Such decentralization is enabled thanks to the recent development of digitizable therapeutics, based on, e.g., short DNA sequences or printable chemical compounds, that can be distributed as digital sequence files and synthesized on location via DNA/3D printing technology. We test our decentralized mitigation under extremely challenging conditions, such as suppressed local production rates or low therapeutic efficacy, and find that thanks to its homogeneous nature, it consistently outperforms the centralized alternative, saving many more lives with significantly less resources.

Results from the second year of a collaborative effort to forecast influenza seasons in the United States

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts.

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

Developments in Surge Research Priorities: A Systematic Review of the Literature Following the Academic Emergency Medicine Consensus Conference, 2007-2015

OBJECTIVES

In 2006, Academic Emergency Medicine (AEM) published a special issue summarizing the proceedings of the AEM consensus conference on the "Science of Surge." One major goal of the conference was to establish research priorities in the field of "disasters" surge. For this review, we wished to determine the progress toward the conference's identified research priorities: 1) defining criteria and methods for allocation of scarce resources, 2) identifying effective triage protocols, 3) determining decision-makers and means to evaluate response efficacy, 4) developing communication and information sharing strategies, and 5) identifying methods for evaluating workforce needs.

METHODS

Specific criteria were developed in conjunction with library search experts. PubMed, Embase, Web of Science, Scopus, and the Cochrane Library databases were queried for peer-reviewed articles from 2007 to 2015 addressing scientific advances related to the above five research priorities identified by AEM consensus conference. Abstracts and foreign language articles were excluded. Only articles with quantitative data on predefined outcomes were included; consensus panel recommendations on the above priorities were also included for the purposes of this review. Included study designs were randomized controlled trials, prospective, retrospective, qualitative (consensus panel), observational, cohort, case-control, or controlled before-and-after studies. Quality assessment was performed using a standardized tool for quantitative studies.

RESULTS

Of the 2,484 unique articles identified by the search strategy, 313 articles appeared to be related to disaster surge. Following detailed text review, 50 articles with quantitative data and 11 concept papers (consensus conference recommendations) addressed at least one AEM consensus conference surge research priority. Outcomes included validation of the benchmark of 500 beds/million of population for disaster surge capacity, effectiveness of simulation- and Internet-based tools for forecasting of hospital and regional demand during disasters, effectiveness of reverse triage approaches, development of new disaster surge metrics, validation of mass critical care approaches (altered standards of care), use of telemedicine, and predictions of optimal hospital staffing levels for disaster surge events. Simulation tools appeared to provide some of the highest quality research.

CONCLUSION

Disaster simulation studies have arguably revolutionized the study of disaster surge in the intervening years since the 2006 AEM Science of Surge conference, helping to validate some previously known disaster surge benchmarks and to generate new surge metrics. Use of reverse triage approaches and altered standards of care, as well as Internet-based tools such as Google Flu Trends, have also proven effective. However, there remains significant work to be done toward standardizing research methodologies and outcomes, as well as validating disaster surge metrics.

Severe virus influenza A H1N1 related pneumonia and community-acquired pneumonia: differences in the evolution

OBJECTIVE

To analyze the clinical, laboratory and evolution data of patients with severe influenza A H1N1 pneumonia and compare the data with that of patients with severe community-acquired bacterial pneumonia.

METHODS

Cohort and retrospective study. All patients admitted to the intensive care unit between May 2009 and December 2010 with a diagnosis of severe pneumonia caused by the influenza A H1N1 virus were included in the study. Thirty patients with severe community-acquired pneumonia admitted within the same period were used as a control group. Severe community-acquired pneumonia was defined as the presence of at least one major severity criteria (ventilator or vasopressor use) or two minor criteria.

RESULTS

The data of 45 patients were evaluated. Of these patients, 15 were infected with H1N1. When compared to the group with community-acquired pneumonia, patients from the H1N1 group had significantly lower leukocyte counts on admission (6,728±4,070 versus 16,038±7,863; p<0.05) and lower C-reactive protein levels (Day 2: 15.1±8.1 versus 22.1±10.9 mg/dL; p<0.05). The PaO2/FiO2 ratio values were lower in the first week in patients with H1N1. Patients who did not survive the H1N1 severe pneumonia had significantly higher levels of C-reactive protein and higher serum creatinine levels compared with patients who survived. The mortality rate was significantly higher in the H1N1 group than in the control group (53% versus 20%; p=0.056, respectivelly).

CONCLUSION

Differences in the leukocyte count, C-reactive protein concentrations and oxygenation profiles may contribute to the diagnosis and prognosis of patients with severe influenza A H1N1 virus-related pneumonia and community-acquired pneumonia.

Intra- and inter-pandemic variations of antiviral, antibiotics and decongestants in wastewater treatment plants and receiving rivers

The concentration of eleven antibiotics (trimethoprim, oxytetracycline, ciprofloxacin, azithromycin, cefotaxime, doxycycline, sulfamethoxazole, erythromycin, clarithromycin, ofloxacin, norfloxacin), three decongestants (naphazoline, oxymetazoline, xylometazoline) and the antiviral drug oseltamivir's active metabolite, oseltamivir carboxylate (OC), were measured weekly at 21 locations within the River Thames catchment in England during the month of November 2009, the autumnal peak of the influenza A[H1N1]pdm09 pandemic. The aim was to quantify the pharmaceutical response to the pandemic and compare this to drug use during the late pandemic (March 2010) and the inter-pandemic periods (May 2011). A large and small wastewater treatment plant (WWTP) were sampled in November 2009 to understand the differential fate of the analytes in the two WWTPs prior to their entry in the receiving river and to estimate drug users using a wastewater epidemiology approach. Mean hourly OC concentrations in the small and large WWTP's influent were 208 and 350 ng/L (max, 2070 and 550 ng/L, respectively). Erythromycin was the most concentrated antibiotic measured in Benson and Oxford WWTPs influent (max=6,870 and 2,930 ng/L, respectively). Napthazoline and oxymetazoline were the most frequently detected and concentrated decongestant in the Benson WWTP influent (1650 and 67 ng/L) and effluent (696 and 307 ng/L), respectively, but were below detection in the Oxford WWTP. OC was found in 73% of November 2009's weekly river samples (max=193 ng/L), but only in 5% and 0% of the late- and inter-pandemic river samples, respectively. The mean river concentration of each antibiotic during the pandemic largely fell between 17-74 ng/L, with clarithromycin (max=292 ng/L) and erythromycin (max=448 ng/L) yielding the highest single measure. In general, the concentration and frequency of detecting antibiotics in the river increased during the pandemic. OC was uniquely well-suited for the wastewater epidemiology approach owing to its nature as a prodrug, recalcitrance and temporally- and spatially-resolved prescription statistics.

Health systems' "surge capacity": state of the art and priorities for future research

CONTEXT

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.

METHODS

A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health.

FINDINGS

The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.

CONCLUSIONS

The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

Health system resource gaps and associated mortality from pandemic influenza across six Asian territories

Background

Southeast Asia has been the focus of considerable investment in pandemic influenza preparedness. Given the wide variation in socio-economic conditions, health system capacity across the region is likely to impact to varying degrees on pandemic mitigation operations. We aimed to estimate and compare the resource gaps, and potential mortalities associated with those gaps, for responding to pandemic influenza within and between six territories in Asia.

Methods and Findings

We collected health system resource data from Cambodia, Indonesia (Jakarta and Bali), Lao PDR, Taiwan, Thailand and Vietnam. We applied a mathematical transmission model to simulate a “mild-to-moderate” pandemic influenza scenario to estimate resource needs, gaps, and attributable mortalities at province level within each territory. The results show that wide variations exist in resource capacities between and within the six territories, with substantial mortalities predicted as a result of resource gaps (referred to here as “avoidable” mortalities), particularly in poorer areas. Severe nationwide shortages of mechanical ventilators were estimated to be a major cause of avoidable mortalities in all territories except Taiwan. Other resources (oseltamivir, hospital beds and human resources) are inequitably distributed within countries. Estimates of resource gaps and avoidable mortalities were highly sensitive to model parameters defining the transmissibility and clinical severity of the pandemic scenario. However, geographic patterns observed within and across territories remained similar for the range of parameter values explored.

Conclusions

The findings have important implications for where (both geographically and in terms of which resource types) investment is most needed, and the potential impact of resource mobilization for mitigating the disease burden of an influenza pandemic. Effective mobilization of resources across administrative boundaries could go some way towards minimizing avoidable deaths.

Pandemic influenza and health system resource gaps in Bali: an analysis through a resource transmission dynamics model

The failure to contain pandemic influenza A(H1N1) 2009 in Mexico has shifted global attention from containment to mitigation. Limited surveillance and reporting have, however, prevented detailed assessment of mitigation during the pandemic, particularly in low- and middle-income countries. To assess pandemic influenza case management capabilities in a resource-limited setting, the authors used a health system questionnaire and density-dependent, deterministic transmission model for Bali, Indonesia, determining resource gaps. The majority of health resources were focused in and around the provincial capital, Denpasar; however, gaps are found in every district for nursing staff, surgical masks, and N95 masks. A relatively low pathogenicity pandemic influenza virus would see an overall surplus for physicians, antivirals, and antimicrobials; however, a more pathogenic virus would lead to gaps in every resource except antimicrobials. Resources could be allocated more evenly across Bali. These, however, are in short supply universally and therefore redistribution would not fill resource gaps.

Modeling for Estimating Influenza Patients from ILI Surveillance Data in Korea

Objective

Prediction of influenza incidence among outpatients from an influenza surveillance system is important for public influenza strategy.

Methods

We developed two influenza prediction models through influenza surveillance data of the Korea Centers for Disease Control and Prevention (each year, each province and metropolitan city; total reported patients with influenza-like illness stratified by age) for 6 years from 2005 to 2010 and disease-specific data (influenza code J09-J11, monthly number of influenza patients, total number of outpatients and hospital visits) from the Health Insurance Review and Assessment service.

Results

Incidence of influenza in each area, year, and month was estimated from our prediction models, which were validated by simulation processes. For example, in November 2009, Seoul and Joenbuk, the final number of influenza patients calculated by prediction models A and B underestimated actual reported cases by 64 and 833 patients, respectively, in Seoul and 6 and 9 patients, respectively, in Joenbuk. R-square demonstrated that prediction model A was more suitable than model B for estimating the number of influenza patients.

Conclusion

Our prediction models from the influenza surveillance system could estimate the nationwide incidence of influenza. This prediction will provide important basic data for national quarantine activities and distributing medical resources in future pandemics.

Assessing the ecotoxicologic hazards of a pandemic influenza medical response

BACKGROUND

The global public health community has closely monitored the unfolding of the 2009 H1N1 influenza pandemic to best mitigate its impact on society. However, little attention has been given to the impact of this response on the environment. Antivirals and antibiotics prescribed to treat influenza are excreted into wastewater in a biologically active form, which presents a new and potentially significant ecotoxicologic challenge to microorganisms responsible for wastewater nutrient removal in wastewater treatment plants (WWTPs) and receiving rivers.

OBJECTIVES

We assessed the ecotoxicologic risks of a pandemic influenza medical response.

METHODS

To evaluate this risk, we coupled a global spatially structured epidemic model that simulates the quantities of antivirals and antibiotics used during an influenza pandemic of varying severity and a water quality model applied to the Thames catchment to determine predicted environmental concentrations. An additional model was then used to assess the effects of antibiotics on microorganisms in WWTPs and rivers.

RESULTS

Consistent with expectations, our model projected a mild pandemic to exhibit a negligible ecotoxicologic hazard. In a moderate and severe pandemic, we projected WWTP toxicity to vary between 0-14% and 5-32% potentially affected fraction (PAF), respectively, and river toxicity to vary between 0-14% and 0-30% PAF, respectively, where PAF is the fraction of microbial species predicted to be growth inhibited (lower and upper 95% reference range).

CONCLUSIONS

The current medical response to pandemic influenza might result in the discharge of insufficiently treated wastewater into receiving rivers, thereby increasing the risk of eutrophication and contamination of drinking water abstraction points. Widespread drugs in the environment could hasten the generation of drug resistance. Our results highlight the need for empirical data on the effects of antibiotics and antiviral medications on WWTPs and freshwater ecotoxicity.

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

BACKGROUND

Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.

RESULTS

We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.

CONCLUSIONS

The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.