Epidemics

Using Medicare claims data on prescriptions of oseltamivir dispensed to people 65 years old and older, we present a descriptive analysis of patterns of influenza activity in the United States for 579 core-based statistical areas (CBSAs) from the 2010-2011 through the 2015-2016 influenza seasons. During this time, 1,010,819 beneficiaries received a prescription of oseltamivir, ranging from 45,888 in 2011-2012 to 380,745 in 2014-2015. For each season, the peak weekly number of prescriptions correlated with the total number of prescriptions (Pearson's r\u2009 65\u20090.88). The variance in peak timing decreased with increasing severity (p\u2009<\u20090.0001). Among these 579 CBSAs, neither peak timing, nor relative timing, nor severity of influenza seasons showed evidence of spatial autocorrelation (0.02\u2009 64\u2009Moran's I\u2009 64\u20090.23). After aggregating data to the state level, agreement between the seasonal severity at the CBSA level and the state level was fair (median Cohen's weighted \u3ba\u2009=\u20090.32, interquartile range\u2009=\u20090.26-0.39). Based on seasonal severity, relative timing, and geographic place, we used hierarchical agglomerative clustering to join CBSAs into influenza zones for each season. Seasonal maps of influenza zones showed no obvious patterns that might assist in predicting influenza zones for future seasons. Because of the large number of prescriptions, these data may be especially useful for characterizing influenza activity and geographic distribution during low severity seasons, when other data sources measuring influenza activity are likely to be sparse.CC999999/Intramural CDC HHS/United States2019-05-15T00:00:00Z30249390PMC6519085626

Spatial distribution and determinants of childhood vaccination refusal in the United States

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012--2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal.Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

Comparative evaluation of methods that adjust for reporting biases in participatory surveillance systems

Over the past decade the widespread proliferation of mobile devices and wearable technology has significantly changed the landscape of epidemiological data gathering and evolved into a field known as Digital Epidemiology. One source of active digital data collection is online participatory syndromic surveillance systems. These systems actively engage the general public in reporting health-related information and provide timely information about disease trends within the community. This dissertation comprehensively addresses how researchers can effectively use this type of data to answer questions about Influenza-like Illness (ILI) disease burden in the general population. We assess the representativeness and reporting habits of volunteers for these systems and use this information to develop statistically rigorous methods that adjust for potential biases. Specifically, we evaluate how different missing data methods, such as complete case and multiple imputation models, affect estimates of ILI disease burden using both simulated data as well as data from the Australian system, Flutracking.net. We then extend these methods to data from the American system, Flu Near You, which has different patterns. Finally, we provide examples of how this data has been used to answer questions about ILI in the general community and promote better understanding of disease surveillance and data literacy among volunteers

Leveraging and adapting global health systems and programs during the COVID-19 pandemic

Overview -- Surveillance, Information, and Laboratory Systems -- Workforce, Institutional, and Public Health Capacity Development -- Clinical and Health Services Delivery and Impact -- Commentaries -- About the Cover.Overview: Partnerships, Collaborations, and Investments Integral to CDC\u2019s International Response to COVID-19 / R. P. Walensky -- Global Responses to the COVID-19 Pandemic / C. H. Cassell et al. -- Surveillance, Information, and Laboratory Systems: Lessons Learned from CDC\u2019s Global COVID-19 Early Warning and Response Surveillance System / P. M. Ricks et al. -- Enhancing Respiratory Disease Surveillance to Detect COVID-19 in Shelters for Displaced Persons, Thailand\u2013Myanmar Border, 2020\u20132021 / B. Knust et al. -- Leveraging International Influenza Surveillance Systems and Programs during the COVID-19 Pandemic / P. Marcenac et al. -- Incorporating COVID-19 into Acute Febrile Illness Surveillance Systems, Belize, Kenya, Ethiopia, Peru, and Liberia, 2020\u20132021 / D. C. Shih et al. -- Extending and Strengthening Routine DHIS2 Surveillance Systems for COVID-19 Responses in Sierra Leone, Sri Lanka, and Uganda / C. Kinkade et al. -- Leveraging PEPFAR-Supported Health Information Systems for COVID-19 Pandemic Response / M. Mirza et al. -- Contribution of PEPFAR-Supported HIV and TB Molecular Diagnostic Networks to COVID-19 Testing Preparedness in 16 Countries / E. Rottinghaus Romano et al. -- A Nationally Representative Survey of COVID-19 in Pakistan, 2021\u20132022 / S. Aheron et al. -- SARS-CoV-2 Prevalence in Malawi Based on Data from Survey of Communities and Health Workers in 5 High-Burden Districts, October 2020 / J. Theu et al. -- Determining Gaps in Publicly Shared SARS-CoV-2 Genomic Surveillance Data by Analysis of Global Submissions / E. C. Ohlsen et al. -- Comparison of COVID-19 Pandemic Waves in 10 Countries in Southern Africa, 2020\u20132021 / J. Smith-Sreen et al. -- Using Population Mobility Patterns to Adapt COVID-19 Response Strategies in 3 East Africa Countries / R. D. Merrill et al. -- Community-Based Surveillance and Geographic Information System\u2012Linked Contact Tracing in COVID-19 Case Identification, Ghana, March\u2012June 2020 / E. Kenu et al. -- The Future of Infodemic Surveillance as Public Health Surveillance / H. Chiou et al. -- Workforce, Institutional, and Public Health Capacity Development: Continuing Contributions of Field Epidemiology Training Programs to Global COVID-19 Response / E. Bell et al. -- India Field Epidemiology Training Program Response to COVID-19 Pandemic, 2020\u20132021 / S. Singh et al. -- COVID-19 Response Roles among CDC International Public Health Emergency Management Fellowship Graduates / S. Krishnan et al. -- Exploratory Literature Review of the Role of National Public Health Institutes in COVID-19 Response / A. Zuber et al. -- Adapting Longstanding Public Health Collaborations between Government of Kenya and CDC Kenya in Response to the COVID-19 Pandemic, 2020\u20132021 / A. Herman-Roloff et al. -- Effect of Nigeria Presidential Task Force on COVID-19 Pandemic, Nigeria / O. Bolu et al. -- Use of Epidemiology Surge Support to Enhance Robustness and Expand Capacity of SARS-CoV-2 Pandemic Response, South Africa / R. Taback-Esra et al. -- Building on Capacity Established through US Centers for Disease Control and Prevention Global Health Programs to Respond to COVID-19, Cameroon / E. Dokubo et al. -- Use of Project ECHO in Response to COVID-19 in Countries Supported by US President\u2019s Emergency Plan for AIDS Relief / J. Wright et al. -- Faith Community Engagement to Mitigate COVID-19 Transmission Associated with Mass Gathering, Uman, Ukraine, September 2021 / L. Erickson-Mamane et al. -- Clinical and Health Services Delivery and Impact: Effects of COVID-19 on Vaccine-Preventable Disease Surveillance Systems in the World Health Organization African Region, 2020 / J. Bigouette et al. -- CDC\u2019s COVID-19 International Vaccine Implementation and Evaluation Program and Lessons from Earlier Vaccine Introductions / H. M. Soeters et al. -- Effects of Decreased Immunization Coverage for Hepatitis B Virus Caused by COVID-19 in World Health Organization Western Pacific and African Regions, 2020 / H. J. Kabore et al. -- Past as Prologue\u2014Use of Rubella Vaccination Program Lessons to Inform COVID-19 Vaccination / M. G. Dixon et al. -- Leveraging Lessons Learned from Yellow Fever and Polio Immunization Campaigns during COVID-19 Pandemic, Ghana, 2021 / K. Amponsa-Achiano et al. -- Effectiveness of Whole-Virus COVID-19 Vaccine among Healthcare Personnel, Lima, Peru / C. S. Arriola et al. -- Leveraging HIV Program and Civil Society to Accelerate COVID-19 Vaccine Uptake, Zambia / P. Bobo et al. -- Adopting World Health Organization Multimodal Infection Prevention and Control Strategies to Respond to COVID-19, Kenya / D. Kimani et al. -- Infection Prevention and Control Initiatives to Prevent Healthcare-Associated Transmission of SARS-CoV-2, East Africa / D. J. Gomes et al. -- Effects of COVID-19 Pandemic on Voluntary Medical Male Circumcision Services for HIV Prevention, Sub-Saharan Africa, 2020 / M. E. Peck et al. -- Sexual Violence Trends before and after Rollout of COVID-19 Mitigation Measures, Kenya / W. Ochieng et al. -- Clinical and Economic Impact of COVID-19 on Agricultural Workers, Guatemala / D. Olson et al. -- Outcomes after Acute Malnutrition Program Adaptations to COVID-19, Uganda, Ethiopia, and Somalia / T. Shragai et al. -- Commentaries: Lessons from Nigeria\u2019s Adaptation of Global Health Initiatives during the COVID-19 Pandemic / C. Ihekweazu -- About the Cover: A United Response to COVID-19\u2014an Artist\u2019s Perspective / B. Breedlove et al

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment

Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products