AUTOMAP: solution for geospatial monitoring in public health

2021 ◽  
Author(s):  
Nelly Lopes de Moraes Gil ◽  
Aline Chotte de Oliveira ◽  
Gabriela Ganassin ◽  
Carolina Luca ◽  
Sandra Pelloso ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Web Application ◽  
Decision Makers ◽  
Health Conditions ◽  
Epidemiological Surveillance ◽  
Free Access ◽  
Evidence Based ◽  
Health Decision ◽  
Shiny Application

Background: Health decision-makers currently face the challenge of accumulating health data in time to inform evidence-based interventions to improve health outcomes. The Brazilian healthcare system is in need of daily primary care data reported in real-time to support evidence-based policy decisions. This study aims to detail the development of a solution for geospatial monitoring in public health called AUTOMAP. Its main objective is to facilitate epidemiological surveillance and promote that rapidly available data improve the provision of health services. Methods: AUTOMAP is an application that articulates concepts inherent to epidemiological surveillance, geographic information systems, and free access technologies to design a monitoring tool of health conditions. The system architecture consists of three modules: user, application, and database. They work together to collect information regarding health conditions, its processing, and dynamic viewing. AUTOMAP design uses the statistical language R, which employs literate programming through a Shiny application package to transform statistical results of health conditions into interactive maps in real-time. AUTOMAP is a web application that has two interfaces: one for loading data and another for generating dynamic epidemiological maps. Conclusion: AUTOMAP allows a variety of clinical solutions, such as risk calculators, spatial evaluation of events of interest, decision models, simulations, and epidemiological patient monitoring. The software is open-source with easy accessibility, allowing anyone to make adjustments and handle a myriad of health conditions, thus being applicable globally. AUTOMAP is a tool that will facilitate and advance data collection for evidence generation and expedite evidence-based health system improvements.

scholarly journals Model Input and Optimization: Improving the Speed and Accuracy of Our COVID-19 Hospitalization Forecasts

2021 ◽  
Vol 1 (S1) ◽  
pp. s9-s9
Author(s):  
Sarah Rhea ◽  
Emily Hadley ◽  
Kasey Jones ◽  
Alexander Preiss ◽  
Marie Stoner ◽  
...  
Keyword(s):  
Public Health ◽  
North Carolina ◽  
Real Time ◽  
Acute Care ◽  
Acute Care Hospitals ◽  
Decision Makers ◽  
Hospital Level ◽  
Public Health Decision ◽  
Health Decision ◽  
Speed And Accuracy

Background: During the COVID-19 pandemic, public-health decision makers have increasingly relied on hospitalization forecasts that are routinely provided, accurate, and based on timely input data to inform pandemic planning. In North Carolina, we adapted an existing agent-based model (ABM) to produce 30-day hospitalization forecasts of COVID-19 and non–COVID-19 hospitalizations for use by public-health decision makers. We sought to continually improve model speed and accuracy during forecasting. Methods: The geospatially explicit ABM included movement of agents (ie, patients) among 104 short-term acute-care hospitals, 10 long-term acute-care hospitals, 421 licensed nursing homes, and the community in North Carolina. Agents were based on a synthetic population of North Carolina residents (ie, >10.4 million agents). We assigned SARS-CoV-2 infections to agents according to county-level susceptible, exposed, infectious, recovered (SEIR) models informed by reported COVID-19 cases by county. Agents’ COVID-19 severity and probability of hospitalization were determined using agent-specific characteristics (eg, age, comorbidities). During May 2020–December 2020, we produced weekly 30-day forecasts of intensive care unit (ICU) and non-ICU bed occupancy for COVID-19 agents and non–COVID-19 agents statewide and by region under a range of SARS-CoV-2 effective reproduction numbers. During the reporting period, we identified optimizations for faster results turnaround. We evaluated the incorporation of real-time hospital-level occupancy data at model initialization on forecast accuracy using mean absolute percent error (MAPE). Results: During May 2020–December 2020, we provided 31 weekly reports of 30-day hospitalization forecasts with a 1-day turnaround time. Reports included (1) raw and smoothed 7-day average values for 42 model output variables; (2) static visuals of ICU and non-ICU bed demand and capacity; and (3) an interactive Tableau workbook of hospital demand variables. Identifying code efficiencies reduced a single model runtime from ~100 seconds to 28 seconds. The use of cloud computing reduced simulation runtime from ~20 hours to 15 minutes. Across forecasts, the average MAPEs were 21.6% and 7.1% for ICU and non-ICU bed demand, respectively. By incorporating hospital-level occupancy data, we reduced the average MAPE to 6.5% for ICU bed demand and 3.9% for non-ICU bed demand, indicating improved accuracy. Conclusions: We adapted an ABM and continually improved it during COVID-19 forecasting by optimizing code and computing resources and including real-time hospital-level occupancy data. Planned SEIR model updates for enhanced forecasts include the addition of compartments for undocumented infections and recoveries as well as permission of reinfection from recovered compartments.Funding: NoDisclosures: None

scholarly journals Workshop: Quantifying burden of disease to support public health policy: exchange of views and experiences

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Public Health ◽  
Health Insurance ◽  
Burden Of Disease ◽  
Disease Burden ◽  
Healthcare Research ◽  
Decision Makers ◽  
Epidemiological Surveillance ◽  
Public Health Decision ◽  
Disease Information ◽  
Health Decision

Abstract Information on disease burden, risk factors, related healthcare costs and their variations over time represents a major concern for public health decision makers. These data could contribute to define priorities and strategies, to allocate resources and to evaluate health policies and interventions at regional and national levels. In this context, the use and synthesis of all available data is essential, whether these data were collected for the purpose of epidemiological surveillance, healthcare, research, and/or reimbursement. This process raises conceptual and methodological issues. The question of the use of these data by decision-makers is also essential and depends not only on their validity, but also on their credibility, their usability, and their capacity to respond to needs in the context of decision. There are now national experiences of production and use of these data. There are also international collaborations. In particular, the Global Burden of Disease (GBD) Study is an extremely structured process with extensive global collaboration. The aim of this workshop is to exchange and share experiences on the different approaches, indicators, methods used in order to quantify the burden of disease; the use of health insurance databases as a source of data for quantifying burden of disease; the use of burden of disease information by public health decision-makers at national and local levels. Key messages Disease burden statistics are a resource for data-informed policy-making. Health insurance databases are a complementary source for quantifying disease burden.

scholarly journals The NCCMT’s dynamic model for evidence-informed public health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
E Clark ◽  
S Neil-Sztramko ◽  
M Dobbins
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Research Evidence ◽  
Decision Makers ◽  
Local Data ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Political Preferences ◽  
Health Decision ◽  
Structured Approach

Abstract Issue It is well accepted that public health decision makers should use the best available research evidence in their decision-making process. However, research evidence alone is insufficient to inform public health decision making. Description of the problem As new challenges to public health emerge, there can be a paucity of high quality research evidence to inform decisions on new topics. Public health decision makers must combine various sources of evidence with their public health expertise to make evidence-informed decisions. The National Collaborating Centre for Methods and Tools (NCCMT) has developed a model which combines research evidence with other critical sources of evidence that can help guide decision makers in evidence-informed decision making. Results The NCCMT's model for evidence-informed public health combines findings from research evidence with local data and context, community and political preferences and actions and evidence on available resources. The model has been widely used across Canada and worldwide, and has been integrated into many public health organizations' decision-making processes. The model is also used for teaching an evidence-informed public health approach in Masters of Public Health programs around the globe. The model provides a structured approach to integrating evidence from several critical sources into public health decision making. Use of the model helps ensure that important research, contextual and preference information is sought and incorporated. Lessons Next steps for the model include development of a tool to facilitate synthesis of evidence across all four domains. Although Indigenous knowledges are relevant for public health decision making and should be considered as part of a complete assessment the current model does not capture Indigenous knowledges. Key messages Decision making in public health requires integrating the best available evidence, including research findings, local data and context, community and political preferences and available resources. The NCCMT’s model for evidence-informed public health provides a structured approach to integrating evidence from several critical sources into public health decision making.

scholarly journals 18.H. Workshop: Bringing evidence during public health emergencies: addressing the challenge through HTA

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Emergency Preparedness ◽  
Public Health Emergency ◽  
Decision Makers ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
Response Planning ◽  
Emergency Preparedness And Response ◽  
Public Health Emergency Preparedness

Abstract Evidence-based decision-making is central to public health. Implementing evidence-informed actions is most challenging during a public health emergency as in an epidemic, when time is limited, scientific uncertainties and political pressures tend to be high, and irrefutable evidence may be lacking. The process of including evidence in public health decision-making and for evidence-informed policy, in preparation, and during public health emergencies, is not systematic and is complicated by many barriers as the absences of shared tools and approaches for evidence-based preparedness and response planning. Many of today's public health crises are also cross-border, and countries need to collaborate in a systematic and standardized way in order to enhance interoperability and to implement coordinated evidence-based response plans. To strengthen the impact of scientific evidence on decision-making for public health emergency preparedness and response, it is necessary to better define mechanisms through which interdisciplinary evidence feeds into decision-making processes during public health emergencies and the context in which these mechanisms operate. As a multidisciplinary, standardized and evidence-based decision-making tool, Health Technology Assessment (HTA) represents and approach that can inform public health emergency preparedness and response planning processes; it can also provide meaningful insights on existing preparedness structures, working as bridge between scientists and decision-makers, easing knowledge transition and translation to ensure that evidence is effectively integrated into decision-making contexts. HTA can address the link between scientific evidence and decision-making in public health emergencies, and overcome the key challenges faced by public health experts when advising decision makers, including strengthening and accelerating knowledge transfer through rapid HTA, improving networking between actors and disciplines. It may allow a 360° perspective, providing a comprehensive view to decision-making in preparation and during public health emergencies. The objective of the workshop is to explore and present how HTA can be used as a shared and systematic evidence-based tool for Public Health Emergency Preparedness and Response, in order to enable stakeholders and decision makers taking actions based on the best available evidence through a process which is systematic and transparent. Key messages There are many barriers and no shared mechanisms to bring evidence in decision-making during public health emergencies. HTA can represent the tool to bring evidence-informed actions in public health emergency preparedness and response.

scholarly journals Nextstrain: real-time tracking of pathogen evolution

2017 ◽  
Author(s):  
James Hadfield ◽  
Colin Megill ◽  
Sidney M. Bell ◽  
John Huddleston ◽  
Barney Potter ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Web Application ◽  
Sequence Data ◽  
Data Types ◽  
Bioinformatics Pipeline ◽  
Public Health Importance ◽  
Viral Genomes ◽  
Effective Public Health ◽  
Interactive Visualisation

AbstractSummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike.Availability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

scholarly journals Health economics methods for public health resource allocation: a qualitative interview study of decision makers from an English local authority

2019 ◽  
Vol 15 (1) ◽  
pp. 128-140 ◽  
Author(s):  
Emma Frew ◽  
Katie Breheny
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Health Economics ◽  
Local Authority ◽  
Decision Makers ◽  
Natural Experiments ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Qualitative Interview Study ◽  
Health Decision

AbstractLocal authorities in England have responsibility for public health, however, in recent years, budgets have been drastically reduced placing decision makers under unprecedented financial pressure. Although health economics can offer support for decision making, there is limited evidence of it being used in practice. The aim of this study was to undertake in-depth qualitative research within one local authority to better understand the context for public health decision making; what, and how economics evidence is being used; and invite suggestions for how methods could be improved to better support local public health decision making. The study included both observational methods and in-depth interviews. Key meetings were observed and semi-structured interviews conducted with participants who had a decision-making role to explore views on economics, to understand the barriers to using evidence and to invite suggestions for improvements to methods. Despite all informants valuing the use of health economics, many barriers were cited: including a perception of a narrow focus on the health sector; lack of consideration of population impact; and problems with translating long timescales to short term impact. Methodological suggestions included the broadening of frameworks; increased use of natural experiments; and capturing wider non-health outcomes that resonate with the priorities of multiple stakeholders.

scholarly journals COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cristian Tebé ◽  
Joan Valls ◽  
Pau Satorra ◽  
Aurelio Tobías
Keyword(s):  
Data Visualization ◽  
Web Application ◽  
Case Fatality ◽  
Epidemiological Surveillance ◽  
Fatality Rate ◽  
Specific Data ◽  
User Friendly ◽  
And Control ◽  
Shiny Application ◽  
Country Specific

Abstract Background Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. Results Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. Conclusions The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

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