Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People

Background. From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. Objective. We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages. Methods. We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China. Results. Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases’ prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94). Conclusion. Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.

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Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

10.21203/rs.3.rs-53367/v1 ◽

2020 ◽

Author(s):

Juan David Gutiérrez

Keyword(s):

Public Health ◽

Machine Learning ◽

Air Pollution ◽

Gradient Boosting ◽

Learning Methods ◽

Machine Learning Methods ◽

Health Authorities ◽

Admission Data ◽

Extreme Gradient Boosting ◽

Public Health Authorities

Abstract Background: Previous authors have evidenced the relationship between air pollution-aerosols and meteorological variables with the occurrence of pneumonia. Forecasting the number of attentions of pneumonia cases may be useful to optimize the allocation of healthcare resources and support public health authorities to implement emergency plans to face an increase in patients. The purpose of this study is to implement four machine-learning methods to forecast the number of attentions of pneumonia cases in the five largest cities of Colombia by using air pollution-aerosols, and meteorological and admission data.Methods: The number of attentions of pneumonia cases in the five most populated Colombian cities was provided by public health authorities between January 2009 and December 2019. Air pollution-aerosols and meteorological data were obtained from remote sensors. Four machine-learning methods were implemented for each city. We selected the machine-learning methods with the best performance in each city and implemented two techniques to identify the most relevant variables in the forecasting developed by the best-performing machine-learning models. Results: According to R2 metric, random forest was the machine-learning method with the best performance for Bogotá, Medellín and Cali; whereas for Barranquilla, the best performance was obtained from the Bayesian adaptive regression trees, and for Cartagena, extreme gradient boosting had the best performance. The most important variables for the forecasting were related to the admission data.Conclusions: The results obtained from this study suggest that machine learning can be used to efficiently forecast the number of attentions of pneumonia cases, and therefore, it can be a useful decision-making tool for public health authorities.

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Tissue Tropism and Transmission Ecology Predict Virulence of Human RNA Viruses

10.1101/581512 ◽

2019 ◽

Cited By ~ 1

Author(s):

Liam Brierley ◽

Amy B. Pedersen ◽

Mark E. J. Woolhouse

Keyword(s):

Public Health ◽

Machine Learning ◽

Risk Factors ◽

Random Forest ◽

Infectious Diseases ◽

Rna Viruses ◽

Rna Virus ◽

Tissue Tropism ◽

Virus Species ◽

Ecological Traits

AbstractNovel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits including human-to-human transmissibility, transmission routes, tissue tropisms and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest model predicted literature-assigned disease severity of test data with 90.3% accuracy, compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection, having renal and/or neural tropism, direct contact or respiratory transmission, and limited (0 < R0 ≤ 1) human-to-human transmissibility were the strongest predictors of severe disease. We present a novel, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections.Author SummaryNewly emerging infectious diseases present potentially serious threats to global health. Although studies have begun to identify pathogen traits associated with the emergence of new human diseases, these do not address why emerging infections vary in the severity of disease they cause, often termed ‘virulence’. We test whether ecological traits of human viruses can act as predictors of virulence, as suggested by theoretical studies. We conduct the first systematic review of virulence across all currently known human RNA virus species. We adopt a machine learning approach by constructing a random forest, a model that aims to optimally predict an outcome using a specific structure of predictors. Predictions matched literature-assigned ratings for 28 of 31 test set viruses. Our predictive model suggests that higher virulence is associated with infection of multiple organ systems, nervous systems or the renal systems. Higher virulence was also associated with contact-based or airborne transmission, and limited capability to transmit between humans. These risk factors may provide novel starting points for questioning why virulence should evolve and identifying causative mechanisms of virulence. In addition, our work could suggest priority targets for infectious disease surveillance and future public health risk strategies.BlurbComparative analysis using machine learning shows specificity of tissue tropism and transmission biology can act as predictive risk factors for virulence of human RNA viruses.

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Influence of Educational Level on the Prevalence of the Metabolic Syndrome in Clients of Health Advice Centres of Regional Public Health Authorities in the Slovak Republic

Hygiena ◽

10.21101/hygiena.a1442 ◽

2016 ◽

Vol 61 (2) ◽

pp. 56-62 ◽

Cited By ~ 1

Author(s):

Tímea Ostrihoňová ◽

Kvetoslava Rimárová ◽

Janka Bérešová ◽

Sylvia Kontrošová

Keyword(s):

Public Health ◽

Metabolic Syndrome ◽

Educational Level ◽

Slovak Republic ◽

The Metabolic Syndrome ◽

Health Authorities ◽

Health Advice ◽

Public Health Authorities ◽

Regional Public Health

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Germs on a Plane: The Transmission and Risks of Airplane-Borne Diseases

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118799709 ◽

2018 ◽

Vol 2672 (29) ◽

pp. 93-102 ◽

Cited By ~ 1

Author(s):

Nereyda L. Sevilla

Keyword(s):

Public Health ◽

Infectious Diseases ◽

Close Contact ◽

Air Travel ◽

Economic Consequences ◽

Disease Spread ◽

Pneumonic Plague ◽

Health Authorities ◽

Public Health Authorities ◽

The Impact

This research explored the role of air travel in the spread of infectious diseases, specifically severe acute respiratory syndrome (SARS), H1N1, Ebola, and pneumonic plague. Air travel provides the means for such diseases to spread internationally at extraordinary rates because infected passengers jump from coast to coast and continent to continent within hours. Outbreaks of diseases that spread from person to person test the effectiveness of current public health responses. This research used a mixed methods approach, including use of the Spatiotemporal Epidemiological Modeler, to model the spread of diseases, evaluate the impact of air travel on disease spread, and analyze the effectiveness of different public health strategies and travel policies. Modeling showed that the spread of Ebola and pneumonic plague is minimal and should not be a major air travel concern if an individual becomes infected. H1N1 and SARS have higher infection rates and air travel will facilitate the spread of disease nationally and internationally. To contain the spread of infectious diseases, aviation and public health authorities should establish tailored preventive measures at airports, capture contact information for ticketed passengers, expand the definition of “close contact,” and conduct widespread educational programs. The measures will put in place a foundation for containing the spread of infectious diseases via air travel and minimize the panic and economic consequences that may occur during an outbreak.

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Prevalence of metabolic syndrome during 2003-2012 among health advise centres clients in Slovakia

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.1082 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

T Ostrihonova ◽

J Beresova ◽

E Dorko

Keyword(s):

Public Health ◽

Metabolic Syndrome ◽

Time Trends ◽

Male Population ◽

Cross Sectional ◽

Health Authorities ◽

Health Advice ◽

Public Health Authorities ◽

Risk Determinants ◽

Regional Public Health

Abstract Background Metabolic syndrome (MetS) arises from insulin resistance and is accompanied with abnormal adipose deposition and frequently with obesity. The aim of our cross-sectional time trends study was to characterize the prevalence of metabolic syndrome and its five risk determinants among the clients of Health Advice Centres of Regional Public Health Authorities in Slovakia during period 2003 - 2012. Methods Prevalence data were estimated in adults and children (≥10 years, N = 79 904) from the nationwide electronic database of Health Advice Centres of Regional Public Health Authorities in Slovak Republic 'Test of healthy heart'. Only first time visitors of the centres were included in the database. SPSS was used for data analysis, arithmetic means and Student´s t-test were used to establish statistical differences based on sex, age and time trends. Results The overall prevalence of metabolic syndrome was 30.2% in males and 26.6% in females, abdominal obesity was confirmed in 48.3% of the male population and 53.9% of females. Increased triglyceride level has higher prevalence among males (33.3%) compared to females (24.2%). Blood pressure (BP) values and fasting glucose values were significantly higher in the male population than comparing to females. We confirmed an increased trend in the age-adjusted prevalence of metabolic syndrome. Abdominal obesity and elevated triglycerides had also increased time trends prevalence in both sexes. The prevalence of people without risk determinants of metabolic syndrome had during following period decreasing trend. A surprising finding is the decrease in the proportion of persons with suboptimal HDL-cholesterol. Conclusions During the period from 2003 - 2012 the results of our large cross-sectional study confirmed unfavourable increasing trends in the age-adjusted prevalence of MetS among the clients of Health Advice Centres and a simultaneous reduction in the number of persons without the presence of MetS risk determinants. Key messages Population study shows highly prevalence of metabolic syndrome in Slovakia. Metabolic syndrome prevalence results are more visible in population considering older age groups and males.

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SIM-D: An Agent-Based Simulator for Modeling Contagion in Population

Applied Sciences ◽

10.3390/app10217745 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7745

Author(s):

Muhammad Waleed ◽

Tai-Won Um ◽

Tariq Kamal ◽

Aftab Khan ◽

Zaka Ullah Zahid

Keyword(s):

Public Health ◽

Infectious Disease ◽

Infectious Diseases ◽

Close Contact ◽

Disease Spread ◽

Agent Based ◽

Disease States ◽

Simulation Engine ◽

Health Authorities ◽

Public Health Authorities

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning.

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A New Model for Predicting Infectious Disease Outbreaks

Medical Research Archives ◽

10.18103/mra.v8i10.2255 ◽

2020 ◽

Vol 8 (10) ◽

Author(s):

Peter Demitry ◽

Darren McKnight ◽

Erin Dale ◽

Elizabeth Bartlett

Keyword(s):

Public Health ◽

Infectious Disease ◽

Infectious Diseases ◽

Early Warning ◽

Preventive Intervention ◽

Disease Outbreaks ◽

World Health ◽

Infectious Disease Outbreaks ◽

Health Authorities ◽

The World

This project integrated tools and hybrid methodologies historically used for early warning, intelligence, counter space, public health, informatics, and medical surveillance applications. A multidiscipline team assembled and explored non-medical prediction and analytical techniques that successfully predict critical events for low probability but high-regret national and global scenarios. The team then created novel approaches needed to fill nuanced and unique gaps for the infectious disease prediction challenge. The team adopted and applied those proven procedures to determine which would be efficacious in foretelling infectious disease outbreaks around the world. One outcome of that effort was a successful two-year development and validation project designated ‘RAID’ (Risk Awareness Framework for Infectious Diseases), which focused on malaria prediction. The project’s objective was to maximize the warning (prediction) window of impending malaria epidemic outbreaks with sufficient time to allow meaningful preventive intervention before widespread human infection. It is generally recognized the more protracted the prediction window extends before an event, the more time available for health authorities to muster and deploy resources, which lessen morbidity, mortality, and harmful economic effects. Also, the value of early warning for an imminent epidemic must have mitigation options, or the warning window would have no beneficial impact on health outcomes. Finally, early notice is preferable over surprise epidemics, as unexpected waves of patients seeking acute care can easily overwhelm most local medical systems, as history repeatedly teaches. This cliché keeps repeating, with recurring Ebola epidemics and the recent COVID-19 pandemic as prominent exemplars. Predictive lead times need to be adequate for an intervention to be relevant. RAID’s focus on malaria prediction met these criteria from a relevant clinical and humanitarian perspective. Subsequent papers will address successful external generalization of these methods in predicting other similar infectious diseases. The model presented in this manuscript supports the conclusion that an additional two weeks advance notice could be available to public health authorities utilizing these techniques. This foreknowledge would allow the deployment of limited health resources into areas where they would do the most good and just in time. The geographical specificity was examined down to 5 km x 5 km grid squares overlaid anywhere in the world. Most of the model’s input data were derived from remote sensing satellite sources that could combine with historical WHO (World Health Organization) or nation-reported existential pathogen loads to improve model accuracy; however, such data harmonization is not required. If ground sensors were integrated into the modeling, the confidence of the risk of infection would logically improve. The model provides a successful global risk assessment via commercially available remote space sensors, even without ground sensing. RAID provides a necessary and useful preliminary means to predictive situational awareness. This improved predictive awareness is sufficiently granular to identify last chance windows for public health interventions globally. This need will become even more pronounced as infectious diseases evolve biologically and migrate geographically at ever-increasing rates.

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