Disease Forecasting Research Articles

Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.

Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease.

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.

Using artificial intelligence to improve public health: a narrative review.

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis.

The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts. We aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations. This study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method. Our study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC. Our study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Influence of Temperature and Wetness on Taphrina deformans Ascospore and Blastospore Germination: Disease Forecasting and Validation

Peach leaf curl is a fungal disease caused by Taphrina deformans, and it can severely affect the health and productivity of peach and nectarine trees (Prunus persica) if left unmanaged. This study was carried out to investigate the temperature and wetness conditions that affect the germination of blastospores and ascospores of local isolates of the fungus T. deformans. The results showed that the rate of both ascospore and blastospore germination was reduced as the temperature increased from 0 to 20 °C. A decrease in temperature from the range of 25 °C to 30 °C caused a reduction in the germination of conidia for both ascospores and blastospores. Ascospore and blastospore germination were totally inhibited at −3 and 35 °C. Under constant temperatures of 20 °C, the percentage of both ascospore and blastospore germination of T. deformans gradually increased as the wetness period increased from 9 to 15 h. However, there was no further increase in germination observed beyond the 15 h wetness period. Additionally, this study aimed to validate the predictive models of T. deformans, developed based on the favorable temperatures and leaf wetness durations, under the specific field conditions of Naoussa, Greece. The results indicate that while both the ascosporic and blastosporic models were capable of correctly predicting infection periods, there were differences in their predictions of infection risk. The ascosporic model predicted lower risk infection, which aligned well with the observed symptoms of the disease. In contrast, the blastosporic model predicted higher risk infection, but this did not match the actual intensity of the symptoms. Finally, this study also provided insights into the potential benefits of using predictive models to guide fungicide applications, potentially leading to more targeted and efficient disease management strategies for commercial peach orchards.

Development of an artificial intelligence system for the forecasting of infectious diseases

Aim. Here, we provided an overview of artificial intelligence (AI) approaches for developing a system for prediction of infectious diseases and designed a respective step-by-step protocol.Materials and Methods. Literature search in PubMed and Google Scholar and PubMed.Key Points. Infectious diseases impose a heavy burden on a healthcare, demanding the development of novel and efficient approaches to prevention as well as sensitive and specific diagnostic tests. Evolution of data science have led to the emergence of promising artificial intelligence (AI) algorithms and tools for the forecasting of infectious diseases. Employing machine learning algorithms, AI systems can rapidly analyze a large amount of data, extract specific disease patterns, and screen for the most efficient AI instruments in relation to specific tasks, thus contributing to prevention, diagnostics, and treatment of infectious diseases in the context of personalized medicine. Importantly, such AI-based systems can determine specific human motor patterns from videos and/or photographs in order to assist physicians in primary diagnosis. Integration of AI tools into the existing healthcare algorithms can be especially useful for public health.

A Deep Learning Model for Multiple Disease Prediction using VGG-16

All health-related problems must be precisely and professionally examined if it is to be prohibited and examined. In the case of a captious illness, the common technique of diagnosis might not be adequate. Early diagnosis of any health issue is crucial, as earlier disease discovery may result in more successful treatments or longer survival times. We may have serious illness, and if we do not address these symptoms right away, it may be too late to reverse the condition. The establishment of a Deep Learning (DL)-based medical analysis systems for disease forecast can lead to a prognosis that is more accurate than one made using historical techniques. Currently, the suggested approach can forecast Covid-19, Brain tumor, Breast Cancer, Alzheimer, Diabetes, Pneumonia. We can employ, a NN (Neural Network) known as CNN (Convolution Neural Network), which VGG 16 architectures for deep learning (visual geometry group) and random forest. For the early detection of a sickness, our diagnostic model can serve as a doctor, enabling timely treatment and the possibility of saving lives. Keywords- CNN (Convolutional neural networks), VGG-16, Deep Learning, Machine Learning and Random Forest.

Methods for modellind and forecasting dynamics of infectious diseases

Introduction.Despite dramatic progress in public health, infectious diseases are common issue leading to significant burden in terms of morbidity and mortality, and emergence and re-emergence of infections and its dynamic are often unpredictable. Infectious diseases modelling and forecasting is effective instrument for policy making in epidemiology.The aim of the reviewis to systematize current literature on modelling and forecasting in infectious disease epidemiology.Materials and methodsLiterature review in field of modelling and forecasting of infectious diseases without restrictions by publication date was conducted. Publication activity was estimated using text mining software.Results and discussion.The following most common classes of modelling methods were marked: regression models, time-series models, compartmental models, agent-based models and artificial neural networks. It was noted that a number of methods (regression analysis, time-series models and artificial neural networks) are relatively simple to implement, but a considerable volume of history data is required for teaching these models. Compartmental models are partially free from this restriction, and they can be rapidly developed for assessment of emerging and reemerging infections, but their implementation presents issues caused by host population heterogeneity. Agent-based models that present most complete descriptions of host population heterogeneity and social interactions within it are extremely complex from the technical point of view.Conclusion.Despite the presence of various mathematical algorithms for disease modelling, the demand for user-friendly statistical software for disease forecasting in field practice is persist.

Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods.

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.

Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. We conducted a systematic review via a PubMed search for the period 1990-2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.

A scoping review of zoonotic parasites and pathogens associated with abattoirs in Eastern Africa and recommendations for abattoirs as disease surveillance sites.

Abattoirs are facilities where livestock are slaughtered and are an important aspect in the food production chain. There are several types of abattoirs, which differ in infrastructure and facilities, sanitation and PPE practices, and adherence to regulations. In each abattoir facility, worker exposure to animals and animal products increases their risk of infection from zoonotic pathogens. Backyard abattoirs and slaughter slabs have the highest risk of pathogen transmission because of substandard hygiene practices and minimal infrastructure. These abattoir conditions can often contribute to environmental contamination and may play a significant role in disease outbreaks within communities. To assess further the risk of disease, we conducted a scoping review of parasites and pathogens among livestock and human workers in abattoirs across 13 Eastern African countries, which are hotspots for zoonoses. Our search results (n = 104 articles) showed the presence of bacteria, viruses, fungi, and macroparasites (nematodes, cestodes, etc.) in cattle, goats, sheep, pigs, camels, and poultry. Most articles reported results from cattle, and the most frequent pathogen detected was Mycobacterium bovis, which causes bovine tuberculosis. Some articles included worker survey and questionnaires that suggested how the use of PPE along with proper worker training and safe animal handling practices could reduce disease risk. Based on these findings, we discuss ways to improve abattoir biosafety and increase biosurveillance for disease control and mitigation. Abattoirs are a 'catch all' for pathogens, and by surveying animals at abattoirs, health officials can determine which diseases are prevalent in different regions and which pathogens are most likely transmitted from wildlife to livestock. We suggest a regional approach to biosurveillance, which will improve testing and data gathering for enhanced disease risk mapping and forecasting. Next generation sequencing will be key in identifying a wide range of pathogens, rather than a targeted approach.

Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately.

Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics

Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.

Occurrence of Ascospores and White Mold Caused by Sclerotinia sclerotiorum in Dry Bean Fields in Alberta, Canada.

White mold caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary is one of the most important biological constraints to dry bean (Phaseolus vulgaris L.) production in Canada. Disease forecasting is one tool that could help growers manage the disease while reducing fungicide use. However, predicting white mold epidemics has remained difficult due to their sporadic occurrence. In this study, over the course of four growing seasons (2018 to 2021), we surveyed dry bean fields in Alberta and collected daily in-field weather data and daily in-field ascospore counts. White mold levels were variable and generally high in all years, confirming that the disease is ubiquitous and a constant threat to dry bean production. Ascospores were present throughout the growing season, and mean ascospore levels varied by field, month, and year. Models based on in-field weather and ascospore levels were not highly predictive of final disease incidence in a field, suggesting that environment and pathogen presence were not limiting factors to disease development. Rather, significant effects of market class on disease were found, with pinto beans, on average, having the highest disease incidence (33%) followed by great northern (15%), black (10%), red (6%), and yellow (5%). When incidence of these market classes was modeled separately, different environmental variables were important in each model; however, average wind speed was a significant variable in all models. Taken together, these findings suggest that white mold management in dry bean should focus on fungicide use, plant genetics, irrigation management, and other agronomic factors.

Machine Learning-Based Multiple Disease Forecaster

Abstract: Accurate prediction of multiple diseases, including diabetes, chronic kidney disease, heart diseases, Parkinson's disease, and breast cancer plays a crucial role in proactive healthcare management and early intervention. In this study, we propose a machine learning-based multiple disease forecaster that leverages advanced algorithms to predict the likelihood of various diseases simultaneously. The forecaster utilizes a comprehensive dataset comprising patient demographics, medical history, and relevant clinical attributes. Feature engineering techniques are employed to extract informative features, which are then input into a diverse set of machine learning models, including Random Forest, Xgboost, and Support Vector Machines. The models are trained and fine-tuned using a dataset collected from kaggle. Performance evaluation is conducted using various metrics, including accuracy, precision, recall, and F1-score, to assess the predictive capability of the forecaster. The proposed forecaster holds promise for improving disease prediction accuracy, facilitating early intervention, and enhancing healthcare outcomes on a broader scale. Future research directions include incorporating additional data sources such as genetic information and exploring interpretability techniques to gain insights into the underlying disease mechanisms

Synthesized Age-Period-Cohort Prediction Method: Application to Lung Cancer Mortality in Taiwan.

Age-period-cohort analysis involves three temporal factors: age (the length of time from birth to diagnosis), period (the calendar time of diagnosis), and cohort (the calendar time of birth). The application of age-period-cohort analysis in disease forecasting can help researchers and health authorities anticipate future disease burden. In this study, a synthesized age-period-cohort prediction method was proposed based on four assumptions: (i) no single model can dominate as the most accurate prediction model in all forecasting scenarios, (ii) historical trends will not continue indefinitely, (iii) a model with the most accurate forecast for the training data will also be appropriate for forecasting future data, and (iv) a model dominated the stochastic temporal change will be the best-selected model with the robust forecasting. An ensemble of age-period-cohort prediction models was constructed and Monte Carlo cross-validation was performed to evaluate forecasting accuracy of these models. Data on lung cancer mortality from 1996 to 2015 in Taiwan were used and projected to the year 2035 to illustrate the method. The actual lung cancer mortality rates from 2016 to 2020 were then used to verify the forecasting accuracy.

Hybrid MRK-Means + + RBM Model: An Efficient Heart Disease Predicting System Using ModifiedRoughK-Means + + Algorithm and Restricted Boltzmann Machine

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means[Formula: see text] (MRK[Formula: see text]) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means[Formula: see text]; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means[Formula: see text] clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means[Formula: see text] clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means[Formula: see text] - RBM model is compared with any single model, it provides the highest accuracy.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health.

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making

BackgroundThe COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future.MethodsUsing archived forecasts from the California Department of Public Health’s California COVID Assessment Tool (https://calcat.covid19.ca.gov/cacovidmodels/), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance.ResultsBased on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations.ConclusionsEnsemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases

Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient’s health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person’s life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient’s data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient’s health status based on abnormal vital signs and enables patients to receive prompt medical attention.