Disease Forecasting Research Articles

Applying the Spatial Transmission Network to the Forecast of Infectious Diseases Across Multiple Regions.

ObjectiveTimely and accurate forecast of infectious diseases is essential for achieving precise prevention and control. A good forecasting method of infectious diseases should have the advantages of interpretability, feasibility, and forecasting performance. Since previous research had illustrated that the spatial transmission network (STN) showed good interpretability and feasibility, this study further explored its forecasting performance for infectious diseases across multiple regions. Meanwhile, this study also showed whether the STN could overcome the challenges of model rationality and practical needs.MethodsThe construction of the STN framework involved three major steps: the spatial kluster analysis by tree edge removal (SKATER) algorithm, structure learning by dynamic Bayesian network (DBN), and parameter learning by the vector autoregressive moving average (VARMA) model. Then, we evaluated the forecasting performance of STN by comparing its accuracy with that of the mechanism models like susceptible-exposed-infectious-recovered-susceptible (SEIRS) and machine-learning algorithm like long-short-term memory (LSTM). At the same time, we assessed the robustness of forecasting performance of STN in high and low incidence seasons. The influenza-like illness (ILI) data in the Sichuan Province of China from 2010 to 2017 were used as an example for illustration.ResultsThe STN model revealed that ILI was likely to spread among multiple cities in Sichuan during the study period. During the whole study period, the forecasting accuracy of the STN (mean absolute percentage error [MAPE] = 31.134) was significantly better than that of the LSTM (MAPE = 41.657) and the SEIRS (MAPE = 62.039). In addition, the forecasting performance of STN was also superior to those of the other two methods in either the high incidence season (MAPE = 24.742) or the low incidence season (MAPE = 26.209), and the superiority was more obvious in the high incidence season.ConclusionThis study applied the STN to the forecast of infectious diseases across multiple regions. The results illustrated that the STN not only had good accuracy in forecasting performance but also indicated the spreading directions of infectious diseases among multiple regions to a certain extent. Therefore, the STN is a promising candidate to improve the surveillance work.

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

Covid-19 Prediction Model Using Data Mining Algorithms

From 2019 until today, the whole world is panicking due to the pandemic of the Corona virus or the so-called COVID-19, so it was inevitable to search for a way to predict the disease before it occurs. Disease forecasting requires huge databases, human hands, and high-speed technologies. Due to the rapid spread of this pandemic, scientists have looked at using data mining methods to predict diseases. Predicting and early diagnosis of disease through mining algorithms reduces human errors, saves money and scientists make the most accurate decision. It then shortens the long time needed to detect COVID-19 using available algorithms and tools that rely on data such as lung images or disease symptoms such as temperature, among others. In this research, two data mining algorithms were used to predict COVID-19 that support vector machine (SVM) and naïve bays (NB) using a dataset from the Korea Centers for Disease Control (KCDC). Feature selection was performed using Correlation based Feature Selection (CFS) after previously processed data. Performance measures for the research proved that SVM is best classifier of NB with accuracy, sensitivity and specificity of SVM being 96.72%, 94.08%, and 97.96% respectively. The receiver operating characteristic (ROC) curve also demonstrated better SVM performance than NB for predicting COVID-19.

A novel generalized fuzzy intelligence-based ant lion optimization for internet of things based disease prediction and diagnosis.

In the modern healthcare system, the function of the Internet of Things (IoT) and the data mining methods with cloud computing plays an essential role in controlling a large number of big data for predicting and diagnosing various categories of diseases. However, when the patients suffer from more than one disease, the physician may not identify it properly. Therefore, in this research, the predictive method using the cloud with IoT-based database is proposed for forecasting the diseases that utilized the biosensors to estimate the constraints of patients. In addition, a novel Generalized Fuzzy Intelligence-based Ant Lion Optimization (GFIbALO) classifier along with a regression rule is proposed for predicting the diseases accurately. Initially, the dataset is filtered and feature extracted using the regression rule that data is processed on the proposed GFIbALO approach for classifying diseases. Moreover, suppose the patient has been affected by any diseases, in that case, the warning signal will be alerted to the patients via text or any other way, and the patients can get advice from doctors or any other medical support. The implementation of the proposed GFIbALO classifier is done with the use of the MATLAB tool. Subsequently, the results from the presented model are compared with state of the art techniques, and it shows that the presented method is more beneficial in diagnosis and disease forecast.

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths.

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.

Fast and accurate influenza forecasting in the United States with Inferno.

Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time.

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

Forecasting Multivariate time-series data using LSTM Neural Network in Mysore district, Karnataka

Advanced and precise forecasting of infectious diseases plays a critical role in planning and providing resources effectively.Time series forecasting for non-linear issues are accessible using deep learning techniques. The association betweenclimatic parameters and dengue occurrences was investigated in this work, and a forecasting model was constructedusing a deep learning approach called long short-term memory (LSTM). Univariate and multivariate LSTM time seriesforecast models were developed using meteorological and dengue incidence data from January 2006 to December 2019.For univariate data, the Pycharm/Google Colab platform was implemented, as the deep learning framework Keras,which is one of the models in the machine learning library based on Tensorflow. The Pandas Python package with builtinsupport for time series data was used for multivariate data. The final model was chosen using the mae loss and theAdam optimizer. Once the model had been fixed, predictions were made using the model. The research showed thatthe meteorological factors such as maximum temperature at lag 3, minimum temperature at lag 3, maximum vapourpressure (lag 0,1 and 2), minimum vapour pressure at lag 1, and vapour pressure daily mean at lag 0,1,4 are all significantpredictors of dengue along with RMSE value of 1.121 . The results indicated that LSTM network has higher predictionaccuracy than any other traditional forecasting methods. Timely management of seasonal diseases such as dengue alongwith meteorological parameters can predict epidemics in the future.

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is conducted on the collected dataset of conjunctivitis disease, and further compared for different performance measures. The result shows that the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), ACF1 (Auto Correlation Function) of the proposed ensemble is decreased significantly. Considering the RMSE, for instance, error values are reduced by 39.23%, 9.13%, 20.42%, and 17.13% in comparison to Auto-ARIMA, NAR, ETS, and HW model respectively. This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA, NAR, ETS, and HW model applied discretely

Swine flu Detection and Location using Machine Learning Techniques and GIS

The H1N1 virus, more commonly referred to as swine flu, is an illness that is extremely infectious and can in some cases be fatal. Because of this, the lives of many individuals have been taken. The disease can be transmitted from pigs to people. This research presents an artificial neural network (ANN) classifier for disease forecasting, as well as a technique for detecting people who are sick based on the geographic region in which they are found. The source codes for these two algorithms are provided below. These coordinates serve as the foundation for the GIS coordinates that are utilized in the method for assessing the extent to which the illness has spread. The ICMR and NCDC datasets were utilized in the study. They used Dynamic Boundary Location algorithm to detect swine flu affected person’s location, the researchers discovered that the accuracy of the proposed classifier was 96 standard classifiers.

A comparison and calibration of integer and fractional-order models of COVID-19 with stratified public response.

The spread of SARS-CoV-2 in the Canadian province of Ontario has resulted in millions of infections and tens of thousands of deaths to date. Correspondingly, the implementation of modeling to inform public health policies has proven to be exceptionally important. In this work, we expand a previous model of the spread of SARS-CoV-2 in Ontario, "Modeling the impact of a public response on the COVID-19 pandemic in Ontario, " to include the discretized, Caputo fractional derivative in the susceptible compartment. We perform identifiability and sensitivity analysis on both the integer-order and fractional-order SEIRD model and contrast the quality of the fits. We note that both methods produce fits of similar qualitative strength, though the inclusion of the fractional derivative operator quantitatively improves the fits by almost 27% corroborating the appropriateness of fractional operators for the purposes of phenomenological disease forecasting. In contrasting the fit procedures, we note potential simplifications for future study. Finally, we use all four models to provide an estimate of the time-dependent basic reproduction number for the spread of SARS-CoV-2 in Ontario between January 2020 and February 2021.

Prouzrokovač čađave pegavosti lista i krastavosti ploda jabuke (Venturia inaequalis (Cooke) Winter)

Apple is one of the most important fruit species, very important in human nutrition, both fresh and as a raw material in the food industry. Apple scab is the most economically significant apple disease worldwide and it is the biggest problem in apple production. Knowledge of the life cycle is of key importance for disease forecasting and control. Forecasting based on the Mills table is still an irreplaceable approach in the forecasting of apple scab occurrence. Control of this pathogen relies mostly on the use of synthetic fungicides, however, a major problem is the emerging resistance of V. inaequalis to the most important fungicides. In recent decades, breeding programs have detected genes for resistance and created many resistant and tolerant varieties, which significantly facilitates apple production and protection. It is necessary to find new ways to control apple scab, especially biological agents and antagonists in order to simplify protection, reduce production costs and preserve the environment, i.e. to make apple production sustainable.

Dengue Cases Prediction Using Machine Learning Approach

Dengue fever, spread by mosquitoes, affects about 3.9 billion people worldwide. Health officials could use indicators of dengue fever outbreaks to start taking preventative measures. Controlling dengue fever may be more straightforward for local authorities if they have timely and accurate disease forecasts. As one of the most rapidly spreading diseases globally, dengue fever is a threat to everyone. Dengue outbreaks can be predicted using machine learning, according to this study. Dengue prediction models could benefit from nature-based algorithms being boosted or used. The only thing that mattered in the prediction and training model was the week of the year, which was the only thing that signified. A standard machine learning algorithm cannot simulate long-term dependencies in time-series data, which is necessary for accurate projections in Dengue fever cases. When it comes to developing risk criteria for severe Dengue, machine learning could be a valuable implement in determining the possible behavior to formulate.

Machine learning applications in healthcare sector: An overview

Machine learning (ML) applications are everywhere and are used in many real-world applications. It is essential in several areas, such as healthcare and medical data protection. ML is applied to analyse medical records and disease forecasts. In our study, we review several ML algorithms, applications, techniques, opportunities, and challenges for the healthcare sector. This paper fills a research gap for efficient use of ML algorithms and applications in the healthcare sector.

Molecular Variability and Host Distribution of 'Candidatus Phytoplasma solani' Strains from Different Geographic Origins.

The knowledge of phytoplasma genetic variability is a tool to study their epidemiology and to implement an effective monitoring and management of their associated diseases. ‘Candidatus Phytoplasma solani’ is associated with “bois noir” disease in grapevines, and yellowing and decline symptoms in many plant species, causing serious damages during the epidemic outbreaks. The epidemiology of the diseases associated with this phytoplasma is complex and related to numerous factors, such as interactions of the host plant and insect vectors and spreading through infected plant propagation material. The genetic variability of ‘Ca. P. solani’ strains in different host species and in different geographic areas during the last two decades was studied by RFLP analyses coupled with sequencing on vmp1, stamp, and tuf genes. A total of 119 strains were examined, 25 molecular variants were identified, and the variability of the studied genes was linked to both geographic distribution and year of infection. The crucial question in ‘Ca. P. solani’ epidemiology is to trace back the epidemic cycle of the infections. This study presents some relevant features about differential strain distribution useful for disease monitoring and forecasting, illustrating and comparing the phytoplasma molecular variants identified in various regions, host species, and time periods.

Prediction and Analysis of Paddy Crops Disease in Artificial Intelligence Techniques

The survival of human beings is generally based on the proper productivity of agriculture. The paddy plant is considered as a major planting crop in improving the economical level of our country. Nowadays, the yield level of paddy crop might be minimized due to several diseases. Bacteria, fungi, virus and certain harmful insects are the main causative agents for such disease occurrence on the paddy crop. The diseases which affect the early stage of the paddy crops influences in the whole stage of crop cultivation. In early days of agriculture, the manual detection of diseases has been carried out by farmers. Image processing is one of the emerging techniques for identifying and classifying the different types of diseases and it overcomes the issues encountered during the manual detection of diseases. Image processing technique solves several issues involved in the cultivation of crops including, recognition and classification of plant diseases, discrimination of certain weeds and disease forecasting.

Using prediction polling to harness collective intelligence for disease forecasting

BackgroundThe global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking.MethodsWe report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases.ResultsConsistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters.ConclusionsCrowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.

Application of Bayesian network and regression method in treatment cost prediction

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Applying deep neural networks to predict incidence and phenology of plant pests and diseases

AbstractA major challenge of agriculture is to improve the sustainability of food production systems in order to provide enough food for a growing human population. Pests and pathogens cause vast yield losses, while crop protection practices raise environmental and human health concerns. Decision support systems provide detailed information on optimal timing and necessity of crop protection interventions, but are often based on phenology models that are time‐, cost‐, and labor‐intensive in development. Here, we aim to develop a data‐driven approach for pest damage forecasting, relying on big data and deep learning algorithms. We present a framework for the development of deep neural networks for pest and pathogen damage classification and show their potential for predicting the phenology of damages. As a case study, we investigate the phenology of the pear leaf blister moth (Leucoptera malifoliella, Costa). We employ a set of 52,322 pictures taken during a period of 19 weeks and establish deep neural networks to categorize the images into six main damage classes. Classification tools achieved good performance scores overall, with differences between the classes indicating that the performance of deep neural networks depends on the similarity to other damages and the number of training images. The reconstructed damage phenology of the pear leaf blister moth matches mine counts in the field. We further develop statistical models to reconstruct the phenology of damages with meteorological data and find good agreement with degree‐day models. Hence, our study indicates a yet underexploited potential for data‐driven approaches to enhance the versatility and cost efficiency of plant pest and disease forecasting.

Validation of the Strawberry Advisory System in the Mid-Atlantic Region.

Anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR) are primary diseases affecting strawberry (Fragaria × ananassa), which typically drive fungicide applications throughout the growing season. The Strawberry Advisory System (StAS), a disease forecasting tool, was originally developed in Florida to better time the fungicide sprays by monitoring AFR and BFR infection risk based on leaf wetness and temperature input in real-time. Thirteen field trials were conducted in Maryland and Virginia between 2017 and 2019 to evaluate the StAS performance in the Mid-Atlantic region. As a result, 55, 18, and 31% fewer sprays were recorded on average in the model-based StAS treatment compared with the grower standard treatment in 2017, 2018, and 2019, respectively. Marketable yield, as well as AFR and BFR incidence, were largely comparable between the two treatments. However, poor disease control occurred during the StAS treatment in four trials in 2017, presumably because of a missed fungicide spray during a high-risk infection event and attributable to heavy rainfall that led to impassable fields. The implementation of the StAS may be further challenged by the employment of floating row covers that are essential for growing strawberries in plasticulture systems in open fields in the Mid-Atlantic region. Preliminary results indicated that row covers can alter canopy-level microclimatic conditions, possibly increasing the risk for disease occurrence. Overall, the StAS can be a valuable tool for Mid-Atlantic growers to control AFR and BFR, but sprays may need to be promptly applied when consecutive or heavy rainfalls are predicted, especially for highly susceptible cultivars. Complications in disease forecasting and management arising from the use of row covers need to be further addressed in this region because of its highly diverse climate.