Prediction Model For Incidence Research Articles

A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management

In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, and (c) computer-aided dispatch for emergency response. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. We conclude by illustrating open challenges and opportunities for future research in this complex domain.

Risk of Postoperative Renal Failure in Radical Nephrectomy and Nephroureterectomy: A Validated Risk Prediction Model

Introduction: The study aimed to construct and validate a risk prediction model for incidence of postoperative renal failure (PORF) following radical nephrectomy and nephroureterectomy. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database years 2005–2014 were used for the derivation cohort. A stepwise multivariate logistic regression analysis was conducted, and the final model was validated with an independent cohort from the ACS-NSQIP database years 2015–2017. Results: In cohort of 14,519 patients, 296 (2.0%) developed PORF. The final 9-factor model included age, gender, diabetes, hypertension, BMI, preoperative creatinine, hematocrit, platelet count, and surgical approach. Model receiver-operator curve analysis provided a C-statistic of 0.79 (0.77, 0.82; p < 0.001), and overall calibration testing R² was 0.99. Model performance in the validation cohort provided a C-statistic of 0.79 (0.76, 0.81; p < 0.001). Conclusion: PORF is a known risk factor for chronic kidney disease and cardiovascular morbidity, and is a common occurrence after unilateral kidney removal. The authors propose a robust and validated risk prediction model to aid in identification of high-risk patients and optimization of perioperative care.

Developing a Prediction Model for Incidence of Newly Detected CKD Among US Veterans, 2009-2018

Developing a Prediction Model for Incidence of Newly Detected CKD Among US Veterans, 2009-2018

Reuse of knowledge by efficient data analytics to fix societal challenges

Technology-intensive industries spend huge resources in the production of products to commercialize successful products. If the appetite on the market continues to change, the capacity to rapidly and cost-effectively refresh the offerings is an important competitive advantage. Even if components and designs need to be modified as new models are released, their underlying technology and designs can generally be reused to allow rapid economic development. Data are considered an important raw material that can influence multidisciplinary analysis, government, and business efficiency. In this paper, the Efficient data analytics (EDA) method has been suggested to fix societal challenges. The proposed methods aim to share the authors' views and perspectives on the emerging opportunities and challenges of the efficient data revolution.EDA provides four key aspects of technology reuse: strategy, method, culture, and information technology. The dimensions are further broken down into concepts supporting this technology reuse, including design on the technology platform and the reuse assessments. In practice, the system can evaluate an organization's existing reuse capabilities and offer an overall theoretical review of activities promoting technology reuse. To prove the system's concepts, industrial scenarios highlighting real questions of technological growth are used. Besides, the possible societal benefits of EDA in six ways are illustrated: enhanced decision management and incident prediction, data-informed technologies and innovative market models, direct social and climate benefits, community engagement, accountability, and public trust. Some best practice is suggested to capture these advantages. The experimental results suggested EDA increases reusability knowledge in the organization (96.3%), operational cost (95.1%), prediction ratio (97.4%), Community engagement ratio (94.1%), and public trust (98.5%).

MO814RISK PREDICTION OF COVID-19 INCIDENCE AND MORTALITY IN A LARGE MULTI-NATIONAL HAEMODIALYSIS COHORT: IMPLICATIONS FOR MANAGEMENT IN OUTPATIENT SETTINGS

Background and AimsExperiences from the first wave of the 2019 coronavirus disease (COVID-19) pandemic can aide in the development of future preventive strategies. To date, risk prediction models for COVID-19-related incidence and outcomes in haemodialysis (HD) patients are missing.MethodWe developed risk prediction models for COVID-19 incidence and mortality among HD patients. We studied 38 256 HD patients from a multi-national dialysis cohort between March 3rd and July 3rd 2020. Risk prediction models were developed and validated, based on predictors readily available in outpatient haemodialysis units. We compared mortality among patients with and without COVID-19, matched for age, sex, and diabetes.ResultsDuring the observational period, 1 259 patients (3.3%) acquired COVID-19. Of these, 62% were hospitalised or died. Mortality was 22% among COVID-19 patients with odds ratios 219.8 (95% CI 80.6-359) to 342.7 (95% CI 60.6-13595.1), compared to matched patients without COVID-19. Since the first wave of the pandemic affected mostly European countries during the study, the risk prediction model for incidence of COVID-19 was developed and validated in European patients only (N=22 826, AUCDev 0.64, AUCVal 0.69). The model for prediction of mortality was developed in all COVID-19 patients (AUCDev 0.71, AUCVal 0.78). Angiotensin receptor blockers were independently associated with a lower incidence of COVID-19 in European patients.ConclusionWe identified modifiable risk factors for COVID-19 incidence and outcome in HD patients. Our risk prediction tools can be readily applied in clinical practice. The current study can aid in the development of preventive strategies for future waves of COVID-19.

Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

BackgroundClimate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia.MethodsMalaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence.ResultsTemperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801–1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m.ConclusionsMeteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

Risk prediction of COVID-19 incidence and mortality in a large multi-national hemodialysis cohort: implications for management of the pandemic in outpatient hemodialysis settings.

BackgroundExperiences from the first wave of the 2019 coronavirus disease (COVID-19) pandemic can aid in the development of future preventive strategies. To date, risk prediction models for COVID-19-related incidence and outcomes in hemodialysis (HD) patients are missing.MethodsWe developed risk prediction models for COVID-19 incidence and mortality among HD patients. We studied 38 256 HD patients from a multi-national dialysis cohort between 3 March and 3 July 2020. Risk prediction models were developed and validated, based on predictors readily available in outpatient HD units. We compared mortality among patients with and without COVID-19, matched for age, sex and diabetes.ResultsDuring the observational period, 1259 patients (3.3%) acquired COVID-19. Of these, 62% were hospitalized or died. Mortality was 22% among COVID-19 patients with odds ratios 219.8 [95% confidence interval (CI) 80.6–359] to 342.7 (95% CI 60.6–13 595.1), compared to matched patients without COVID-19. Since the first wave of the pandemic affected most European countries during the study, the risk prediction model for incidence of COVID-19 was developed and validated in European patients only [N = 22 826 area under the ROC curve(AUC)Dev 0.64, AUCVal 0.69]. The model for prediction of mortality was developed in all COVID-19 patients (AUCDev 0.71, AUCVal 0.78). Angiotensin receptor blockers were independently associated with a lower incidence of COVID-19 in European patients.ConclusionsWe identified modifiable risk factors for COVID-19 incidence and outcome in HD patients. Our risk prediction tools can be readily applied in clinical practice. This can aid in the development of preventive strategies for future waves of COVID-19.

Risk-Based Selection of Individuals for Oral Cancer Screening.

We evaluated proof of principle for resource-efficient, risk-based screening through reanalysis of the Kerala Oral Cancer Screening Trial. The cluster-randomized trial included three triennial rounds of visual inspection (seven clusters, n = 96,516) versus standard of care (six clusters, n = 95,354) and up to 9 years of follow-up. We developed a Cox regression-based risk prediction model for oral cancer incidence. Using this risk prediction model to adjust for the oral cancer risk imbalance between arms, through intention-to-treat (ITT) analyses that accounted for cluster randomization, we calculated the relative (hazard ratios [HRs]) and absolute (rate differences [RDs]) screening efficacy on oral cancer mortality and compared screening efficiency across risk thresholds. Oral cancer mortality was reduced by 27% in the screening versus control arms (HR = 0.73; 95% CI, 0.54 to 0.98), including a 29% reduction in ever-tobacco and/or ever-alcohol users (HR = 0.71; 95% CI, 0.51 to 0.99). This relative efficacy was similar across oral cancer risk quartiles (P interaction = .59); consequently, the absolute efficacy increased with increasing model-predicted risk-overall trial: RD in the lowest risk quartile (Q1) = 0.5/100,000 versus 13.4/100,000 in the highest quartile (Q4), P trend = .059 and ever-tobacco and/or ever-alcohol users: Q1 RD = 1.0/100,000 versus Q4 = 22.5/100,000; P trend = .026. In a population akin to the Kerala trial, screening of 100% of individuals would provide 27.1% oral cancer mortality reduction at number needed to screen (NNS) = 2,043. Restriction of screening to ever-tobacco and/or ever-alcohol users with no additional risk stratification would substantially enhance efficiency (43.4% screened for 23.3% oral cancer mortality reduction at NNS = 1,029), whereas risk prediction model-based screening of 50% of ever-tobacco and/or ever-alcohol users at highest risk would further enhance efficiency with little loss in program sensitivity (21.7% screened for 19.7% oral cancer mortality reduction at NNS = 610). In the Kerala trial, the efficacy of oral cancer screening was greatest in individuals at highest oral cancer risk. These results provide proof of principle that risk-based oral cancer screening could substantially enhance the efficiency of screening programs.

Critical incident prediction model in advanced abdominal surgery

Critical incident prediction model in advanced abdominal surgery

An ensemble clinical and MR-image deep learning model predicts 8-year knee pain trajectory: Data from the osteoarthritis initiative

We applied data-driven feature learning to classify the presence and 8-years incidence of knee pain from MRI. We analyzed the data from the Osteoarthritis Initiative. Self-reported pain scores from knees with chronic pain were used to generate binary labels for the pain presence. A 3D DenseNet was then trained to classify the presence of pain from sagittal intermediate-weighted 2D turbo spin-echo fat suppression MRI. Next, functional principal component analysis (FPCA) was performed to model the temporal patterns of pain incidence among non-symptomatic knees at baseline. Bayesian Gaussian mixture models was fitted to the FPCA scores to identify clusters of pain trajectories. Cluster membership were used as labels and learned by the 3D DenseNet from MRI only and MRI combined with clinical features. The pain presence and pain incidence prediction models included 14,606 and 3,828 samples, respectively. The pain presence classification model achieved an AUC of 0.898 (95%CI: 0.882-0.914). We identified two distinct subtypes of the temporal progression of pain score: stable (87%) and worsening group (13%). Our model showed high performance in subtype classification using both imaging and clinical features (AUC: 0.794 [95%CI: 0.761-0.823]). Our data-driven approach showed high performance in extracting MRI features associated with chronic knee pain presence and incident knee pain. Our results provide a basis for a more comprehensive quantitative analysis of MRI to uncover latent relationships between OA imaging and knee pain. Further studies are needed to improve our understanding of the extracted features.

Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Clinical impact of fine particulate matter (PM2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837–0.853]) than existing traditional regression models using CHA2DS2-VASc (0.654 [0.646–0.661]), CHADS2 (0.652 [0.646–0.657]), or HATCH (0.669 [0.661–0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM2.5 as a highly important variable, we applied PM2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM2.5 (c-indices: boosted ensemble ML, 0.954 [0.949–0.959]; PM-CHA2DS2-VASc, 0.859 [0.848–0.870]; PM-CHADS2, 0.823 [0.810–0.836]; or PM-HATCH score, 0.849 [0.837–0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM2.5 data was found to predict incident AF better than models without PM2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.

Prediction of Suicide Attempts Using the Box-Jenkins Model.

Objective: Suicide is a preventable social harm in the field of health. The present study aimed to design a prediction model for suicide incidence based on Box-Jenkins model in Ilam province. Method : Using a retrospective method all completed and attempted suicide data were collected during 1993-2013. Then, using the autoregressive integrated moving average (ARIMA) model, the time series analysis of the Box-Jenkins model was conducted to predict suicide status in the coming years (2014-2015). Results: According to the actual results obtained from the suicide data in 2014, a total of 1078 suicides were recorded and compared to the predicted results, according to the fitted model of the time series, which showed the selected model predicted 931 suicide cases, showing 86% accuracy of prediction. The series’ prediction indicated 931 suicides in 2014 with a negative growth rate of 25.3% compared to 2013 and 969 suicide cases in 2015 with a positive growth rate of 3.93% compared to 2014. Conclusion: The results of this study showed the designed model provides a high diagnostic value to predict suicide rates. These types of models can help to predict suicide in future and plan to control and prevent suicide attempts.

Prediction of hepatitis E using machine learning models.

BackgroundAccurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.MethodsAutoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).ResultsBy analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively.ConclusionsComparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number.

Influence of Meteorological Factors on HFMD and Construction of Prediction Model in Sichuan Province

To analyze the epidemiological characteristics of hand, foot and mouth disease (HFMD) in Sichuan Province from 2013 to 2019. To study the correlation between meteorological factors and the incidence of HFMD and construct a prediction model. The HMFD surveillance data and meteorological data from 2013 to 2019 in Sichuan Province were collected through the Chinese Center for Disease Control and Prevention and the China Meteorological data Network. Spearman correlation was used to analyze the relationship between HFMD incidence and meteorological factors. Multiple regression model and support vector regression (SVR) model were used to construct HFMD incidence prediction models respectively. A total of 615 840 cases of HFMD and 81 deaths were reported from 2013 to 2019. The average annual incidence rate was 107.31/105, and the mortality rate was 0.16/106. Spearman correlation analysis showed that the monthly incidence rate of HFMD was correlated with monthly average relative humidity (r=0.342), monthly average temperature (r=0.284), monthly average water vapor pressure (r=0.304) and monthly average days of precipitation (r=0.259). The prediction effect of the SVR model (R2=0.836) was better than the multiple regression model (R2=0.375). The SVR model provided a good fit to the monthly incidence of HFMD from 2013 to 2018, and can predict the peak incidence of HFMD in 2019. Relative humidity has the greatest influence on the incidence of HFMD. The fitting value of SVR model is in good agreement with the actual value, which is valuable in predicting the incidence of HFMD in Sichuan Province.

Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches

Coffee Leaf Rust (CLR) is a disease that leads to considerable losses in the worldwide coffee industry; as those that have been reported recently in Colombia and Central America. The early detection of favorable conditions for epidemics could be used to improve decision making for the coffee grower and thus reduce the losses due to the disease. Researchers tried to predict the occurrence of the disease earlier through statistical and machine learning models from crop properties, disease indicators and weather conditions. These studies considered the impact of weather variables in a common period for all. Assuming that the dynamics of weather that most impact the development of the disease occur in the same time periods is simplistic. We propose an approach to discover the time period (window) for each weather variables and crop related features that most explain a future observed CLR incidence, in order to obtain a prediction model through machine learning. The selection of the variables more related with coffee rust incidence and rejection of the features with no significant contribution of information in machine learning tasks were approached from Feature Selection methods (Filter, Wrapper, Embedded). In this way, a CLR incidence prediction model based on the features with the greatest impact on the development of the disease was obtained. Moreover, the use of SHapley Additive exPlanations allowed us to identify the impact of features in the model prediction. The monitoring of coffee rust incidence is the most important predictor, since it provides information about current inoculum and this determines how much can the incidence grow or decrease. Temperature is a determining driver for germination and penetration phases in days 9 to 6 and 4 to 1 before the date of prediction. Additionally, the amount of rain determines whether uredospore dispersal or washing conditions occurred. The mean absolute error expected in the model is 6.94% of incidence, trained with XGBoost algorithm and the dataset reduced by Embedded method. The estimation of the disease incidence 28 days later can be used to improve decision making in control and nutrition practices.

A risk stratification scoring system for new-onset atrial fibrillation after ischemic stroke: A National cohort study.

Atrial fibrillation (AF) is a major independent risk factor of stroke and anticoagulation therapy is needed in patients with AF after ischemic stroke. However, the detection rate of AF is low after ischemic stroke. Developing a prediction model for newly diagnosed AF after ischemic stroke will help to assess the subclinical AF.We identified 98,103 patients with diabetes mellitus (DM) and 261,893 patients without DM, who were not AF history and admitted for newly ischemic stroke from the National Health Insurance Research Database in Taiwan. The prediction model for 3-year incidence of AF after ischemic stroke was derived from multivariate logistic regression and also the accuracy rate of the prediction model was compared with CHA2DS2-VASC and CHADS2 scores as a reference.Four thousand nine hundred seventy six patients in the DM cohort and 16,127 patients in the non-DM cohort developed AF during 3 years of follow-up. The variables in the point-based prediction model for non-DM patients (range: -3-28), included age, heart failure, coronary artery disease, gout, obstructive pulmonary disease, hypertension, female, and statin use, while those for DM patients (range: -2-30) included age, heart failure, coronary artery disease, chronic kidney disease, hypertension, obstructive pulmonary disease, and statin use. Compared to the CHADS2 and CHA2DS2-VASc scoring systems, this scoring system was better at predicting 3-year risk of AF after ischemic stroke in both cohorts.This model might be useful in evaluating the benefit of insertable cardiac monitor implantation and anticoagulation agents in individual patients after ischemic stroke.

Outlier Analysis to Improve the Performance of an Incident Duration Estimation and Incident Management System

Traffic incidents, as non-recurrent events, are one of the major causes of congestion in the transportation network. To mitigate the impacts of such incidents and to recover the performance of transportation systems as safely and quickly as possible, most responsible agencies over the past decades have implemented various traffic incident management systems, and an incident duration prediction model is one of the key components to estimate the impact of time-varying incidents on the network. Many studies have been undertaken to develop a robust prediction model of incident duration, but they have struggled to provide a reliable and accurate estimation result because of various data and modeling issues, such as a highly skewed distribution, complex correlations, heteroscedasticity, many outliers, and so forth. This study proposes an outlier analysis process for estimating the outlier-ness of each detected incident and utilizing such outlier information to improve accuracy of prediction of incident duration estimation and detect any system deficiency. An ensemble modeling technique and various outlier detection methodologies have been used to estimate the outlier-ness, and a hybrid association rule mining method has been applied to classify the detected outliers as anomalies or noises. Lastly, through the model evaluation and application example in this study, we can conclude that the proposed outlier analysis process can improve the accuracy of incident duration estimation and detect the potential system deficiencies associated with incident response, data recording, resource management, and so forth.

Machine learning algorithms, bull genetic information, and imbalanced datasets used in abortion incidence prediction models for Iranian Holstein dairy cattle

The ability to predict abortion incidence, especially in regions with high abortion rates (e.g., Iran), helps improve reproductive performance and, thereby, dairy farm profitability. The objective of this study was to predict pregnancy loss in Iranian dairy herds. For this purpose, the cow history records and bull genetic information available at 6 large commercial dairy farms with cows calved between 2005 and 2014 were extracted from an on-farm record-keeping software. Using WEKA, 12 commonly used machine learning (ML) algorithms were applied to the dataset. The algorithms belonged to 5 classifier groups which were Bayes, meta, functions, rules, and trees. The original dataset including herd-cow factors was randomly divided into 2 subsets: a training dataset and a test one (at a ratio of 60:40). The original dataset was combined with the bull genetic information to create a full dataset. The average abortion rate was 15.4 %, which represented an imbalanced dataset. Therefore, 2 down- and up-sampling techniques were additionally implemented on the original dataset (more specifically on the training one) to create 2 balanced datasets. This ultimately yielded 4 datasets; original, full, down-sampling, and up-sampling. Different algorithms and models were evaluated based on F-measure and area under the curve (AUC). Based on the results obtained, ML algorithms exhibited a high performance in predicting abortion when applied to the balanced dataset. However, their performance varied from 32.3 % (poor) to 69.2 % (medium upward) when applied to the imbalanced original dataset. In addition to the imbalance in the original dataset, the reason for these poor results were attributed to the high proportion of unknown risk factors underlying abortion incidence. Even when including the bull genetic information, it did not lead to any significant improvements in the prediction model. From among the datasets used, the Bayes algorithms outperformed the others in predicting pregnancy losses while rules had the worst performance. Furthermore, while the Bayes algorithms were not affected by the type of dataset (balanced or imbalanced), substantial increases in F-measure and AUC were observed for rules, trees, and functions with balanced datasets. Overall, the balanced models outperformed the others, with the down-sampling method exhibiting the highest performance. Despite the fact that the prediction models used in this study did not perform as expected, it was shown that they can be beneficially used to predict and reduce pregnancy losses, despite their moderate accuracy, especially when used for herds with high abortion rates and low reproductive performances.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

BackgroundThis study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.MethodsIn this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.ResultsIn total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R2) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.ConclusionFrom 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

Computational intelligence-based model for diarrhea prediction using Demographic and Health Survey data

Diarrhea is one of the leading public health problems and the third main cause of death among young children in developing countries. Solutions to tackling the infectious disease require both preventive and control efforts. However, efforts toward improving the control measures require comprehending the factors associated with diarrhea incidence and the ability to accurately forecast the incidence of the disease. Therefore, the present study develops a diarrhea incidence prediction model based on the 2013 Nigeria Demographic and Health Survey data using artificial neural network. The empirical results of the model indicate that, by using only 44 demographic, socioeconomic and environmental variables, diarrhea incidence can be predicted with high accuracy of 95.78 and 95.63% during training and testing phases, respectively. The model is useful for health policymakers in devising proactive intervention measures, including preparing healthcare systems and improving diarrhea prevention and control capabilities. It could also benefit future studies in predicting epidemics that are affected by similar variables.