Prediction method of centrifugal pump running in turbine mode based on losses analysis.

Interest of Pump as Turbine (PAT) is growing with diverse applications in engineering. Usually, the data of PATs are not available in the hands of pump manufacturers. Therefore, performance prediction methods appear as an important research area of PATs. The current prediction methods reposed on expensive, inaccurate and time consuming experimental methods. In the scope of this work, a generic and robust prediction method is built up for a centrifugal impeller PAT. The most significant hydraulic losses were derived in PAT mode, these are namely, the shock losses at the impeller inlet, the swirling losses at the impeller outlet and the impeller wall frictional losses. The Euler head, the available total head and the hydraulic efficiency were computed as well. The global efficiency was computed taking into account the machine mechanical and volumetric efficiencies, enabling therefore to perform comparison of the new prediction method with experimental, computational fluid dynamic (CFD), Rossi and Perez performances prediction methods. From where it resulted a good agreement between the given prediction methods for the entire range of operation, confirming the robustness and the applicability of the developed prediction method. The relative difference between the new prediction method and CFD data and between the new prediction method and experimental data remained higher for lower discharge conditions, notably for extreme part load conditions, where a small error could result in very high relative difference.

A review of existing methods for predicting leachate production from municipal solid waste landfills.

Landfilling is one of the predominant methods of municipal solid waste (MSW) disposal worldwide, while the generation of leachate, a kind of toxic wastewater, is among the primary factors behind landfill instability and environmental contamination problems. Precise prediction of leachate production is crucial to landfill safety evaluation and design. This paper presents a comprehensive review of methods for predicting leachate production from MSW landfills. Firstly, compositional characteristics of MSW and leachate generation mechanism are analysed. Factors influencing leachate production are summarised based on the generation mechanism, including the components of MSW, climatic conditions, landfill structure, and environmental factors. Then, we classified the existing methods for predicting leachate production into four categories: water balance formula, water balance model, empirical formula, and artificial intelligence model methods. Advantages, disadvantages, and applicability of different leachate production prediction methods are compared and analysed. Furthermore, limitations in the existing leachate production prediction methods for MSW landfills and scope for future research are discussed.

Factors affecting the accuracy of genomic prediction in joint pig populations.

Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.

The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins.

Disorder prediction methods that can discriminate between ordered and disordered regions have contributed fundamentally to our understanding of the properties and prevalence of intrinsically disordered proteins (IDPs) in proteomes as well as their functional roles. However, a recent large-scale assessment of the performance of these methods indicated that there is still room for further improvements, necessitating novel approaches to understand the strengths and weaknesses of individual methods. In this study, we compared two methods, IUPred and disorder prediction, based on the pLDDT scores derived from AlphaFold2 (AF2) models. We evaluated these methods using a dataset from the DisProt database, consisting of experimentally characterized disordered regions and subsets associated with diverse experimental methods and functions. IUPred and AF2 provided consistent predictions in 79% of cases for long disordered regions; however, for 15% of these cases, they both suggested order in disagreement with annotations. These discrepancies arose primarily due to weak experimental support, the presence of intermediate states, or context-dependent behavior, such as binding-induced transitions. Furthermore, AF2 tended to predict helical regions with high pLDDT scores within disordered segments, while IUPred had limitations in identifying linker regions. These results provide valuable insights into the inherent limitations and potential biases of disorder prediction methods.

Risk Factors and Prediction Methods for Endometriosis Combined with Ureteral Stricture Based on Logistic-Regression Analysis.

OBJECTIVE: This study aimed to explore the risk factors of patients with endometriosis (EMS) and ureteral stricture and to establish a prediction model based on logistic-regression analysis. METHODS: The clinical data of 228 EMS patients in Jiaozhou Central Hospital of Qingdao from May 2019 to May 2022 were selected for a retrospective study. According to the results of ureteroscopic biopsy, they were divided into concurrent (n = 32) and nonconcurrent (n = 196) groups. Univariate analysis was performed on the general data and situations of clinical treatment in both groups. Single factor with statistically significant differences was included in unconditional logistic-regression analysis with multiple factors to explore the risk factors of such patients and establish a prediction model. RESULTS: Overt differences were found in previous history of ureter operation (odds ratio (OR) = 3.711, p = 0.006), course of EMS (OR = 3.987, p = 0.007), presence or absence of haematuria (OR = 3.586, p = 0.009) and lateral abdominal pain (OR = 4.451, p = 0.002), and invasion depth of lesion (OR = 7.271, p < 0.001) between the two groups (p < 0.05), without distinct difference in age, menstrual duration, BMI values, history of dysmenorrhea, previous history of drug therapy, smoking history, and drinking history (p > 0.05). Logistic-regression analysis showed that previous history of ureter operation (a1), course of EMS (b2), occurrence of haematuria (c3) and lateral abdominal pain (d4), and invasion depth of lesion ≥5 mm (e5) were risk factors for EMS combined with ureteral stricture (p < 0.05), taking logit (p) = -4.990 + 1.311a1 + 1.383b2 + 1.277c3 + 1.493d4 + 1.984e5 as regression model. The receiver operating characteristic (ROC) curve analysis based on this model showed that the area under the curve (AUC), standard error, and 95% confidence interval (CI) were 0.813, 0.062, and 0.692-0.934, respectively. One hundred EMS patients were re-included, whose values for predictive sensitivity, specificity, and kappa coefficient were 71.40%, 91.10% and 0.615. CONCLUSIONS: Previous history of ureter operation, course of EMS, occurrence of haematuria and lateral abdominal pain, and invasion depth of lesion ≥5 mm were risk factors for EMS combined with ureteral stricture. Therefore, the use of this model has a certain clinical value.

A novel integrated optimization model for carbon emission prediction: A case study on the group of 20.

Carbon emission is a central factor in the study of the greenhouse effect and a crucial consideration in environmental policy making. Therefore, it is essential to establish carbon emission prediction models to provide scientific guidance for leaders in implementing effective carbon reduction policies. However, existing research lacks comprehensive roadmaps that integrate both time series prediction and analysis of influencing factors. This study combines the environmental Kuznets curve (EKC) theory to classify and qualitatively analyzes research subjects based on national development patterns and levels. Considering the autocorrelated characteristics of carbon emissions and their correlation with other influencing factors, we propose an integrated carbon emission prediction model named SSA-FAGM-SVR. This model optimizes the fractional accumulation grey model (FAGM) and support vector regression (SVR) using the sparrow search algorithm (SSA), considering both time series and influencing factors. The model is subsequently applied to predict the carbon emissions of the G20 for the next 10 years. The results demonstrate that this model significantly improves prediction accuracy compared to other mainstream prediction algorithms, exhibiting strong adaptability and high accuracy.

Risk Factors and Prediction Methods for Endometriosis Combined with Ureteral Stricture Based on Logistic-Regression Analysis

Objective: This study aimed to explore the risk factors of patients with endometriosis (EMS) and ureteral stricture and to establish a prediction model based on logistic-regression analysis. Methods: The clinical data of 228 EMS patients in Jiaozhou Central Hospital of Qingdao from May 2019 to May 2022 were selected for a retrospective study. According to the results of ureteroscopic biopsy, they were divided into concurrent (n = 32) and nonconcurrent (n = 196) groups. Univariate analysis was performed on the general data and situations of clinical treatment in both groups. Single factor with statistically significant differences was included in unconditional logistic-regression analysis with multiple factors to explore the risk factors of such patients and establish a prediction model. Results: Overt differences were found in previous history of ureter operation (odds ratio (OR) = 3.711, p = 0.006), course of EMS (OR = 3.987, p = 0.007), presence or absence of haematuria (OR = 3.586, p = 0.009) and lateral abdominal pain (OR = 4.451, p = 0.002), and invasion depth of lesion (OR = 7.271, p < 0.001) between the two groups (p < 0.05), without distinct difference in age, menstrual duration, BMI values, history of dysmenorrhea, previous history of drug therapy, smoking history, and drinking history (p > 0.05). Logistic-regression analysis showed that previous history of ureter operation (a1), course of EMS (b2), occurrence of haematuria (c3) and lateral abdominal pain (d4), and invasion depth of lesion ≥5 mm (e5) were risk factors for EMS combined with ureteral stricture (p < 0.05), taking logit (p) = –4.990 + 1.311a1 + 1.383b2 + 1.277c3 + 1.493d4 + 1.984e5 as regression model (AU)

Cancer driver mutations: predictions and reality.

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors.

Research in the field of biochemistry and cellular biology has entered a new phase due to the discovery of phase separation driving the formation of biomolecular condensates, or membraneless organelles, in cells. The implications of this novel principle of cellular organization are vast and can be applied at multiple scales, spawning exciting research questions in numerous directions. Of fundamental importance are the molecular mechanisms that underly biomolecular condensate formation within cells and whether insights gained into these mechanisms provide a gateway for accurate predictions of protein phase behavior. Within the last six years, a significant number of predictors for protein phase separation and condensate localization have emerged. Herein, we compare a collection of state-of-the-art predictors on different tasks related to protein phase behavior. We show that the tested methods achieve high AUCs in the identification of biomolecular condensate drivers and scaffolds, as well as in the identification of proteins able to phase separate in vitro. However, our benchmark tests reveal that their performance is poorer when used to predict protein segments that are involved in phase separation or to classify amino acid substitutions as phase-separation-promoting or -inhibiting mutations. Our results suggest that the phenomenological approach used by most predictors is insufficient to fully grasp the complexity of the phenomenon within biological contexts and make reliable predictions related to protein phase behavior at the residue level.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies.

Immunogenicity is an important concern to therapeutic antibodies during antibody design and development. Based on the co-crystal structures of idiotypic antibodies and their antibodies, one can see that anti-idiotypic antibodies usually bind the complementarity-determining regions (CDR) of idiotypic antibodies. Sequence and structural features, such as cavity volume at the CDR region and hydrophobicity of CDR-H3 loop region, were identified for distinguishing immunogenic antibodies from non-immunogenic antibodies. These features were integrated together with a machine learning platform to predict immunogenicity for humanized and fully human therapeutic antibodies (PITHA). This method achieved an accuracy of 83% in a leave-one-out experiment for 29 therapeutic antibodies with available crystal structures. The web server of this method is accessible at http://mabmedicine.com/PITHA or http://sysbio.unl.edu/PITHA . This method, as a step of computer-aided antibody design, helps evaluate the safety of new therapeutic antibody, which can save time and money during the therapeutic antibody development.

Toward an Automatic Assessment of Cognitive Dysfunction in Relapsing-Remitting Multiple Sclerosis Patients Using Eye Movement Analysis.

Despite the importance of cognitive function in multiple sclerosis, it is poorly represented in the Expanded Disability Status Scale (EDSS), the commonly used clinical measure to assess disability, suggesting that an analysis of eye movement, which is generated by an extensive and well-coordinated functional network that is engaged in cognitive function, could have the potential to extend and complement this more conventional measure. We aimed to measure the eye movement of a case series of MS patients with relapsing−remitting MS to assess their cognitive status using a conventional gaze tracker. A total of 41 relapsing−remitting MS patients and 43 age-matched healthy controls were recruited for this study. Overall, we could not find a clear common pattern in the eye motor abnormalities. Vertical eye movement was more impaired in MS patients than horizontal movement. Increased latencies were found in the prosaccades and reflexive saccades of antisaccade tests. The smooth pursuit was impaired with more corrections (backup and catchup movements, p<0.01). No correlation was found between eye movement variables and EDSS or disease duration. Despite significant alterations in the behavior of the eye movements in MS patients, which are compatible with altered cognitive status, there is no common pattern of these alterations. We interpret this as a consequence of the patchy, heterogeneous distribution of white matter involvement in MS that provokes multiple combinations of impairment at different points in the different networks involved in eye motor control. Further studies are therefore required.

Global natural projections.

The paper contributes to the debate on natural interest rates and potential growth rates. We build a model-based projection of the world's most significant economies/areas to improve understanding of their change over the long run and the factors behind their decline. We use a general equilibrium overlapping generation model to understand the simultaneous role of demographics, technology, and globalization. The novelty of the model lies in the way it constructs a human capital index based on UN population projections and an estimated increasing returns production function for major economies worldwide. We find that the decline in interest rates is well explained through labor market dynamics and the increasing obsolescence of capital goods. We also find that a reduced share of labor income has caused movement in the opposite direction, leading to an increase in natural interest rates, which runs counter to the empirical evidence. Moreover, the dynamics of economic integration predict an endogenous adjustment of global imbalances over the long run, with an increasing weight of the Chinese economy and, consequently, a phase of weakness in United States growth between 2030 and 2040. The model is also used to perform shock scenario analysis. We find that demographic decline can adversely affect the growth dynamics for European countries, while a change in the dynamics of globalization can have serious consequences, especially for the United States, with significant benefits for European countries and China.

Combining dense elements with attention mechanisms for 3D radiotherapy dose prediction on head and neck cancers.

PURPOSE: External radiation therapy planning is a highly complex and tedious process as it involves treating large target volumes, prescribing several levels of doses, as well as avoiding irradiating critical structures such as organs at risk close to the tumor target. This requires highly trained dosimetrists and physicists to generate a personalized plan and adapt it as treatment evolves, thus affecting the overall tumor control and patient outcomes. Our aim is to achieve accurate dose predictions for head and neck (H&N) cancer patients on a challenging in-house dataset that reflects realistic variability and to further compare and validate the method on a public dataset. METHODS: We propose a three-dimensional (3D) deep neural network that combines a hierarchically dense architecture with an attention U-net (HDA U-net). We investigate a domain knowledge objective, incorporating a weighted mean squared error (MSE) with a dose-volume histogram (DVH) loss function. The proposed HDA U-net using the MSE-DVH loss function is compared with two state-of-the-art U-net variants on two radiotherapy datasets of H&N cases. These include reference dose plans, computed tomography (CT) information, organs at risk (OARs), and planning target volume (PTV) delineations. All models were evaluated using coverage, homogeneity, and conformity metrics as well as mean dose error and DVH curves. RESULTS: Overall, the proposed architecture outperformed the comparative state-of-the-art methods, reaching 0.95 (0.98) on D95 coverage, 1.06 (1.07) on the maximum dose value, 0.10 (0.08) on homogeneity, 0.53 (0.79) on conformity index, and attaining the lowest mean dose error on PTVs of 1.7% (1.4%) for the in-house (public) dataset. The improvements are statistically significant ( p < 0.05 $p<0.05$ ) for the homogeneity and maximum dose value compared with the closest baseline. All models offer a near real-time prediction, measured between 0.43 and 0.88 s per volume. CONCLUSION: The proposed method achieved similar performance on both realistic in-house data and public data compared to the attention U-net with a DVH loss, and outperformed other methods such as HD U-net and HDA U-net with standard MSE losses. The use of the DVH objective for training showed consistent improvements to the baselines on most metrics, supporting its added benefit in H&N cancer cases. The quick prediction time of the proposed method allows for real-time applications, providing physicians a method to generate an objective end goal for the dosimetrist to use as reference for planning. This could considerably reduce the number of iterations between the two expert physicians thus reducing the overall treatment planning time.

Predicting Protein Conformational Disorder and Disordered Binding Sites.

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Large-Scale Discovery of Microbial Fibrillar Adhesins and Identification of Novel Members of Adhesive Domain Families.

Fibrillar adhesins are bacterial cell surface proteins that mediate interactions with the environment, including host cells during colonization or other bacteria during biofilm formation. These proteins are characterized by a stalk that projects the adhesive domain closer to the binding target. Fibrillar adhesins evolve quickly and thus can be difficult to computationally identify, yet they represent an important component for understanding bacterium-host interactions. To detect novel fibrillar adhesins, we developed a random forest prediction approach based on common characteristics we identified for this protein class. We applied this approach to Firmicutes and Actinobacteria proteomes, yielding over 6,500 confidently predicted fibrillar adhesins. To verify the approach, we investigated predicted fibrillar adhesins that lacked a known adhesive domain. Based on these proteins, we identified 24 sequence clusters representing potential novel members of adhesive domain families. We used AlphaFold to verify that 15 clusters showed structural similarity to known adhesive domains, such as the TED domain. Overall, our study has made a significant contribution to the number of known fibrillar adhesins and has enabled us to identify novel members of adhesive domain families involved in bacterial pathogenesis. IMPORTANCE Fibrillar adhesins are a class of bacterial cell surface proteins that enable bacteria to interact with their environment. We developed a machine learning approach to identify fibrillar adhesins and applied this classification approach to the Firmicutes and Actinobacteria Reference Proteomes database. This method allowed us to detect a high number of novel fibrillar adhesins and also novel members of adhesive domain families. To confirm our predictions of these potential adhesin protein domains, we predicted their structure using the AlphaFold tool.

Prediction of global spread of COVID-19 pandemic: a review and research challenges.

Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyze and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modeling tools to the latest artificial intelligence technology, the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response-whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance, including lockdowns. On the one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand, it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt to compile different research methods and comparing their accuracy in predicting the spread of COVID-19.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Prediction model for the spread of the COVID-19 outbreak in the global environment.

COVID-19 has long become a worldwide pandemic. It is responsible for the death of over two million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy, we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.