Dose prediction of lopinavir/ritonavir for 2019-novel coronavirus (2019-nCoV) infection based on mathematic modeling

Wuhan novel coronavirus or 2019-novel coronavirus(2019-nCoV)infection is a rapidly emerging respiratory viral disease[1].2019-nCoV infection is characterized as febrile illness with possible severe lung complication[1].The disease was firstly reported in China in December 2019 and then spread to many countries(such as Thailand,Japan and Singapore)[2,3].As a new disease,there is a limited knowledge of treatment for the infection.Lu recently proposed that some drug might be useful in treatment of 2019-nCoV infection[3].

Prediction of Kappa Number in Eucalyptus Kraft Pulp Continuous Digester Using the Box &Jenkins Methodology

The quality of the resulting pulping continuous digesters is monitored by measuring the Kappa number, which is a reference of residual lignin. The control of the kappa number is carried out mainly in the top of the digester, therefore it is important to get some indication of this analysis beforehand. In this context, the aim of this work was to obtain a prediction model of the kappa number in advance to the laboratory results. This paper proposes a new approach using the Box & Jenkins methodology to develop a dynamic model for predicting the kappa number from a Kamyr continuous digester from an eucalyptus Kraft pulp mill in Brazil. With a database of 1500 observations over a period of 30 days of operation, some ARMA models were studied, leading to the choice of ARMA (1, 2) as the best forecasting model. After fitting the model, we performed validation with a new set of data from 30 days of operation, achieving a model of 2.7% mean absolute percent error.

The Number of Five Basic Elements' Motion of Traditional Chinese Medicine and Cancer Prediction

"The number of five basic elements'motion"varies according to the date of birth,which is tightly tied to human health.To predict cancer with"the number of five basic elements'motion"is simple,accurate,worth of research and popularizing.The theory of“five basic elements'motion and six climatic changes”is a key part of Traditional Chinese Medicine,“the number of five basic elements'motion”is strongly related to the physiology and pathology of human body.Combine with folk pathology prediction,“the number of five basic elements'motion”has been proved to have some scientific validity and of high accuracy in cancer prediction,it is worth to study,research and be discussed in-depth.

Mach Number Prediction for a Wind Tunnel Based on the CNN-LSTM-Attention Method

The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance.The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions.The process flow in wind tunnel testing is inherently complex,resulting in a system characterized by nonlinearity,time lag,and multiple working conditions.To implement the predictive control algorithm,a precise Mach number prediction model must be created.Therefore,this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions.Firstly,this paper introduces a continuous transonic wind tunnel.The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process.Secondly,considering the nonlinear and time-lag characteristics of the wind tunnel system,a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model,which has long and short-term memory functions.Then,the attention mechanism is incorporated into the CNN-LSTM prediction model to enable the model to focus more on data with greater information importance,thereby enhancing the model's training effectiveness.The application results ultimately demonstrate the efficacy of the proposed approach.

Eosinopenia is a predictive factor for the severity of acute ischemic stroke

Previous data have revealed an association between eosinopenia and mortality of acute ischemic stroke.However,the relationship of eosinopenia with infarct volume,infection rate,and poor outcome of acute ischemic stroke is still unknown.The retrospective study included 421 patients(273 males,65%;mean age,68.0± 13.0 years)with first acute ischemic stroke who were hospitalized in the Second Affiliated Hospital of Soochow University,China,from January 2017 to February 2018.Laboratory data,neuroimaging results,and modified Rankin Scale scores were collected.Patients were divided into four groups according to their eosinophil percentage level(<0.4%,0.4-1.1%,1 1-2.3%,≥2.3%).Spearman’s correlation analysis showed that the percentage of eosinophils was negatively correlated with infarct volume(rs=-0.514,P<0.001).Receiver operating characteristic analysis demonstrated that eosinopenia predicted a large infarct volume more accurately than neutrophilia;the area under curve was 0.906 and 0.876,respectively;a large infarct was considered as that with a diameter larger than 3 cm and involving more than two major arterial blood supply areas.Logistic regression analysis revealed that eosinophil percentage was an independent risk factor for acute ischemic stroke(P=0.002).Moreover,eosinophil percentage was significantly associated with large infarct volume,high infection rate(pulmonary and urinary tract infections),and poor outcome(modified Rankin Scale score>3)after adjusting for potential confounding factors(P-trend<0.001).These findings suggest that eosinopenia has the potential to predict the severity of acute ischemic stroke.This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University,China(approval number:K10)on November 10,2015.

Relevance of MUC1 mucin variable number of tandem repeats polymorphism in H pylori adhesion to gastric epithelial cells

AIM:To evaluate the influence of MUC1 mucin variable number of tandem repeats (VNTR) variability on H pylori adhesion to gastric cells. METHODS: Enzyme linked immunosorbent assay (ELISA)-based adhesion assays were performed to measure the adhesion of different H pylori strains (HP26695 and HPTx30a) to gastric carcinoma cell lines (GP202 and MKN45) and GP202 clones expressing recombinant MUC1 with different VNTR lengths. RESULTS: Evaluation of adhesion results shows that H pylori pathogenic strain HP26695 has a significantly higher (P < 0.05) adhesion to all the cell lines and clones tested, when compared to the non-pathogenic strain HPTx30a. Bacteria showed a significantly higher (P < 0.05) adhesion to the GP202 cell line, when compared to the MKN45 cell line. Furthermore, both strains showed a significantly higher (P < 0.05) adhesion to GP202 clones with larger MUC1 VNTR domains. CONCLUSION: This work shows that MUC1 mucin variability conditions H pylori binding to gastric cells. The extent of bacterial adhesion depends on the size of theMUC1 VNTR domain. The adhesion is further dependent on bacterial pathogenicity and the gastric cell line. MUC1 mucin variability may contribute to determine H pylori colonization of the gastric mucosa.

Scan of the endogenous retrovirus sequences across the swine genome and survey of their copy number variation and sequence diversity among various Chinese and Western pig breeds

In pig-to-human xenotransplantation,the transmission risk of porcine endogenous retroviruses(PERVs)is of great concern.However,the distribution of PERVs in pig genomes,their genetic variation among Eurasian pigs,and their evolutionary history remain unclear.We scanned PERVs in the current pig reference genome(assembly Build 11.1),and identified 36 long complete or near-complete PERVs(lc PERVs)and 23 short incomplete PERVs(si PERVs).Besides three known PERVs(PERV-A,-B,and-C),four novel types(PERV-JX1,-JX2,-JX3,and-JX4)were detected in this study.According to evolutionary analyses,the newly discovered PERVs were more ancient,and PERV-Bs probably experienced a bottleneck~0.5 million years ago(Ma).By analyzing63 high-quality porcine whole-genome resequencing data,we found that the PERV copy numbers in Chinese pigs were lower(32.0±4.0)than in Western pigs(49.1±6.5).Additionally,the PERV sequence diversity was lower in Chinese pigs than in Western pigs.Regarding the lc PERV copy numbers,PERV-A and-JX2 in Western pigs were higher than in Chinese pigs.Notably,Bama Xiang(BMX)pigs had the lowest PERV copy number(27.8±5.1),and a BMX individual had no PERV-C and the lowest PERV copy number(23),suggesting that BMX pigs were more suitable for screening and/or modification as xenograft donors.Furthermore,we identified 451 PERV transposon insertion polymorphisms(TIPs),of which 86 were shared by all 10 Chinese and Western pig breeds.Our findings provide systematic insights into the genomic distribution,variation,evolution,and possible biological function of PERVs.

Mathematical Modeling of the Co-Infection Dynamics of HIV and Tuberculosis Incorporating Inconsistency in HIV Treatment

A non-linear HIV-TB co-infection has been formulated and analyzed. The positivity and invariant region has been established. The disease free equilibrium and its stability has been determined. The local stability was determined and found to be stable under given conditions. The basic reproduction number was obtained and according to findings, co-infection diminishes when this number is less than unity, and persists when the number is greater than unity. The global stability of the endemic equilibrium was calculated. The impact of HIV on TB was established as well as the impact of TB on HIV. Numerical solution was also done and the findings indicate that when the rate of HIV treatment increases the latent TB increases while the co-infected population decreases. When the rate of HIV treatment decreases the latent TB population decreases and the co-infected population increases. Encouraging communities to prioritize the consistent treatment of HIV infected individuals must be emphasized in order to reduce the scourge of HIV-TB co-infection.

The Genesis of Prime Numbers—Revealing the Underlying Periodicity of Prime Numbers

Prime numbers are the integers that cannot be divided exactly by another integer other than one and itself. Prime numbers are notoriously disobedient to rules: they seem to be randomly distributed among natural numbers with no laws except that of chance. Questions about prime numbers have been perplexing mathematicians over centuries. How to efficiently predict greater prime numbers has been a great challenge for many. Most of the previous studies focus on how many prime numbers there are in certain ranges or patterns of the first or last digits of prime numbers. Honestly, although these patterns are true, they help little with accurately predicting new prime numbers, as a deviation at any digit is enough to annihilate the primality of a number. The author demonstrates the periodicity and inter-relationship underlying all prime numbers that makes the occurrence of all prime numbers predictable. This knowledge helps to fish all prime numbers within one net and will help to speed up the related research.

Mathematical Model for the Transmission Dynamics of Zika Virus Infection with Combined Vaccination and Treatment Interventions

In this paper, we studied the transmission dynamics of ZIKV in the presence of a vector under the combined effects of treatment and vaccination in a hypothetical population. The disease-free εo and endemic ε1 equilibria were established with local stability on εo. We established the basic reproduction number Ro which served as a threshold for measuring the spread of the infection in the population using the next-generation matrix and computed its numerical value to be Ro = 0.0185903201 using the parameter values. It was established that the disease-free equilibrium εo is locally asymptotically stable since Ro < 1;meaning ZIKV infection would be eradicated from the population. The computational results of the study revealed that combining the two interventions of vaccination and treatment concomitantly proffers an optimal control strategy in taming the transmission of the virus than a single intervention strategy.

Predictions of Galapagos Volcanic Eruption, El Niño, Ecuadorian Earthquake, Global Volcanic Eruption and Forest Fire by Sunspot Number

The earthquake number was correlated (R2 = 0.8781) with the volcano number in the East Pacific and Central American countries. The year of volcanic eruptions in the Galapagos Hot Spot (GHS) was proportional to the year of El Ni&#241o events (R2 = 0.9939) as well as to the year of earthquake events (R2 = 0.9943) in Ecuador, Colombia, and Peru from 1977 to 2016. Therefore, there can be El Ni&#241o and earthquake events in Ecuador, Colombia and Peru if there are strong volcanic eruptions either from aboveground volcanoes or undersea seamounts in the GHS during the maximal sunspot number’s period. Global volcanic eruptions were occurred during the maximal temperature departure, the latter being reversely proportional (R2 = 0.4512) to the sunspot number from 1980 to 2019. Forest fires occurred in middle latitude countries (South Korea, California, western Russia, Australia) around 35 degree from Pacific Equator during La Ni&#241a events at the time of the minimal sunspot number. Since there were intense forest fires in South Korea, Australia and California in 2019, more are expected in 2030 due to the 11 year cyclical variation in the sunspot number. The sequence of forest fires can be schematically summarized as below. 1) La Ni&#241a event in Equator causes the low vapor pressure of water due to the low sea surface temperature (SST);2) There is the pumping of freshwater from the warm Pacific Ocean to cold Equator with simultaneous transfer of heat (warm Pacific Ocean to cold Equator) and mass (water enriched Pacific Ocean to water lean Equator);3) Strong winds from dry zone pass Mountains to cause the forest fires in the residential land with bushes and trees. 4) According to the Bernoulli’s principle, the pressure gradient between the high Mountains and the low Ocean, induces the decrease of static velocity gradient for vigorously upwards flares in the Mountains and the residential land during the forest fires, which may be why it is difficult to extinguish the forest fires until burnt up bushes and trees in the Mountains and residential land with serious damages. Most effective solution to forest fires is to change the events from La Ni&#241a to El Ni&#241o for reductions of temperature and pressure gradients by Gay-Lussac’s law. Such a transition may be induced by artificial submarine volcanic eruptions among volcanoes of Fernandina, Sierra Negra and Wolf in the GHS for Korea and California. As for Australia, artificial volcanic eruptions can be caused in huge underwater volcano chain in Tasmania. The strong damages of forest fires in South Korea, Australia and California were observed in the period of the minimal sunspot number, as were in October of 2019 to February of 2020. Simultaneous transfers of momentum (velocity), heat (temperature) and mass (freshwater) were occurred from the dry zone or the hot Deserts via the Mountains and the residential land with bushes and trees to the cold Sea or Ocean. It is expected the lowest SST during the year of the minimal sunspot number to induce the strong damages of forest fires, as was in South Korea, Australia and California during the years from 2019 to 2020. CO2 emissions in Australia showed 34.5% increase, 70% coal power and no nuclear power to induce bushfires because of increases of ozone hole area and UV radiation for the hot land in the period of La Ni&#241a. Fossil fuel operation should be cut down to decrease the ozone hole area and UV radiation for weak bushfires in Australia. A few coming years may still have bushfires in Australia although their damages may not be as terrible as was in 2020. A simple remedy can be the reduction of CO2 emissions as low as possible. It was proposed that the maximal sunspot number induced El Ni&#241o event, GHS volcanic eruption and Ecuadorian earthquakes, while the minimal sunspot number induced La Ni&#241a events in Ecuador, Colombia, Peru and intense forest fires in middle latitude countries of South Korea (forest fires), Australia (bushfires) and California (wildfires).

NARX-GA-Elman Method for Mach Number Prediction of Wind Tunnel Flow Field

Mach number is a key metric in the evaluation of wind tunnel flow field performance.This complex process of wind tunnel test mainly has the problems of nonlinearity and time lag.In order to overcome the problems and control the Mach number stability,this paper proposes a new method of Mach number prediction based on a nonlinear autoregressive exogenous-genetic algorithm-Elman(NARX-GA-Elman)model,which adopts NARX as the basic framework,determines the order of the input variables by using the false nearest neighbor(FNN),and uses the dynamic nonlinear network Elman to fit the model,and finally uses the global optimization algorithm GA to optimize the weight thresholds in the model to establish the Mach number prediction model with optimal performance under single working condition.By comparing with the traditional algorithm,the prediction accuracy of the model is improved by 61.5%,and the control accuracy is improved by 55.7%,which demonstrates that the model has very high prediction accuracy and good stability performance.

Mathematical Model of HIV-1 Circulating Recombinants Forms in Mali

In this paper, we propose a determinist mathematical model for the co-circulating into two circulating recombinants forms (CRFs) Of HIV-disease in Mali. We divide the sexually active population within three compartments (susceptible, CRF-1 infected and CRF-2 or CRF-12 infected) and study the dynamical behavior of this model. Then, we define a basic reproduction number of the CRF-2 or CRF-12 infected individuals R0 and shown that the CRF-2 or CRF-12 infected-free equilibrium is locally-asymptotically stable if R0 1 (thus the CRF-2 or CRF-12 infected invade in the population). Fur-thermore, we prove that under certain conditions on the parameters of the model the controllability of CRF-2 or CRF-12 infected with regard to the CRF-1 infected. Numerical simulations are given to illustrate the results.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

Predicting lower limb periprosthetic joint infections: A review of risk factors and their classification

AIM To undertook a systematic review to determine factors that increase a patient's risk of developing lower limb periprosthetic joint infections(PJI).METHODS This systematic review included full-text studies that reviewed risk factors of developing either a hip or knee PJI following a primary arthroplasty published from January 1998 to November 2016. A variety of keywords were used to identify studies through international databases referencing hip arthroplasty, knee arthroplasty, infection, and risk factors. Studies were only included if they included greater than 20 patients in their study cohort, and there was clear documentation of the statistical parameter used; specifically P-value, hazard ratio, relative risk, or/and odds ratio(OR). Furthermore a quality assessment criteria for the individual studies was undertaken to evaluate the presence of record and reporting bias. RESULTS Twenty-seven original studies reviewing risk factors relating to primary total hip and knee arthroplasty infections were included. Four studies(14.8%) reviewed PJI of the hip, 3(11.21%) of the knee, and 20(74.1%) reviewed both joints. Nineteen studies(70.4%) were retrospective and 8(29.6%) prospective. Record bias was identified in the majority of studies(66.7%). The definition of PJI varied amongst the studies but there was a general consensus to define infection by previously validated methods. The most significant risks were the use of preoperative high dose steroids(OR = 21.0, 95%CI: 3.5-127.2, P < 0.001), a BMI above 50(OR = 18.3, P < 0.001), tobacco use(OR = 12.76, 95%CI: 2.47-66.16, P= 0.017), body mass index below 20(OR = 6.00, 95%CI: 1.2-30.9, P = 0.033), diabetes(OR = 5.47, 95%CI: 1.77-16.97, P = 0.003), and coronary artery disease(OR = 5.10, 95%CI: 1.3-19.8, P = 0.017).CONCLUSION We have highlighted the need for the provider to optimise modifiable risk factors, and develop strategies to limit the impact of non-modifiable factors.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma

BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management.

Evaluation of preoperative blood markers for predicting intraabdominal infection during colorectal cancer resection:A commentary on recent findings

This commentary evaluates the study by Liu et al.This study investigates the predictive utility of the neutrophil-lymphocyte ratio,platelet-lymphocyte ratio,systemic immune-inflammation index,and carcinoembryonic antigen levels for post-operative intra-abdominal infection following colorectal cancer(CRC)surge-ry.The study highlights the critical need for analyzing diverse patient demogra-phics and delves into the potential impact of various confounding factors on the predictive accuracy of these markers.Additionally,the commentary advocates for the initiation of prospective studies aimed at validating and enhancing the clinical utility of these biomarkers in the context of CRC treatment.The commentary aims to underscore the importance of broadening the research framework to include a wider patient demographic and more comprehensive factor analyses,thereby enriching the predictive model's applicability and relevance in clinical settings.

Estimating the Level of Asymptomatic COVID-19 Infections in Northern Ireland in 2020

The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R0, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.

Study on Prediction Model of Number of Rainstorm Days in Summer Based on C5.0 Decision Tree Algorithm

Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.