大数据毕设零基础也能搞定：传染病可视化系统完整版Spark+Django实现方案

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传染病可视化系统-系统功能

基于大数据的中国常见传染病数据分析与可视化系统是一套运用现代大数据技术构建的综合性疾病监测分析平台，该系统采用Hadoop+Spark大数据框架作为核心技术架构，结合Python开发语言和Django后端框架，构建了强大的数据处理和分析能力。系统前端采用Vue+ElementUI+Echarts技术栈实现交互式数据可视化界面，通过HDFS分布式文件系统存储海量传染病数据，利用Spark SQL、Pandas、NumPy等技术进行高效的数据分析和处理。该系统具备疾病流行病学特征分析、人口特征与疾病关系分析、医疗干预与疾病控制效果分析以及公共卫生系统效能评估四大核心功能模块，能够深入分析疾病的发病率与死亡率、季节性分布特征、地域分布差异、城乡分布差异、年龄性别关联性、疫苗接种效果、隔离措施控制效果等多维度指标，为疾病防控决策提供科学依据。系统通过大数据技术实现了对2018-2022年中国常见传染病的全面监测和预警，构建了传染病综合风险评估模型，能够有效评估区域防控能力，为公共卫生管理部门提供了强有力的技术支撑和决策参考工具。

传染病可视化系统-技术选型

大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL

传染病可视化系统-背景意义

选题背景 近年来传染病防控面临严峻挑战，2023年我国法定传染病发病人数达到1096.07万例，传染病发病数量呈现较高幅度增长趋势。随着全球化进程加速和人口流动频繁，新发传染病不断涌现，传统的传染病监测方式已难以满足现代公共卫生管理的需求。传统的监测预警方法主要针对某一种或某一系统传染病进行预警，监测预警方法很难得到全面推广，而以大数据为基础的监测预警方法进展迅速，在甲型H1N1、埃博拉、手足口、肠道传染病防控等方面取得了显著成效。当前我国正在积极推进智慧化预警多点触发机制建设，把增强早期监测预警能力作为健全公共卫生体系当务之急，迫切需要运用先进的大数据技术构建综合性传染病数据分析系统，实现对传染病疫情的精准监测、科学分析和有效预警。 选题意义 基于大数据的传染病数据分析与可视化系统具有重要的实践价值和社会意义。该系统能够整合海量的传染病监测数据，通过Hadoop和Spark等大数据技术实现高效的数据处理和深度挖掘，为疾病防控决策提供科学依据。系统通过多维度分析传染病的时空分布规律、人群特征关联性以及防控措施效果，帮助卫生管理部门准确识别疾病传播风险点，制定针对性防控策略。基于大数据的传染病监测预警方法为提高传染病预警的灵敏度、特异度、准确度提供了重要途径，能够显著提升疫情早期发现和响应能力。该系统的建设对于完善我国传染病监测网络、增强公共卫生应急管理能力具有积极意义，同时也为相关领域的科研工作者和管理人员提供了强有力的技术工具和数据支撑平台，推动传染病防控从经验驱动向数据驱动转变，提升整体防控效率和科学化水平。

传染病可视化系统-演示视频

系统-演示视频

传染病可视化系统-演示图片

传染病可视化系统-代码展示

# 核心功能1：疾病流行病学特征分析
def analyze_disease_epidemiology(disease_data):
   disease_stats = {}
   for disease in disease_data['Disease'].unique():
       disease_subset = disease_data[disease_data['Disease'] == disease]
       total_cases = disease_subset['Reported_Cases'].sum()
       total_deaths = disease_subset['Deaths'].sum()
       mortality_rate = (total_deaths / total_cases * 100) if total_cases > 0 else 0
       seasonal_distribution = disease_subset.groupby('Season')['Reported_Cases'].sum().to_dict()
       provincial_distribution = disease_subset.groupby('Province')['Reported_Cases'].sum().sort_values(ascending=False).head(10).to_dict()
       urban_cases = disease_subset[disease_subset['Urban_Rural'] == 'Urban']['Reported_Cases'].sum()
       rural_cases = disease_subset[disease_subset['Urban_Rural'] == 'Rural']['Reported_Cases'].sum()
       urban_deaths = disease_subset[disease_subset['Urban_Rural'] == 'Urban']['Deaths'].sum()
       rural_deaths = disease_subset[disease_subset['Urban_Rural'] == 'Rural']['Deaths'].sum()
       urban_mortality = (urban_deaths / urban_cases * 100) if urban_cases > 0 else 0
       rural_mortality = (rural_deaths / rural_cases * 100) if rural_cases > 0 else 0
       yearly_trend = disease_subset.groupby('Year')['Reported_Cases'].sum().to_dict()
       monthly_pattern = disease_subset.groupby('Month')['Reported_Cases'].mean().to_dict()
       symptom_fever_rate = (disease_subset['Symptom_Fever'].sum() / len(disease_subset) * 100) if len(disease_subset) > 0 else 0
       symptom_cough_rate = (disease_subset['Symptom_Cough'].sum() / len(disease_subset) * 100) if len(disease_subset) > 0 else 0
       symptom_rash_rate = (disease_subset['Symptom_Rash'].sum() / len(disease_subset) * 100) if len(disease_subset) > 0 else 0
       high_risk_provinces = [province for province, cases in provincial_distribution.items() if cases > total_cases * 0.1]
       peak_season = max(seasonal_distribution, key=seasonal_distribution.get)
       peak_month = max(monthly_pattern, key=monthly_pattern.get)
       disease_stats[disease] = {
           'total_cases': total_cases, 'total_deaths': total_deaths, 'mortality_rate': round(mortality_rate, 2),
           'seasonal_distribution': seasonal_distribution, 'provincial_distribution': provincial_distribution,
           'urban_rural_comparison': {'urban_mortality': round(urban_mortality, 2), 'rural_mortality': round(rural_mortality, 2)},
           'yearly_trend': yearly_trend, 'monthly_pattern': monthly_pattern, 'peak_season': peak_season, 'peak_month': peak_month,
           'symptom_rates': {'fever': round(symptom_fever_rate, 2), 'cough': round(symptom_cough_rate, 2), 'rash': round(symptom_rash_rate, 2)},
           'high_risk_provinces': high_risk_provinces
       }
   return disease_stats
# 核心功能2：人口特征与疾病关系分析
def analyze_population_disease_relationship(disease_data):
   population_analysis = {}
   age_disease_matrix = disease_data.groupby(['Age_Group', 'Disease'])['Reported_Cases'].sum().unstack(fill_value=0)
   gender_disease_matrix = disease_data.groupby(['Gender', 'Disease'])['Reported_Cases'].sum().unstack(fill_value=0)
   for age_group in disease_data['Age_Group'].unique():
       age_subset = disease_data[disease_data['Age_Group'] == age_group]
       total_cases = age_subset['Reported_Cases'].sum()
       total_deaths = age_subset['Deaths'].sum()
       icu_cases = age_subset['ICU_Admission'].sum()
       avg_hospitalization_days = age_subset['Days_Hospitalized'].mean()
       mortality_rate = (total_deaths / total_cases * 100) if total_cases > 0 else 0
       icu_rate = (icu_cases / total_cases * 100) if total_cases > 0 else 0
       comorbidity_impact = age_subset[age_subset['Comorbidity'] == 1]['Deaths'].sum() / age_subset[age_subset['Comorbidity'] == 1]['Reported_Cases'].sum() * 100 if age_subset[age_subset['Comorbidity'] == 1]['Reported_Cases'].sum() > 0 else 0
       travel_related_cases = age_subset[age_subset['Travel_History'] == 1]['Reported_Cases'].sum()
       travel_case_rate = (travel_related_cases / total_cases * 100) if total_cases > 0 else 0
       most_susceptible_diseases = age_subset.groupby('Disease')['Reported_Cases'].sum().sort_values(ascending=False).head(5).to_dict()
       population_analysis[age_group] = {
           'total_cases': total_cases, 'mortality_rate': round(mortality_rate, 2), 'icu_rate': round(icu_rate, 2),
           'avg_hospitalization_days': round(avg_hospitalization_days, 2), 'comorbidity_mortality_impact': round(comorbidity_impact, 2),
           'travel_related_rate': round(travel_case_rate, 2), 'most_susceptible_diseases': most_susceptible_diseases
       }
   gender_comparison = {}
   for gender in disease_data['Gender'].unique():
       gender_subset = disease_data[disease_data['Gender'] == gender]
       gender_total_cases = gender_subset['Reported_Cases'].sum()
       gender_mortality = (gender_subset['Deaths'].sum() / gender_total_cases * 100) if gender_total_cases > 0 else 0
       gender_diseases = gender_subset.groupby('Disease')['Reported_Cases'].sum().sort_values(ascending=False).to_dict()
       gender_comparison[gender] = {'total_cases': gender_total_cases, 'mortality_rate': round(gender_mortality, 2), 'disease_distribution': gender_diseases}
   population_analysis['gender_comparison'] = gender_comparison
   population_analysis['age_disease_matrix'] = age_disease_matrix.to_dict()
   population_analysis['gender_disease_matrix'] = gender_disease_matrix.to_dict()
   return population_analysis
# 核心功能3：医疗干预与疾病控制效果分析
def analyze_medical_intervention_effectiveness(disease_data):
   intervention_analysis = {}
   vaccinated_data = disease_data[disease_data['Vaccinated'] == 1]
   unvaccinated_data = disease_data[disease_data['Vaccinated'] == 0]
   vaccinated_mortality = (vaccinated_data['Deaths'].sum() / vaccinated_data['Reported_Cases'].sum() * 100) if vaccinated_data['Reported_Cases'].sum() > 0 else 0
   unvaccinated_mortality = (unvaccinated_data['Deaths'].sum() / unvaccinated_data['Reported_Cases'].sum() * 100) if unvaccinated_data['Reported_Cases'].sum() > 0 else 0
   vaccine_effectiveness = ((unvaccinated_mortality - vaccinated_mortality) / unvaccinated_mortality * 100) if unvaccinated_mortality > 0 else 0
   quarantine_data = disease_data[disease_data['Quarantined'] == 1]
   non_quarantine_data = disease_data[disease_data['Quarantined'] == 0]
   quarantine_control_rate = (quarantine_data['Recovered'].sum() / quarantine_data['Reported_Cases'].sum() * 100) if quarantine_data['Reported_Cases'].sum() > 0 else 0
   non_quarantine_control_rate = (non_quarantine_data['Recovered'].sum() / non_quarantine_data['Reported_Cases'].sum() * 100) if non_quarantine_data['Reported_Cases'].sum() > 0 else 0
   quarantine_effectiveness = quarantine_control_rate - non_quarantine_control_rate
   contact_tracing_data = disease_data[disease_data['Contact_Tracing'] == 1]
   non_tracing_data = disease_data[disease_data['Contact_Tracing'] == 0]
   tracing_control_effectiveness = (contact_tracing_data['Recovered'].sum() / contact_tracing_data['Reported_Cases'].sum() * 100) - (non_tracing_data['Recovered'].sum() / non_tracing_data['Reported_Cases'].sum() * 100) if non_tracing_data['Reported_Cases'].sum() > 0 and contact_tracing_data['Reported_Cases'].sum() > 0 else 0
   hospitalized_data = disease_data[disease_data['Hospitalized'] == 1]
   hospitalized_recovery_rate = (hospitalized_data['Recovered'].sum() / hospitalized_data['Reported_Cases'].sum() * 100) if hospitalized_data['Reported_Cases'].sum() > 0 else 0
   hospitalized_mortality_rate = (hospitalized_data['Deaths'].sum() / hospitalized_data['Reported_Cases'].sum() * 100) if hospitalized_data['Reported_Cases'].sum() > 0 else 0
   avg_hospital_stay = hospitalized_data['Days_Hospitalized'].mean()
   lab_confirmed_data = disease_data[disease_data['Lab_Confirmed'] == 1]
   lab_confirmation_rate = (len(lab_confirmed_data) / len(disease_data) * 100) if len(disease_data) > 0 else 0
   lab_confirmed_accuracy = (lab_confirmed_data['Recovered'].sum() + lab_confirmed_data['Deaths'].sum()) / lab_confirmed_data['Reported_Cases'].sum() * 100 if lab_confirmed_data['Reported_Cases'].sum() > 0 else 0
   follow_up_data = disease_data[disease_data['Follow_Up'] == 1]
   follow_up_recovery_rate = (follow_up_data['Recovered'].sum() / follow_up_data['Reported_Cases'].sum() * 100) if follow_up_data['Reported_Cases'].sum() > 0 else 0
   intervention_analysis = {
       'vaccination_impact': {'vaccinated_mortality': round(vaccinated_mortality, 2), 'unvaccinated_mortality': round(unvaccinated_mortality, 2), 'vaccine_effectiveness': round(vaccine_effectiveness, 2)},
       'quarantine_effectiveness': {'quarantine_control_rate': round(quarantine_control_rate, 2), 'non_quarantine_control_rate': round(non_quarantine_control_rate, 2), 'effectiveness_difference': round(quarantine_effectiveness, 2)},
       'contact_tracing_impact': {'tracing_effectiveness': round(tracing_control_effectiveness, 2)},
       'hospitalization_outcomes': {'recovery_rate': round(hospitalized_recovery_rate, 2), 'mortality_rate': round(hospitalized_mortality_rate, 2), 'avg_stay_days': round(avg_hospital_stay, 2)},
       'laboratory_diagnosis': {'confirmation_rate': round(lab_confirmation_rate, 2), 'diagnostic_accuracy': round(lab_confirmed_accuracy, 2)},
       'follow_up_management': {'follow_up_recovery_rate': round(follow_up_recovery_rate, 2)}
   }
   return intervention_analysis

传染病可视化系统-结语

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