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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Project Overview</title>
<link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="css/styles.css">
</head>
<body>
<div class="container mt-4">
<h1 class="text-center mb-4">Leveraging Machine Learning to Assess Market-Level Food Safety and Zoonotic Disease
Risks in China</h1>
<section id="project-description">
<h2>Project Description & Overview</h2>
<p>
In recent years, China has faced significant challenges with zoonotic diseases originating from its
wholesale and wet markets, highlighting the critical need to balance food safety regulations with market
sustainability. This project aims to develop a data-driven approach using machine learning to evaluate
and mitigate these risks effectively.
</p>
</section>
<section id="objectives">
<h2>Objectives</h2>
<p>
The primary objective of this project is to assess the correlation between food safety risks
(specifically, food adulteration) in wholesale markets (WSMs) and wet markets (WMs) across China and the
incidence of zoonotic diseases. By analyzing extensive datasets derived from regulatory food safety
tests and zoonotic disease outbreaks, the project seeks to:
</p>
<ul>
<li>
<span>Develop Market-Level Risk Scores</span>: Utilize machine learning algorithms to create food
adulteration risk scores for individual markets based on historical regulatory data from China's
Administration for Market Regulation (AMR).
</li>
<li><span>Correlate Risk Scores with Zoonotic Diseases</span>: Investigate and establish statistical
correlations between these market-level risk scores and the occurrence of zoonotic disease outbreaks
using epidemiological data.</li>
<li><span>Identify High-Risk Markets</span>: Identify and prioritize markets with elevated food
adulteration risks, potentially linked to zoonotic outbreaks, to guide targeted regulatory
interventions.</li>
</ul>
</section>
<section id="methodology">
<h2>Methodology</h2>
<p>
The project employs a comprehensive methodology integrating machine learning techniques and
epidemiological analyses:
</p>
<ul>
<li><span>Data Collection and Preparation</span>: Aggregate and preprocess a vast dataset of over 4
million food safety test records conducted between 2014 and 2020 by AMRs at various levels (state,
provincial, prefecture).</li>
<li><span>Machine Learning for Market Identification</span>: Develop novel clustering algorithms
tailored for Chinese text to associate food safety test records with specific markets, overcoming
challenges in inconsistent market naming conventions.</li>
<li><span>Risk Score Calculation</span>: Compute market-level food adulteration risk scores based on
failure rates from AMR tests, focusing on markets handling high volumes of animal products.</li>
<li><span>Statistical Analysis</span>: Apply regression models to assess the relationship between these
risk scores and zoonotic disease outbreaks, controlling for regional factors like livestock
production
and market density.</li>
</ul>
</section>
<section id="amr-pipeline">
<h2>AMR Pipeline Process</h2>
<img src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41598-022-25817-8/MediaObjects/41598_2022_25817_Fig1_HTML.png?as=webp"
alt="AMR Pipeline Process">
<p>This image illustrates the flow chart diagram of the AMR Pipeline process:</p>
<ul>
<li><span>Step I</span>: AMR Food Adulteration Database Integration</li>
<li><span>Step II</span>: Identify AMR Database Test Records of Animal Products at Markets</li>
<li><span>Step III</span>: Unsupervised Clustering to Connect Records to Specific Markets</li>
<li><span>Step IV</span>: Market Risk Scores (Failure Rates)</li>
<li><span>Step V</span>: Province Risk Scores (Number of AMR Tests at Markets in the Top 20% of Risk
Scores)</li>
<li><span>Step VI</span>: Province Analysis (Association of Province Risk Scores with Zoonotic Flu
Isolates in
Humans)</li>
</ul>
</section>
<section id="results-impact">
<h2>Results and Impact</h2>
<p>
The project's outcomes aim to provide actionable insights and tools for policymakers, regulators, and
public health officials:
</p>
<ul>
<li><span>Decision Support Tool</span>: Develop an operational tool for visualizing market risk scores
and associated food safety test results, aiding regulators in prioritizing inspections and
interventions.</li>
<li><span>Policy Recommendations</span>: Inform evidence-based policies for targeted regulatory
interventions that enhance food safety and mitigate zoonotic disease risks while preserving market
operations
vital to China's food supply chain.</li>
<li><span>Contribution to Public Health</span>: Advance understanding of the systemic factors
contributing to zoonotic disease transmission in market settings, facilitating proactive public
health measures.
</li>
</ul>
</section>
<section id="dataset-sample">
<h2>Sample Subset Table of AMR Dataset</h2>
<p>File: <code>Zhejiang_Zhejiang_msb_20220730/3.食品抽检合格-20201111.xls</code>.</p>
<table class="table table-bordered">
<thead class="thead-dark">
<tr>
<th>抽样编号</th>
<th>序号</th>
<th>标称生产企业名称</th>
<th>标称生产企业地址</th>
<th>被抽样单位名称</th>
<th>被抽样单位所在省份</th>
<th>食品名称</th>
<th>规格型号</th>
<th>生产日期/批号</th>
<th>分类</th>
<th>公告号</th>
<th>公告日期</th>
<th>任务来源/项目名称</th>
<th>备注</th>
<th>公告网址链接</th>
</tr>
</thead>
<tbody>
<tr>
<td>GC20330000300530998</td>
<td>1</td>
<td>金华市蜂御医养蜂专业合作社蜂产品加工厂</td>
<td>浙江省金华市胜利北街气象巷5号</td>
<td>金华市蜂御医养蜂专业合作社</td>
<td>浙江</td>
<td>洋槐蜂蜜</td>
<td>500克/瓶</td>
<td>6/10/2020</td>
<td>蜂产品</td>
<td>浙江省食品2020年第45期</td>
<td>2020.11.11</td>
<td>浙江/(总局国抽)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300530999</td>
<td>2</td>
<td>嵊州市香富制酱调味品厂</td>
<td>嵊州市经济开发区(浦口街道)原农机厂</td>
<td>嵊州市香富制酱调味品厂</td>
<td>浙江</td>
<td>母油王液态复合调味料</td>
<td>散装称重</td>
<td>6/2/2020</td>
<td>调味品</td>
<td>浙江省食品2020年第45期</td>
<td>2020.11.11</td>
<td>浙江/(总局国抽)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300531000</td>
<td>3</td>
<td>金微克食品配料（杭州）有限公司</td>
<td>杭州市萧山区经济开发区红泰六路489号16幢</td>
<td>金微克食品配料（杭州）有限公司</td>
<td>浙江</td>
<td>复配防腐剂5号</td>
<td>1kg/袋</td>
<td>7/27/2020</td>
<td>食品添加剂</td>
<td>浙江省食品2020年第45期</td>
<td>2020.11.11</td>
<td>浙江/(总局国抽)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000005131798</td>
<td>4</td>
<td>象山德丰食品有限公司</td>
<td>浙江省宁波市象山县大徐镇城东工业园万隆路615号</td>
<td>象山德丰食品有限公司</td>
<td>浙江</td>
<td>千张</td>
<td>/</td>
<td>8/14/2020</td>
<td>豆制品</td>
<td>浙江省食品2020年第45期</td>
<td>2020.11.11</td>
<td>浙江/(总局国抽)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300531001</td>
<td>5</td>
<td>绍兴市四方食品有限公司</td>
<td>绍兴市诸暨阮市镇董公村</td>
<td>绍兴市四方食品有限公司</td>
<td>浙江</td>
<td>精制老抽王调味精品</td>
<td>800ml/壶</td>
<td>8/6/2020</td>
<td>调味品</td>
<td>浙江省食品2020年第45期</td>
<td>2020.11.11</td>
<td>浙江/(总局国抽)</td>
<td>/</td>
<td>/</td>
</tr>
</tbody>
</table>
</section>
<section id="dataset-sample-english">
<h2>Sample Subset Table of AMR Dataset (English Translations)</h2>
<p>File: <code>Zhejiang_Zhejiang_msb_20220730/3.Food sampling qualified-20201111.xls</code>.</p>
<table class="table table-bordered">
<thead class="thead-dark">
<tr>
<th>Sampling Number</th>
<th>Serial Number</th>
<th>Nominal Manufacturer Name</th>
<th>Nominal Manufacturer Address</th>
<th>Sampled Unit Name</th>
<th>Sampled Unit Province</th>
<th>Food Name</th>
<th>Specification Model</th>
<th>Production Date/Batch Number</th>
<th>Classification</th>
<th>Announcement Number</th>
<th>Announcement Date</th>
<th>Task Source/Project Name</th>
<th>Remarks</th>
<th>Announcement Website Link</th>
</tr>
</thead>
<tbody>
<tr>
<td>GC20330000300530998</td>
<td>1</td>
<td>Jinhua Fengyuyi Beekeeping Professional Cooperative Bee Products Processing Factory</td>
<td>No. 5, Qixiang Lane, Shengli North Street, Jinhua City, Zhejiang Province</td>
<td>Jinhua Fengyuyi Beekeeping Professional Cooperative</td>
<td>Zhejiang</td>
<td>Acacia Honey</td>
<td>500g/bottle</td>
<td>6/10/2020</td>
<td>Bee Products</td>
<td>Zhejiang Provincial Food 2020 No. 45</td>
<td>2020.11.11</td>
<td>Zhejiang/(General Administration of National Sampling)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300530999</td>
<td>2</td>
<td>Shengzhou Xiangfu Sauce and Seasoning Factory</td>
<td>Shengzhou Economic Development Zone (Pukou Street) Former Agricultural Machinery Factory
</td>
<td>Shengzhou Xiangfu Sauce and Seasoning Factory</td>
<td>Zhejiang</td>
<td>Mother Oil King Liquid Compound Seasoning</td>
<td>Bulk Weighing</td>
<td>6/2/2020</td>
<td>Condiment</td>
<td>Zhejiang Provincial Food 2020 No. 45</td>
<td>2020.11.11</td>
<td>Zhejiang/(General Administration of State Administration of Food)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300531000</td>
<td>3</td>
<td>Jinweike Food Ingredients (Hangzhou) Co., Ltd.</td>
<td>Building 16, No. 489, Hongtai 6th Road, Economic Development Zone, Xiaoshan District,
Hangzhou</td>
<td>Jinweike Food Ingredients (Hangzhou) Co., Ltd.</td>
<td>Zhejiang</td>
<td>Compound Preservative No. 5</td>
<td>1kg/bag</td>
<td>7/27/2020</td>
<td>Food Additives</td>
<td>Zhejiang Provincial Food 2020 No. 45</td>
<td>2020.11.11</td>
<td>Zhejiang/(General Administration of State Administration of Food)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000005131798</td>
<td>4</td>
<td>Xiangshan Defeng Food Co., Ltd.</td>
<td>No. 615, Wanlong Road, Chengdong Industrial Park, Daxu Town, Xiangshan County, Ningbo City,
Zhejiang Province</td>
<td>Xiangshan Defeng Food Co., Ltd.</td>
<td>Zhejiang</td>
<td>Qianzhang</td>
<td>/</td>
<td>8/14/2020</td>
<td>Soy Products</td>
<td>Zhejiang Provincial Food 2020 No. 45</td>
<td>2020.11.11</td>
<td>Zhejiang/(General Administration of State Administration of Soybean)</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>GC20330000300531001</td>
<td>5</td>
<td>Shaoxing Sifang Food Co., Ltd.</td>
<td>Donggong Village, Ruanshi Town, Zhuji City, Shaoxing</td>
<td>Shaoxing Sifang Food Co., Ltd.</td>
<td>Zhejiang</td>
<td>Refined Dark Soybean King Seasoning Boutique</td>
<td>800ml/pot</td>
<td>8/6/2020</td>
<td>Condiments</td>
<td>Zhejiang Provincial Food 2020 No. 45</td>
<td>2020.11.11</td>
<td>Zhejiang/(General Administration of State Administration of Soybean)</td>
<td>/</td>
<td>/</td>
</tr>
</tbody>
</table>
</section>
<section id="faq-link" class="text-center">
<h2>FAQ</h2>
<p>For frequently asked questions, visit our FAQ page:</p>
<div>
<a href="faq.html" class="btn btn-info">Visit FAQ Page</a>
</div>
</section>
<section id="signup-section" class="text-center">
<h2>Sign Up to Access Data and Visualizations</h2>
<p>Stay informed and explore detailed data insights and visualizations by signing up for
access.</p>
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