diff --git a/scripts/src/cowidev/vax/incremental/china.py b/scripts/src/cowidev/vax/incremental/china.py
index 8d9941f82379..6d7123084d35 100644
--- a/scripts/src/cowidev/vax/incremental/china.py
+++ b/scripts/src/cowidev/vax/incremental/china.py
@@ -3,7 +3,7 @@
 
 import pandas as pd
 
-from cowidev.utils.clean import clean_count, extract_clean_date
+from cowidev.utils.clean import clean_count, extract_clean_date, clean_date
 from cowidev.utils.web.scraping import get_driver
 from cowidev.vax.utils.base import CountryVaxBase
 
@@ -11,14 +11,24 @@
 class China(CountryVaxBase):
     location: str = "China"
     source_url: str = "http://www.nhc.gov.cn/xcs/yqjzqk/list_gzbd.shtml"
+    source_url_complete: str = "http://www.nhc.gov.cn/xcs/s2906/new_list.shtml"
     regex: dict = {
         "date": r"截至(20\d{2})年(\d{1,2})月(\d{1,2})日",
         "total_vaccinations": "([\d\.]+\s*万)剂次",
     }
+    regex_complete = {
+        "title": r"国务院联防联控机制(20\d\d)年(\d{1,2})月(\d{1,2})日新闻发布会文字实录",
+        "doses": r"截至(\d{1,2})月(\d{1,2})日.*计报告接种新冠疫苗([\d\.]+)亿([\d\.]+)万剂次",
+        "people": r"接种总人数达([\d\.]+)亿([\d\.]+)万",
+        "fully": r"已完成全程接种([\d\.]+)亿([\d\.]+)万人，覆盖人数占全国总人口的",
+        "boosters": r"完成加强免疫接种([\d\.]+)亿([\d\.]+)万人，其中序贯加强免疫接种",
+    }
+    num_links_complete = 3
+    timeout = 30
 
     def read(self, last_update: str):
         data = []
-        with get_driver(firefox=True) as driver:
+        with get_driver(firefox=True, timeout=self.timeout) as driver:
             driver.get(self.source_url)
             time.sleep(5)
             links = self._get_links(driver)
@@ -42,6 +52,46 @@ def _get_links(self, driver) -> list:
         elems = driver.find_elements_by_css_selector("li>a")
         return [elem.get_property("href") for elem in elems]
 
+    def read_complete(self):
+        records = []
+        with get_driver(firefox=True, timeout=self.timeout) as driver:
+            driver.get(self.source_url_complete)
+            time.sleep(5)
+            links = self._get_links_complete(driver)
+            for link in links[: self.num_links_complete]:
+                records.append(self._parse_data_complete(driver, link))
+        return pd.DataFrame(records)
+
+    def _get_links_complete(self, driver):
+        elems = driver.find_elements_by_css_selector("li>a")
+        return [elem.get_property("href") for elem in elems if re.search(self.regex_complete["title"], elem.text)]
+
+    def _parse_data_complete(self, driver, url):
+        def _estimate_metric(position_1, position_2):
+            return int(float(position_1) * 1e8 + float(position_2) * 1e4)
+
+        driver.get(url)
+        elem = driver.find_element_by_id("xw_box")
+        # Apply regex
+        month, day, total_vaccinations_1, total_vaccinations_2 = re.search(
+            self.regex_complete["doses"], elem.text
+        ).groups()
+        people_vaccinated_1, people_vaccinated_2 = re.search(self.regex_complete["people"], elem.text).groups()
+        people_fully_vaccinated_1, people_fully_vaccinated_2 = re.search(
+            self.regex_complete["fully"], elem.text
+        ).groups()
+        total_boosters_1, total_boosters_2 = re.search(self.regex_complete["boosters"], elem.text).groups()
+        # Get metrics
+        metrics = {
+            "total_vaccinations": _estimate_metric(total_vaccinations_1, total_vaccinations_2),
+            "people_vaccinated": _estimate_metric(people_vaccinated_1, people_vaccinated_2),
+            "people_fully_vaccinated": _estimate_metric(people_fully_vaccinated_1, people_fully_vaccinated_2),
+            "total_boosters": _estimate_metric(total_boosters_1, total_boosters_2),
+            "date": clean_date(f"2022-{month}-{day}", "%Y-%m-%d"),
+            "source_url": url,
+        }
+        return metrics
+
     def pipe_metadata(self, df: pd.DataFrame) -> pd.DataFrame:
         return df.assign(
             location=self.location,
@@ -56,10 +106,20 @@ def pipeline(self, df: pd.DataFrame) -> pd.DataFrame:
     def export(self):
         last_update = self.load_datafile().date.max()
         df = self.read(last_update)
+        df_complete = self.read_complete()
+        # Transform
         if not df.empty:
             df = df.pipe(self.pipeline)
-            # print(df.tail())
-            self.export_datafile(df, attach=True)
+        if not df_complete.empty:
+            df_complete = df_complete.pipe(self.pipeline)
+        # Merge
+        if df.empty:
+            df = df_complete
+        else:
+            msk = ~df.date.isin(df_complete.date)
+            df = pd.concat([df_complete, df.loc[msk]])
+        # Export
+        self.export_datafile(df, attach=True)
 
 
 def main():