{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:51:37Z","timestamp":1768427497278,"version":"3.49.0"},"reference-count":100,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The COVID-19 pandemic has drawn attention to the interconnected roles of environmental conditions and public health beyond conventional medical explanations. The One Health (OH) perspective offers a collaborative and interdisciplinary perspective that integrates humans, animals, plants, and their shared environment to achieve optimal health outcomes. The 12 cities in Hubei Province that experienced lockdown during the peak phase of COVID-19 (February 1 to March 4, 2020) provided unique samples. In this study, land cover was selected as the environmental variable, and the COVID-19 growth rate was used as the infectious disease indicator to examine their relationship, thereby investigating the potential role of environmental factors in epidemic control.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the most influential variables for subsequent analyses. Spatial autocorrelation was assessed using Moran\u2019s I in RStudio, while spatial dependence was explicitly modeled through the Spatial Autoregressive (SAR) and Spatial Lag of X (SLX) models to evaluate the effects of explanatory variables while accounting for spatial interactions. All results were interpreted within the One Health perspective, considering the source of infection, routes of transmission, and susceptible populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO regression identified wetland, cultivated land, orchard land, forest land, and population density as the main factors associated with the COVID-19 growth rate. Wetland coverage exhibited a significant positive association with growth rate, whereas cultivated land showed a negative but marginally significant relationship. Orchard land and forest land were associated with weak negative effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The statistical results indicate that variations in land cover influence the growth rate of COVID-19 cases, suggesting that environmental management, including wetland and wastewater control, agricultural landscape configuration, forest vegetation preservation, and control population density, may help mitigate infectious disease growth. From the One Health perspective, sustainable habitat design and planning strategies and land use policies were proposed for future research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1682820","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:41:07Z","timestamp":1768394467000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Does land cover affect the growth rate of COVID-19? Rethinking sustainable habitat from the One Health perspective using data from 12 cities during lockdown in Hubei Province, China"],"prefix":"10.3389","volume":"13","author":[{"given":"Yidan","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology","place":["Guangzhou, China"]},{"name":"School of Architecture, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology","place":["Guangzhou, China"]},{"name":"School of Architecture, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Architecture and Design, China University of Mining and Technology","place":["Xuzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"ref1","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"100133","DOI":"10.1016\/j.onehlt.2020.100133","article-title":"The risk of SARS-CoV-2 transmission to pets and other wild and domestic animals strongly mandates a one-health strategy to control the COVID-19 pandemic","volume":"10","author":"Leroy","year":"2020","journal-title":"One Health"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e264","DOI":"10.1016\/S2542-5196(18)30084-6","article-title":"The growth and strategic functioning of one health networks: a systematic analysis","volume":"2","author":"Khan","year":"2018","journal-title":"Lancet Planet Health"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.jiph.2020.03.019","article-title":"Coronavirus disease 2019 (COVID-19): a literature review","volume":"13","author":"Harapan","year":"2020","journal-title":"J Public Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.3390\/vaccines9111305","article-title":"Vaccine versus variants (3Vs): are the COVID-19 vaccines effective against the variants? 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