This is the research on the relationships between the COVID-19 health outcome and land cover and NDVI in the USA.
Chao Li, Shunsuke Managi
Coronavirus disease 2019 (COVID‐19) poses special challenges for societies, as the disease causes millions of deaths. Although the direct prevention measures affect the prevalence and mortality the most, the other indirect factors, including natural environments and economics, could not be neglected. Assessing whether natural land cover impacts COVID-19 health outcomes is an urgent and crucial public health topic. Here, we examine the relationships between natural land cover and the prevalence and mortality of COVID-19 in the United States. A 1% increase of open water or deciduous forest is associated with a 0.004-death and 0.163-conformed-case, or 0.006-death and 0.099-confirmed-case decrease in every 1,000 people. Converting them into monetary value, for the mortality, a 1% increase in open water, deciduous forest, or evergreen forest in a county is equivalent to a 212-, 313-, or 219-USD increase in household income in the long term. Moreover, for the prevalence, a 1% change in open water, deciduous forest, or mixed forest is worth a 382-, 230-, or 650-USD increase in household income. Furthermore, a rational development intensity is also critical to prevent the COVID-19 pandemic. More greenery in the short term could also slash the prevalence and mortality. Our research highlights that societies could prevent other pandemics similar to the COVID-19 and improve public health in the future by adding natural land cover.
Natural Land Cover Improves COVID-19 Health Outcomes
COVID19 Package: confirmed cases, deaths, population, restrictions information.
LC_2001.dbf - LC_2019.dbf: downlaoded from https://www.mrlc.gov/data?f%5B0%5D=category%3ALand%20Cover. This data set is about 2001 - 2019 land cover, eight tif files, which should be extracted by geoprocessiong. The data are about percentage in the counties.
Unemployment.xls: downlaoded from https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/.
PovertyEstimates.csv: downloaded from https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/.
Education.csv: downloaded from https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/.
cc-est2019-alldata.csv (Not uploaded): downloaded from https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-detail.html. This data set is about county-level age group, sex, and race.
(obese_what_inactive.xls)[02_RawData/obese_what_inactive.xls]: downloaded from https://www.countyhealthrankings.org/explore-health-rankings/rankings-data-documentation.
(hospitalBed.csv)[02_RawData/hospitalBed.csv]: downloaded from https://hifld-geoplatform.opendata.arcgis.com/datasets/hospitals/data.
(temp_seasonal_county.csv)[02_RawData/temp_seasonal_county.csv]: downloaded from https://www.northwestknowledge.net/metdata/data/. This data set is about temperature, and humidity.
(county_pm25.csv)[02_RawData/county_pm25.csv]: downloaded from https://github.com/MichaelChaoLi-cpu/On-road_Transportation_PM2.5/blob/main/Data/dataset.csv.
"NDVI" this is raw data, which should be multiply by the factor (0.0001). MOD13A3 1 km monthly NDVI
"DayTem" this is raw data, which should be multiply by the factor (0.02 K). MOD11C3 0.05 monthly
"NightTem" this is raw data, which should be multiply by the factor (0.02 K). MOD11C3 0.05 monthly
"NTL" this is nightlight data. VNP46A3 15 arc second
COVID19 Package: confirmed cases, deaths, population, restrictions information. panel_mod.csv: "NDVI", "DayTem", "NightTem", "NTL" from 02_DW_NDVITemperatureQuarterly_v1.R
01_DW_MortalityPrevalenceLandCoverCross_v1.R: This script is to wash the data to get the cross-sectional dataset in the analysis. The result of this script is the dataset, including the variable of the county-level prevalence and mortality (capita/1000) of the COVID-19 in the U.S, due by 1st Nov. This dataset also contain the county-level land cover data (hm/capi), weather, income, population, etc.
02_DW_NDVITemperatureQuarterly_v1.R: This script is to wash the data to get the monthly panel dataset of the NDVI and Temperature data in the analysis. The result of this script is the (panel_mod.csv)[02_RawData/panel_mod.csv] due by the third quarter in the 2021.
03_AN_OLSPrevalenceMortalityCross_v1.R: This script perform OLS on the dataset to detect the relationship between land cover and mortality as well as prevalence of COVID-19. The estimated parameters in the OLS results are fixed based on the bp tests. The residuals from the OLS are spatially clustering, according to Moran's I test. Therefore, in this scripts, the GWR models are also included. However, the local parameters from the GWR are scarcely signifcant. Obviously, the GWR is not the ideal model to solve this spatial spillover. This script output two results, SeperatedOLS.MR.html and SeperatedOLS.PR.html, which are the OLS results of the associations of land cover with mortality and prevalence, respectively.
04_AN_SpatialModelPrevalenceMortalityCross_v1.R: This script tries to use other spatial models to trackle the spillover of COVID-19. The first one is SAR.
05_DW_MortalityPrevalenceQuarterlyPanel_v1.R: This script make the data set, panel_NDVI_mortality_prevalence.csv, which includes "stringency_index", "NDVI_perc", "tem_c", "NLT", "confirmed_per1000", and "deaths_per1000". With this quarterly panel data set, we could perform the panel regressions and even spatial panel regressions. "NDVI"" represents greenery. "tem_c" represents temperature. "NLT" represents nighttime light. The counties should be richer, whose nighttime light are brighter.
06_AN_RegressionPrevalenceMortalityNDVIPanel_v0.R: This script mainly perform panel model, including "plm" and "splm". Currently, we use both spatial lag and spatial error in the main model. The basic data is quarterly, panel_NDVI_mortality_prevalence.csv, including "tem_c", "NTL", "NDVI_perc", etc.
07_AF_OutputSplmImpactFunction_v1.R: this script is the function to output spml model impacts, which has been stored in the AssistantFunction repo https://github.com/MichaelChaoLi-cpu/AssistantFuctions.
08_DW_MortalityPrevalenceMonthlyPanel_v1.R: This script make the data set, panel_NDVI_mortality_prevalence_monthly.csv, which includes "stringency_index", "NDVI_perc", "tem_c", "NLT", "confirmed_per1000", and "deaths_per1000". With this monthly panel data set, we could perform the panel regressions and even spatial panel regressions.
09_AN_PrevalenceMortalityNDVIMonthlyPanel_v0.R: This script mainly perform panel model, including "plm" and "splm", based on the monthly panel data set, panel_NDVI_mortality_prevalence_monthly.csv. However, they are out of memory. Therefore, aborted!
10_VI_DistributionStatistics_v0.R: This script mainly make the figure in the article.prevalence.png and mortality.png are to show the spatial distribution of total prevalence and mortality.
WF.A: 01 -> 03 -> 04 -> END
WF.A.01.03: This step provides the data to perform OLS. According to the results of OLS, the residuals are spatially clustering. Though GWR is considered, the poor results of GWR make us reject it.
WF.A.03.04: This step makes us try to use other spatial models.
WF.B: 02 -> 05 -> 06 -> END
WF.B.02.05: This step help us get the quarterly panel data set to detect whether greenery is able to prevent COVID-19.
WF.B.05.06: This step performs the panel regressions including ordinary (plm) and spatial (splm) based on the quarterly panel data.
WF.B: 02 -> 08 -> 09 -> END
Out of memory! Aborted!
- Email: Prof. Shunsuke Managi managi@doc.kyushu-u.ac.jp
- Email: Chao Li chaoli0394@gmail.com
Authors/funders retain copyright (where applicable) of code on this Github repo. This GitHub repo and its contents herein, including data, link to data source, and analysis code that are intended solely for reproducing the results in the manuscript "Natural Land Cover Improves the COVID-19 Health Outcomes" The analyses rely upon publicly available data from multiple sources, that are often updated without advance notice. We hereby disclaim any and all representations and warranties with respect to the site, including accuracy, fitness for use, and merchantability. By using this site, its content, information, and software you agree to assume all risks associated with your use or transfer of information and/or software. You agree to hold the authors harmless from any claims relating to the use of this site.