What environmental data are relevant to the study of infectious diseases like COVID-19?

The COVID-19 pandemic has triggered an increase in infectious disease modeling studies, some of which incorporate environmental parameters. These studies are driven by questions about the potential seasonality of disease transmission, potential comorbidities associated with other environmentally-linked respiratory diseases, and a desire to improve predictions to inform future national and local policies to control transmission. This page has been developed to facilitate access to environmental data commonly used in infectious disease modeling. Email questions or feedback about this page to one.health@noaa.gov.

Environmental datasets for infectious disease modeling

The table below was designed to help users quickly locate environmental datasets for a given variable and timescale. Within each cell, one or more datasets are referenced along with key information, a link to download the data, and a link to read the metadata for the source. Additional information on the data types is available below this table.

The table below was designed to help users quickly locate environmental datasets for a given variable and timescale. Within each cell, one or more datasets are referenced along with key information, a link to download the data, and a link to read the metadata for the source. Additional information on the data types is available below this table.

Parameter

Observational Climate Record and Reanalyses

Short-term Forecasts and Predictions (deterministic and probabilistic)

Long-term Projections (scenario-based)

Temperature

  • Avg
  • Min
  • Max
  • Anomaly

Global Station Daily

Global Historical Climatology Network Daily - GHCN Data Access
Station-based
Spatial: global, coverage varies (see Understanding Data Sources below)
Temporal: daily, coverage varies by station (19th century to present)
Practicalities: available as *.csv files (HTTPS access).

U.S. Gridded Monthly

Gridded 5km GHCN-Daily Dataset (U.S. only) nClimGrid (aggregated to monthly values)
Gridded
Spatial: U.S., 5km
Temporal: monthly, 19th century to present
Practicalities: Available as ASCII text

Global Gridded Monthly

NOAAGlobalTemp (V5)
Spatial: global, 5° × 5°
Temporal: monthly, 19th century to present
Practicalities: Available as *.nc

Global Comprehensive (Reanalysis) Hourly

Historical Climate Forecast System (CFS) Time Series)
Spatial: Global, 0.5° (approximately 56 km)
Temporal: hourly, 1979 - 2011
Practicalities: Available as *.grib2

Operational Climate Forecast System (CFS) Time Series
Spatial: Global, 0.5° (approximately 56 km)
Temporal: hourly, 2011 - present
Practicalities:Available as *.grib2
Filename (high-res)
tmp2m.gdas.YYYYMM.grib2
Filename (low-res)
tmp2m.l.gdas.YYYYMM.grib2

Global Gridded Reanalysis Hourly

NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2)
Spatial: global, 0.5° x 0.625°
Temporal: hourly, 1980 - present

Global Station Sub-Daily

Integrated Surface Database
Temporal: sub-hourly, 1901 - present
Practicalities: available as *.csv or via Common Access or GIS services

European Global Comprehensive (Reanalysis) Hourly

ECMWF ERA5 climate reanalysis
Spatial: global, 0.25°x0.25°
Temporal: hourly, 1971 - present
Practicalities: available as *.grib files

NOAA NDFD Forecast

Metadata

Lead 1-3 days
Daily Max Temp
Daily Min Temp
Hourly Temp

Lead 4-7 days
Daily Max Temp
Daily Min Temp
Hourly Temp
Spatial: U.S., 5km
Temporal: hourly, 1 - 7 day lead time
Practicalities: available as *.bin
(rename to *.grib2)

Multi-Model Ensemble

NOAA National Blend of Models
Lead 0-10 days
Visualize Time Series and download CSV files by station.
(Extendable for CSV output, longer than 10 days to subseasonal, other parameters)
Spatial: U.S., coverage varies by station
Temporal: hourly, 10 day lead time
Practicalities: available as *.csv

Monthly & Seasonal

North American Multi-Model Ensemble Spatial: global, varies
Temporal: 2019-2020, monthly and seasonal
Practicalities: See BAMS article for more information

CMIP6 Climate Projections

CMIP6-GFDL Data Search | CMIP6-GFDL | ESGF-CoG Spatial: global, varied resolution
Temporal: 1850 - 2100, varied resolution
Practicalities: guidance for users

Humidity

  • Relative
  • Absolute
  • Specific
  • Dew point

European Global Comprehensive (Reanalysis) Hourly

ECMWF ERA5 climate reanalysis
Spatial: global, 0.25°x0.25°
Temporal: hourly, 1971 - present
Practicalities: available as *.grib files

Global Station Sub-Daily

Integrated Surface Database
Temporal: sub-hourly, 1901 - present
Practicalities: available as *.csv or via Common Access or GIS services

NOAA NDFD Forecast

Metadata

Lead 1-3 days
Daily Max RH
Daily Min RH
Hourly RH
6hr Dew Point

Lead 4-7 days
Daily Max RH
Daily Min RH
Hourly RH
6hr Dew Point
Spatial: U.S., 5km
Temporal: hourly, 1 - 7 day lead time
Practicalities: available as *.bin
(rename to *.grib2)

CMIP6 Climate Projections

CMIP6-GFDL Data Search | CMIP6-GFDL | ESGF-CoG Spatial: global, varied resolution
Temporal: 1850 - 2100, varied resolution
Practicalities: guidance for users

Ultraviolet Index Wm-2

European Global Comprehensive (Reanalysis) Hourly

ECMWF ERA5 climate reanalysis
Spatial: global, 0.25°x0.25°
Temporal: hourly, 1971 - present
Practicalities: available as *.grib files

County-Level

CDC National Environmental Public Health Tracking Network
Spatial: U.S., state or county level
Temporal: monthly or annual avg, 2005 to 2015
Practicalities: Available as *.csv

Global Station Sub-Daily

Integrated Surface Database
Temporal: sub-hourly, 1901 - present
Practicalities: available as *.csv or via Common Access or GIS services

NOAA Forecast

NWS Global UV Index Forecast
Spatial: global, 0.5x0.5 degree
Temporal: hourly, --5 day lead time
Practicalities: available as *.grib2

CMIP6 Climate Projections

CMIP6-GFDL Data Search | CMIP6-GFDL | ESGF-CoG
Spatial: global, varied resolution
Temporal: 1850 - 2100, varied resolution
Practicalities: guidance for users

Understanding Data Sources

Station Data

Real-time and historical data are available for surface-based weather stations on every continent. The most commonly measured climate variables are temperature and precipitation, the latter being available for more than 100,000 locations worldwide. Some stations (such as those at airports) also observe additional variables—including pressure, wind, and cloud cover—on an hourly basis. Coverage of the stations is sparse in some regions; however, most airports have a station.

Interpolated Gridded Data

Many modeling applications require data in locations that do not have weather stations. Gridded datasets bridge this gap. These datasets consist of estimated data at evenly spaced intervals, such as every 5 kilometers. Researchers construct these datasets by using station data in combination with statistical methods (e.g., by interpolating station data to a uniform grid or fitting a statistical surface through the original observations). Gridded datasets are valuable in many modeling applications that require evenly spaced data as input. They are also useful in computing averages for societally relevant areas, such as counties and census tracts. Interpolated grids estimate unknown values at locations by using nearby points where values are known. Read more about interpolation

Comprehensive Climate Monitoring Data

Comprehensive Climate Monitoring data, also known as Reanalysis datasets, are produced by running climate models over a historical period while constraining many of the values of the model to match observed values. These data are multivariate, spatially and temporally complete, and gridded. This process provides a consistent high-resolution output that smooths over data gaps by providing the best possible estimate of the true values by using both observations and models to estimate what value most likely would have been observed. For example, in a reanalysis for the period from 1950 to 2000, if a station was only active from 1970 onward, the 20 years of missing values can be estimated by constraining the model using existing observations that were active so that the temperature (and other values) that might have been observed at that station can be filled in. Read more about reanalyses »

Climate Model Output

Climate models break the globe into a 3-dimensional grid and simulate a large number of variables in each grid cell at each time step using fundamental physics equations. Climate models are “spun up” by initializing them with observations and letting them run over many years of timesteps, solving the equations for each grid cell during each step, and allowing for interactions between adjacent grid cells. Climate model output should be used to estimate longer-term, probabilistic climate statistics rather than to predict deterministic values. Read more about climate models »

Multi-Model Ensemble

A Multi-Model Ensemble (MME) is a product containing outputs from one or more models from different modeling centers that have been averaged together to create what is called an ensemble. In some cases, more than one model run (realization) per model is included, and each realization is run with slightly perturbed parameters to better represent uncertainty in the MME mean. MMEs are often more skillful than individual models, and better represent prediction error, because the ensemble as a whole balances out individual model biases to some extent.  Read more about Multi-Model Ensembles »

This section describes the most commonly encountered data sets for representing environmental information as well as packages available for accessing and analyzing environmental information in specific programming languages.

Understanding File Formats

Language Specific Sources

  • CSV

    • A comma separated values file, which can be opened in most text or spreadsheet editing applications.
  • NC
    • A NetCDF file, which is used to display geospatial information stored in an array of points
  • GRIB2
    • The GRIdded Binary, or GRIB, file format was defined by the World Meteorological Organization to store two-dimensional data.
  • BIN
    • BIN files are binary files, but for the purpose of analyzing the datasets represented here, you can rename *.bin files to *.grib2 files and most readers will be able to display them.
R

Python

Glossary of Terms & Acronyms

  • CDC = Centers for Disease Control
  • CFS = Climate Forecast System
  • CMIP6 = Coupled Model Intercomparison Project, version 6
  • CoG = University of Colorado collaboration environment in support of the ESGF
  • ECMWF = European Centre for Medium-range Weather Forecasts
  • ERA5 = ECMWF Reanalysis of the Atmosphere, 5th generation
  • ESGF = Earth System Grid Federation
  • GFDL = NOAA’s Geophysical Fluid Dynamics Laboratory
  • GHCN = Global Historical Climatology Network
  • GIS = Geographical Information System
  • HTTPS = HyperText Transfer Protocol Secure
  • MERRA = Modern-Era Retrospective analysis for Research and Applications
  • NDFD = NOAA’s National Digital Forecast Database
  • NMME = North American Multi-Model Ensemble
  • NWS = National Weather Service
  • RH = Relative Humidity
  • UV = Ultraviolet
  • Wm2 = Watts per square meter

Acknowledgments

This page is developed through NOAA’s One Health Team to serve the COVID-19 and broader health research and decision making community. Acknowledgment and thanks go to: Hunter Jones, Mary Lindsey, Richard Glupker, Stan Benjamin, Georg Grell, and Juli Trtanj, NOAA Research (OAR), and Jennifer Runkle, Russell Vose, and Jeff Privette, National Environmental Satellite and Data Information Service (NESDIS).

Example Model Studies

This list of studies was compiled by the NOAA Central Library on August 7, 2020. This list is a sampling of COVID-19 studies employing climate variables and will be updated periodically. The presence of a study in this list does not imply endorsement. Many of the listed studies have not been peer reviewed.

Abdollahi, A., & Rahbaralam, M. (2020). Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain. medRxiv https://doi.org/10.1101/2020.05.01.20087759

Adhikari, A., Ghosh, S., Sen, M. M., & Adhikari, R. (2020). Models of Transmission of COVID-19 with Time under the Influence of Meteorological Determinants. medRxiv https://doi.org/10.1101/2020.05.26.20113985

Adhikari, A., & Yin, J. (2020). Short-Term Effects of Ambient Ozone, Pm2.5, and Meteorological Factors on COVID-19 Confirmed Cases and Deaths in Queens, New York. International Journal of Environmental Research and Public Health, 17(11), 4047 https://doi.org/10.3390/ijerph17114047

Ahmadi, M., Sharifi, A., Dorosti, S., Jafarzadeh Ghoushchi, S., & Ghanbari, N. (2020). Investigation of Effective Climatology Parameters on COVID-19 Outbreak in Iran. Science of The Total Environment, 729, 138705 https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138705

Ahmed, A., & Rahman, M. M. (2020). COVID-19 Trend in Bangladesh: Deviation from Epidemiological Model and Critical Analysis of the Possible Factors. medRxiv https://doi.org/10.1101/2020.05.31.20118745

Al-Rousan, N., & Al-Najjar, H. (2020). The Correlation between the Spread of COVID-19 Infections and Weather Variables in 30 Chinese Provinces and the Impact of Chinese Government Mitigation Plans. European review for medical and pharmacological sciences, 24(8), 4565-4571 https://doi.org/10.26355/eurrev_202004_21042

Alipio, M. M. (2020). Do Latitude and Ozone Concentration Predict Covid-2019 Cases in 34 Countries? medRxiv https://doi.org/10.1101/2020.04.09.20060202

Alvarez-Ramirez, J., & MERAZ, M. (2020). Role of Meteorological Temperature and Relative Humidity in the January-February 2020 Propagation of 2019-Ncov in Wuhan, China. medRxiv https://doi.org/10.1101/2020.03.19.20039164

Amin, H. N. M., & Amin, H. N. M. (2020). Climate Analysis to Predict Potential Spread and Seasonality for Global (COVID-19) in Iraqi Kurdistan Region. Kurdistan Journal of Applied Research, 72-83 https://doi.org/10.24017/covid.9

Andree, B. P. J. (2020). Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands. medRxiv https://doi.org/10.1101/2020.04.27.20081562

Anis, A. (2020). The Effect of Temperature Upon Transmission Of covid-19 : Australia and Egypt Case Study. Research Square https://doi.org/10.21203/rs.3.rs-28360/v1

Araujo, M. B., & Naimi, B. (2020). Spread of Sars-CoV-2 Coronavirus Likely to Be Constrained by Climate. medRxiv https://doi.org/10.1101/2020.03.12.20034728

Asyary, A., & Veruswati, M. (2020). Sunlight Exposure Increased Covid-19 Recovery Rates: A Study in the Central Pandemic Area of Indonesia. Science of The Total Environment, 729, 139016 https://doi.org/10.1016/j.scitotenv.2020.139016

Auler, A. C., Cássaro, F. A. M., da Silva, V. O., & Pires, L. F. (2020). Evidence That High Temperatures and Intermediate Relative Humidity Might Favor the Spread of COVID-19 in Tropical Climate: A Case Study for the Most Affected Brazilian Cities. Science of The Total Environment, 139090 https://doi.org/10.1016/j.scitotenv.2020.139090

Avhad, A. S., Sutar, P. P., Mohite, O. T., & Pawar, V. S. (2020). On the COVID-19 Pandemic in Indian State of Maharashtra: Forecasting & Effect of Different Parameters. medRxiv https://doi.org/10.1101/2020.05.23.20111179

Awasthi, R., Nagori, A., Singh, P., Pal, R., Joshi, V., & Sethi, T. (2020). Temperature and Humidity Do Not Influence Global COVID-19 Incidence as Inferred from Causal Models. medRxiv, 2020.2006.2029.20142307 https://doi.org/10.1101/2020.06.29.20142307

Backer, A. (2020). Follow the Sun: Slower COVID-19 Morbidity and Mortality Growth at Higher Irradiances. SSRN https://doi.org/10.2139/ssrn.3567587

Bäcker, A. (2020). Slower COVID-19 Morbidity and Mortality Growth at Higher Solar Irradiance and Elevation. SSRN https://doi.org/10.2139/ssrn.3604729

Baker, R. E., Yang, W., Vecchi, G. A., Metcalf, C. J. E., & Grenfell, B. T. (2020). Susceptible Supply Limits the Role of Climate in the COVID-19 Pandemic. In medRxiv (pp. 2020.2004.2003.20052787). Retrieved from https://www.medrxiv.org/content/medrxiv/early/2020/04/07/2020.04.03.20052787.full.pdf

Baker, R. E., Yang, W., Vecchi, G. A., Metcalf, C. J. E., & Grenfell, B. T. (2020). Susceptible Supply Limits the Role of Climate in the Early Sars-CoV-2 Pandemic. Science, eabc2535 https://doi.org/10.1126/science.abc2535

Bannister-Tyrrell, M., Meyer, A., Faverjon, C., & Cameron, A. (2020). Preliminary Evidence That Higher Temperatures Are Associated with Lower Incidence of COVID-19, for Cases Reported Globally up to 29th February 2020. medRxiv https://doi.org/10.1101/2020.03.18.20036731

Bashir, M. F., Ma, B., Bilal, Komal, B., Bashir, M. A., Tan, D., & Bashir, M. (2020). Correlation between Climate Indicators and COVID-19 Pandemic in New York, USA. Science of The Total Environment, 728, 138835 https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138835

Bashir, M. F., Ma, B. J., Bilal, Komal, B., Bashir, M. A., Farooq, T. H., . . . Bashir, M. (2020). Correlation between Environmental Pollution Indicators and COVID-19 Pandemic: A Brief Study in Californian Context. Environmental Research, 187, 109652 https://doi.org/10.1016/j.envres.2020.109652

Behnood, A., Golafshani, E. M., & Hosseini, S. M. (2020). Determinants of the Infection Rate of the COVID-19 in the U.S. Using Anfis and Virus Optimization Algorithm (Voa). Chaos Solitons & Fractals, 139, 110051 https://doi.org/10.1016/j.chaos.2020.110051

Bellali, H., Chtioui, N., & Chahed, M. (2020). Factors Associated with Country-Variation in COVID-19 Morbidity and Mortality Worldwide: An Observational Geographic Study. medRxiv https://doi.org/10.1101/2020.05.27.20114280

Benedetti, F., Pachetti, M., Marini, B., Ippodrino, R., Gallo, R. C., Ciccozzi, M., & Zella, D. (2020). Inverse Correlation between Average Monthly High Temperatures and COVID-19-Related Death Rates in Different Geographical Areas. Journal of Translational Medicine, 18(1), 251 https://doi.org/10.1186/s12967-020-02418-5

Berumen, J., Schmulson, M., Guerrero, G., Barrera, E., Larriva-Sahd, J., Olaiz, G., . . . Tapia-Conyer, R. (2020). Trends of Sars-Cov-2 Infection in 67 Countries: Role of Climate Zone, Temperature, Humidity and Curve Behavior of Cumulative Frequency on Duplication Time. medRxiv https://doi.org/10.1101/2020.04.18.20070920

Bherwani, H., Gupta, A., Anjum, S., Anshul, A., & Kumar, R. (2020). Exploring Dependence of COVID-19 on Environmental Factors and Spread Prediction in India. Research Square https://doi.org/10.21203/rs.3.rs-25644/v1

Biktasheva, I. V. (2020). Role of a Habitat's Air Humidity in Covid-19 Mortality. Science of The Total Environment, 736, 138763 https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138763

Biryukov, J., Boydston, J. A., Dunning, R. A., Yeager, J. J., Wood, S., Reese, A. L., . . . Altamura, L. A. (2020). Increasing Temperature and Relative Humidity Accelerates Inactivation of Sars-CoV-2 on Surfaces. mSphere, 5(4) https://doi.org/10.1128/msphere.00441-20

Braiman, M. (2020). Latitude Dependence of the COVID-19 Mortality Rate—a Possible Relationship to Vitamin D Deficiency? SSRN https://doi.org/10.2139/ssrn.3561958

Briz-Redón, Á., & Serrano-Aroca, Á. (2020). A Spatio-Temporal Analysis for Exploring the Effect of Temperature on COVID-19 Early Evolution in Spain. Science of The Total Environment, 728, 138811 https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138811

Bu, J., Peng, D.-D., Xiao, H., Yue, Q., Han, Y., Lin, Y., . . . Chen, J. (2020). Analysis of Meteorological Conditions and Prediction of Epidemic Trend of 2019-Ncov Infection in 2020. medRxiv https://doi.org/10.1101/2020.02.13.20022715

Bukhari, Q., & Jameel, Y. (2020). Will Coronavirus Pandemic Diminish by Summer. SSRN https://doi.org/10.2139/ssrn.3556998

Bukhari, Q., Massaro, J. M., D'Agostino, R. B., & Khan, S. (2020). Effects of Weather on Coronavirus Pandemic. International Journal of Environmental Research and Public Health, 17(15), 5399 https://doi.org/10.3390/ijerph17155399

Byass, P. (2020). Eco-Epidemiological Assessment of the COVID-19 Epidemic in China, January–February 2020. Global Health Action, 13(1), 1760490 https://doi.org/10.1080/16549716.2020.1760490

Cai, Y., Huang, T., Liu, X., & Xu, G. (2020). The Effects of "Fangcang, Huoshenshan, and Leishenshan" Makeshift Hospitals and Temperature on the Mortality of COVID-19. medRxiv https://doi.org/10.1101/2020.02.26.20028472

Cai, Y., Huang, T., Liu, X., & Xu, G. (2020). The Effects of “Fangcang, Huoshenshan, and Leishenshan” Hospitals and Environmental Factors on the Mortality of COVID-19. PeerJ, 8, e9578 https://doi.org/10.7717/peerj.9578

Cao, H., Li, B., Gu, T., Liu, X., Meng, K., & Zhang, L. (2020). Associations of Ambient Air Pollutants and Meteorological Factors with COVID-19 Transmission in 31 Chinese Provinces: A Time-Series Study. medRxiv, 2020.2006.2024.20138867 https://doi.org/10.1101/2020.06.24.20138867

Carleton, T., Cornetet, J., Huybers, P., Meng, K., & Proctor, J. (2020). Ultraviolet Radiation Decreases COVID-19 Growth Rates: Global Causal Estimates and Seasonal Implications. SSRN https://doi.org/10.2139/ssrn.3588601

Carleton, T., & Meng, K. C. (2020). Causal Empirical Estimates Suggest COVID-19 Transmission Rates Are Highly Seasonal. medRxiv https://doi.org/10.1101/2020.03.26.20044420

Caspi, G., Shalit, U., Kristensen, S. L., Aronson, D., Caspi, L., Rossenberg, O., . . . Caspi, O. (2020). Climate Effect on COVID-19 Spread Rate: An Online Surveillance Tool. medRxiv https://doi.org/10.1101/2020.03.26.20044727

Chen, B., Liang, H., Yuan, X., Hu, Y., Xu, M., Zhao, Y., . . . Zhu, X. (2020). Roles of Meteorological Conditions in COVID-19 Transmission on a Worldwide Scale. medRxiv https://doi.org/10.1101/2020.03.16.20037168

Chen, C., Li, X., Meng, X., Ma, Z., Li, W., & Dong, L. (2020). A Retrospective Study: Meteorological Factors and COVID-19. Research Square https://doi.org/10.21203/rs.3.rs-28151/v1

Chen, S., Prettner, K., Kuhn, M., Geldsetzer, P., Wang, C., Baernighausen, T., & Bloom, D. E. (2020). COVID-19 and Climate: Global Evidence from 117 Countries. medRxiv https://doi.org/10.1101/2020.06.04.20121863

Chennakesavulu, K., & Reddy, G. R. (2020). The Effect of Latitude and Pm2.5 on Spreading of Sars-CoV-2 in Tropical and Temperate Zone Countries. Environmental Pollution, 266(Pt 3), 115176 https://doi.org/10.1016/j.envpol.2020.115176

Chien, L.-C., & Chen, L.-W. (2020). Meteorological Impacts on the Incidence of COVID-19 in the U.S. Stochastic Environmental Research and Risk Assessment, 1-6 https://doi.org/10.1007/s00477-020-01835-8

Chiyomaru, K., & Takemoto, K. (2020). Global COVID-19 Transmission Rate Is Influenced by Precipitation Seasonality and the Speed of Climate Temperature Warming. medRxiv https://doi.org/10.1101/2020.04.10.20060459

Choi, Y.-W., Tuel, A., & Eltahir, E. A. B. (2020). An Environmental Determinant of Viral Respiratory Disease. medRxiv https://doi.org/10.1101/2020.06.05.20123349

Choma, J., Mellado, B., Lieberman, B., Correa, F., Maslo, C., Naude, J., . . . Stevenson, F. D. (2020). Evaluating Temperature and Humidity Gradients of COVID-19 Infection Rates in Light of Non-Pharmaceutical Interventions. medRxiv https://doi.org/10.1101/2020.07.20.20158071

Coccia, M. (2020). Diffusion of COVID-19 Outbreaks: The Interaction between Air Pollution-to-Human and Human-to-Human Transmission Dynamics in Hinterland Regions with Cold Weather and Low Average Wind Speed. SSRN https://doi.org/10.2139/ssrn.3567841

Coccia, M. (2020). Factors Determining the Diffusion of COVID-19 and Suggested Strategy to Prevent Future Accelerated Viral Infectivity Similar to COVID. The Science of The Total Environment, 729, 138474 https://doi.org/10.1016/j.scitotenv.2020.138474

Coccia, M. (2020). How High Wind Speed Can Reduce Negative Effects of Confirmed Cases and Total Deaths of COVID-19 Infection in Society. SSRN https://doi.org/10.2139/ssrn.3603380

Coccia, M. (2020). Two Mechanisms for Accelerated Diffusion of COVID-19 Outbreaks in Regions with High Intensity of Population and Polluting Industrialization: The Air Pollution-to-Human and Human-to-Human Transmission Dynamics. medRxiv https://doi.org/10.1101/2020.04.06.20055657

Collivignarelli, M. C., Abb, amp, agrave, A., Caccamo, F. M., Bertanza, G., . . . Miino, M. C. (2020). Covid-19 Outbreak in Northern Italy: Did Particulate Matter Really Play a Key Role? medRxiv, 2020.2006.2011.20128215 https://doi.org/10.1101/2020.06.11.20128215

Conticini, E., Frediani, B., & Caro, D. (2020). Can Atmospheric Pollution Be Considered a Co-Factor in Extremely High Level of Sars-CoV-2 Lethality in Northern Italy? Environmental Pollution, 261, 114465 https://doi.org/10.1016/j.envpol.2020.114465

Correa-Araneda, F., Ulloa-Yañez, A., Núñez, D., Boyero, L., Tonin, A. M., Cornejo, A., . . . Esse, C. (2020). Environmental Determinants of COVID-19 Transmission across a Wide Climatic Gradient in Chile. Research Square https://doi.org/10.21203/rs.3.rs-30393/v1

Corripio, J. G., & Raso, L. (2020). Weather Variables Impact on COVID-19 Incidence. medRxiv https://doi.org/10.1101/2020.06.08.20125377

da Silva, F. L., Gomes, M. D. A., da Silva, A. P. L., de Sousa, S. C., de Souza, M. F. S., & da Silva, G. L. P. (2020). Correlation between Meteorological Factors and COVID-19 Infection in the Belem Metropolitan Region. medRxiv https://doi.org/10.1101/2020.06.10.20127506

Dangi, R. R., & George, M. (2020). Temperature, Population and Longitudinal Analysis to Predict Potential Spread for COVID-19. SSRN https://doi.org/10.2139/ssrn.3560786

Danon, L., Brooks-Pollock, E., Bailey, M., & Keeling, M. J. (2020). A Spatial Model of Covid-19 Transmission in England and Wales: Early Spread and Peak Timing. medRxiv https://doi.org/10.1101/2020.02.12.20022566

Das, K., & Chatterjee, N. D. (2020). Examine the Impact of Weather and Ambient Air Pollutant Parameters on Daily Case of COVID-19 in India. medRxiv https://doi.org/10.1101/2020.06.08.20125401

Del Rio, C., & Camacho-Ortiz, A. (2020). Will Environmental Changes in Temperature Affect the Course of COVID-19? The Brazilian Journal of Infectious Diseases https://doi.org/https://doi.org/10.1016/j.bjid.2020.04.007

Demongeot, J., Flet-Berliac, Y., & Seligmann, H. (2020). Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics. Biology (Basel), 9(5) https://doi.org/10.3390/biology9050094

Devara, P., Kumar, A., Sharma, P. B., Banerjee, P., Khan, A. A., Tripathi, A., . . . Beig, G. (2020). Influence of Air Pollution on Coronavirus (COVID-19): Some Evidences from Studies at Auh, Gurugram, India. SSRN https://doi.org/10.2139/ssrn.3588060

Deyal, N., Tiwari, V., & Bisht, N. (2020). Impact of Climatic Parameters on COVID-19 Pandemic Progression in India: Analysis and Prediction. medRxiv https://doi.org/10.1101/2020.07.25.20161919

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