Climate change and Vibrio : Environmental determinants for predictive risk assessment

Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 122; no. 33; p. e2420423122
Main Authors Brumfield, Kyle D., Usmani, Moiz, Long, Daniel M., Lupari, Henry A., Pope, Robert K., Jutla, Antarpreet S., Huq, Anwar, Colwell, Rita R.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 19.08.2025
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Abstract Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic Vibrio spp ., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of Vibrio spp. in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for Vibrio cholerae have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant Vibrio spp . and other waterborne pathogens.
AbstractList Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic ., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant . and other waterborne pathogens.
Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic Vibrio spp ., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of Vibrio spp. in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for Vibrio cholerae have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant Vibrio spp . and other waterborne pathogens.
Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic Vibrio spp., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of Vibrio spp. in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for Vibrio cholerae have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant Vibrio spp. and other waterborne pathogens.Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to humans and animals. Shifts in conditions favorable for microbial growth have expanded the geographic range of many pathogens, contributing to the emergence and reemergence of infectious diseases. Waterborne diseases pose severe risks in regions where adverse climate conditions intersect with population vulnerabilities, especially inadequate water, sanitation, and hygiene infrastructure. Since many waterborne pathogens play crucial roles in the environment, such as in carbon and nitrogen cycling, their eradication is not possible. However, predictive intelligence models that identify environmental heuristics conducive to the growth of pathogenic strains, integrating microbiological, sociological, and weather data, can offer anticipatory decision-making capabilities, reducing infection risks. Here, the objective was to analyze data from studies since the 1960s to identify environmental determinants driving the occurrence and distribution of pathogenic Vibrio spp., enabling predictive modeling of the effects of climate change on cholera and noncholera vibriosis. The proliferation of Vibrio spp. in aquatic ecosystems has been linked to climate change and, concomitantly, with increased environmental disease transmission, notably cholera in Southeast Asia and parts of Africa and noncholera vibriosis in Northern Europe and along the Eastern seaboard of North America. Global predictive risk models for Vibrio cholerae have contributed to reduction in case fatality rates when coupled with individual and large-scale intervention early in outbreaks. These models, when appropriately modified, hold the potential to predict disease caused by all clinically relevant Vibrio spp. and other waterborne pathogens.
Author Jutla, Antarpreet S.
Brumfield, Kyle D.
Usmani, Moiz
Long, Daniel M.
Huq, Anwar
Lupari, Henry A.
Colwell, Rita R.
Pope, Robert K.
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Cites_doi 10.1371/journal.pone.0137828
10.1073/pnas.97.4.1438
10.1038/s41598-022-22946-y
10.1128/CMR.15.4.757-770.2002
10.1111/1462-2920.15040
10.1038/nrmicro.2015.13
10.1061/(ASCE)WR.1943-5452.0000929
10.1029/2022GH000681
10.1073/pnas.0237386100
10.4269/ajtmh.12-0721
10.1128/aem.52.5.1209-1211.1986
10.1073/pnas.0337468100
10.1111/1462-2920.15041
10.1007/978-94-009-6735-9_12
10.1007/s10499-023-01325-y
10.1073/pnas.1810138116
10.1371/journal.ppat.1012767
10.1073/pnas.1609157113
10.1007/PL00012151
10.1128/AEM.03540-14
10.1128/AEM.02894-07
10.1097/EDE.0b013e31815c09ea
10.1128/mBio.01425-17
10.1086/717177
10.1126/science.aad5901
10.1111/1462-2920.14967
10.4269/ajtmh.23-0077
10.1016/j.socscimed.2021.113716
10.1016/S2542-5196(21)00169-8
10.1073/pnas.0809654105
10.1128/aem.00307-23
10.3109/10408418209113506
10.1093/femsec/fiw072
10.1016/j.marpolbul.2021.112785
10.1080/01431161.2019.1577575
10.1093/cid/cis243
10.1128/aem.45.1.275-283.1983
10.1111/jam.12624
10.1038/s41558-022-01426-1
10.1371/journal.pone.0020275
10.1111/1462-2920.13955
10.1016/j.ecss.2023.108558
10.1016/j.fsi.2013.08.017
10.1080/20477724.2021.1981716
10.1016/S0140-6736(82)92340-6
10.1016/j.tim.2011.04.003
10.1128/spectrum.02631-22
10.1146/annurev.micro.54.1.641
10.1128/mbio.01476-23
10.1128/aem.44.5.1047-1058.1982
10.1126/science.164.3885.1286
10.1038/s41598-018-37129-x
10.1007/s11430-017-9229-x
10.3389/fmicb.2016.00567
10.2166/wh.2022.029
10.1007/BF00327795
10.1073/pnas.1207359109
10.1128/JCM.38.6.2156-2161.2000
10.1029/2023GH001005
10.3201/eid2812.220748
10.1128/JB.00795-17
10.3390/tropicalmed6030147
10.3201/eid2602.190362
10.1016/j.jip.2011.02.006
10.4269/ajtmh.14-0648
10.1016/j.foodres.2010.04.001
10.1111/j.1574-6976.2009.00200.x
10.1128/mbio.00529-23
10.1038/s41598-023-28247-2
10.4269/ajtmh.17-0048
10.1128/jb.113.1.24-32.1973
10.1128/JCM.38.2.578-585.2000
10.1016/j.envres.2023.117940
10.1111/1462-2920.15716
10.1111/j.1752-1688.2010.00448.x
10.1289/EHP2904
10.1126/science.274.5295.2025
10.4269/ajtmh.2008.78.823
10.1056/NEJMoa051594
10.1016/j.scitotenv.2025.179412
10.1128/AEM.01698-08
10.2471/BLT.11.092189
10.1111/j.1348-0421.1998.tb02357.x
10.1111/1758-2229.13135
10.1016/S0140-6736(11)60273-0
10.1016/j.tim.2016.09.008
10.1073/pnas.0408992102
10.3201/eid2811.212066
10.1128/aem.48.2.420-424.1984
10.1007/s00248-012-0168-x
10.1128/spectrum.02680-23
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References e_1_3_4_3_2
Colwell R. R. (e_1_3_4_89_2) 1977; 198
e_1_3_4_61_2
e_1_3_4_84_2
e_1_3_4_7_2
e_1_3_4_80_2
e_1_3_4_23_2
e_1_3_4_69_2
e_1_3_4_42_2
e_1_3_4_27_2
e_1_3_4_65_2
e_1_3_4_46_2
e_1_3_4_88_2
e_1_3_4_72_2
e_1_3_4_95_2
e_1_3_4_30_2
e_1_3_4_91_2
e_1_3_4_11_2
e_1_3_4_34_2
e_1_3_4_57_2
e_1_3_4_53_2
e_1_3_4_15_2
e_1_3_4_38_2
e_1_3_4_76_2
e_1_3_4_19_2
e_1_3_4_2_2
e_1_3_4_62_2
e_1_3_4_85_2
e_1_3_4_6_2
e_1_3_4_81_2
e_1_3_4_20_2
e_1_3_4_43_2
e_1_3_4_24_2
e_1_3_4_47_2
e_1_3_4_66_2
e_1_3_4_28_2
e_1_3_4_73_2
e_1_3_4_96_2
e_1_3_4_50_2
e_1_3_4_92_2
e_1_3_4_12_2
e_1_3_4_58_2
e_1_3_4_54_2
e_1_3_4_31_2
e_1_3_4_16_2
e_1_3_4_77_2
e_1_3_4_35_2
e_1_3_4_39_2
e_1_3_4_1_2
e_1_3_4_82_2
e_1_3_4_9_2
e_1_3_4_63_2
e_1_3_4_40_2
e_1_3_4_5_2
e_1_3_4_44_2
e_1_3_4_21_2
e_1_3_4_48_2
e_1_3_4_86_2
e_1_3_4_25_2
e_1_3_4_67_2
e_1_3_4_29_2
e_1_3_4_93_2
e_1_3_4_74_2
e_1_3_4_51_2
e_1_3_4_70_2
e_1_3_4_55_2
e_1_3_4_32_2
e_1_3_4_59_2
e_1_3_4_97_2
e_1_3_4_13_2
e_1_3_4_36_2
e_1_3_4_78_2
e_1_3_4_17_2
e_1_3_4_60_2
e_1_3_4_83_2
e_1_3_4_8_2
e_1_3_4_41_2
e_1_3_4_4_2
e_1_3_4_22_2
e_1_3_4_45_2
e_1_3_4_68_2
e_1_3_4_26_2
e_1_3_4_49_2
e_1_3_4_64_2
e_1_3_4_87_2
e_1_3_4_71_2
e_1_3_4_94_2
e_1_3_4_52_2
e_1_3_4_90_2
e_1_3_4_79_2
e_1_3_4_33_2
e_1_3_4_10_2
e_1_3_4_75_2
e_1_3_4_98_2
e_1_3_4_37_2
e_1_3_4_14_2
e_1_3_4_56_2
e_1_3_4_18_2
References_xml – ident: e_1_3_4_57_2
– ident: e_1_3_4_50_2
  doi: 10.1371/journal.pone.0137828
– ident: e_1_3_4_47_2
  doi: 10.1073/pnas.97.4.1438
– ident: e_1_3_4_56_2
– ident: e_1_3_4_54_2
  doi: 10.1038/s41598-022-22946-y
– ident: e_1_3_4_62_2
  doi: 10.1128/CMR.15.4.757-770.2002
– ident: e_1_3_4_11_2
  doi: 10.1111/1462-2920.15040
– ident: e_1_3_4_30_2
  doi: 10.1038/nrmicro.2015.13
– ident: e_1_3_4_97_2
  doi: 10.1061/(ASCE)WR.1943-5452.0000929
– ident: e_1_3_4_53_2
  doi: 10.1029/2022GH000681
– ident: e_1_3_4_22_2
  doi: 10.1073/pnas.0237386100
– ident: e_1_3_4_49_2
  doi: 10.4269/ajtmh.12-0721
– ident: e_1_3_4_18_2
  doi: 10.1128/aem.52.5.1209-1211.1986
– ident: e_1_3_4_66_2
  doi: 10.1073/pnas.0337468100
– ident: e_1_3_4_76_2
  doi: 10.1111/1462-2920.15041
– ident: e_1_3_4_93_2
  doi: 10.1007/978-94-009-6735-9_12
– ident: e_1_3_4_41_2
  doi: 10.1007/s10499-023-01325-y
– ident: e_1_3_4_38_2
  doi: 10.1073/pnas.1810138116
– ident: e_1_3_4_40_2
  doi: 10.1371/journal.ppat.1012767
– ident: e_1_3_4_92_2
  doi: 10.1073/pnas.1609157113
– ident: e_1_3_4_35_2
  doi: 10.1007/PL00012151
– ident: e_1_3_4_34_2
  doi: 10.1128/AEM.03540-14
– ident: e_1_3_4_19_2
  doi: 10.1128/AEM.02894-07
– ident: e_1_3_4_84_2
  doi: 10.1097/EDE.0b013e31815c09ea
– ident: e_1_3_4_72_2
  doi: 10.1128/mBio.01425-17
– ident: e_1_3_4_24_2
  doi: 10.1086/717177
– ident: e_1_3_4_65_2
  doi: 10.1126/science.aad5901
– ident: e_1_3_4_8_2
  doi: 10.1111/1462-2920.14967
– ident: e_1_3_4_16_2
  doi: 10.4269/ajtmh.23-0077
– ident: e_1_3_4_46_2
  doi: 10.1016/j.socscimed.2021.113716
– ident: e_1_3_4_83_2
  doi: 10.1016/S2542-5196(21)00169-8
– ident: e_1_3_4_88_2
  doi: 10.1073/pnas.0809654105
– ident: e_1_3_4_12_2
  doi: 10.1128/aem.00307-23
– ident: e_1_3_4_94_2
  doi: 10.3109/10408418209113506
– ident: e_1_3_4_21_2
  doi: 10.1093/femsec/fiw072
– ident: e_1_3_4_42_2
  doi: 10.1016/j.marpolbul.2021.112785
– ident: e_1_3_4_52_2
  doi: 10.1080/01431161.2019.1577575
– ident: e_1_3_4_59_2
  doi: 10.1093/cid/cis243
– ident: e_1_3_4_3_2
  doi: 10.1128/aem.45.1.275-283.1983
– ident: e_1_3_4_45_2
– ident: e_1_3_4_91_2
  doi: 10.1111/jam.12624
– ident: e_1_3_4_1_2
  doi: 10.1038/s41558-022-01426-1
– ident: e_1_3_4_32_2
  doi: 10.1371/journal.pone.0020275
– ident: e_1_3_4_58_2
  doi: 10.1111/1462-2920.13955
– ident: e_1_3_4_14_2
  doi: 10.1016/j.ecss.2023.108558
– ident: e_1_3_4_33_2
  doi: 10.1016/j.fsi.2013.08.017
– ident: e_1_3_4_63_2
  doi: 10.1080/20477724.2021.1981716
– ident: e_1_3_4_86_2
  doi: 10.1016/S0140-6736(82)92340-6
– ident: e_1_3_4_25_2
  doi: 10.1016/j.tim.2011.04.003
– ident: e_1_3_4_27_2
  doi: 10.1128/spectrum.02631-22
– ident: e_1_3_4_31_2
  doi: 10.1146/annurev.micro.54.1.641
– ident: e_1_3_4_5_2
  doi: 10.1128/mbio.01476-23
– ident: e_1_3_4_78_2
  doi: 10.1128/aem.44.5.1047-1058.1982
– ident: e_1_3_4_68_2
  doi: 10.1126/science.164.3885.1286
– volume: 198
  start-page: 394
  year: 1977
  ident: e_1_3_4_89_2
  article-title: Vibrio cholerae, Vibrio parahaemolyticus, and other vibrios: Occurrence and distribution in Chesapeake Bay
  publication-title: Science
– ident: e_1_3_4_95_2
  doi: 10.1038/s41598-018-37129-x
– ident: e_1_3_4_17_2
  doi: 10.1007/s11430-017-9229-x
– ident: e_1_3_4_71_2
  doi: 10.3389/fmicb.2016.00567
– ident: e_1_3_4_29_2
  doi: 10.2166/wh.2022.029
– ident: e_1_3_4_37_2
  doi: 10.1007/BF00327795
– ident: e_1_3_4_79_2
– ident: e_1_3_4_77_2
  doi: 10.1073/pnas.1207359109
– ident: e_1_3_4_70_2
  doi: 10.1128/JCM.38.6.2156-2161.2000
– ident: e_1_3_4_13_2
  doi: 10.1029/2023GH001005
– ident: e_1_3_4_43_2
  doi: 10.3201/eid2812.220748
– ident: e_1_3_4_26_2
  doi: 10.1128/JB.00795-17
– ident: e_1_3_4_80_2
– ident: e_1_3_4_61_2
  doi: 10.3390/tropicalmed6030147
– ident: e_1_3_4_73_2
  doi: 10.3201/eid2602.190362
– ident: e_1_3_4_44_2
  doi: 10.1016/j.jip.2011.02.006
– ident: e_1_3_4_87_2
  doi: 10.4269/ajtmh.14-0648
– ident: e_1_3_4_90_2
  doi: 10.1016/j.foodres.2010.04.001
– ident: e_1_3_4_36_2
  doi: 10.1111/j.1574-6976.2009.00200.x
– ident: e_1_3_4_55_2
  doi: 10.1128/mbio.00529-23
– ident: e_1_3_4_10_2
  doi: 10.1038/s41598-023-28247-2
– ident: e_1_3_4_51_2
  doi: 10.4269/ajtmh.17-0048
– ident: e_1_3_4_20_2
  doi: 10.1128/jb.113.1.24-32.1973
– ident: e_1_3_4_69_2
  doi: 10.1128/JCM.38.2.578-585.2000
– ident: e_1_3_4_60_2
  doi: 10.1016/j.envres.2023.117940
– ident: e_1_3_4_2_2
  doi: 10.1111/1462-2920.15716
– ident: e_1_3_4_48_2
  doi: 10.1111/j.1752-1688.2010.00448.x
– ident: e_1_3_4_98_2
  doi: 10.1289/EHP2904
– ident: e_1_3_4_6_2
  doi: 10.1126/science.274.5295.2025
– ident: e_1_3_4_85_2
  doi: 10.4269/ajtmh.2008.78.823
– ident: e_1_3_4_82_2
  doi: 10.1056/NEJMoa051594
– ident: e_1_3_4_64_2
  doi: 10.1016/j.scitotenv.2025.179412
– ident: e_1_3_4_75_2
  doi: 10.1128/AEM.01698-08
– ident: e_1_3_4_15_2
  doi: 10.2471/BLT.11.092189
– ident: e_1_3_4_28_2
  doi: 10.1111/j.1348-0421.1998.tb02357.x
– ident: e_1_3_4_9_2
  doi: 10.1111/1758-2229.13135
– ident: e_1_3_4_96_2
  doi: 10.1016/S0140-6736(11)60273-0
– ident: e_1_3_4_7_2
  doi: 10.1016/j.tim.2016.09.008
– ident: e_1_3_4_39_2
  doi: 10.1073/pnas.0408992102
– ident: e_1_3_4_81_2
– ident: e_1_3_4_67_2
  doi: 10.3201/eid2811.212066
– ident: e_1_3_4_4_2
  doi: 10.1128/aem.48.2.420-424.1984
– ident: e_1_3_4_23_2
  doi: 10.1007/s00248-012-0168-x
– ident: e_1_3_4_74_2
  doi: 10.1128/spectrum.02680-23
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Snippet Climate change significantly impacts the incidence and abundance of microorganisms, including those essential for environmental cycles and those pathogenic to...
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StartPage e2420423122
SubjectTerms Animals
Aquatic ecosystems
Bacteria
Carbon cycle
Cholera
Cholera - epidemiology
Cholera - microbiology
Cholera - transmission
Climate Change
Climate effects
Climate prediction
Climatic conditions
Decision making
Disease control
Disease transmission
Ecosystem
Environmental diseases
Health risks
Humans
Hygiene
Infectious diseases
Meteorological data
Microorganisms
Nitrogen cycle
Pathogens
Prediction models
Risk Assessment
Sanitation
Vibrio
Vibrio - pathogenicity
Vibrio Infections - epidemiology
Vibrio Infections - microbiology
Vibrio Infections - transmission
Vibriosis
Water Microbiology
Waterborne diseases
Title Climate change and Vibrio : Environmental determinants for predictive risk assessment
URI https://www.ncbi.nlm.nih.gov/pubmed/40789031
https://www.proquest.com/docview/3243694210
https://www.proquest.com/docview/3238716818
Volume 122
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