Under the Weather: Climate, Ecosystems, and Infectious Disease (2001)
Chapter: Index
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B Speakers/Presentations at the Committee Meetings
Bjerknes, Vilhelm, 15
Bjerknes, Jacob, 17
Bubonic plague, 12-13, 78
Cryptosporidiosis, 1, 33, 46, 56-57
Databases, 107
see also Internet
El Niño, 97
Geographic Information System (GIS), 4, 7, 76-77, 105, 108
standards for surveillance, 6, 66, 74-75, 107
Definitional issues
early warning system, 86
epidemiology, 28-30
glossary, 110-114
risk assessment terminology, 68-69, 111, 112, 113
weather vz climate, 20-22
Demographic factors, 3, 27, 41-42, 70
see also Population density; Urban areas
early warning systems, 90
emerging diseases, 29
SEIR modeling, 33
Dengue virus, 1, 9, 10, 33, 34, 41, 42, 43, 45-48, 74
El Niño, 10
humidity, 46, 47, 48
temperature factors, 33, 34, 47-48
urban areas, 42, 85
water, 25, 42, 46, 47
Department of Defense, 73-74
Developing countries, general, 3, 74, 90, 91
Diagnosis
dengue virus, 74
epidemics, 13, 28
Disasters, see Extreme weather events
Dose-response models, 68, 69, 71, 111
Drought, 33, 35, 36-37, 38-39
cholera, 38-39
early warning systems, 5, 101-102
risk assessment, 38
Drug resistance, 2, 43
gonorrhea, 40
historical perspectives, 9
malaria, 48
Drugs, see Pharmaceuticals
Early-warning systems, 2, 4-7, 10, 11, 27, 86- 102, 105-106
committee meetings, 133-134, 135
cost factors, 4-5, 68, 91, 93
defined, 86
demographic factors, 90
drought/famine, 5, 101-102
ecological factors, 5, 86-87, 89-90
fire, 99
funding, 93
historical perspectives, 14-16
hurricanes, 101
local factors, 88, 90, 91, 92, 93-97, 101, 102, 106, 135
models, 26-27, 88, 89
national-level factors, 14-15, 88, 90, 92, 102
population density, 90
public communication, 89, 91, 95-96, 102, 135
public health services, 5, 88, 90-92, 93
regional factors, 90, 92, 93
risk assessment, 87-88, 89, 90, 94-95
sanitation, 90, 94
sentinel animals, 86, 87, 89
state-level factors, 92
temporal factors, 91, 92
uncertainty, 87
vaccines, 90
waterborne diseases, general, 90, 94, 96
Ecological factors, xi, xii , 9, 10, 11, 35, 36, 75, 80-85, 103, 104, 107-108
aggregation bias, 67
committee study methodology, 2, 134
early warning systems, 5, 86-87, 89-90
emerging diseases, 29, 30
historical perspectives, 17
interdisciplinary studies, 7, 71-72
remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
SEIR modeling, 33
time-series analysis, 60
Economic factors, 27, 74, 90, 134
see also Cost and cost-effectiveness; Socioeconomic factors
developing countries, general, 3, 74, 90, 91
Education, see Internet; Professional education; Public communication
agricultural planning, 97-98
cholera, 9, 57-58
databases, 97
defined, 111
dengue virus, 10
global climate, general, 22, 23
historical perspectives, 17, 25
Internet, 23, 98
malaria, 48-49
prospective observations, 60
time-series analysis, 60
Emerging diseases, 29-30, 73-74, 104
Emigration, see Migration
Endemic outbreaks, general, 78, 96-97
defined, 28-29, 111
Environmental Protection Agency, 68
Epidemics, 4, 9, 35
see also Early warning systems
committee meetings, 132
defined, 28, 111
diagnostic issues, 13, 28
extreme weather events, 38
hantavirus, 51-52
historical perspectives, 12-13, 36, 38, 49-50
malaria, 48-49
St. Louis encephalitis, 49-50
urban areas, 36
Epidemiology, xii , 4, 5, 6, 10, 28-33, 66, 67- 68, 73-75, 105-106, 107
see also Early warning systems; Risk assessment; Surveillance systems
committee meetings, 134, 135
definitional issues, 28-30
dengue fever, 47-48
extreme weather conditions, 38
historical perspectives, 13, 14, 16-17, 18, 19
meta-analysis, 67
sanitation and, 16
Error of measurement, 24-25, 66-67, 82-85
aggregation bias, 67, 72, 73
Experimental studies, 5, 11, 62-63, 66, 67, 80, 82, 83, 107
interdependence with observational and modeling studies, 6, 67
Expert opinion, xi , 10, 62, 66, 99
Exposure and exposure assessment, 65, 69-70
cholera, 70, 71
dose-response models, 68, 69, 71, 111
food and waterborne diseases, 65
hantavirus, 52
housing and, 38
immunity and, 30
vector-borne diseases, 38, 43, 49, 50
Extreme weather events
see also Drought
flooding, 33, 36-37, 38, 63, 78-79
hurricanes, 36-37, 38, 101
monsoons, 14, 81
risk assessment, 38
temporal factors, 36-37
Famine
cholera, 38-39
early warning systems, 5, 101-102
Famine Early Warning System, 101-102
Federal Emergency Management Agency, 92
Federal government, 1-2, 7, 73, 107-108
Centers for Disease Control and Prevention, 7, 73, 74, 108
committee meetings, 132
Department of Defense, 73-74
Environmental Protection Agency, 68
Federal Emergency Management Agency, 92
Forest Service, 99
funding, 7, 62, 93
National Center for Ecological Analysis and Synthesis, 6, 107
National Institute of Allergy and Infectious Diseases, 7, 108
National Oceanic and Atmospheric Administration, 60, 76, 93
National Weather Service, 15, 87
Fires and fire control, 99
Flooding, 36-37, 78
cryptosporidiosis, 33
meningitis, 33
remote sensing, 78-79
Rift Valley fever, 33, 63
risk assessment, 38
Food, see Famine; Nutrition and malnutrition
Food-borne pathogens, 44, 46, 57, 58, 68, 76, 96
Forest Service, 99
Funding
early warning systems, 93
interdisciplinary studies, 7, 62
General circulation models, 27
Genetic factors, 3, 35, 36, 105
emerging diseases, 29-30, 73-74, 104
Geographic factors, 1, 3-4, 6
see also Local factors; National-level factors; Regional factors; Spatial factors; State-level factors
cholera, 16-17
cryptosporidiosis, 56
historical perspectives, 12, 13, 17
modeling studies, 27, 65, 66
Geographic Information System (GIS), 4, 7, 76-77, 105, 108
Global Change Research Program, xi-xii, 7, 9, 108
Global climate, general, 2, 17, 81, 82, 104
El Niño, 22, 23
surveillance and, 74
Global Emerging Infections Surveillance and Response Systems, 74
Global warming, 3, 9, 10, 22-24, 27, 37, 81, 82, 104
greenhouse gases, 22, 24, 27
influenza, 55
integrated assessment, 72
mosquito vectors, 9, 49
West Nile virus, 97
Gonorrhea, 40
Greenhouse gases, 22, 24, 27
Hantavirus, 46, 51-52, 78, 94
Hill, Austin Bradford, 17
Historical perspectives, 1, 9-10, 12-19, 99
cholera, 16-17, 58
committee meetings, 132
cryptosporidiosis, 56
early warning systems, 14-16
El Niño, 17, 25
epidemics, 12-13, 36, 38, 49-50
epidemiology, 13, 14, 16-17, 18, 19
geographic factors, 12, 13, 17
interdisciplinary approaches, 17
precipitation, 14, 25
public health, 18, 19
regional factors, 12-13, 14, 17
research methodology, 13-15, 61
sanitation, 14, 16, 18
seasonal variation, 8, 12, 14, 21, 25
surveillance, 89
time-series analysis, 58, 59-60, 61
vaccines, 9, 42-43
waterborne diseases and water treatment, 14, 16, 18, 44
HIV/AIDS, 1, 40, 90
Housing
early warning systems, 90
Lyme disease, 40
regional factors, 3
vector-borne diseases, 38, 40, 42
Humidity, 2, 46
see also Precipitation
coccidioidomycosis, 33
cryptosporidiosis, 33, 46
dengue virus, 46, 47, 48
influenza, 55
malaria, 46, 48, 65
meningitis, 33, 39
modeling studies, 65
Rift Valley fever, 33
Hurricanes, 36-37, 38, 101
Immigration, see Migration
Immune response, 35-36, 111-112
see also Drug resistance; Vaccines
cholera, 57
influenza, 54-55
refugees, 37, 40
Rift Valley fever, 51
travelers and migrants, 40
Incidence and prevalence, 6, 9, 10, 70, 74
see also Endemic outbreaks; Epidemics; Surveillance
cholera, 57
cryptosporidiosis, 56
defined, 28, 112, 113
dengue fever, 45-46
emerging diseases, 29
influenza, 54
Lyme disease, 10, 52-53
time-series analysis, 60
Indian Ocean, 17, 21, 25, 51
Influenza, 1, 8, 46, 54-55, 134
temperature factors, 33, 55
travelers, 40
Insects, 36, 81
see also Lyme disease; Mosquitos; Pesticides; Vector-borne diseases
eradication of vectors, 36, 43, 46, 49
Integrated assessment, 59, 71-73
Inter-American Institute, 93
Interannual variation, 1, 2-3, 21, 81, 82-83, 103, 104
see also El Niño/Southern Oscillation
Interdisciplinary approaches, 2, 7, 62
ecological factors, 7, 71-72
funding, 7, 62
historical perspectives, 17
integrated assessment, 59, 71-73
modeling studies, 6, 7, 104-105, 107-108
professional education, 6, 7, 62
remote sensing and, 76
research centers, 6, 62
social factors, 7, 71-72
socioeconomic factors, 7, 72, 107
Intergovernmental Panel on Climate Change (IPCC), 9, 24, 37
International Research Institute for Climate Prediction, 93
Internet, 74
El Niño, 23, 98
Koch, Robert, 16-17
Land cover and land use, 3, 22, 24, 35, 39-40, 42
agriculture, 5, 22, 42, 44, 78, 85, 97-98
forests and deforestation, 22, 24, 42, 78, 99
Lyme disease, 39-40, 54
seasonal variation, 25-26
soil, 33, 34, 54, 76, 79
urban areas, 35, 36, 40, 41, 42, 46, 49-50, 55, 79, 85, 93-97
vegetation, general, 22, 24, 54, 76, 78, 81, 83-84
wetlands, 79
Local factors, 24
early warning systems, 88, 90, 91, 92, 93-97, 101, 102, 106, 135
epidemics, 13
modeling studies, 65
surveillance systems, 73
Lorenz, Edward, 16
Lyme disease, 39-40, 46, 52-54, 78, 79
Malaria, 1, 8, 43, 46, 48-49, 78, 79, 85, 135
air transport, 41
drug-resistant, 48
global warming, 9
humidity, 46, 48, 65
incidence, 10, 48-49
migrants, 40
modeling studies, 65
precipitation, 46, 48, 65
seasonal variation, 33
temperature, 33, 34, 46, 48
water, standing, 42, 46
wind, 48
Malnutrition, see Nutrition and malnutrition
Mass media, 10
Mathematical, 9, 13, 15-16, 63-68, 88, 112
committee meetings, 133
SEIR framework, 31-33, 36, 63, 88
Mechanistic models, 63-64, 65, 106-107
Meningitis, 1, 22, 33, 39
Meta-analysis, 67, 112
Methodology, see Research methodology
Migration, 10, 35, 40-41
see also Travel and tourism
refugees, 37, 40
Modeling studies, 5-6, 11, 59, 70, 98, 104-105, 106-107
agricultural land uses, 98
animal models, 16-17, 55
cholera, 70-71
climate, 7, 9
computer models, 5, 6, 15-16, 24, 25, 105
disease transmission, 30
dose-response, 68, 69, 71, 111
early warning systems, 26-27, 88, 89
epidemiology, 4, 107
forest fire conditions, 99
general circulation models, 27
geographic factors, 27, 65, 66
historical perspectives, 9
humidity, 65
influenza, 55
interdisciplinary approaches, 6, 7, 104- 105, 107-108
land/atmosphere, 26
malaria, 65
mathematical, 9, 13, 15-16, 63-68, 71, 72, 88
SEIR framework, 31-33, 36, 63, 88
mechanistic models, 63-64, 65, 106-107
multivariate models, 64, 65
observational studies and, 6, 67
ocean, seasonal variation, 25
ocean/land/atmosphere, 27
precipitation, 27, 65
prediction and prevention, 10
public health services, 66
spatial factors, 61, 65, 66, 67
statistical-empirical models, 6, 64, 66, 106
temperature, 65
temporal factors, 66
uncertainty, 3, 27, 103-104
Monsoons, 14, 81
Mosquitos, 1, 9, 30, 34, 43, 47-49, 135
see also Dengue virus; Malaria; Yellow fever
modeling studies, 65
remote sensing, 76, 78-79
Rift Valley fever, 33, 35, 46, 50-51, 63, 76, 78, 88
St. Louis encephalitis, 46, 49-50
West Nile virus, 90, 93-97
Multidisciplinary approaches, see Interdisciplinary approaches
Multivariate models, 64, 65
National Center for Ecological Analysis and Synthesis, 6, 107
National Institute of Allergy and Infectious Diseases, 7, 108
National-level factors, 135
early warning systems, 14-15, 88, 90, 92, 102
meteorological systems, 75
surveillance systems, 73, 74
National Oceanic and Atmospheric Administration, 60, 76, 93
National Weather Service, 15, 87
Natural disasters, see Extreme weather events
Normalized Difference Vegetation Index, 76
North Atlantic Oscillation, 21
Nutrition and malnutrition, 37, 38-39, 41-42, 43
see also Famine; Food-borne pathogens
Observational studies, 5, 11, 59-62, 73, 75, 80-81, 88, 105, 107
historical perspectives, 9
interdependence with experimental and modeling studies, 6, 67
prospective observations, 60-61
remote sensing, 6, 7, 10, 70, 75-77, 90, 101, 105
temporal factors, 59-60, 61, 80-81
uncertainty, 3, 103-104
Oceans
see also El Niño/Southern Oscillation; North Atlantic Oscillation
Atlantic Ocean, 21, 25
color, 79
Indian Ocean, 17, 21, 25, 51
Pacific Ocean, 17, 25, 51
seasonal variability models, 25
surface height, 22, 58, 79
surface temperature, general, 9, 27, 51, 57-58, 79
Office of Global Programs (NOAA), 60, 93
Outbreaks, see Epidemics
Pacific Ocean, 17, 25, 51
see also El Niño/Southern Oscillation; North Atlantic Oscillation
Parasitic diseases, 1, 9, 35
see also Malaria
cryptosporidiosis, 1, 33, 46, 56-57
schistosomiasis, 39, 43, 78, 79
Pesticides, 43, 96
historical perspectives, 9
resistance to, 2, 9
see also Drug resistance; Vaccines
Population density, 3, 35, 37, 41, 46, 55
early warning systems, 90
influenza, 33, 55
see also Urban areas
Population factors, other, see Demographic
factors; Social factors;
Socioeconomic factors
Precipitation, 21, 46, 90, 97
see also Drought; Flooding
cryptosporidiosis, 1, 46, 56
dengue fever, 46, 47
general circulation models, 27
historical perspectives, 14, 25
malaria, 46, 48, 65
modeling studies, 27, 65
monsoons, 14, 81
Rift Valley fever, 46, 51
seasonal, general, 25
vegetative cover and, 24, 78
Prevalence, see Incidence and prevalence
Professional education
interdisciplinary, 6, 7, 62
response strategies, 94
Prospective observations, 60-61
Public communication, 89, 91, 95-96, 98, 102, 135
see also Internet
Mass media, 10
Public health, 3, 10, 36, 42-43, 68, 135
see also Housing; Sanitation; Vaccines; Waterborne diseases and water treatment
bubonic plague, 13
early warning systems, 5, 88, 90-92, 93
emerging diseases, 29
historical perspectives, 18, 19
hurricanes, 100
interdisciplinary studies, 7
modeling studies, 66
quarantine, 13, 36
vector eradication, 36, 43, 46, 49
Public opinion, 10, 91
Quantitative risk characterization, 2, 4, 27, 62, 64, 65, 69, 71, 72, 96, 105
Quarantine, 13, 36
Rain, see Precipitation
Regional factors, 3, 21, 22, 24, 61
see also Epidemics
developing countries, general, 3, 74, 90, 91
early warning systems, 90, 92, 93
epidemics, 12-13
general circulation models, 27
global warming, 24
historical perspectives, 12-13, 14, 17
modeling studies, 65, 66
monsoons, 14
precipitation, 24
remote sensing, 73
surveillance systems, 73, 105-106
temperature, general, 24
time-series analysis, 60
Remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
Reporting bias, 66
Research methodology, 5, 59-79
see also Interdisciplinary approaches; Modeling studies; Observational studies; Uncertainty
aggregation bias, 67, 72, 73
causal relations, general, 83-84
committee study at hand, 2, 132-135
error of measurement, 24-25, 66-67, 82- 85
experimental studies, 5, 6, 11, 62-63, 66, 67, 80, 82, 83, 107
expert opinion, xi, 10, 62, 66, 99
historical perspectives, 13-15, 61
meta-analysis, 67, 112
remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
stochastic processes, 73
time-series analysis, 58, 59-60, 61
Retrospective analysis of historical trends, 61
Retrospective analysis of natural variation, 59-60
Richardson, Lewis Frye, 15
Rift Valley fever, 33, 35, 42, 46, 50-51, 63, 76, 78, 88
Risk assessment, 59, 68-71
see also Exposure and exposure assessment
cholera, 70-71
defined, 113
dose-response models, 68, 69, 71, 111
drought, 38
early warning systems, 87-88, 89, 90, 94-95
extreme weather events, 38
flooding, 33, 63
forest fire conditions, 99
integrated assessment, 59, 71-73
modeling studies, 65
quantitative, 2, 4, 27, 62, 64, 65, 69, 71, 72, 96, 105
sanitation, 38
waterborne diseases, 38, 68
West Nile virus, 94-95
Sanitation, 3, 36, 41, 43
see also Waterborne diseases and water treatment
dengue fever, 42
early warning systems, 90, 94
epidemiology and, 16
historical perspectives, 14, 16, 18
malaria, 49
regional factors, 3
response strategies, 94
risk assessment, 38
St. Louis encephalitis, 49-50
urban areas, 41-42
Satellite technology, see Remote sensing
Schistosomiasis, 39, 43, 78, 79
Seasonal variation, 2-3, 21, 25-27, 81, 82-83, 103, 104
see also El Niño/Southern Oscillation
cholera, 57-58
computer models, 25
cryptosporidiosis, 56-57
dengue virus, 33
historical perspectives, 8, 12, 21, 14, 25
influenza, 33, 55
malaria, 33
modeling of, 25
monsoons, 14, 81
vibrios, 57-58
Sea surface
height, 22, 58, 79
temperature, 9, 27, 51, 57-58, 79;
see also El Niño/Southern Oscillation; North Atlantic Oscillation
SEIR framework, 31-33, 36, 63, 88
Sentinel animals, 86, 87, 89
Social factors, 9, 36, 41-42
see also Demographic factors
committee study methodology, 2
interdisciplinary studies, 7, 71-72
population density, 3, 33, 35, 37, 41, 46, 55, 90;
see also Urban areas
time-series analysis, 60
Socioeconomic factors
developing countries, general, 3, 74, 90, 91
interdisciplinary studies, 7, 72, 107
Soil conditions
Lyme disease, 54
remote sensing, 76, 79
wind-blown dust, 33, 34
Spatial factors, 2, 5, 6, 11, 13, 20, 59, 61, 80- 85, 107
see also Geographic factors
aggregation bias, 67, 72
climate defined, 20-21
cryptosporidiosis, 56
general circulation models, 27
global warming, 24
Lyme disease, 53
modeling studies, 61, 65, 66, 67
temperature factors and, 20-21, 24, 61
uncertainty, 3-4, 82-83, 104
Standards
databases, 6, 66, 74-75, 107
public health response, 92
reporting bias, 66
State-level factors
early warning systems, 92
surveillance systems, 73
Statistical-empirical models, 6, 64, 66, 106
St. Louis encephalitis, 46, 49-50, 61, 89-90
Stochastic processes, 73
Storms, 21
flooding, 33, 36-37, 38, 63, 78-79
hurricanes, 36-37, 38, 101
monsoons, 14, 81
Surveillance systems, 4, 5-7, 10, 66, 69, 73- 75, 89-90, 93, 95, 105-108
see also Early warning systems; Epidemiology; Observational studies
committee study methodology, 2, 135
cost factors, 74
influenza, 54
prospective observations, 60
regional factors, 73, 105-106
reporting bias, 66
time-series analysis, 60
Temperature, 1, 2, 22, 81
see also Global warming; El Niño/Southern Oscillation
cholera, 57-58
climate defined, 20-21
dengue virus, 33, 34, 47-48
historical perspectives, 14
influenza, 33, 55
integrated assessment, 71-72
Lyme disease, 53-54
malaria, 33, 34, 46, 48
microbe replication rate, 34
modeling studies, 65
sea surface, general, 9, 27, 51, 57-58, 79
spatial factors and, 20-21, 24, 61
St. Louis encephalitis, 50
vector-borne diseases, general, 34, 65
vibrios, other than cholera, 58
Temporal factors, 5, 59, 80-85
see also Interannual variation; Seasonal variation
climate defined, 20, 21-22
early warning systems, 91, 92
epidemics, 13
extreme weather events, 36-37
Lyme disease, 53
modeling studies, 66
observational studies, 59-60, 61, 80-81
uncertainty, 3-4, 72, 82-83, 104
Time-series analysis, 58, 59-60, 61
Togavirus, see Yellow fever
Travel and tourism, 3, 10, 29, 35, 40-41, 97, 104
see also Migration
influenza, 55
quarantine, 36
surveillance systems, 73
Ultraviolet radiation, 33, 55
Uncertainty, 3-4, 82-85, 104
aggregation bias, 67, 72, 73
early warning systems, 87
error of measurement, 24-25, 66-67, 82- 85
integrated assessments, 72, 73
modeling studies, 3, 27, 103-104
observational studies, 3, 103-104
reporting bias, 66
spatial factors, 3-4, 82-83, 104
temporal factors, 3-4, 72, 82-83, 104
United Nations, 75
Urban areas, 35, 36, 40, 41, 79
dengue fever, 42, 85
influenza, 55
sanitation, 41-42
St. Louis encephalitis, 46, 49-50
West Nile virus, 93-97
Vaccines, 3, 7, 36, 42-43, 94
early warning systems, 90
historical perspectives, 9, 42-43
Vector-borne diseases, 1, 2, 30, 31, 38, 94
see also Dengue virus; Insects; Lyme disease; Malaria; Mosquitos; Pesticides; Rift Valley fever
air transport, 41
bubonic plague, 12-13, 78
committee meetings, 133, 135
control of vectors, 7
definitions, 113-114
eradication of vectors, 36, 43, 46, 49
hantavirus, 46, 51-52, 78, 94
housing, 38, 40, 42
land cover, 39-40
migration and travel, 46
modeling studies, 65
St. Louis encephalitis, 46, 49-50, 61, 89-90
temperature factors, 34, 65
Vegetation, 22, 24, 81, 83-84
agriculture, 5, 22, 42, 44, 78, 85, 97-98
forests and deforestation, 22, 24, 42, 78, 99
Lyme disease, 54
remote sensing, 76, 78
Vibrios, 46, 57-58
see also Cholera
Viruses, 1
see also Dengue virus
hantavirus, 46, 51-52, 78, 94
HIV/AIDS, 1, 40, 90
St. Louis encephalitis, 46, 49-50, 61, 89-90
West Nile virus, 90, 93-97
yellow fever, 1, 9, 78
Waterborne diseases and water treatment, 7, 38, 42, 44, 46, 79
see also Flooding; Sanitation; Vibrios; Wetlands
cholera, 16-17, 34, 38-39, 57-58, 70-71, 79
cryptosporidiosis, 1, 33, 46, 56-57
dengue fever, 25, 42, 46, 47
drought and, 38-39
early warning systems, 90, 94, 96
historical perspectives, 14, 16, 18, 44
malaria, 42, 46
response strategies, 90, 94
Rift Valley fever, 42
risk assessment, 38, 68
schistosomiasis, 39
temperature factors, 34
vibrios, other than cholera, 58
West Nile virus, 90, 93-97
Wetlands, 79
Wind, 12, 14, 20, 21, 34, 38, 48
World Health Organization, 9, 43, 54, 73, 134
World Meteorological Organization, 75, 134
World Weather Watch, 75
Yellow fever, 1, 9, 78
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 137
Index
A
Advanced Very High Resolution Radiometer,
76
Aerosols, 24, 27, 30, 54
Africa, 35
see also Rift Valley fever; West Nile virus
cryptosporidiosis, 56
famine, 101-102
malaria, 40, 48
meningitis, 22, 39
Aggregation bias, 67, 72, 73
Agriculture, 5, 22, 42, 44, 85
El Niño, 97-98
remote sensing, 78
AIDS, see HIV/AIDS
American Association for the Advancement of Science, 16
Asia, 14, 39
cryptosporidiosis, 56
influenza, 40
malaria, 40, 65
Atlantic Ocean, 25
North Atlantic Oscillation, 21
Aerosols, 24, 27, 30, 54
Africa, 35
see also Rift Valley fever; West Nile virus
cryptosporidiosis, 56
famine, 101-102
malaria, 40, 48
meningitis, 22, 39
Aggregation bias, 67, 72, 73
Agriculture, 5, 22, 42, 44, 85
El Niño, 97-98
remote sensing, 78
AIDS, see HIV/AIDS
American Association for the Advancement of Science, 16
Asia, 14, 39
cryptosporidiosis, 56
influenza, 40
malaria, 40, 65
Atlantic Ocean, 25
North Atlantic Oscillation, 21
B
Bjerknes, Vilhelm, 15
Bjerknes, Jacob, 17
Bubonic plague, 12-13, 78
C
Centers for Disease Control and Prevention,
7,
73,
74,
108
Cholera, 34, 57-58, 79
drought/famine, 38-39
El Niño, 9, 57-58
geographic factors, 16-17
historical perspectives, 16-17, 58
modeling studies/risk assessment, 70-71
temperature factors, 57-58
Clouds, general circulation models, 27
Coccidioidomycosis, 33, 38
Communication, see Public communication
Computer databases, see Databases
Computer models, 5, 6, 15-16, 24, 105
seasonal variations, 25
Cost and cost-effectiveness, 105
agricultural planning, El Niño, 98
bubonic plague, 13
early warning systems, 4-5, 68, 91
surveillance, 74
Cholera, 34, 57-58, 79
drought/famine, 38-39
El Niño, 9, 57-58
geographic factors, 16-17
historical perspectives, 16-17, 58
modeling studies/risk assessment, 70-71
temperature factors, 57-58
Clouds, general circulation models, 27
Coccidioidomycosis, 33, 38
Communication, see Public communication
Computer databases, see Databases
Computer models, 5, 6, 15-16, 24, 105
seasonal variations, 25
Cost and cost-effectiveness, 105
agricultural planning, El Niño, 98
bubonic plague, 13
early warning systems, 4-5, 68, 91
surveillance, 74
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 138
Crowding, see Population densityCryptosporidiosis, 1, 33, 46, 56-57
D
Databases, 107
see also Internet
El Niño, 97
Geographic Information System (GIS), 4, 7, 76-77, 105, 108
standards for surveillance, 6, 66, 74-75, 107
Definitional issues
early warning system, 86
epidemiology, 28-30
glossary, 110-114
risk assessment terminology, 68-69, 111, 112, 113
weather vz climate, 20-22
Demographic factors, 3, 27, 41-42, 70
see also Population density; Urban areas
early warning systems, 90
emerging diseases, 29
SEIR modeling, 33
Dengue virus, 1, 9, 10, 33, 34, 41, 42, 43, 45-48, 74
El Niño, 10
humidity, 46, 47, 48
temperature factors, 33, 34, 47-48
urban areas, 42, 85
water, 25, 42, 46, 47
Department of Defense, 73-74
Developing countries, general, 3, 74, 90, 91
Diagnosis
dengue virus, 74
epidemics, 13, 28
Disasters, see Extreme weather events
Dose-response models, 68, 69, 71, 111
Drought, 33, 35, 36-37, 38-39
cholera, 38-39
early warning systems, 5, 101-102
risk assessment, 38
Drug resistance, 2, 43
gonorrhea, 40
historical perspectives, 9
malaria, 48
Drugs, see Pharmaceuticals
E
Early-warning systems, 2, 4-7, 10, 11, 27, 86- 102, 105-106
committee meetings, 133-134, 135
cost factors, 4-5, 68, 91, 93
defined, 86
demographic factors, 90
drought/famine, 5, 101-102
ecological factors, 5, 86-87, 89-90
fire, 99
funding, 93
historical perspectives, 14-16
hurricanes, 101
local factors, 88, 90, 91, 92, 93-97, 101, 102, 106, 135
models, 26-27, 88, 89
national-level factors, 14-15, 88, 90, 92, 102
population density, 90
public communication, 89, 91, 95-96, 102, 135
public health services, 5, 88, 90-92, 93
regional factors, 90, 92, 93
risk assessment, 87-88, 89, 90, 94-95
sanitation, 90, 94
sentinel animals, 86, 87, 89
state-level factors, 92
temporal factors, 91, 92
uncertainty, 87
vaccines, 90
waterborne diseases, general, 90, 94, 96
Ecological factors, xi, xii , 9, 10, 11, 35, 36, 75, 80-85, 103, 104, 107-108
aggregation bias, 67
committee study methodology, 2, 134
early warning systems, 5, 86-87, 89-90
emerging diseases, 29, 30
historical perspectives, 17
interdisciplinary studies, 7, 71-72
remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
SEIR modeling, 33
time-series analysis, 60
Economic factors, 27, 74, 90, 134
see also Cost and cost-effectiveness; Socioeconomic factors
developing countries, general, 3, 74, 90, 91
Education, see Internet; Professional education; Public communication
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 139
El Niño/Southern Oscillation (ENSO), 1, 4, 5, 21, 22, 25, 37, 83, 85, 88, 105agricultural planning, 97-98
cholera, 9, 57-58
databases, 97
defined, 111
dengue virus, 10
global climate, general, 22, 23
historical perspectives, 17, 25
Internet, 23, 98
malaria, 48-49
prospective observations, 60
time-series analysis, 60
Emerging diseases, 29-30, 73-74, 104
Emigration, see Migration
Endemic outbreaks, general, 78, 96-97
defined, 28-29, 111
Environmental Protection Agency, 68
Epidemics, 4, 9, 35
see also Early warning systems
committee meetings, 132
defined, 28, 111
diagnostic issues, 13, 28
extreme weather events, 38
hantavirus, 51-52
historical perspectives, 12-13, 36, 38, 49-50
malaria, 48-49
St. Louis encephalitis, 49-50
urban areas, 36
Epidemiology, xii , 4, 5, 6, 10, 28-33, 66, 67- 68, 73-75, 105-106, 107
see also Early warning systems; Risk assessment; Surveillance systems
committee meetings, 134, 135
definitional issues, 28-30
dengue fever, 47-48
extreme weather conditions, 38
historical perspectives, 13, 14, 16-17, 18, 19
meta-analysis, 67
sanitation and, 16
Error of measurement, 24-25, 66-67, 82-85
aggregation bias, 67, 72, 73
Experimental studies, 5, 11, 62-63, 66, 67, 80, 82, 83, 107
interdependence with observational and modeling studies, 6, 67
Expert opinion, xi , 10, 62, 66, 99
Exposure and exposure assessment, 65, 69-70
cholera, 70, 71
dose-response models, 68, 69, 71, 111
food and waterborne diseases, 65
hantavirus, 52
housing and, 38
immunity and, 30
vector-borne diseases, 38, 43, 49, 50
Extreme weather events
see also Drought
flooding, 33, 36-37, 38, 63, 78-79
hurricanes, 36-37, 38, 101
monsoons, 14, 81
risk assessment, 38
temporal factors, 36-37
F
Famine
cholera, 38-39
early warning systems, 5, 101-102
Famine Early Warning System, 101-102
Federal Emergency Management Agency, 92
Federal government, 1-2, 7, 73, 107-108
Centers for Disease Control and Prevention, 7, 73, 74, 108
committee meetings, 132
Department of Defense, 73-74
Environmental Protection Agency, 68
Federal Emergency Management Agency, 92
Forest Service, 99
funding, 7, 62, 93
National Center for Ecological Analysis and Synthesis, 6, 107
National Institute of Allergy and Infectious Diseases, 7, 108
National Oceanic and Atmospheric Administration, 60, 76, 93
National Weather Service, 15, 87
Fires and fire control, 99
Flooding, 36-37, 78
cryptosporidiosis, 33
meningitis, 33
remote sensing, 78-79
Rift Valley fever, 33, 63
risk assessment, 38
Food, see Famine; Nutrition and malnutrition
Food-borne pathogens, 44, 46, 57, 58, 68, 76, 96
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 140
Forests and deforestation, 22, 24, 42, 78, 99Forest Service, 99
Funding
early warning systems, 93
interdisciplinary studies, 7, 62
G
General circulation models, 27
Genetic factors, 3, 35, 36, 105
emerging diseases, 29-30, 73-74, 104
Geographic factors, 1, 3-4, 6
see also Local factors; National-level factors; Regional factors; Spatial factors; State-level factors
cholera, 16-17
cryptosporidiosis, 56
historical perspectives, 12, 13, 17
modeling studies, 27, 65, 66
Geographic Information System (GIS), 4, 7, 76-77, 105, 108
Global Change Research Program, xi-xii, 7, 9, 108
Global climate, general, 2, 17, 81, 82, 104
El Niño, 22, 23
surveillance and, 74
Global Emerging Infections Surveillance and Response Systems, 74
Global warming, 3, 9, 10, 22-24, 27, 37, 81, 82, 104
greenhouse gases, 22, 24, 27
influenza, 55
integrated assessment, 72
mosquito vectors, 9, 49
West Nile virus, 97
Gonorrhea, 40
Greenhouse gases, 22, 24, 27
H
Hantavirus, 46, 51-52, 78, 94
Hill, Austin Bradford, 17
Historical perspectives, 1, 9-10, 12-19, 99
cholera, 16-17, 58
committee meetings, 132
cryptosporidiosis, 56
early warning systems, 14-16
El Niño, 17, 25
epidemics, 12-13, 36, 38, 49-50
epidemiology, 13, 14, 16-17, 18, 19
geographic factors, 12, 13, 17
interdisciplinary approaches, 17
precipitation, 14, 25
public health, 18, 19
regional factors, 12-13, 14, 17
research methodology, 13-15, 61
sanitation, 14, 16, 18
seasonal variation, 8, 12, 14, 21, 25
surveillance, 89
time-series analysis, 58, 59-60, 61
vaccines, 9, 42-43
waterborne diseases and water treatment, 14, 16, 18, 44
HIV/AIDS, 1, 40, 90
Housing
early warning systems, 90
Lyme disease, 40
regional factors, 3
vector-borne diseases, 38, 40, 42
Humidity, 2, 46
see also Precipitation
coccidioidomycosis, 33
cryptosporidiosis, 33, 46
dengue virus, 46, 47, 48
influenza, 55
malaria, 46, 48, 65
meningitis, 33, 39
modeling studies, 65
Rift Valley fever, 33
Hurricanes, 36-37, 38, 101
I
Immigration, see Migration
Immune response, 35-36, 111-112
see also Drug resistance; Vaccines
cholera, 57
influenza, 54-55
refugees, 37, 40
Rift Valley fever, 51
travelers and migrants, 40
Incidence and prevalence, 6, 9, 10, 70, 74
see also Endemic outbreaks; Epidemics; Surveillance
cholera, 57
cryptosporidiosis, 56
defined, 28, 112, 113
dengue fever, 45-46
emerging diseases, 29
influenza, 54
Lyme disease, 10, 52-53
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 141
malaria, 10, 48-49time-series analysis, 60
Indian Ocean, 17, 21, 25, 51
Influenza, 1, 8, 46, 54-55, 134
temperature factors, 33, 55
travelers, 40
Insects, 36, 81
see also Lyme disease; Mosquitos; Pesticides; Vector-borne diseases
eradication of vectors, 36, 43, 46, 49
Integrated assessment, 59, 71-73
Inter-American Institute, 93
Interannual variation, 1, 2-3, 21, 81, 82-83, 103, 104
see also El Niño/Southern Oscillation
Interdisciplinary approaches, 2, 7, 62
ecological factors, 7, 71-72
funding, 7, 62
historical perspectives, 17
integrated assessment, 59, 71-73
modeling studies, 6, 7, 104-105, 107-108
professional education, 6, 7, 62
remote sensing and, 76
research centers, 6, 62
social factors, 7, 71-72
socioeconomic factors, 7, 72, 107
Intergovernmental Panel on Climate Change (IPCC), 9, 24, 37
International Research Institute for Climate Prediction, 93
Internet, 74
El Niño, 23, 98
K
Koch, Robert, 16-17
L
Land cover and land use, 3, 22, 24, 35, 39-40, 42
agriculture, 5, 22, 42, 44, 78, 85, 97-98
forests and deforestation, 22, 24, 42, 78, 99
Lyme disease, 39-40, 54
seasonal variation, 25-26
soil, 33, 34, 54, 76, 79
urban areas, 35, 36, 40, 41, 42, 46, 49-50, 55, 79, 85, 93-97
vegetation, general, 22, 24, 54, 76, 78, 81, 83-84
wetlands, 79
Local factors, 24
early warning systems, 88, 90, 91, 92, 93-97, 101, 102, 106, 135
epidemics, 13
modeling studies, 65
surveillance systems, 73
Lorenz, Edward, 16
Lyme disease, 39-40, 46, 52-54, 78, 79
M
Malaria, 1, 8, 43, 46, 48-49, 78, 79, 85, 135
air transport, 41
drug-resistant, 48
global warming, 9
humidity, 46, 48, 65
incidence, 10, 48-49
migrants, 40
modeling studies, 65
precipitation, 46, 48, 65
seasonal variation, 33
temperature, 33, 34, 46, 48
water, standing, 42, 46
wind, 48
Malnutrition, see Nutrition and malnutrition
Mass media, 10
Mathematical, 9, 13, 15-16, 63-68, 88, 112
committee meetings, 133
SEIR framework, 31-33, 36, 63, 88
Mechanistic models, 63-64, 65, 106-107
Meningitis, 1, 22, 33, 39
Meta-analysis, 67, 112
Methodology, see Research methodology
Migration, 10, 35, 40-41
see also Travel and tourism
refugees, 37, 40
Modeling studies, 5-6, 11, 59, 70, 98, 104-105, 106-107
agricultural land uses, 98
animal models, 16-17, 55
cholera, 70-71
climate, 7, 9
computer models, 5, 6, 15-16, 24, 25, 105
disease transmission, 30
dose-response, 68, 69, 71, 111
early warning systems, 26-27, 88, 89
epidemiology, 4, 107
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 142
experimental studies and, 6, 67forest fire conditions, 99
general circulation models, 27
geographic factors, 27, 65, 66
historical perspectives, 9
humidity, 65
influenza, 55
interdisciplinary approaches, 6, 7, 104- 105, 107-108
land/atmosphere, 26
malaria, 65
mathematical, 9, 13, 15-16, 63-68, 71, 72, 88
SEIR framework, 31-33, 36, 63, 88
mechanistic models, 63-64, 65, 106-107
multivariate models, 64, 65
observational studies and, 6, 67
ocean, seasonal variation, 25
ocean/land/atmosphere, 27
precipitation, 27, 65
prediction and prevention, 10
public health services, 66
spatial factors, 61, 65, 66, 67
statistical-empirical models, 6, 64, 66, 106
temperature, 65
temporal factors, 66
uncertainty, 3, 27, 103-104
Monsoons, 14, 81
Mosquitos, 1, 9, 30, 34, 43, 47-49, 135
see also Dengue virus; Malaria; Yellow fever
modeling studies, 65
remote sensing, 76, 78-79
Rift Valley fever, 33, 35, 46, 50-51, 63, 76, 78, 88
St. Louis encephalitis, 46, 49-50
West Nile virus, 90, 93-97
Multidisciplinary approaches, see Interdisciplinary approaches
Multivariate models, 64, 65
N
National Center for Ecological Analysis and Synthesis, 6, 107
National Institute of Allergy and Infectious Diseases, 7, 108
National-level factors, 135
early warning systems, 14-15, 88, 90, 92, 102
meteorological systems, 75
surveillance systems, 73, 74
National Oceanic and Atmospheric Administration, 60, 76, 93
National Weather Service, 15, 87
Natural disasters, see Extreme weather events
Normalized Difference Vegetation Index, 76
North Atlantic Oscillation, 21
Nutrition and malnutrition, 37, 38-39, 41-42, 43
see also Famine; Food-borne pathogens
O
Observational studies, 5, 11, 59-62, 73, 75, 80-81, 88, 105, 107
historical perspectives, 9
interdependence with experimental and modeling studies, 6, 67
prospective observations, 60-61
remote sensing, 6, 7, 10, 70, 75-77, 90, 101, 105
temporal factors, 59-60, 61, 80-81
uncertainty, 3, 103-104
Oceans
see also El Niño/Southern Oscillation; North Atlantic Oscillation
Atlantic Ocean, 21, 25
color, 79
Indian Ocean, 17, 21, 25, 51
Pacific Ocean, 17, 25, 51
seasonal variability models, 25
surface height, 22, 58, 79
surface temperature, general, 9, 27, 51, 57-58, 79
Office of Global Programs (NOAA), 60, 93
Outbreaks, see Epidemics
P
Pacific Ocean, 17, 25, 51
see also El Niño/Southern Oscillation; North Atlantic Oscillation
Parasitic diseases, 1, 9, 35
see also Malaria
cryptosporidiosis, 1, 33, 46, 56-57
schistosomiasis, 39, 43, 78, 79
Pesticides, 43, 96
historical perspectives, 9
resistance to, 2, 9
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 143
Pharmaceuticals, 3, 9, 36see also Drug resistance; Vaccines
Population density, 3, 35, 37, 41, 46, 55
early warning systems, 90
influenza, 33, 55
see also Urban areas
Population factors, other, see Demographic
factors; Social factors;
Socioeconomic factors
Precipitation, 21, 46, 90, 97
see also Drought; Flooding
cryptosporidiosis, 1, 46, 56
dengue fever, 46, 47
general circulation models, 27
historical perspectives, 14, 25
malaria, 46, 48, 65
modeling studies, 27, 65
monsoons, 14, 81
Rift Valley fever, 46, 51
seasonal, general, 25
vegetative cover and, 24, 78
Prevalence, see Incidence and prevalence
Professional education
interdisciplinary, 6, 7, 62
response strategies, 94
Prospective observations, 60-61
Public communication, 89, 91, 95-96, 98, 102, 135
see also Internet
Mass media, 10
Public health, 3, 10, 36, 42-43, 68, 135
see also Housing; Sanitation; Vaccines; Waterborne diseases and water treatment
bubonic plague, 13
early warning systems, 5, 88, 90-92, 93
emerging diseases, 29
historical perspectives, 18, 19
hurricanes, 100
interdisciplinary studies, 7
modeling studies, 66
quarantine, 13, 36
vector eradication, 36, 43, 46, 49
Public opinion, 10, 91
Q
Quantitative risk characterization, 2, 4, 27, 62, 64, 65, 69, 71, 72, 96, 105
Quarantine, 13, 36
R
Rain, see Precipitation
Regional factors, 3, 21, 22, 24, 61
see also Epidemics
developing countries, general, 3, 74, 90, 91
early warning systems, 90, 92, 93
epidemics, 12-13
general circulation models, 27
global warming, 24
historical perspectives, 12-13, 14, 17
modeling studies, 65, 66
monsoons, 14
precipitation, 24
remote sensing, 73
surveillance systems, 73, 105-106
temperature, general, 24
time-series analysis, 60
Remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
Reporting bias, 66
Research methodology, 5, 59-79
see also Interdisciplinary approaches; Modeling studies; Observational studies; Uncertainty
aggregation bias, 67, 72, 73
causal relations, general, 83-84
committee study at hand, 2, 132-135
error of measurement, 24-25, 66-67, 82- 85
experimental studies, 5, 6, 11, 62-63, 66, 67, 80, 82, 83, 107
expert opinion, xi, 10, 62, 66, 99
historical perspectives, 13-15, 61
meta-analysis, 67, 112
remote sensing, 6, 7, 10, 70, 75-79, 90, 101, 105
stochastic processes, 73
time-series analysis, 58, 59-60, 61
Retrospective analysis of historical trends, 61
Retrospective analysis of natural variation, 59-60
Richardson, Lewis Frye, 15
Rift Valley fever, 33, 35, 42, 46, 50-51, 63, 76, 78, 88
Risk assessment, 59, 68-71
see also Exposure and exposure assessment
cholera, 70-71
defined, 113
dose-response models, 68, 69, 71, 111
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 144
drought, 38
early warning systems, 87-88, 89, 90, 94-95
extreme weather events, 38
flooding, 33, 63
forest fire conditions, 99
integrated assessment, 59, 71-73
modeling studies, 65
quantitative, 2, 4, 27, 62, 64, 65, 69, 71, 72, 96, 105
sanitation, 38
waterborne diseases, 38, 68
West Nile virus, 94-95
S
Sanitation, 3, 36, 41, 43
see also Waterborne diseases and water treatment
dengue fever, 42
early warning systems, 90, 94
epidemiology and, 16
historical perspectives, 14, 16, 18
malaria, 49
regional factors, 3
response strategies, 94
risk assessment, 38
St. Louis encephalitis, 49-50
urban areas, 41-42
Satellite technology, see Remote sensing
Schistosomiasis, 39, 43, 78, 79
Seasonal variation, 2-3, 21, 25-27, 81, 82-83, 103, 104
see also El Niño/Southern Oscillation
cholera, 57-58
computer models, 25
cryptosporidiosis, 56-57
dengue virus, 33
historical perspectives, 8, 12, 21, 14, 25
influenza, 33, 55
malaria, 33
modeling of, 25
monsoons, 14, 81
vibrios, 57-58
Sea surface
height, 22, 58, 79
temperature, 9, 27, 51, 57-58, 79;
see also El Niño/Southern Oscillation; North Atlantic Oscillation
SEIR framework, 31-33, 36, 63, 88
Sentinel animals, 86, 87, 89
Social factors, 9, 36, 41-42
see also Demographic factors
committee study methodology, 2
interdisciplinary studies, 7, 71-72
population density, 3, 33, 35, 37, 41, 46, 55, 90;
see also Urban areas
time-series analysis, 60
Socioeconomic factors
developing countries, general, 3, 74, 90, 91
interdisciplinary studies, 7, 72, 107
Soil conditions
Lyme disease, 54
remote sensing, 76, 79
wind-blown dust, 33, 34
Spatial factors, 2, 5, 6, 11, 13, 20, 59, 61, 80- 85, 107
see also Geographic factors
aggregation bias, 67, 72
climate defined, 20-21
cryptosporidiosis, 56
general circulation models, 27
global warming, 24
Lyme disease, 53
modeling studies, 61, 65, 66, 67
temperature factors and, 20-21, 24, 61
uncertainty, 3-4, 82-83, 104
Standards
databases, 6, 66, 74-75, 107
public health response, 92
reporting bias, 66
State-level factors
early warning systems, 92
surveillance systems, 73
Statistical-empirical models, 6, 64, 66, 106
St. Louis encephalitis, 46, 49-50, 61, 89-90
Stochastic processes, 73
Storms, 21
flooding, 33, 36-37, 38, 63, 78-79
hurricanes, 36-37, 38, 101
monsoons, 14, 81
Surveillance systems, 4, 5-7, 10, 66, 69, 73- 75, 89-90, 93, 95, 105-108
see also Early warning systems; Epidemiology; Observational studies
committee study methodology, 2, 135
cost factors, 74
influenza, 54
prospective observations, 60
regional factors, 73, 105-106
reporting bias, 66
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 145
standards, 6, 66, 74-75, 107time-series analysis, 60
T
Temperature, 1, 2, 22, 81
see also Global warming; El Niño/Southern Oscillation
cholera, 57-58
climate defined, 20-21
dengue virus, 33, 34, 47-48
historical perspectives, 14
influenza, 33, 55
integrated assessment, 71-72
Lyme disease, 53-54
malaria, 33, 34, 46, 48
microbe replication rate, 34
modeling studies, 65
sea surface, general, 9, 27, 51, 57-58, 79
spatial factors and, 20-21, 24, 61
St. Louis encephalitis, 50
vector-borne diseases, general, 34, 65
vibrios, other than cholera, 58
Temporal factors, 5, 59, 80-85
see also Interannual variation; Seasonal variation
climate defined, 20, 21-22
early warning systems, 91, 92
epidemics, 13
extreme weather events, 36-37
Lyme disease, 53
modeling studies, 66
observational studies, 59-60, 61, 80-81
uncertainty, 3-4, 72, 82-83, 104
Time-series analysis, 58, 59-60, 61
Togavirus, see Yellow fever
Travel and tourism, 3, 10, 29, 35, 40-41, 97, 104
see also Migration
influenza, 55
quarantine, 36
surveillance systems, 73
U
Ultraviolet radiation, 33, 55
Uncertainty, 3-4, 82-85, 104
aggregation bias, 67, 72, 73
early warning systems, 87
error of measurement, 24-25, 66-67, 82- 85
integrated assessments, 72, 73
modeling studies, 3, 27, 103-104
observational studies, 3, 103-104
reporting bias, 66
spatial factors, 3-4, 82-83, 104
temporal factors, 3-4, 72, 82-83, 104
United Nations, 75
Urban areas, 35, 36, 40, 41, 79
dengue fever, 42, 85
influenza, 55
sanitation, 41-42
St. Louis encephalitis, 46, 49-50
West Nile virus, 93-97
V
Vaccines, 3, 7, 36, 42-43, 94
early warning systems, 90
historical perspectives, 9, 42-43
Vector-borne diseases, 1, 2, 30, 31, 38, 94
see also Dengue virus; Insects; Lyme disease; Malaria; Mosquitos; Pesticides; Rift Valley fever
air transport, 41
bubonic plague, 12-13, 78
committee meetings, 133, 135
control of vectors, 7
definitions, 113-114
eradication of vectors, 36, 43, 46, 49
hantavirus, 46, 51-52, 78, 94
housing, 38, 40, 42
land cover, 39-40
migration and travel, 46
modeling studies, 65
St. Louis encephalitis, 46, 49-50, 61, 89-90
temperature factors, 34, 65
Vegetation, 22, 24, 81, 83-84
agriculture, 5, 22, 42, 44, 78, 85, 97-98
forests and deforestation, 22, 24, 42, 78, 99
Lyme disease, 54
remote sensing, 76, 78
Vibrios, 46, 57-58
see also Cholera
Viruses, 1
see also Dengue virus
hantavirus, 46, 51-52, 78, 94
HIV/AIDS, 1, 40, 90
Suggested Citation:
"Index." National Research Council. 2001. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: The National Academies Press.
doi: 10.17226/10025.
Page 146
St. Louis encephalitis, 46, 49-50, 61, 89-90
West Nile virus, 90, 93-97
yellow fever, 1, 9, 78
W
Waterborne diseases and water treatment, 7, 38, 42, 44, 46, 79
see also Flooding; Sanitation; Vibrios; Wetlands
cholera, 16-17, 34, 38-39, 57-58, 70-71, 79
cryptosporidiosis, 1, 33, 46, 56-57
dengue fever, 25, 42, 46, 47
drought and, 38-39
early warning systems, 90, 94, 96
historical perspectives, 14, 16, 18, 44
malaria, 42, 46
response strategies, 90, 94
Rift Valley fever, 42
risk assessment, 38, 68
schistosomiasis, 39
temperature factors, 34
vibrios, other than cholera, 58
West Nile virus, 90, 93-97
Wetlands, 79
Wind, 12, 14, 20, 21, 34, 38, 48
World Health Organization, 9, 43, 54, 73, 134
World Meteorological Organization, 75, 134
World Weather Watch, 75
Y
Yellow fever, 1, 9, 78
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