Dataset for: Infectious disease responses to human climate change adaptations
Authors/Creators
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1.
Colorado State University
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2.
University of California, Santa Barbara
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3.
Duke University
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4.
University of Wisconsin–Madison
Description
Original and derived data products referenced in the original manuscript are provided in the data package.
Description of the data and file structure
Original data:
Table_1_source_papers.csv: Papers that met review criteria and which are summarized in Table 1 of the manuscript.
- ID: The paper identification number
- Topic: The broad topic (i.e., each row of Table 1)
- Authors: The names of the authors of the paper
- Article Title: The title of the paper
- Source Title: The name of the journal in which the paper was published
- Abstract: The paper's abstract, retrieved from the Web of Science search
- study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.
- pathogen_broad: Broad classification of the type of pathogen discussed in the paper.
- transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.
- pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.
- country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).
Derived data:
change_livestock_country.csv: A dataframe containing values used to generate Figure 4a in the manuscript.
- County Name: The name of the county in Kenya
- Sheep and goats 1980: The estimated number of sheep and goats in 1980
- Sheep and goats 2016: The estimated number of sheep and goats in 2016
- pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016
- Cattle 1980: The estimated number of cattle in 1980
- Cattle 2016: The estimated number of cattle in 2016
- pct_change_cattle: The percent change in cattle numbers from 1980 to 2016
- Camel 1980: The estimated number of camels in 1980
- Camel 2016: The estimated number of camels in 2016
- pct_change_camel: The percent change in camel numbers from 1980 to 2016
- human_pop 1980: The estimated human population in the county in 1980
- human_pop 2016: The estimated human population in the county in 1980
- pct_change_human: The percent change in the human population from 1980 to 2016
- area_sq_km: The land area of the county
- change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016
- change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016
- change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016
country_avg_schist_wormy_world.csv: A dataframe containing values used to generate Figure 3 in the manuscript.
- Country: The country in which the schistosome prevalence studies were performed.
- Latitude: The latitute in decimal degrees
- Longitude: The longitute in decimal degrees
- Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.
kenya_precip_change_1951_2020.csv: A dataframe containing values used to generate Figure 4b in the manuscript.
- Precipitation (mm): Binned annual precipitation values
- 1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period
- 1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period
- 1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period
Sharing/Access information
Data were derived from the following sources:
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Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249
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London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0
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World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections
Abstract
Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes: they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have under-appreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes non-intuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that pathogens rely upon.
Here, we consider the ways that human responses to change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.
Methods
This dataset includes original data sources and data that have been extracted from other sources that are referenced in the manuscript entitled "Infectious disease responses to human climate change adaptations".
Original data:
Table_1_source_papers
We conducted a Web of Science search following PRISMA guidelines (SI I). Search terms included each topic, followed by “AND (infectious disease* OR zoono* OR pathogen* OR parasit*) AND (human OR people).” Papers were assessed for any positive, negative, or neutral link between each topic (dam construction, crop shifts, rainwater harvesting, mining, migration, carbon sequestration, and public transit) and human infectious diseases. Searches on poultry and transit returned >5,000 papers, so searches were restricted to review topics only. We further restricted the 3479 results for livestock shifts to those with ‘shift’ in the abstract. Following screening of 3485 papers (6964 including all livestock), 108 papers met initial review criteria of being relevant to each adaptation or mitigation and discussing a human infectious disease; of which only 14 were quantitative studies with a control or reference group.
Extracted data:
- change_livestock_country
- Data were extracted from Ogutu 2016 supplementary materials and include percent change calculations for different livestock in different Kenyan counties.
- Original data source citation:
Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249
- country_avg_schist_wormy_world
- Schistosomiasis survey data were obtained from the Global Atlas of Helminth Infection and were generated by downloading map data in csv format. Prevalence values were calculated by taking the mean maximum prevalence.
- Original data source citation:
London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0
- kenya_precip_change_1951_2020
- Data were extracted from the Climate Change Knowledge Portal and downloaded in csv format.
- Original data source citation:
World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections
Files
change_livestock_county.csv
Additional details
Funding
- National Institutes of Health
- Land Use Change, Transmission Potential Networks and Disease Spread in Madagascar R01-TW011493