Climate-Sensitive Companion Animal Zoonotic Diseases: A scoping review protocol
Bibliographic record
Abstract
Climate change is expected to increase the incidence and risk of zoonotic diseases in animal populations via changes in temperature, precipitation patterns, extreme weather events, and other climatic factors (Berezowski et al., 2023; McIntyre et al., 2017; Omazic et al., 2019; Rees et al., 2019). Zoonotic diseases are transmitted from animals to people and are caused by pathogens that can infect both animals and humans (Weese & Fullford, 2011). Zoonotic pathogens can be found in all major taxonomic groups: fungi, helminths, protozoa, viruses, and bacteria (McIntyre et al., 2017). Like other infectious pathogens, zoonotic pathogens can be climate-sensitive, and may be more sensitive to climate than animal- or human-only pathogens (McIntyre et al., 2017). Climate-sensitive pathogens and diseases are characterized by their susceptibility to changes in meteorological factors, including temperature and precipitation, which can alter the distribution of disease vectors and reservoirs, and alter migration and mobility patterns, allowing for geographic range expansion (Brankston et al., 2018; Brunn et al., 2019; Canadian Counsel of Ministers of the Environment, 2021; Cousins et al., 2020; Gardner et al., 2019; Greer et al., 2009). The impacts of changing meteorological factors on zoonotic pathogens have been observed globally and across various species including wildlife, humans, and domestic animals (Bowser & Anderson, 2018; McIntyre et al.,2017). It is estimated that zoonotic diseases contribute to approximately 60% of emerging diseases in human populations (Jones et al., 2008). Emerging infectious diseases are characterized as newly appearing or affecting a population for the first time or having previously existed in the population but experiencing a rapid increase in infections or expanding geographically (World Health Organization, 2014). Zoonotic diseases in wildlife and production animals are well researched, but knowledge gaps exist for risk factors influencing disease in companion animals, particularly in the context of climate change. Due to the global popularity of companion animals, a notable risk of zoonotic transmission exists given the close and frequent contact of pets and people in shared living environments (Agriculture and Agri-Food Canada, 2021; Smith & Whitfield, 2012; Weese & Fullfor, 2011; Whitfield & Smith, 2014). Current knowledge regarding the degree of climate-sensitivity across zoonotic diseases is limited (Berezowski et al., 2023; McIntrye et al.,2017). Although there is evidence of climate-sensitivity in certain pathogens, we lack a deeper understanding of factors that influence individual pathogens (Booth, 2018; Gnat et al., 2021; Jenkins et al., 2011; McIntrye et al., 2017; Smith & Whitfield, 2012). Varying responses to climate across pathogens are likely, as there is an abundance of unique species that exist in a wide range of environments and hosts (McIntyre et al., 2017). Further, we lack a comprehensive understanding of which zoonotic diseases are sensitive to climate change, the meteorological factors individual diseases are sensitive to, or their degree of sensitivity (McIntyre et al., 2017; Patil & Pandya, 2021). Understanding how climate-sensitive zoonotic diseases respond to individual meteorological factors in specific regions will contribute to improved disease surveillance and forecasting abilities (Berezowski et al., 2023; Booth, 2018; Dixon et al., 2022; Gnat et al., 2021; Jenkins et al., 2011; McIntrye et al., 2017; Mubareka et al., 2023; Smith & Whitfield, 2012; World Health Organization, 2005). The purpose of this review is to 1) identify relevant climate-sensitive zoonotic diseases found in companion animals, 2) identify meteorological factors that influence the disease burden and/or epidemiology of climate-sensitive zoonotic diseases, and 3) describe the projected impacts of climate change on zoonotic diseases in companion animal populations. These objectives align with the intended application of a scoping review. This review will synthesize existing knowledge regarding how climate change influences zoonotic disease epidemiology, and inform the direction of future research to understand, monitor, and forecast zoonotic diseases in companion animal populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.090 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".