Table 1.
Examples of published studies with applications of ENM to pathogen distribution
| Pathogen/disease/species | Pathogen type | Scale | Algorithm | Validation | Time | Ref. |
| Vibrio cholerae | Free-living bacterium | Central California | Mantel | Bootstrap | Current | (97) |
| Yersinia pestis | Vector-borne bacterium | Western Usambara Mountains of Tanzania | GARP | Jackknife | Current | (98) |
| H5N1 avian influenza | Directly transmitted virus | India, Bangladesh, Nepal, and Pakistan | GARP | Actual outbreak locations | Current | (99) |
| Coccidiomycosis | Fungus with environmental spores | Southern California, Arizona, and Sonora | GARP | Available epidemiological data | Current | (100) |
| Bacillus anthracis | Bacterium with environmental spores | United States | GARP | AUC | Current | (101) |
| Triatoma brasiliensis | Vector-borne protozoan | Northeastern Brazil | GARP | Points sample from test data | Current | (102) |
| Campylobacter jejuni | Enteric bacterium | 100 km2 around Cheshire, United Kingdom | GAM, UPGMA | Simulation data from the null model | Current | (103) |
| Range of parasites | Microparasites (e.g., viruses, bacteria, protozoa), macroparasites (helminths), and ectoparasites (arthropods) | North America | Correlations | N/A | Current | (104) |
| Bat-related pathogens | N/A | South America | MaxEnt | Jacknife, ROC, AUC | Current | (105) |
| West Nile encephalitis | Vector-borne virus, Culex pipiens | Illinois, Indiana, and Ohio | GARP | Independent datasets | Current | (106) |
| Chagas, Trypanosoma cruzi | Vector-borne protozoan | South America | NODF | Bootstrap | Current | (107) |
| H5N1 | Directly transmitted virus | West Africa | GARP | Binomial probabilities | Current | (108) |
| Filoviruses | Directly transmitted virus | Africa | GARP | N/A | Current | (55) |
| Chagas, Trypanosoma cruzi | Vector-borne protozoan | Mexico | GARP | None | Current | (56) |
| Leishmaniasis | Vector-borne protozoan | North America | MaxEnt | AUC | Future | (95) |
| Leishmaniasis | Vector-borne protozoan, Lutzomyia | South America | GARP | Bootstrap | Future | (109) |
| Leishmaniasis | Vector-borne protozoan | Spain | Negative binomial regression | Independent dataset | Future | (110) |
| Malaria | Vector-borne protozoan | Africa | GARP | Independent dataset | Future | (46) |
| Dengue | Vector-borne virus | Mexico | GARP | Actual case data | Past | (111) |
Scales of studies varied from state or county levels (e.g., Illinois; Cheshire, United Kingdom) to continental scales (e.g., Africa). Few studies focused on the effects of climate change on the distribution of directly transmitted pathogens, focusing instead on vector-borne or free-living pathogens. A combination of key words was used to search the International Statistical Institute Web of Science: (environmental niche model* OR ecological niche model* OR species distribution model* OR predictive habitat distribution model* OR climate envelope model* and disease* OR pathogen*); nearly 73% of ENM studies referred to vectors or an environmental reservoir (vector* OR environ* reservoir* OR environ*), whereas only 27% of studies referenced a directly transmitted pathogen without vectors or an environmental reservoir [host*NOT (vector* OR environ* reservoir* OR environ*)]. AUC, area under the curve; GAM, Generalized Additive Model; GARP, Genetic Algorithm for Rule-set Production; N/A, Not Applicable; NODF, Nestedness overlap and decreasing fills; ROC, receiver operating characteristic; UPGMA, Unweighted Pair Group Method with Arithmetic Mean.