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Volume 32, Number 12—December 2026

Research

Estimated Illness Linked to Animal Contact for 7 Pathogens, United States, 2019

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Discussion
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Author affiliation: Centers for Disease Control and Prevention, Atlanta, Georgia, USA (R. Medford, Z. Cui, R. Tierney, A Straily, S. Miko, L. Francois-Watkins, S. Verlander, D.L. Weller, R.P. Louis, K.M. Benedict); Colorado School of Public Health, Aurora, Colorado, USA (E.J. Scallan Walter)

Suggested citation for this article

Abstract

Diseases transmitted through direct or indirect animal contact are a public health concern in the United States. Improvements in laboratory detection methods and illness estimates warrant an update of illness estimates linked to animal contact. We used surveillance data and other sources to estimate the annual number of US illnesses, hospitalizations, and deaths for 7 pathogens. We adjusted for underreporting and underdiagnosis by using established methods. Campylobacter, Cryptosporidium, Shiga toxin–producing Escherichia coli, Listeria monocytogenes, nontyphoidal Salmonella, and non-pestis Yersinia caused an estimated 995,022 (90% credible interval [CrI] 476,251–1,833,238) animal contact illnesses around 2019. Together with Toxoplasma gondii, those illnesses caused an estimated 9,592 (90% CrI 5,603‒15,557) hospitalizations and 236 (90% CrI 119‒411) deaths in the United States. Increased awareness among health professionals, government officials, industry partners, and the public about animal contact as a mode of disease transmission can aid prevention efforts and enhanced surveillance.

Zoonotic diseases are spread between animals and humans, causing an estimated 60% of infectious diseases and 75% of emerging infectious diseases globally (1). Zoonotic diseases spread through direct or indirect animal contact, foodborne, waterborne, and vectorborne transmission (2). In this analysis, we focus on direct (e.g., touching animals) and indirect (e.g., contact with contaminated objects or environments) animal contact. An estimate published in 2012 attributed 14% of US enteric illnesses to animal contact, corresponding to an estimated 450,000 illnesses, 5,000 hospitalizations, and 70 deaths annually based on circa 2006 data (3).

Campylobacter outbreaks have been attributed to pet store puppies and raccoons from a wildlife rehabilitation center (4,5). Cryptosporidium (6,7) and Shiga toxin–producing Escherichia coli (STEC) (8–12) outbreaks have been linked to farm animals, and nontyphoidal Salmonella outbreaks have been linked to pet hedgehogs (13,14), guinea pigs (15,16), live poultry (17,18), wild songbirds (19), dairy calves (20), and various reptiles and amphibians (21–29).

Toxoplasma gondii is an important parasitic pathogen; felids are definitive hosts (30). Transmission can occur through indirect animal contact through ingestion of environmentally matured oocysts from contaminated cat feces or soil (31). The prevalence of T. gondii infection in the United States is relatively low (32); however, serious health consequences linked to pregnancy and immune compromise underscore a need to better understand the proportions of hospitalizations and deaths that may be linked to animal contact (31).

The Centers for Disease Control and Prevention recently updated the estimates for 7 major foodborne pathogens by using improved methods (33). By using this methodology, we updated the estimated annual prevalence of US illnesses linked to animal contact for the pathogens from the previous animal contact estimates (Campylobacter, Cryptosporidium, Listeria monocytogenes, nontyphoidal Salmonella, STEC, and Yersinia). We expanded the Yersinia estimates to include non-pestis Yersinia spp. beyond Y. enterocolitica and added estimates of hospitalizations and deaths attributable to T. gondii.

Methods

We estimated the annual prevalence of illnesses, hospitalizations, and deaths linked to animal contact for Campylobacter, Cryptosporidium, L. monocytogenes, nontyphoidal Salmonella, STEC, and non-pestis Yersinia and of hospitalizations and deaths for T. gondii by adapting the modeling approach for the recent estimate of foodborne pathogens to account for the proportion attributable to animal contact (33). We did not estimate T. gondii infections because most were asymptomatic and the mild illnesses were unquantified. We based estimates on the average US population during 2017–2019. We described model inputs by pathogen (Appendix Table 1).

Estimating Illnesses
Laboratory-Confirmed Illnesses Reported to Surveillance Systems

The Foodborne Diseases Active Surveillance Network (FoodNet) conducts active surveillance of laboratory-confirmed infections in 10 sites that are the historic catchment area and represent 15% of the US population (34). We used 2017–2019 data from FoodNet for Campylobacter, nontyphoidal Salmonella, STEC, and non-pestis Yersinia. We used 2016–2019 data from the National Notifiable Diseases Surveillance System (NNDSS) for Cryptosporidium and 2017–2019 data from the Listeria Initiative for invasive L. monocytogenes; both are national passive surveillance systems (35,36). We defined invasive L. monocytogenes as isolation of Listeria from a specimen collected from a normally sterile site (cerebrospinal fluid or blood) or product of conception (for pregnancy-associated illnesses, products were amniotic fluid, placenta, chorion, or umbilical cord). We counted illness in mother and infant as separate if L. monocytogenes were isolated from that person’s specimen or if the mother or infant of a person with listeriosis had symptoms (e.g., vomiting, fever, or diarrhea) (33). We proportionally distributed infants with unknown outcomes according to infants with known outcomes.

Estimating Total Domestically Acquired Animal Contact Illnesses

The methods were reported in the recent estimate of foodborne pathogens and applied to this analysis (33). In brief, we estimated illnesses as follows.

Projected US Laboratory-Confirmed Illnesses

We used laboratory-confirmed illnesses caused by a pathogen by year (by site and year for FoodNet pathogens) to project US illnesses. We multiplied the ratio of the average US population during 2017–2019 to the population in the surveillance area in that year.

Accounting for Underreporting

We assumed no underreporting in FoodNet active surveillance (i.e., all diagnosed illnesses in the FoodNet surveillance area were reported to public health authorities). For NNDSS (Cryptosporidium), we adjusted for underreporting by using a multiplier estimated by comparing FoodNet-projected annual laboratory-confirmed illnesses with annual illnesses reported to NNDSS for parasites in both surveillance systems (i.e., Cryptosporidium and Cyclospora). Because FoodNet stopped collecting Cryptosporidium data after 2017, we used 2016–2017 Cryptosporidium data and 2016–2019 Cyclospora data from FoodNet and NNDSS. For L. monocytogenes, we did not adjust for underreporting because we assumed all diagnosed cases were reported given the severity of listeriosis.

Adjusting for Underdiagnosis

We created underdiagnosis multipliers to account for underascertainment from care-seeking, specimen submission, and laboratory reporting. The 2018–2019 FoodNet Population Survey randomly selected residents from the 10 FoodNet sites, collecting information on symptoms and medical care sought for an acute diarrheal illness (37). By using age, sex, race/ethnicity, bloody diarrhea, and fever, Bayesian regression models predicted the probabilities of seeking medical care among persons reporting an acute diarrheal illness and of submitting a specimen for laboratory testing among persons who sought medical care. We applied those models to each person with a laboratory-confirmed infection in the 2017–2019 FoodNet surveillance data to inflate the count to the reciprocals of the probabilities. Because NNDSS did not collect symptom information, we used models with age, sex, and race/ethnicity to predict those probabilities for persons with a Cryptosporidium infection.

The proportion of laboratories routinely testing for each pathogen, except for Listeria, was available from the FoodNet Laboratory Survey. We estimated test sensitivity for Campylobacter, non-typhoidal Salmonella, STEC, and non-pestis Yersinia. Most culture-independent diagnostic tests (CIDTs) are PCR-based and highly sensitive (38); therefore, we assumed 100% CIDT sensitivity. We estimated culture sensitivity using CIDT and reflex culture (cultures performed after a positive CIDT) data from FoodNet. We assumed the proportion of positive reflex culture as the lower parameter of the program evaluation and review technique (PERT) distribution. For Cryptosporidium, estimated test sensitivity determined by using a combination of conventional (microscopic analysis and antigen tests) was 74% and test sensitivity determined by using molecular tests with proportional weighted random sampling was 26% (38–40). For listeriosis, we assumed high probabilities of medical care seeking, specimen submission, and testing. We based test sensitivity on a published study of blood culture sensitivity (41).

Estimating Domestically Acquired Illnesses

We used FoodNet, the Listeria Initiative, and NNDSS data to estimate the percentage of illnesses acquired while traveling outside the United States. We considered the remainder to be domestically acquired.

Estimating Illnesses Acquired through Animal Contact

We applied pathogen-specific animal contact attribution proportions derived from a structured expert judgment (SEJ) of 5 transmission pathways (foodborne, waterborne, person-to-person, animal contact, and environmental) (42). Because Listeria was not included in the SEJ, we used the proportion from the previous animal contact estimates (3).

Estimating Hospitalizations and Deaths

We estimated the percentage of illnesses causing hospitalizations and deaths and applied them to projected US laboratory-confirmed illnesses after accounting for underreporting. We used the 2017–2019 data from FoodNet to estimate hospitalizations and deaths caused by Campylobacter, STEC, nontyphoidal Salmonella, and non-pestis Yersinia. We used data from the 2017–2019 Listeria Initiative to estimate hospitalizations and deaths caused by invasive L. monocytogenes. We used the 2016–2019 data from the National Inpatient Sample (NIS) to estimate hospitalizations and deaths caused by Cryptosporidium and T. gondii (43,44). NIS is nationally representative of all short-term, nonfederal hospitals, representing 97% of all discharges in the United States, and includes all patients, regardless of payer. Because NNDSS did not collect hospitalization or death data during the study period, we estimated the percentage of cryptosporidiosis causing hospitalization or death as the ratio of inpatients diagnosed with cryptosporidiosis in the 2016–2019 NIS (using codes from the International Classification of Diseases, 10th Revision, Clinical Modification) to the illnesses in NNDSS during the same period. For T. gondii, we estimated the hospitalization and death rates in the US population on the basis of the NIS during 2016–2019 and applied those data to the average US population during 2017–2019. We adjusted for underdiagnosis for hospitalizations or deaths, domestic acquisition, and animal contact as the mode of transmission.

Uncertainty Analysis

Models described in each step are multiplicative. For each factor, we drew a random sample (n = 10,000) from an empirical, degenerate, PERT, or posterior distribution. We applied successive factors by multiplication to obtain proportional increases or decreases in the count. That calculation usually produces wider ranges in the final distribution than additive models. We summarized the final distribution by using a point estimate (posterior mean) and 90% CrI. We report values at the precision of the direct model output; those values should not be interpreted as reflecting equivalent scientific precision, given the assumptions and uncertainty in model inputs.

We applied empirical distribution to laboratory-confirmed illnesses, percentages of hospitalization or death, and proportion of laboratories that routinely tested a pathogen from surveillance data. We applied simple nonparametric bootstrapping to illnesses in FoodNet by site and year and to illnesses in national surveillance systems by year. We used degenerate distribution for the ratio of US population to population in the surveillance area, the underreporting multiplier for active surveillance, and sensitivity of CIDTs. We used a modified PERT distribution with 4 parameters when data from surveillance were not available or insufficient to account for uncertainty. The first 3 parameters were low, modal, and high; the fourth parameter controls the flatness of the distribution, and the default value was 4. We used mean and confidence limits from literature as mode and the lower and upper bounds (Appendix Table 1). We used the default value for the fourth parameter. When only 1 of the low, modal, and high parameter was available, we calculated the other 2 parameters assuming that the upper bound was 1.5 and the lower bound was the reciprocal (1/1.5 times of the mode) on odds scale. When combining distributions from 2 sources, we used random sampling with weights equal to the number of events from each source.

We used R version 4.1.1 for analyses package (The R Project for Statistical Computing, https://www.rproject.org). We generated posterior distributions from Bayesian regression models by using the brms package in R.

Results

Campylobacter, Cryptosporidium, L. monocytogenes, nontyphoidal Salmonella, STEC (O157 and non-O157 serogroups), and non-pestis Yersinia caused an estimated 995,022 (90% CrI 476,251–1,833,238) domestically acquired illnesses linked to animal contact (Table 1). Together with T. gondii, those pathogens caused an estimated 9,592 (90% CrI 5,603‒15,557) hospitalizations and 236 (90% CrI 119‒411) deaths (Table 2).

Illnesses

Campylobacter caused the most domestically acquired estimated illnesses linked to animal contact (520,832 [90% CrI 86,949–1,333,098]), followed by nontyphoidal Salmonella (249,139 [90% CrI 110,141–468,266]), STEC (134,738 [90% CrI 31,457–315,575]), Cryptosporidium (82,757 [90% CrI 20,491–172,973]), non-pestis Yersinia (7,543 [90% CrI 2–29,706]), and L. monocytogenes (14 [90% CrI 8–20]). For nontyphoidal Salmonella, 138,909 (90% CrI 23,667–345,624) estimated illnesses were caused by serotypes other than the 5 we estimated: Typhimurium (30,926 [90% CrI 8,385–67,175]), Javiana (22,659 [90% CrI 3,272–63,051]), Newport (20,159 [90% CrI 3,859–53,952]), Enteritidis (19,333 [90% CrI 2,661–59,187]), and I 4,[5],12:i- (17,153 [90% CrI 4,504–39,443]). STEC non-O157 serogroups caused an estimated 117,477 (90% CrI 18,902–296,123) illnesses, and STEC O157 caused an estimated 17,261 (90% CrI 3,573–45,939) illnesses. L. monocytogenes caused an estimated 11 (90% CrI 7–17) non–pregnancy associated illnesses and 2 (1 mother [90% CrI 1–2] and 1 infant [90% CrI 1–2]) pregnancy-associated illnesses.

Hospitalizations

Campylobacter caused the highest estimated number of hospitalizations from domestically acquired illnesses linked to animal contact (3,601 [90% CrI 619–9,199]), followed by nontyphoidal Salmonella (2,415 [90% CrI 1,067–4,530]), T. gondii (1,773 [90% CrI 939–2,607]), STEC (960 [90% CrI 291–2,069]), Cryptosporidium (753 [90% CrI 185‒1,565]), non-pestis Yersinia (80 [90% CrI 0–327]), and L. monocytogenes (12 [90% CrI 7–17]). STEC non-O157 caused 613 (90% CrI 91–1,549) hospitalizations, and STEC O157 caused 346 (90% CrI 69–951) hospitalizations. An estimated 10 non–pregnancy-associated hospitalizations (90% CrI 6–15) were attributed to L. monocytogenes, compared with 2 pregnancy-associated hospitalizations (1 mother [90% CrI 0–1] and 1 infant [90% CrI 0–1]).

Deaths

T. gondii caused the highest estimated deaths from domestically acquired illnesses linked to animal contact (91 [90% CrI 46–144]), followed by Campylobacter (55 [90% CrI 0–184]), nontyphoidal Salmonella (46 [90% CrI 1–138]), STEC (19 [90% CrI 0–66]), Cryptosporidium (20 [90% CrI 4‒49]), non-pestis Yersinia (3 [90% CrI 0–11]), and L. monocytogenes (2 [90% CrI 1–3]). For nontyphoidal Salmonella, 25 (90% CrI 0–99) estimated deaths were caused by serotypes other than the 5 we estimated: Typhimurium (9 [90% CrI 0–43]), I 4,[5],12:i− (5 [90% CrI 0–26]), Enteritidis (4 [90% CrI 0–21]), Newport (2 [90% CrI 0–12]), and Javiana (1 [90% CrI 0–6]). STEC non-O157 caused 11 (90% CrI 0–45) deaths, and STEC O157 caused 8 (90% CrI 0–39) deaths annually. Two non–pregnancy-associated deaths (90% CrI 1–3) and no pregnancy-associated deaths were attributed to L. monocytogenes.

Discussion

We estimated that Campylobacter, Cryptosporidium, STEC, L. monocytogenes, nontyphoidal Salmonella, and non-pestis Yersinia caused nearly 1 million illnesses linked to animal contact annually in the United States. Together with T. gondii, those pathogens caused ≈9,500 hospitalizations and ≈230 deaths. Campylobacter caused most illnesses and hospitalizations, followed by nontyphoidal Salmonella. T. gondii led in deaths, followed by Campylobacter and nontyphoidal Salmonella. Despite methodologic differences between studies, Campylobacter was the leading cause of animal contact illnesses in both analyses (3). However, in this report, estimated hospitalizations and deaths attributable to Campylobacter surpassed those caused by nontyphoidal Salmonella, which previously caused most hospitalizations and deaths. That change is attributable to a markedly higher estimated number of illnesses for Campylobacter versus nontyphoidal Salmonella in this study compared with the 2012 estimates (3). Although Campylobacter accounts for a large proportion of the illnesses, various animal types (e.g., ruminants, poultry, and swine) contribute to exposure pathways. However, Campylobacter outbreaks linked to those animal types are poorly characterized, highlighting the need for further investigations to inform prevention strategies (45,46).

The first syndromic gastrointestinal panel tests, which are types of CIDTs, were approved by the US Food and Drug Administration in 2013. As described in a recent estimate of 7 major foodborne pathogens (33), the number of laboratories using CIDTs for enteric illness diagnosis has increased (40). CIDTs generally are considered more sensitive than traditional culture-based diagnostics. Consequently, the widespread adoption of CIDTs has increased detection of illnesses that would previously have gone undiagnosed and unreported, probably improving the accuracy of the estimates and contributing substantially to the higher illness estimates linked to animal contact (34,47).

We saw higher estimated illnesses for certain pathogens in this study compared with the 2012 estimates (3). For Campylobacter, 187,481 (90% CrI 66,259–372,359) illnesses were previously estimated, compared with 520,832 (90% CrI 86,949‒1,333,098) illnesses in this report. Campylobacter is fastidious and requires specific culture conditions, and increased use of CIDTs probably has improved detection. For STEC O157, previous estimates were 5,960 (90% CrI 1,518–14,606) illnesses, compared with 17,261 (90% CrI 3,573‒45,939) illnesses in this report. The 117,477 (90% CrI 18,902‒296,123) STEC non-O157 estimated illnesses in this report was >10 times higher than the 10,097 (90% CrI 1,005–27,073) illnesses in the previous estimates. Identifying specific pathotypes causing E. coli infections other than O157 is difficult using culture-based methods; however, CIDTs have helped overcome many challenges, contributing to the higher estimated STEC illnesses in the current report. Nontyphoidal Salmonella also showed higher estimated illnesses: 127,155 (90% CrI 66,502–219,886) in the previous estimates and 249,139 (90% CrI 110,141–468,266) in this report. Diagnostic specificity of clinical and CIDT-based tests across these pathogens detected in ill persons who sought care are assumed to be accurate; thus, differences in estimates are probably not driven by false-positive results. Instead, observed increases are probably attributable to improved case detection.

Cryptosporidium illnesses were lower than in previous estimates; 113,344 (90% CrI 22,570–331,635) illnesses were estimated previously and 82,757 (90% CrI 20,491‒172,973) illnesses in this report. However, the estimated 735 (90% CrI 185–1,565) hospitalizations and 20 (90% CrI 4–49) deaths in this report were higher than the 412 (90% CrI 108–1,039) hospitalizations and 7 (90% CrI 0–37) deaths previously estimated. The lower estimate of Cryptosporidium illnesses is primarily attributable to a lower underdiagnosis multiplier, reflecting a new approach for accounting for medical care seeking and specimen submission (33). A higher underdiagnosis multiplier was used in the 2012 estimates under the assumption that most illnesses caused by Cryptosporidium were mild. L. monocytogenes estimates were similar between the previous estimates and this report; most illnesses were attributable to foodborne transmission (33). Yersinia illnesses, hospitalizations, and deaths estimated in this report represent all non-pestis Yersinia species, whereas the 2012 estimates included only those caused by Y. enterocolitica. Increased use of CIDTs has improved detection of yersiniosis cases that would have previously gone undiagnosed, serving as the probable explanation for the higher estimated illnesses, hospitalizations, and deaths caused by Yersinia in this report (45).

T. gondii was not included in the previous estimates but showed the highest number of estimated deaths linked to animal contact in this report (91 deaths annually). An SEJ identified animal contact as the primary transmission pathway for T. gondii (58%), compared with foodborne (28%), environmental (9%), and waterborne (5%) sources (42). Of note, the SEJ defined animal contact transmission as direct or indirect, including contact with excreta in the local environment. Toxoplasmosis is thought to be primarily transmitted through ingesting the parasite after this type of indirect contact. Direct animal contact transmission is less likely because oocysts shed in felid feces require 1–5 days to sporulate and become infective, and felids typically shed oocysts for only 1–3 weeks after exposure. In contrast, environmental transmission refers to exposure or contact with contaminated soil that is not attributable to animal contact. The recent estimate of 7 major foodborne pathogens reported both hospitalizations and deaths caused by T. gondii were lower than in previous estimates (33). Immunocompromising conditions, such as HIV, are risk factors for severe illness from T. gondii, and increased access to antiretroviral drugs might contribute to lower estimates of hospitalizations and deaths in recent reports (48). The high percentage of animal contact transmission might explain the higher number of deaths associated with T. gondii compared with other pathogens.

The zoonotic pathogens in this report are linked to different animal types and settings where persons might interact with those animals. Although we did not distinguish illnesses by animal type or setting, a report on enteric pathogen outbreaks in the United States linked to animal contact reported to the Animal Contact Outbreak Surveillance System during 2009–2021 identified 557 outbreaks, highlighting the most common animal types and settings (49). The etiologies in order of highest to lowest number of outbreaks included Salmonella, Cryptosporidium, STEC infections, and Campylobacter and caused 14,377 illnesses, 2,656 hospitalizations, and 22 deaths. Outbreaks of T. gondii, non-pestis Yersinia, and L. monocytogenes linked to animal contact were not reported to Animal Contact Outbreak Surveillance System during that timeframe. Backyard poultry–associated salmonellosis outbreaks and illnesses were the most common and have increased in recent years (18,49). Reptiles were the second most common source of salmonellosis outbreaks, including those linked to small turtles despite a federal ban on their sale (50). Overall, most animal contact during outbreaks occurred in private homes (49). Although pet ownership rates remained relatively unchanged during 2012–2022 (https://faunalytics.org/u-s-pet-ownership-demographics-sourcebook-2012, https://ebusiness.avma.org/files/ProductDownloads/eco-pet-demographic-report-22-low-res.pdf), the higher estimated illnesses for most pathogens in this study compared with previous estimates underscore the need to improve prevention knowledge among pet owners. National data on pet ownership does not include wildlife or animals housed in public settings, but enteric disease outbreaks associated with animal contact have been linked to additional settings outside the private home, including farms and dairies, festivals or fairs, and petting zoos (49). Therefore, stable pet ownership rates may not fully reflect opportunities for animal contact and associated transmission because national estimates do not capture exposures in those settings (https://faunalytics.org/u-s-pet-ownership-demographics-sourcebook-2012, https://ebusiness.avma.org/files/ProductDownloads/eco-pet-demographic-report-22-low-res.pdf). In addition, advancements in diagnostic testing and outbreak detection may have contributed to the higher prevalence estimated in our analysis. Future analyses describing estimated illnesses by animal type and setting might identify higher-risk exposures to these pathogens and guide targeted prevention measures.

One limitation of our study is that we used similar methods to those in the estimate for 7 major foodborne pathogens, preventing direct comparison with previous animal contact estimates (3). The source data have many unquantified uncertainties, several of which are described in methodologic limitations elsewhere (33). For example, the FoodNet catchment area is a critical contributor of unquantified uncertainty because of geographic variation in incidence rates for all pathogens. Limited health department personnel, funding, and laboratory capacity across jurisdictions might reduce the detection and reporting of animal contact–associated outbreaks, potentially affecting disease prevalence estimates. Although national reporting definitions exist, variations in how health departments define animal contact might affect how illnesses and outbreaks are categorized and reported (47). In addition, differences in surveillance, outbreak detection, and pathogen transmission might influence model estimates, limit direct comparisons, and contribute to underreporting. Estimates derived from SEJ are subject to expert opinion and might not reflect evidence that emerged later. Furthermore, our analysis focused on the primary pathogens causing animal contact illnesses in the United States. As national surveillance data become available, future analyses should include additional pathogens that cause animal contact illnesses to improve disease prevalence estimates and associated economic costs.

Our study found that ≈1 million illnesses, ≈9,500 hospitalizations, and ≈230 deaths linked to animal contact occur each year. Those findings underscore the need for enhanced education, awareness, and prevention measures to reduce the prevalence of disease in the United States. Continued surveillance, outbreak investigations, and epidemiologic studies are needed to improve understanding of transmission pathways and guide future disease prevalence estimates and prevention priorities. Recognizing animal contact as an important mode of transmission is essential for disease prevention priorities in addition to other routes, including foodborne, waterborne, environmental, and person-to-person transmission.

Dr. Medford is a veterinary epidemiologist who conducted this work as an Epidemic Intelligence Service Officer in the National Center for Emerging and Zoonotic Infectious Diseases at the Centers for Disease Control and Prevention, Atlanta, Georgia, USA. She also investigates multistate outbreaks of enteric illnesses linked to animal contact by using a One Health approach.

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Acknowledgments

We thank Erin Imada and Susan Montgomery for providing data and expert advice for estimates described in this article. We also thank Sean Stapleton for assistance in verifying the accuracy of tabular data.

This activity was reviewed by CDC, deemed not research, and was conducted consistent with applicable federal law and CDC policy (e.g., 45 C.F.R. part 46.102(l)(2); 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.).

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References

  1. Taylor  LH, Latham  SM, Woolhouse  ME. Risk factors for human disease emergence. Philos Trans R Soc Lond B Biol Sci. 2001;356:983–9. PubMed DOIGoogle Scholar
  2. Centers for Disease Control and Prevention. About zoonotic diseases. One Health. 2025 Apr 14 [cited 2026 Feb 27]. https://www.cdc.gov/one-health/about/one-health-history.html
  3. Hale  CR, Scallan  E, Cronquist  AB, Dunn  J, Smith  K, Robinson  T, et al. Estimates of enteric illness attributable to contact with animals and their environments in the United States. Clin Infect Dis. 2012;54(Suppl 5):S472–9. PubMed DOIGoogle Scholar
  4. Francois Watkins  LK, Laughlin  ME, Joseph  LA, Chen  JC, Nichols  M, Basler  C, et al. Ongoing outbreak of extensively drug-resistant Campylobacter jejuni infections associated with US pet store puppies, 2016–2020. JAMA Netw Open. 2021;4:e2125203. PubMed DOIGoogle Scholar
  5. Saunders  S, Smith  K, Schott  R, Dobbins  G, Scheftel  J. Outbreak of campylobacteriosis associated with raccoon contact at a wildlife rehabilitation centre, Minnesota, 2013. Zoonoses Public Health. 2017;64:222–7. PubMed DOIGoogle Scholar
  6. Gharpure  R, Perez  A, Miller  AD, Wikswo  ME, Silver  R, Hlavsa  MC. Cryptosporidiosis outbreaks—United States, 2009–2017. MMWR Morb Mortal Wkly Rep. 2019;68:568–72. PubMed DOIGoogle Scholar
  7. Webb  LM, Tubach  SA, Hunt  DC; Centers for Disease Control and Prevention (CDC). Outbreak of cryptosporidiosis among responders to a rollover of a truck carrying calves - Kansas, April 2013. [PubMed]. MMWR Morb Mortal Wkly Rep. 2014;63:1185–8.PubMedGoogle Scholar
  8. Lanier  WA, Hall  JM, Herlihy  RK, Rolfs  RT, Wagner  JM, Smith  LH, et al. Outbreak of Shiga-toxigenic Escherichia coli O157:H7 infections associated with rodeo attendance, Utah and Idaho, 2009. Foodborne Pathog Dis. 2011;8:1131–3. PubMed DOIGoogle Scholar
  9. Nichols  MC, Gacek  P, Phan  Q, Gambino-Shirley  KJ, Gollarza  LM, Schroeder  MN, et al. Agritourism and kidding season: a large outbreak of human Shiga toxin–producing Escherichia coli O157 (STEC O157) infections linked to a goat dairy farm—Connecticut, 2016. Front Vet Sci. 2021;8:744055. PubMed DOIGoogle Scholar
  10. Thomas  CM, Foster  A, Boop  S, Kirschke  D, Mooney  H, Reid  I, et al. Shiga toxin–producing Escherichia coli O157:H7 outbreak associated with school field trips at a farm animal exhibit-Tennessee, September–October 2023. Zoonoses Public Health. 2024;71:829–35. PubMed DOIGoogle Scholar
  11. Luna-Gierke  RE, Wymore  K, Sadlowski  J, Clogher  P, Gierke  RW, Tobin-D’Angelo  M, et al. Multiple-aetiology enteric infections involving non-O157 Shiga toxin–producing Escherichia coli—FoodNet, 2001–2010. Zoonoses Public Health. 2014;61:492–8. PubMed DOIGoogle Scholar
  12. Tack  DM, Kisselburgh  HM, Richardson  LC, Geissler  A, Griffin  PM, Payne  DC, et al. Shiga toxin–producing Escherichia coli outbreaks in the United States, 2010–2017. Microorganisms. 2021;9:1529. PubMed DOIGoogle Scholar
  13. Anderson  TC, Marsden-Haug  N, Morris  JF, Culpepper  W, Bessette  N, Adams  JK, et al. Multistate outbreak of human Salmonella Typhimurium infections linked to pet hedgehogs—United States, 2011–2013. Zoonoses Public Health. 2017;64:290–8. PubMed DOIGoogle Scholar
  14. Hoff  C, Nichols  M, Gollarza  L, Scheftel  J, Adams  J, Tagg  KA, et al. Multistate outbreak of Salmonella Typhimurium linked to pet hedgehogs, United States, 2018–2019. Zoonoses Public Health. 2022;69:167–74. PubMed DOIGoogle Scholar
  15. Bartholomew  ML, Heffernan  RT, Wright  JG, Klos  RF, Monson  T, Khan  S, et al. Multistate outbreak of Salmonella enterica serotype enteritidis infection associated with pet guinea pigs. Vector Borne Zoonotic Dis. 2014;14:414–21. PubMed DOIGoogle Scholar
  16. Robertson  S, Burakoff  A, Stevenson  L, Tompkins  B, Patel  K, Tolar  B, et al. Recurrence of a multistate outbreak of Salmonella Enteritidis infections linked to contact with guinea pigs—eight States, 2015–2017. MMWR Morb Mortal Wkly Rep. 2018;67:1195–6. PubMed DOIGoogle Scholar
  17. Basler  C, Nguyen  TA, Anderson  TC, Hancock  T, Behravesh  CB. Outbreaks of human Salmonella infections associated with live poultry, United States, 1990–2014. Emerg Infect Dis. 2016;22:1705–11. PubMed DOIGoogle Scholar
  18. Stapleton  GS, Habrun  C, Nemechek  K, Gollarza  L, Ellison  Z, Tolar  B, et al. Multistate outbreaks of salmonellosis linked to contact with backyard poultry—United States, 2015–2022. Zoonoses Public Health. 2024;71:708–22. PubMed DOIGoogle Scholar
  19. Patel  K, Stapleton  GS, Trevejo  RT, Tellier  WT, Higa  J, Adams  JK, et al. Human salmonellosis outbreak linked to Salmonella Typhimurium epidemic in wild songbirds, United States, 2020–2021. Emerg Infect Dis. 2023;29:2298–306. PubMed DOIGoogle Scholar
  20. Nichols  M, Gollarza  L, Sockett  D, Aulik  N, Patton  E, Francois Watkins  LK, et al. Outbreak of multidrug-resistant Salmonella Heidelberg infections linked to dairy calf exposure, United States, 2015–2018. Foodborne Pathog Dis. 2022;19:199–208. PubMed DOIGoogle Scholar
  21. Basler  C, Bottichio  L, Higa  J, Prado  B, Wong  M, Bosch  S. Multistate outbreak of human Salmonella Poona infections associated with pet turtle exposure—United States, 2014. MMWR Morb Mortal Wkly Rep. 2015;64:804. PubMed DOIGoogle Scholar
  22. Dewart  CM, Waltenburg  MA, Dietrich  S, Machesky  K, Singh  A, Brandt  E, et al. Cluster of human Salmonella Guinea infections: reported reptile exposures and associated opportunities for infection prevention—Ohio, 2019–2020. Prev Vet Med. 2022;198:105530. PubMed DOIGoogle Scholar
  23. Gambino-Shirley  K, Stevenson  L, Concepción-Acevedo  J, Trees  E, Wagner  D, Whitlock  L, et al. Flea market finds and global exports: four multistate outbreaks of human Salmonella infections linked to small turtles, United States—2015. Zoonoses Public Health. 2018;65:560–8. PubMed DOIGoogle Scholar
  24. Jenson  HB. Outbreaks of salmonellosis associated with turtles. Infect Dis Alert. 2016;35:52 https://www.clinician.com/articles/137134-outbreaks-of-salmonellosis-associated-with-turtles cited 2026 Feb 27.
  25. Kiebler  CA, Bottichio  L, Simmons  L, Basler  C, Klos  R, Gurfield  N, et al. Outbreak of human infections with uncommon Salmonella serotypes linked to pet bearded dragons, 2012–2014. Zoonoses Public Health. 2020;67:425–34. PubMed DOIGoogle Scholar
  26. Koski  L, DeBess  E, Rosen  HE, Reporter  R, Waltz  T, Leeper  M, et al. An investigation of Salmonella Fluntern illnesses linked to leopard geckos—United States, 2018. Zoonoses Public Health. 2019;66:974–7. PubMed DOIGoogle Scholar
  27. Paphitis  K, Habrun  CA, Stapleton  GS, Reid  A, Lee  C, Majury  A, et al. Salmonella Vitkin outbreak associated with bearded dragons, Canada and United States, 2020–2022. Emerg Infect Dis. 2024;30:225–33. PubMed DOIGoogle Scholar
  28. Waltenburg  MA, Perez  A, Salah  Z, Karp  BE, Whichard  J, Tolar  B, et al. Multistate reptile- and amphibian-associated salmonellosis outbreaks in humans, United States, 2009–2018. Zoonoses Public Health. 2022;69:925–37. PubMed DOIGoogle Scholar
  29. Xiaoli  L, Huth  P, Sherman  C, Wroblewski  D, Anderson  S, Ferraro  L, et al. Reoccurring Salmonella Cotham outbreak linked to pet bearded dragons—United States, 2024. MMWR Morb Mortal Wkly Rep. 2025;74:474–9. PubMed DOIGoogle Scholar
  30. de Barros  RAM, Torrecilhas  AC, Marciano  MAM, Mazuz  ML, Pereira-Chioccola  VL, Fux  B. Toxoplasmosis in human and animals around the world. Diagnosis and perspectives in the One Health approach. Acta Trop. 2022;231:106432. PubMed DOIGoogle Scholar
  31. Tenter  AM, Heckeroth  AR, Weiss  LM. Toxoplasma gondii: from animals to humans. Int J Parasitol. 2000;30:1217–58. PubMed DOIGoogle Scholar
  32. Jones  JL, Kruszon-Moran  D, Elder  S, Rivera  HN, Press  C, Montoya  JG, et al. Toxoplasma gondii infection in the United States, 2011–2014. Am J Trop Med Hyg. 2018;98:551–7. PubMed DOIGoogle Scholar
  33. Scallan Walter  EJ, Cui  Z, Tierney  R, Griffin  PM, Hoekstra  RM, Payne  DC, et al. Foodborne illness acquired in the United States—major pathogens, 2019. [PubMed]. Emerg Infect Dis. 2025;31:669–77.PubMedGoogle Scholar
  34. Shah  HJ, Jervis  RH, Wymore  K, Rissman  T, LaClair  B, Boyle  MM, et al. Reported incidence of infections caused by pathogens transmitted commonly through food: impact of increased use of culture-independent diagnostic tests—Foodborne Diseases Active Surveillance Network, 1996–2023. MMWR Morb Mortal Wkly Rep. 2024;73:584–93. PubMed DOIGoogle Scholar
  35. Centers for Disease Control and Prevention. National Notifiable Diseases Surveillance System (NNDSS). 2024 Apr 17 [cited 2025 Aug 25]. https://www.cdc.gov/ncird-surveillance/about/nndss.html
  36. Centers for Disease Control and Prevention. Listeria annual summary, 2019 [cited 2023 Dec 5]. https://www.cdc.gov/listeria/php/surveillance/annual-summary-2019.html
  37. Devine  CJ, Molinari  NA, Shah  HJ, Blackstock  AJ, Geissler  A, Marder  EP, et al. The 2018–2019 FoodNet Population Survey: a tool to estimate risks and behaviors associated with enteric infections. Am J Epidemiol. 2025;194:5–11. PubMed DOIGoogle Scholar
  38. Buss  SN, Leber  A, Chapin  K, Fey  PD, Bankowski  MJ, Jones  MK, et al. Multicenter evaluation of the BioFire FilmArray gastrointestinal panel for etiologic diagnosis of infectious gastroenteritis. J Clin Microbiol. 2015;53:915–25. PubMed DOIGoogle Scholar
  39. Scallan  E, Hoekstra  RM, Angulo  FJ, Tauxe  RV, Widdowson  MA, Roy  SL, et al. Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis. 2011;17:7–15. PubMed DOIGoogle Scholar
  40. Ray  LC, Griffin  PM, Wymore  K, Wilson  E, Hurd  S, LaClair  B, et al. Changing diagnostic testing practices for foodborne pathogens, Foodborne Diseases Active Surveillance Network, 2012–2019. Open Forum Infect Dis. 2022;9:ofac344. PubMed DOIGoogle Scholar
  41. Mylonakis  E, Hohmann  EL, Calderwood  SB. Central nervous system infection with Listeria monocytogenes. 33 years’ experience at a general hospital and review of 776 episodes from the literature. Medicine (Baltimore). 1998;77:313–36. PubMed DOIGoogle Scholar
  42. Beshearse  E, Bruce  BB, Nane  GF, Cooke  RM, Aspinall  W, Hald  T, et al. Attribution of illnesses transmitted by food and water to comprehensive transmission pathways using structured expert judgment, United States. Emerg Infect Dis. 2021;27:182–95. PubMed DOIGoogle Scholar
  43. Burke  RM, Mattison  CP, Pindyck  T, Dahl  RM, Rudd  J, Bi  D, et al. Burden of norovirus in the United States, as estimated based on administrative data: updates for medically attended illness and mortality, 2001–2015. Clin Infect Dis. 2021;73:e1–8. PubMed DOIGoogle Scholar
  44. Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. AHRQ HCUP NIS overview [cited 2023 Dec 5]. https://hcup-us.ahrq.gov/nisoverview.jsp
  45. Centers for Disease Control and Prevention. BEAM (Bacteria, Enterics, Ameba, and Mycotics) dashboard [cited 2026 July 29]. https://www.cdc.gov/beam/dashboard/index.html
  46. Su  CP, Stover  DT, Buss  BF, Carlson  AV, Luckhaupt  SE. Occupational animal exposure among persons with campylobacteriosis and cryptosporidiosis—Nebraska, 2005–2015. MMWR Morb Mortal Wkly Rep. 2017;66:955–8. PubMed DOIGoogle Scholar
  47. Ray  LC, Payne  DC, Rounds  J, Trevejo  RT, Wilson  E, Burzlaff  K, et al. Syndromic gastrointestinal panel diagnostic tests have changed our understanding of the epidemiology of Yersiniosis—Foodborne Diseases Active Surveillance Network, 2010–2021. Open Forum Infect Dis. 2024;11:ofae199. PubMed DOIGoogle Scholar
  48. Wang  ZD, Wang  SC, Liu  HH, Ma  HY, Li  ZY, Wei  F, et al. Prevalence and burden of Toxoplasma gondii infection in HIV-infected people: a systematic review and meta-analysis. Lancet HIV. 2017;4:e177–88. PubMed DOIGoogle Scholar
  49. Eisenstein  T, Wong  M, Vahey  G, Toepfer  AP, Gleason  B, Benedict  K. Enteric disease outbreaks associated with animal contact—Animal Contact Outbreak Surveillance System, United States, 2009–2021. MMWR Surveill Summ. 2025;74:1–12. PubMed DOIGoogle Scholar
  50. Centers for Disease Control and Prevention. Menu of state turtle-associated salmonellosis laws. 2016 [cited 2026 Feb 27]. https://www.cdc.gov/phlp/docs/turtle-menu.pdf

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Suggested citation for this article: Medford R, Cui Z, Tierney R, Straily A, Miko S, Francois-Watkins L, et al. Estimated illness linked to animal contact for 7 pathogens, United States, 2019. Emerg Infect Dis. 2026 Dec [date cited]. https://doi.org/10.3201/eid3212.260787

DOI: 10.3201/eid3212.260787

Original Publication Date: October 27, 2026

1These senior authors contributed equally to this article.

Table of Contents – Volume 32, Number 12—December 2026

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Page created: September 21, 2026
Page updated: October 27, 2026
Page reviewed: October 27, 2026
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
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