Inside the fight against the parasite Cyclospora 35%

By Fechi Inyama0%

7/17/2026, 9:26:09 PM

BS Summary: This article contains 23 faulty reasoning types, including Pessimism Bias, Biased Writer Voice, and Post Hoc (False Cause), with Appeal to Authority as the most egregious example at 31.5% saturation with 255 hits. Analysis detected 1,476 faulty-reasoning hits from 809 analyzed words, generating a BS Score of 42.3% and a BS Rank of 35% (13,910 of 21,097 articles). This article is better (less manipulative) than 65.90% of the article peer group.

Intense abdominal pain. 
Extreme fatigue. 
Explosive diarrhea. 
These are just some of the symptoms of cyclosporiasis, a foodborne parasitic infection that has sickened thousands and hospitalized more than 100 people in the United States since May 1. 
Although public health officials may have finally identified one culprit  shredded lettuce distributed to Taco Bells in some states  the outbreak of the parasite Cyclospora cayetanensis shows no sign of letting up. 
Researchers are racing to track down what’s driving the surge in cases, but the parasite’s wiliness plus cuts to federal funding for public health presents a unique challenge. 
“In terms of this year’s outbreak, we haven’t seen such large, common source outbreaks identified in the past few years,” says epidemiologist Craig Hedberg of the University of Minnesota School of Public Health in Minneapolis. 
Of 34 states reporting illness, Michigan has been hardest hit. 
As of July 17, the state has reported 5,002 cases and 102 hospitalizations  the most of any of the affected states. 
New York has the second highest case load: 510 as of early July. 
It is unclear whether the chasm reflects a true difference in infection rates or more awareness among Michigan residents prompting more testing. 
The influx is putting a strain on Michigan’s public health infrastructure. 
The state’s 45 local health districts are responsible for identifying potential cases, collecting human fecal samples and conducting patient interviews. 
Samples then go to the Michigan Department of Health and Human Services for processing and analysis. 
Typically, Michigan logs fewer than 50 cases per year, says MDHHS genomic epidemiologist Heather Blankenship, who is based in Lansing. 
The deluge of clinical samples has prompted the agency to lean on the state’s Emergency Preparedness Group and the Michigan Department of Agriculture for help. 
It takes a minimum of three days to process a sample, and with the sheer volume of cases, it is taking the MDHHS much longer, Blankenship says. 
This backlog delays the work needed to find the outbreak’s origins. 
Early results from MDHHS’ investigation point to lettuce and salad greens as potential suspects. 
On July 16, the U.S. 
Centers for Disease Control and Prevention and the U.S. 
Food and Drug Administration announced that they linked some cases in Michigan, Indiana, Kentucky, Ohio and West Virginia to shredded iceberg lettuce served at Taco Bell. 
The restaurant chain has voluntarily removed lettuce from its menus in the affected states. 
And Taylor Farms, the company that supplied the lettuce, announced a recall on July 17. 
Tracking Cyclospora is tricky 
Once a person ingests contaminated food, it can take up to two weeks for symptoms to appear, Hedberg says. 
Compared with common foodborne illnesses that take just hours or days to develop, the extended timeframe can confound efforts to trace Cyclospora to its source. 
Public health officials often find that patients have difficulty recalling the food they ate so far in the past. 
The parasite’s biology also conspires against finding quick answers. 
It can’t be grown in the lab like other foodborne pathogens can, which means more samples from infected people are needed to collect enough parasite DNA to nab its identity. 
Blankenship and colleagues rely on eight of the parasite’s genes to confirm cases, but that isn’t enough to determine how cases might be related. 
Sequencing Cyclospora’s entire genome would help, but its relatively large size (it’s about nine times as large as E. coli’s) makes that exceedingly difficult, Blankenship says. 
“There’s a lot of unknowns with the investigation,” Blankenship says. 
“Doing [genetic] characterization is going to help us better understand what is happening.” 
The Cyclospora outbreak exposes cracks in food safety 
Clinical microbiologist Kimberly McCullor, also of MDHHS, says staff are trained to assist in almost all aspects of an investigation. 
“We’re always kind of looking for the next potential pop-up outbreak,” McCullor says. 
“We try to limit the surprise, right? 
We’re able to pivot  we use that word a lot.” 
This reliance on pivoting isn’t unique to Michigan and highlights a much larger issue facing the country: The chronic underfunding of public health services. 
Funding for food safety in particular has been stagnant for years, says epidemiologist Barbara Kowalcyk of George Washington University in Washington, D.C. 
Funding pinches are partly behind the CDC’s move in 2025 to reduce its surveillance for Cyclospora along with the foodborne pathogens Campylobacter, Listeria, Shigella, Vibrio and Yersinia. 
While the move shouldn’t impact the response to the current cyclosporiasis outbreak, it could hamstring responses to future ones, Dan Jernigan, former director of the CDC’s Center for Emerging and Zoonotic Infectious Diseases, told the Washington Post. 
Data show that investing in surveillance at the local, state and federal levels leads “to a decrease in the amount of time it takes to identify an outbreak and consequently the number of cases associated with an outbreak,” Kowalcyk says. 
Article reasoning-pattern comparisonThis article: 10.9%Fechi Inyama: 4.3%Science News: 2.6%Confirmation Bias10.9%This article: 5.7%Fechi Inyama: 1.6%Science News: 1.1%Anchoring Bias5.7%This article: 9.8%Fechi Inyama: 3.9%Science News: 2.5%Availability Heuristic9.8%This article: 0.0%Fechi Inyama: 0.6%Science News: 1.5%Representativeness Heuristic0.0%This article: 0.0%Fechi Inyama: 1.0%Science News: 0.5%Hindsight Bias0.0%This article: 6.3%Fechi Inyama: 1.2%Science News: 2.5%Overconfidence Bias6.3%This article: 6.7%Fechi Inyama: 7.6%Science News: 4.2%Framing Effect6.7%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.3%Loss Aversion0.0%This article: 1.4%Fechi Inyama: 0.3%Science News: 0.3%Status Quo Bias1.4%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.2%Sunk Cost Effect0.0%This article: 1.6%Fechi Inyama: 2.3%Science News: 4.2%Optimism Bias1.6%This article: 17.3%Fechi Inyama: 5.1%Science News: 1.2%Pessimism Bias17.3%This article: 9.0%Fechi Inyama: 5.0%Science News: 4.1%Negativity Bias9.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.4%Self-Serving Bias0.0%This article: 3.0%Fechi Inyama: 0.6%Science News: 0.5%Fundamental Attribution Error3.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%Actor-Observer Bias0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%In-Group Bias0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Fechi Inyama: 0.6%Science News: 1.4%Halo Effect0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Horn Effect0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Dunning-Kruger Effect0.0%This article: 7.0%Fechi Inyama: 1.4%Science News: 0.8%Recency Bias7.0%This article: 2.7%Fechi Inyama: 0.9%Science News: 0.3%Primacy Effect2.7%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%Blind-Spot Bias0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.2%Ad Hominem0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.2%Straw Man0.0%This article: 31.5%Fechi Inyama: 9.1%Science News: 4.5%Appeal to Authority31.5%This article: 2.7%Fechi Inyama: 0.5%Science News: 1.1%False Dilemma2.7%This article: 7.5%Fechi Inyama: 1.5%Science News: 1.1%Slippery Slope7.5%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%Circular Reasoning0.0%This article: 11.0%Fechi Inyama: 4.7%Science News: 4.0%Hasty Generalization11.0%This article: 0.0%Fechi Inyama: 0.3%Science News: 0.0%Red Herring0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.4%Bandwagon0.0%This article: 4.2%Fechi Inyama: 2.4%Science News: 2.6%Appeal to Emotion4.2%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.5%Begging the Question0.0%This article: 14.1%Fechi Inyama: 4.1%Science News: 2.5%Post Hoc (False Cause)14.1%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Tu Quoque0.0%This article: 0.0%Fechi Inyama: 0.7%Science News: 0.4%Burden of Proof0.0%This article: 0.0%Fechi Inyama: 0.6%Science News: 0.2%Appeal to Nature0.0%This article: 0.0%Fechi Inyama: 0.6%Science News: 0.2%Composition/Division0.0%This article: 2.3%Fechi Inyama: 0.8%Science News: 1.2%Anecdotal2.3%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%No True Scotsman0.0%This article: 4.6%Fechi Inyama: 2.1%Science News: 1.6%Ambiguity (Equivocation)4.6%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.2%Middle Ground0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.1%Personal Incredulity0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Special Pleading0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Genetic Fallacy0.0%This article: 4.6%Fechi Inyama: 3.2%Science News: 0.8%Unattributed Quote4.6%This article: 0.0%Fechi Inyama: 1.3%Science News: 0.7%Quote-first Misdirection0.0%This article: 15.6%Fechi Inyama: 8.0%Science News: 2.6%Biased Writer Voice15.6%This article: 3.0%Fechi Inyama: 0.6%Science News: 0.9%Indoctrination3.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Fechi Inyama: 0.0%Science News: 0.4%Attempt to Sell a Product or S…0.0%

809 words analyzed.

Speakers

6speakers40%attributed speech483writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 7 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageCraig Hedberg • 35 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageHeather Blankenship • 20 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageHeather Blankenship • 27 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageCenters for Disease Control and Prevention • 26 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageCraig Hedberg • 19 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageHeather Blankenship • 26 words • 0.0% coverageHeather Blankenship • 10 words • 0.0% coverageHeather Blankenship • 13 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageKimberly McCullor • 20 words • 0.0% coverageKimberly McCullor • 13 words • 0.0% coverageKimberly McCullor • 7 words • 0.0% coverageKimberly McCullor • 11 words • 0.0% coverageWriter's voice • 24 words • 100.0% coverageBarbara Kowalcyk • 22 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageDan Jernigan • 37 words • 100.0% coverageBarbara Kowalcyk • 40 words • 0.0% coverage
Selected voice

Dan Jernigan

100%flagged-word coverage
37 attributed words11% of attributed speech72% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%Dan Jernigan: 100.0%100.0%Biased Writer Voice-26.1 ptsWriter: 26.1%Dan Jernigan: 0.0%0.0%Indoctrination-5.0 ptsWriter: 5.0%Dan Jernigan: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.