No, Wildlife is not Teeming with SARS-CoV-2 Virus

A new study claims that wild animals are filled with SARS-CoV-2 virus. Here's precisely why this is bunk.

The COVID-19 pandemic was met with a high priority on diagnostic testing in managing public health crises. Central to this effort has been the widespread deployment of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, widely regarded as the gold standard for detecting SARS-CoV-2.

However, the entire testing enterprise is fraught. As the pandemic has progressed, significant concerns have emerged regarding the reliability of these tests, mainly because unacceptably high cycle thresholds (CT values) are employed. These concerns are not just theoretical—they have profound implications for public health, resource allocation, and, critically, the surveillance of wildlife populations.

RT-PCR testing, though powerful, is inherently limited by its sensitivity to even the smallest fragments of viral RNA from off-target nucleotide sources, such as other viruses or bacteria, or the patient’s or animal’s genome. When CT values exceed 35 cycles, the likelihood of detecting clinically irrelevant, off-target viral fragments increases dramatically, leading to false positives. Such results can misinform public health policies, drive unnecessary quarantines, and, as this critique will argue, skew our understanding of the presence of the virus in wildlife. These issues have been addressed in high technical detail from early 2020 (see articles here, here, and here).

Unfortunately, methods like one published by Ceci et al., 2021 are dangerously lax. The implications of misinterpreting RT-PCR results are far-reaching. In human populations, they can lead to inflated infection rates, misdiagnosed cases, and an exaggerated sense of the virus's spread. In wildlife studies, these false positives can create a misleading narrative that wildlife species are significant reservoirs of SARS-CoV-2, prompting unnecessary and potentially harmful interventions. As I have pointed out, since early 2020, using high CT values without proper validation, such as sequencing to confirm amplicons, has led to an overestimation of viral prevalence, with severe consequences for science and policy.

This critique will dissect the flaws in RT-PCR testing methodologies used in a recent study of the prevalence of SARS-CoV-2 in wildlife that employed the flawed method of Ceci et al. (2021), and explore the broader risks of relying on these results. By scrutinizing these practices with precision and rigor, we aim to challenge the notion that wildlife is teeming with SARS-CoV-2 and advocate for more reliable, scientifically sound approaches to disease surveillance.

METHODOLOGICAL FLAWS in Goldberg et al., 2024

The methodology employed in the wildlife study, which claims widespread detection of SARS-CoV-2 in various animal species, is central to understanding the validity of the conclusions by Goldberg et al. (2024). A thorough examination of the methodological approach reveals significant concerns that undermine the reliability of the findings. These concerns revolve around the design of the study, the RT-PCR testing protocol used, and the handling of samples. Each of these aspects contributes to the overall accuracy and credibility of the study, making it essential to scrutinize them in detail.

The study aimed to assess the prevalence of SARS-CoV-2 across various wildlife species. However, several critical flaws in the study’s design raise questions about the validity of the results. The selection of species appears to have been influenced by convenience rather than a strategic approach considering ecological or epidemiological relevance. This introduces a potential bias, as certain species might be overrepresented while others that could be significant reservoirs of zoonotic diseases are underrepresented or excluded entirely. Without a clear rationale for species selection, the study risks drawing conclusions that may not be generalizable to other wildlife populations. The potential for bias in species selection can lead to skewed data, where the results might incorrectly suggest that certain species are more likely to carry SARS-CoV-2 than others. This could lead to inappropriate or misdirected conservation and public health efforts, potentially focusing resources on species that do not play a significant role in public health.

Another concern is the sample sizes used in the study. Insufficient sample sizes can lead to a lack of statistical power, making it difficult to detect true differences or associations. Furthermore, the geographical distribution of the samples is critical in understanding the scope of the study. If the samples are concentrated in specific areas, the findings may not accurately reflect the broader wildlife population. This limitation is particularly problematic in studies of zoonotic diseases, where regional differences can be significant due to variations in species ecology, human-wildlife interaction, and environmental factors. Wildlife populations are not uniform across regions; factors like climate, human population density, and local biodiversity can all influence viral prevalence. A study with a limited or skewed geographical focus might miss these nuances, leading to results that do not accurately reflect the accurate distribution of the virus.

The study does not appear to have adequately controlled for potential confounding factors that could influence the detection of SARS-CoV-2. The RT-PCR testing protocol is the cornerstone of the methodology of the study, and it is essential to examine the choice of primers, cycle thresholds, and the use of controls, as these elements directly impact the accuracy of the results. The primers and probes used in RT-PCR are critical for the specificity of the test. The choice of primers must be scrutinized to ensure they are highly specific to SARS-CoV-2 and do not cross-react with other coronaviruses or non-target sequences from the animals’ genomes. The virus has evolved significantly, and tests will thereby also “age out” unless the primers are updated every three or four months.

Without rigorous testing for cross-reactivity, there is a risk that the detected viral RNA could belong to a related but non-pathogenic coronavirus, leading to incorrect conclusions about the presence of SARS-CoV-2 in wildlife. This is particularly concerning in wildlife studies, where multiple coronaviruses might coexist.

The study employed high CT values, often exceeding 35 cycles, to detect SARS-CoV-2 in wildlife samples. High CT values are associated with a greater likelihood of detecting off-target fragments or non-specific sequences, leading to false positives. Using such high thresholds without appropriate validation significantly undermines the credibility of the results. In scientific studies, particularly those involving public health, the choice of parameters such as CT values must be rigorously justified. The absence of such justification in this study indicates a potential oversight that could have led to the overestimation of viral prevalence. Proper controls are essential in any RT-PCR assay to ensure the reliability of the results. The failure of the authors of the paper to include negative controls (e.g, to use a template to calculate sample-specific cycle thresholds), such as the Delta Delta CT method) is a major methodological flaw. Without these controls, it is impossible to accurately distinguish true positive results from false positives caused by non-specific amplification. Adequate controls, including positive and negative controls, are critical to validating the results of PCR assays. Their absence in this study is a significant oversight that calls into question the reliability of the reported findings. Furthermore, sequencing the amplicons to confirm their identity is the only way to be 100% certain that the virus, not something else, is triggering a positive PCR result.

The integrity and handling of samples are crucial in wildlife studies, where field conditions are often less controlled than in clinical settings. The conditions under which samples are collected and stored can significantly affect the quality of the RNA extracted for RT-PCR. Degradation of RNA due to improper storage or delays in processing can lead to false negatives or unreliable results. The study does not provide sufficient detail on how samples were handled from collection to analysis, leaving a gap in understanding potential pre-analytical variables that could influence the outcome. Wildlife studies often involve collecting samples under challenging conditions where maintaining the integrity of RNA is difficult. Without detailed protocols and justifications for the methods used, it is hard to assess whether the RNA in the samples was of sufficient quality to yield reliable RT-PCR results.

The methodology used in the wildlife study is fraught with issues that cast doubt on the reliability of its findings. The study design lacks clarity in species selection, sample size, and geographical distribution, while the RT-PCR protocol employed is prone to false positives due to high CT values and inadequate controls. Moreover, the handling of samples raises concerns about the integrity of the data. These methodological flaws must be addressed to ensure that conclusions drawn from the study are scientifically valid and do not mislead public health or conservation efforts. Future studies should incorporate rigorous controls, including sequencing, to confirm the identity of amplified products and ensure that sample handling procedures are clearly documented and justified. By addressing these issues, researchers can produce more reliable data that accurately reflects the true prevalence of SARS-CoV-2 in wildlife populations.

The Critical Flaw in the Wildlife Study: Misuse of RT-PCR

The most significant and ultimately fatal flaw in the wildlife study by Goldberg et al. (2024) lies in the misuse of the RT-PCR testing protocol. This error is not merely a minor oversight; it fundamentally compromises the reliability of the conclusions, rendering them not only suspect but potentially misleading. RT-PCR, while a powerful tool for detecting viral RNA, is highly sensitive to the parameters under which it is used, particularly the cycle threshold (CT) values, the specificity of primers, and the adequacy of controls. In this study, these critical factors were mishandled, leading to a high probability of false positives that skew the data and create an inaccurate picture of SARS-CoV-2 prevalence in wildlife populations.

Central to this flaw is the use of excessively high CT values, often exceeding 35 cycles. RT-PCR operates by amplifying viral RNA to detectable levels, doubling the amount of genetic material each cycle. While this high sensitivity allows for detecting even minute amounts of viral RNA, it also dramatically increases the likelihood of amplifying non-specific signals or even degraded and non-viable viral fragments. At such high thresholds, the amplified genetic material may not represent an active infection but rather non-specific sequences or remnants of past infections. This is particularly problematic in wildlife studies, where the presence of other coronaviruses are significant risks, and where non-specific amplification may not be evenly distributed among species. By failing to justify or validate these high CT values, the study introduces a critical error that undermines the validity of its findings.

The consequences of this misuse are severe and wide-ranging. False positives resulting from high CT values can lead to the erroneous conclusion that wildlife populations are significant reservoirs of SARS-CoV-2, when the detected RNA might actually belong to other coronaviruses or come from the animal’s genome. This can mislead public health authorities and conservationists, prompting unnecessary interventions such as the culling of wildlife species or the misallocation of resources toward managing an overstated zoonotic threat. Without proper controls—such as negative controls or a template control for normalization—the study cannot distinguish between true positive results and those that are false positives, further exacerbating the issue. This lack of adequate controls leaves the findings vulnerable to misinterpretation and overestimation of the prevalence of the virus in the studied populations.

The authors’ failure to incorporate sequencing to confirm the identity of the RT-PCR products is another critical oversight. Sequencing is the gold standard for confirming that the amplified RNA indeed belongs to SARS-CoV-2 and not to another source. Without this confirmatory step, verifying that the detected sequences are specific to the virus in question is impossible. This omission leaves a significant gap in the study’s validation process, as it relies solely on RT-PCR results that could easily be confounded by cross-reactivity with other coronaviruses or non-specific amplification. Sequencing would have provided an additional layer of verification, ensuring that the detected RNA was, in fact, indicative of an active SARS-CoV-2 infection rather than an artifact of the testing process.

Moreover, the study's broader scientific context further highlights the severity of this flaw. During the COVID-19 pandemic, numerous studies have demonstrated the dangers of relying on high CT values in RT-PCR tests, particularly without confirmatory sequencing. Other studies have shown how high CT values can lead to significant rates of false positives, especially in low-prevalence settings where the probability of a positive result being a true positive is already reduced. By failing to account for these well-documented issues, the wildlife study repeats a critical error that has been highlighted many times over in other research contexts, exacerbating the potential for misleading conclusions.

To address potential counterarguments, some might suggest that high CT values are necessary to detect low viral loads, particularly in wildlife where viral prevalence might be low. However, detecting low viral loads at such high thresholds is often clinically or epidemiologically irrelevant, as it may not indicate an active or transmissible infection. Detecting off-target RNA at these high CT values does not contribute to understanding the risk of zoonotic transmission but rather inflates the perceived prevalence of the virus in wildlife populations. This could lead to misguided public health strategies and conservation efforts based on an inflated threat.

The misuse of RT-PCR in this wildlife study is a fatal flaw that severely undermines the credibility of its findings. The use of high CT values without proper validation, the lack of necessary controls, and the failure to confirm the identity of the amplified products through sequencing collectively create a perfect storm for generating false positives. These methodological errors cast severe doubt on the study’s conclusions and highlight the dangers of relying on poorly executed RT-PCR protocols in wildlife research. To draw any meaningful conclusions about the presence of SARS-CoV-2 in wildlife, it is imperative that future studies use rigorously validated RT-PCR protocols with appropriate CT thresholds, comprehensive controls, and confirmatory sequencing. Without these safeguards, the risk of misinformation and misguided public health responses remains unacceptably high.

Evaluation of Statistical Analysis and Interpretation of Data

The statistical analysis of the wildlife study is fundamentally compromised by the flawed RT-PCR data, illustrating the principle of "Garbage In, Garbage Out" (GIGO). When the input data is flawed—specifically due to the misuse of RT-PCR with high CT values—the statistical outputs are inevitably unreliable, regardless of the sophistication of the analysis.

High CT values in RT-PCR testing are known to generate a significant number of false positives by detecting non-specific signals or non-viable viral fragments. When these false positives are included in the data set, they inflate the apparent prevalence of SARS-CoV-2 in wildlife, skewing the statistical models used to analyze the data. As a result, the study's estimates of infection rates, confidence intervals, and p-values are distorted, leading to erroneous conclusions.

The statistical models likely used in the study, such as logistic regression, operate on the assumption that the input data is accurate and representative. However, when a substantial portion of the data consists of false positives, these models produce biased results, misidentifying associations between variables (e.g., species or geographic location) and infection rates. This bias misleads the interpretation of the data and could result in incorrect inferences about the epidemiological significance of the findings.

Additionally, the study does not account for the sensitivity-specificity trade-off inherent in using high CT values. By not adjusting for this trade-off, the analysis fails to address how inflated sensitivity at the expense of specificity leads to an overestimation of the presence of the virus in the first place. This oversight further undermines the credibility of the statistical conclusions.

The failure to incorporate sequencing to confirm RT-PCR results removes a critical validation layer. Without sequencing, the study cannot ensure that the detected viral RNA is truly SARS-CoV-2, making any statistical analysis derived from such data inherently suspect.

Moreover, the absence of sensitivity analyses to test how varying the CT thresholds or implementing stricter controls would affect the results reveals a lack of rigor. Sensitivity analyses are crucial for determining the robustness of statistical findings, and their omission means the study's conclusions are untested against alternative scenarios.

The flawed input data from the RT-PCR tests renders the statistical analysis in this study unreliable. The principle of GIGO is fully at play here—no matter how advanced the statistical methods used, if the data is compromised, the conclusions will be too. For future studies, robust validation of RT-PCR protocols, including confirmatory sequencing and thorough sensitivity analyses, is essential to produce accurate and meaningful results.

Broader Implications for Wildlife Research and Public Policy

The misuse of RT-PCR in the wildlife study has far-reaching implications that extend beyond the scope of this single research project. The errors in methodology and data interpretation, particularly the reliance on high CT values without proper validation, have the potential to mislead not only scientific understanding but also public policy and conservation efforts. These broader implications not only highlight how the flawed study could distort wildlife management strategies, but also the impact on public health responses, and our general understanding of zoonotic diseases.

The erroneous conclusions about the prevalence of SARS-CoV-2 in wildlife populations can lead to significant missteps in wildlife management and conservation. If wildlife is incorrectly identified as a major reservoir of the virus based on flawed data, it could prompt unnecessary and potentially harmful interventions. For instance, there could be calls for widespread culling of certain species perceived as threats, which could disrupt ecosystems and harm biodiversity without actually mitigating the risk of disease transmission. These actions, driven by incorrect data, could have long-lasting negative effects on wildlife populations and their habitats.

Moreover, the flawed study risks skewing public health strategies. Public health decisions rely heavily on accurate data to assess risk and allocate resources. If SARS-CoV-2 is falsely reported as widespread in wildlife, it could lead to the misallocation of public health resources, diverting attention from more significant sources of zoonotic transmission. This could weaken the overall response to the pandemic, as efforts might be focused on controlling a perceived threat in wildlife rather than addressing more pressing human-to-human transmission dynamics.

The misinterpretation of wildlife as a significant SARS-CoV-2 reservoir based on unreliable RT-PCR results also has broader implications for our understanding of zoonotic diseases. Zoonotic spillover events are complex and multifactorial, involving various ecological, environmental, and host-related factors. Misleading data from this study could distort the scientific discourse on zoonotic diseases, leading to a skewed understanding of how viruses like SARS-CoV-2 interact with wildlife and what role they play in transmission cycles. This misunderstanding could influence future research directions, potentially leading to the neglect of more pertinent areas of study that are crucial for preventing future pandemics.

The study's shortcomings highlight the critical need for rigorous scientific standards in wildlife research. The flaws in the RT-PCR methodology underscore the importance of using validated, reliable techniques and ensuring that data interpretation is rooted in robust statistical analysis. The failure to meet these standards in the wildlife study is a cautionary tale for researchers and policymakers alike, emphasizing that scientific rigor must not be compromised, especially when the stakes are high.

The broader implications of the wildlife study's flaws are profound. Misleading conclusions about the prevalence of SARS-CoV-2 in wildlife could result in misguided conservation efforts, misinformed public health policies, and a distorted understanding of zoonotic disease dynamics. To avoid these outcomes, it is essential that future research adheres to stringent methodological standards, including the proper use of RT-PCR, validation of results through sequencing, and careful statistical analysis. We can ensure that wildlife research contributes positively to public health and conservation efforts by maintaining high scientific standards, rather than undermining them.

Conclusion and Recommendations

The wildlife study's reliance on flawed RT-PCR methodologies, particularly the misuse of high CT values without proper validation, has led to unreliable data and misleading conclusions. These methodological errors not only undermine the study’s findings but also have broader implications for wildlife management, public health policies, and our understanding of zoonotic diseases. Addressing these issues with rigorous scientific standards is essential to ensure that future research contributes positively to these fields.

One of the key failures of this study is the absence of robust controls, especially when compared to commercial PCR test kits for other pathogens like MPox and H1Nx influenza viruses. These commercial kits incorporate internal negative controls to prevent false positives, a practice that likely contributed to the lack of an explosion of human cases. This comparison highlights the critical importance of including such controls in wildlife studies to ensure the reliability of the results.

To rectify the issues identified and improve the quality of future research, the following recommendations are essential:

1. Adopt Lower CT Thresholds: Future studies should use more conservative CT thresholds, ideally below 27 cycles, to minimize the detection of non-specific or non-viable viral fragments. This approach will help ensure that only relevant viral loads are detected, reducing the risk of false positives.

2. Implement Rigorous Controls: The use of internal negative controls, as seen in commercial PCR kits for MPox and H1Nx, should be standard practice in wildlife studies. These controls are crucial for distinguishing true positives from false positives and preventing the overestimation of virus prevalence.

3. Conduct Sensitivity Analyses: Researchers should perform and report sensitivity analyses to assess how changes in CT thresholds and the inclusion of additional controls impact the study’s results. This practice will help ensure that the findings are robust and reliable.

4. Incorporate Confirmatory Sequencing: Sequencing should be an integral part of the validation process in wildlife studies using RT-PCR. Confirmatory sequencing ensures that the detected RNA is specific to the target virus, eliminating the possibility of cross-reactivity or contamination.

5. Enhance Transparency and Rigor: Future studies must prioritize transparency in their methodologies, including clear documentation of sample handling, RT-PCR protocols, and statistical methods. This transparency will facilitate peer review and enable other researchers to replicate and verify findings, ultimately strengthening the scientific foundation for public health and conservation decisions.

By implementing these recommendations, researchers can produce more accurate and reliable data that reflects the true prevalence of zoonotic pathogens in wildlife populations. High scientific standards, including the use of internal controls and confirmatory sequencing, are essential not only for advancing our understanding of zoonotic diseases but also for ensuring that public health and conservation efforts are based on accurate and meaningful data. Through rigorous and validated research, we can better manage and mitigate the risks associated with emerging infectious diseases, safeguarding both human health and biodiversity.

Currently, only CDC can test human samples for H1N5, and there is grave concern among scientists outside of the CDC and FDA that somehow they will try to develop a way to use PCR tests without internal negative controls (template controls). Sanger sequencing is inexpensive, and sequencing even 1 out of every 10 or 100 samples will allow an empirical estimate of false-positive rates. There is no excuse for low-specificity testing for bird flu under any circumstances.

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References

Goldberg, A.R., Langwig, K.E., Brown, K.L. et al. Widespread exposure to SARS-CoV-2 in wildlife communities. Nat Commun 15, 6210 (2024). https://doi.org/10.1038/s41467-024-49891-w

Ceci, A., Muñoz-Ballester, C., Tegge, A.N. et al. Development and implementation of a scalable and versatile test for COVID-19 diagnostics in rural communities. Nat Commun 12, 4400 (2021). https://doi.org/10.1038/s41467-021-24552-4