by Saima Kabir, Owen Peterson, and Peter F. Orazem
Livestock disease outbreaks can affect commodity prices through supply disruptions and customer perceptions of quality or food safety. Figure 1 displays the impact of animal disease outbreaks as more flocks and herds get infected. Since these diseases cause a loss in livestock, due to natural death or animal culling, the supply curve will shift to the left and the price will rise. Conversely, knowledge of an animal disease outbreak will cause consumers to worry about food safety, the demand will shift to the left and the price will fall. An outbreak of animal disease will affect both the supply and demand for livestock, and whether the price rises or falls depends on which effect is greater. Since the effect on demand is influenced by a consumer’s level of knowledge, finding their source of information will allow the consumer’s impact on prices to be recorded.
Google was first introduced to the public in 1998 and quickly established itself as the dominant internet search engine. Google Search has a market share of around 90% of the online web search applications. Early on, epidemiologists thought that surges in Google searches could provide an early signal of human disease outbreaks and that the location of searches might help localize where those outbreaks were most serious. Google Flu Trends was touted as being able to track individuals searching common flu symptoms before any of the more serious cases started showing up at doctor’s offices. An early study by Carneiro and Mylonakis (2009) suggests that Google Flu Trends detected flu outbreaks 7–10 days earlier than the case reports provided by the Centers for Disease Control and Prevention (CDC). However, later research suggests that the number of searches was less reliable than hoped, reporting twice as many flu cases as the CDC (Lazer et al. 2014). In their review of relevant studies, Cervellin et al. (2017) conclude that Google searches were driven more by viral media trends than by viral epidemiological disease.
Several papers examine whether Google searches could inform commodity price movements. Nicita (2008) finds that searches on avian influenza were correlated with higher poultry and egg prices. Attavanich et al. (2011) finds that searches on swine influenza were correlated with lower pork prices, suggesting that demand-side responses were more important in that sector. Wang et al. (2024) finds that search intensity for one type of livestock disease could influence the prices of other commodities. This study reexamines the role of Google searches on livestock prices using consistent measures of poultry, pork, and cattle disease keyword searches over a 20-year period and monitors price responses across all three livestock types. We find that online search intensity can provide meaningful signals of market demand and supply conditions that can influence commodity prices, but the relationship depends heavily on whether a disease affects prices through the supply or demand channel.
Data and methods
Google Trends reports how frequently specific search terms are entered relative to total search volume. We compiled estimates of daily Relative Search Volume (RSV) series for the keywords ‘avian flu,’ ‘swine influenza,’ and ‘brucellosis’ from 2004 to 2024.1 We focused our choice of livestock price outcomes on markets that were consistently traded over the 20 years. USDA Agricultural Marketing Service provides daily Midwest breaking stock egg prices that we use as our poultry price measure. The Chicago Mercantile Exchange provides daily nearby lean hog and live cattle futures prices. Since markets are closed on weekends and holidays, we only include trading days.
Once we begin to record the search terms for each disease, we are able to quickly locate periods of interest for each term. In figure 2, we show the reported global searches for avian flu. There are three major surges in search intensity: 2005–2006, 2010–2019, and 2020–2024. The peak occurred in April 2022 (index = 100), coinciding with the most extensive global highly pathogenic avian influenza (HPAI) outbreak on record. The 2021–2022 epidemic caused 2,467 outbreaks in Europe and 185 million US birds lost by April 2025.
Searches for swine flu in figure 3 show a single dramatic spike that occurred in November 2009 (index = 100), corresponding to the global H1N1 pandemic. Relative search intensity remained minimal before and after, with only occasional small spikes. Notably, searches reflected both human health concerns as well as swine health impacts. Because the 2009 spike was driven by human health concerns rather than livestock losses, the searches for swine influenza have a greater negative impact of searches to prices due to this outbreak having a greater impact on demand than the supply impact H1N1 had on hogs.
Search intensity on brucellosis remained relatively stable (20–50 range; see figure 4) throughout 2004–2019, with a sharp isolated peak in September 2020 (index = 100) when the Lanzhou, China, laboratory accidentally released Brucella bacteria causing an outbreak with over 10,000 reported infections. Otherwise, global interest remained steady, reflecting the disease’s chronic nature and continued relevance for animal health.
Figure 5 displays the three price series for beef, pork, and eggs. To make the three series comparable, we divide the daily prices by the mean of the price series, and so each price series reflects deviations around 1. Egg prices showed the greatest volatility. There are clear price surges that align with the three surges in avian flu searches. Pork prices demonstrated more moderate volatility with no apparent price surge associated with the 2009 spike in swine influenza searches. Beef prices showed the least volatility and virtually no correlation with brucellosis searches, possibly because most cattle diseases including brucellosis do not result in mass herd culling. However, a definitive assessment of the relationship between Google searches and commodity prices will require a more formal analysis.
The relationship between livestock prices and Google searches
Table 1 summarizes the regressions of commodity prices on the three Google search measures. The regressions also controlled for export demand and for monthly seasonal factors that would also affect livestock prices.2,3 The coefficients show how a one-percentage-point increase in relative search intensity affects percentage changes in livestock prices.
| Variable | Egg Price | Hog Price | Cattle Price |
|---|---|---|---|
| Poultry disease | 0.060*** | –0.003*** | 0.012*** |
| Swine Disease | –0.011*** | –0.009*** | –0.007*** |
| Cattle Disease | –0.009*** | <0.001 | 0.002*** |
| Model Fit (R-squared) | 0.47 | 0.42 | 0.40 |
Egg Prices: A one-percentage-point increase in Google searches for avian flu results in a 0.06% increase in egg prices. This relatively small effect suggests that small changes in Google searches will have only modest impacts on egg prices. However, avian flu outbreaks should result in a large surge in Google searches. At the peak search intensity of 100, egg prices will increase by 6%. The positive effect suggests that avian flu searches primarily correspond with shrinking supply. Increased search intensity for swine or cattle diseases results in small and negative but statistically significant effects on egg prices.
Hog Prices: When public searches for swine flu rise by one percentage point, pork prices fall by 0.009%, a small but statistically significant effect. At peak search intensity, pork prices fall by only 0.9%. The negative effect suggests that swine disease has larger effects on demand than supply. Searches concerning poultry diseases also slightly reduce hog prices by 0.003% per one percentage point in avian flu searches. Cattle disease searches had essentially no effect on pork prices.
Cattle Prices: The beef market shows the weakest direct relationship with its corresponding disease key words. A one unit increase in brucellosis searches raised prices by about 0.002%. The limited response likely reflects the fact that brucellosis, unlike avian flu, does not typically lead to mass culling of herds in the US beef sector. However, searches related to avian and swine flu have larger effects on cattle prices. A one-percentage-point increase in avian flu raises cattle prices by 0.012% while a one-percentage-point increase in swine flu searches lowers cattle prices by 0.007%.
Overall, the results show that worldwide searches for livestock diseases, measured through Google Trends, have only modest effects on livestock prices. Even very large surges in Google searches result in relatively modest changes in prices, suggesting that investors will not gain much information on livestock price movements from surges in Google searches on livestock disease. Small fluctuations in Google searches generate price changes that are too small to affect investor behavior. Search surges that are large enough to affect prices are so large that investors must already know about the disease. So, while the correlations between Google searches and livestock prices are statistically different from zero, they are not large enough to be economically important.
Footnotes
1. Because RSV values are normalized within each period, we combine and adjust multiple six month windows using monthly averages to build continuous daily series for each disease.
2. To control for international demand effects, we constructed three export demand indices using Principal Component Analysis (PCA) of 10 trade categories from the USDA Foreign Agricultural Service database (2004–2024). Export data were obtained from the USDA Foreign Agricultural Service’s Global Agricultural Trade System (GATS) database (https://apps.fas.usda.gov/gats) at annual frequency. Ten product categories were used, each measured by total export value in US dollars. Annual index values were applied uniformly across the 12 months of each year to match the monthly frequency of the price and search data. Because the 10 export categories are highly correlated, PCA was used to reduce dimensionality and avoid multicollinearity. Three components with eigenvalues greater than 1 were retained, grouped as live animal exports (bulls, live hogs, heifers), processed meat exports (processed cattle, processed pork, variety pork, canned pork), and poultry exports (egg products, live poultry, poultry meat). The first principal component explained 56.5% of the covariation in live animals, 89.4% in processed meats, and 86.5% in poultry. These indices (live animal exports, processed meat exports and poultry exports) serve as control variables in the regression models.
3. In the regression we control for monthly seasonal factors, which are fixed effects on commodity prices that recur every year with seasonal demand. These are predictable, recurring price patterns that happen every year on a calendar schedule, such as egg prices tending to rise around Easter in spring and beef and pork prices shifting with summer grilling season. By adjusting these recurring calendar patterns before measuring the effect of disease searches, ensures price changes we are measuring are genuinely driven by disease-related search activity and not due to seasonal demand.
References
Attavanich, W., B.A. McCarl, and D.A. Bessler. 2011. “The H1N1 Outbreak and US Livestock Prices: An Application of Directed Acyclic Graphs and Error Correction Models.” Journal of Agricultural and Applied Economics 43(4):475-488. https://doi.org/10.1017/S107407080000448X
Carneiro, H.A., and E. Mylonakis. 2009. “Google Trends: A Web-based Tool for Real-time Surveillance of Disease Outbreaks.” Clinical Infectious Diseases 49(10):1557-1564. https://doi.org/10.1086/644528
Cervellin, G., I. Comelli, and G. Lippi. 2017. “Is Google Trends a Reliable Tool for Digital Epidemiology? Insights from Different Clinical Settings.” Journal of Epidemiology and Global Health 7(3):185-189. https://doi.org/10.1016/j.jegh.2017.06.001
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Lazer, D., R. Kennedy, G. King, and A. Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343(6176):1203-1205. https://doi.org/10.1126/science.1248506
Nicita, A. 2008. “The Impact of Avian Influenza on Poultry Markets.” Policy research working paper No. 4554. World Bank.
Nzayisenga, E., and Y. Zhu. 2020. “The Import Trade Forecasting Model Based on PCA: Evidence from Rwanda.” Open Journal of Statistics 10(4):678-693. https://doi.org/10.4236/ojs.2020.104042
Pappas, S. 2023. “Global Health Trends and Surveillance.” American Psychological Association. https://www.apa.org
US Department of Agriculture Foreign Agricultural Service. (n.d.). Global Agricultural Trade System (GATS). https://apps.fas.usda.gov/gats
Wang, Y., O. Isengildina Massa, and S.L. Stewart. 2024. “Time-varying Reaction of U.S. Meat Demand to Animal Disease Outbreaks.” Applied Economic Perspectives and Policy 46(3):983-1009. https://doi.org/10.1002/aepp.13431
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Suggested citation
Kabir, S., O. Peterson, and P.F. Orazem. 2026. “Google Searches and Livestock Prices, 2004–2024.” Agricultural Policy Review, Spring 2026. Center for Agricultural and Rural Development, Iowa State University. https://agpolicyreview.card.iastate.edu/spring-2026/google-searches-and-livestock-prices-2004-2024