Abstract
Disease names that convey the location of discovery, the discovering scientists, the species of discovery, or the most impacted populations have been increasingly recognized as problematic—often leading to or amplifying xenophobia, disrepute, and stigma. In this context, in 2022 the World Health Organization proposed naming diseases after their causative pathogen or symptomatology instead. This recent guidance has been retrospectively applied to a disease at the center of an epidemic rife with intolerance and misinformation, predominantly affecting the already-stigmatized LGBTQ + community: mpox (formerly known as ‘monkeypox’). We used publicly available data from Google Trends to determine which countries or territories adopted this name change and to identify factors that influenced the preferential use of ‘mpox’ over its stigmatizing predecessor ‘monkeypox’. Specifically, we built regression models to quantify the relationship between ‘mpox’ search intensity in a given country or territory and a suite of sociopolitical, health system, and inequality variables. Key results suggest that search intensity for ‘mpox’ was higher than that for ‘monkeypox’ in locations with greater LGBTQ + acceptance and lower in locations governed by leaders who had recently propagated infectious disease-related misinformation. While the adoption of a given disease name will always be context-specific—depending in part on its origins and the affected populations—our study provides generalizable insights that are applicable to future changes in disease nomenclature.
Similar content being viewed by others
Introduction
Historically, diseases have often been named after the animal that first presented with the pathogen (e.g., monkeypox, swine flu, bird flu); the location of first diagnosis or report (e.g., Spanish flu, Marburg virus, Ebola Zaire virus); or the discovering scientist or affected patient’s name (e.g., Hodgkin’s lymphoma, Alzheimer’s disease, Chagas disease)1,2,3. However, since the initial release of specific guidelines in 2015, the World Health Organization (WHO) has recommended avoiding such practices and instead favoring symptomatology or the causative pathogen when naming new diseases4. This recommendation is responsive to rising xenophobia and avoidance related to location-centric names (e.g., anti-Mexican sentiment following the "Mexican swine flu" moniker for the 2009 H1N1 pandemic5), culling of animal populations wrongly believed to be reservoirs for a disease (e.g., pig culling in Egypt due to the 2009 H1N1 “swine flu” pandemic6), and occasionally, the prejudicial past of a discovering scientist (e.g., Rett syndrome, named after an Austrian neurologist who was a member of the Nazi party in his youth7). In some cases, a disease name may not even accurately reflect its origins. This scenario is best illustrated by the Spanish flu, which likely originated in North America, but was first reported in Spain due to limited communication during World War I8. Further, the species of discovery may not be the reservoir species, adding to confusion. The case of mpox fits into this scenario: the virus was discovered in 1958 in a captive cynomolgus monkey in Denmark, yet rodents are far more likely than monkeys to be the true reservoir species9,10. As such, during the COVID-19 pandemic, particular care was taken in naming the disease caused by the novel coronavirus (SARS-CoV-2), precisely by using the new WHO naming conventions. Despite this care, the magnitude of the COVID-19 pandemic highlighted pervasive xenophobia and racism tied to the origins of its causative pathogen and the importance of appropriate risk communication11,12. While authors of scientific publications generally used the formal disease name ‘COVID-19’, prominent figures in politics and the media have not similarly followed suit, often emphasizing the origins of COVID-19 by using inflammatory language to refer to the virus. One such example is US President Trump’s reference to SARS-CoV-2 as the “Chinese virus” in 202013. Given the use of such stigmatizing language in tweets and press briefings, both online anti-Asian sentiment and physical violence against Asians and Asian Americans rose dramatically13,14,15,16,17,18,19.
While the COVID-19 pandemic raged on, an epidemic of mpox (formerly known as ‘monkeypox’) simultaneously spread to over 50 countries in 2022–202320. Mpox is native to west and central Africa, with only a few sporadic outbreaks recorded in other countries up until 2022—when an unprecedented epidemic, comprised of multiple country-level outbreaks, unfolded21,22,23. Sexual transmission of mpox was observed for the first time during the 2022–2023 epidemic24. Given the high visibility of this mode of transmission throughout this epidemic, comparisons with the early HIV/AIDS epidemic were drawn, leading to increased stigmatization of gay men in particular25,26,27. Additionally, given the epidemic’s unprecedented global reach, the presentation and naming of the disease were more visible than ever before. The name ‘monkeypox’ not only constituted a misnomer, but also perpetuated an offensive stereotype of African populations, among whom the bulk of mpox infections had previously occurred28,29,30. Because of this egregious comparison and the use of African patients to portray mpox lesions early in the epidemic, Africans and other Black populations unjustly faced the initial burden of discrimination and stigma associated with mpox31. However, the heightened transmission among gay, bisexual, and other men who have sex with men (GBMSM) throughout the 2022–2023 epidemic subsequently made GBMSM and other LGBTQ + populations the main target of discrimination. In order to combat pre-existing and newly emerging stigma, in August 2022 the WHO advocated for renaming the disease. Following this call, the name ‘monkeypox’ was formally changed to ‘mpox’ on November 28, 2022, allowing for one year of simultaneous use of the two names before the former was fully phased out32,33.
The transition from ‘monkeypox’ to ‘mpox’ is the first known example of a formal name change for an infectious disease in the Digital Age, designed to explicitly address prejudice and misinformation. It echoes former experiences with AIDS in the early 1980s, when the disease was referred to as "Gay-Related Immune Deficiency" or GRID34,35. Similar to mpox, which was named after its first identification in a monkey9, ‘GRID’ was named after its first presentation in gay men in Los Angeles in 198036, resulting in tremendous stigmatization of the LGBTQ + community. Ultimately, in August 1982, the Centers for Disease Control introduced a non-stigmatizing name for this novel disease: Acquired Immune Deficiency Syndrome (AIDS). Nevertheless, the damage associated with the initial name had long-term repercussions—and this stigma persists today37. As such, the 2015 WHO recommendation endeavored to avoid the use of stigmatizing language for newly-named diseases4. Yet no formal and sufficiently broad guidance has been provided to date for retroactively changing existing disease names en masse, despite countless examples with prejudicial connotations that would require such intervention. In the absence of formal retrospective naming guidelines, the 2022–2023 mpox epidemic not only provides an interesting case study but also creates a precedent for the WHO and its national counterparts to build upon.
Unfortunately, despite the formal guidance released in 2022, the global adoption of the newly recommended term ‘mpox’ has been inconsistent. Indeed, in a more recent outbreak of mpox, which was declared a Public Health Emergency of International Concern (PHEIC) in August 2024, news sources across many countries still used the now-outdated term ‘monkeypox’38,39,40,41,42,43,44,45. We view the use of ‘monkeypox’ in lieu of ‘mpox’ as perpetuating racist and bigoted ideas by continuing to use this outdated and offensive terminology, as mpox historically most impacted Black individuals but now disproportionately affects LGBTQ + individuals. Thus, while our objective was to understand how stigmatizing attitudes and language relate to social factors such as acceptance of diverse identities, the use of stigmatizing language can also be interpreted as a reflection of existing societal stigma. In this study, our goal was to understand which factors influenced whether a country or territory was a timely adopter of the name change from ‘monkeypox’ to ‘mpox’. Specifically, we leveraged Google Trends (GT) to examine national-level Internet search query intensity of the terms ‘mpox’ and ‘monkeypox’ between November 2022 and December 2023, the time period over which the WHO recommended the dual use of both ‘mpox’ and ‘monkeypox’. Previously, GT has been used to study infectious diseases such as seasonal influenza, Zika, SARS-CoV-2, and measles, among others—from predicting and monitoring their spread, to understanding spatiotemporal variation in health-seeking behaviors, to contextualizing symptom searches and developing tailored risk communication plans, and more46,47,48,49,50,51,52,53,54,55,56.
Our main analytic goal was to understand the drivers of the adoption of the term ‘mpox’ at the national level. To that end, we defined two key outcomes: (1) a binary variable reflecting whether the average Google search intensity was higher for ‘mpox’ than for ‘monkeypox’ during our study period; and (2) a continuous variable defined as the fraction of the average Google search intensity for ‘mpox’ and ‘monkeypox’ attributable to ‘mpox’ during our study period (i.e., \(\frac{{s_{mpox} }}{{s_{mpox} + s_{monkeypox} }}\), where \(s_{mpox}\) and \(s_{monkeypox}\) represent the search intensity for ‘mpox’ and ‘monkeypox’, respectively). Through outcome (1), our objective was to identify the characteristics of countries and territories where a majority of Google search users had adopted the term ‘mpox’. Yet we recognize that the broad adoption of a new disease name by the general public is a time-intensive process. Following a change in disease name, adoption typically happens incrementally rather than immediately. Through outcome (2), we thus sought to better understand factors associated with incremental improvements in the uptake of ‘mpox’, even when a country or territory was not considered a ‘majority adopter’. We constructed regression models to associate these two outcomes with static national-level factors such as type of political regime, robustness of sociopolitical and health systems, level of pandemic preparedness, extent of gender and educational inequalities, temporal evolution of confirmed local mpox cases during the 2022–2023 epidemic, and distribution of mpox cases since its discovery. To our knowledge, no analyses have investigated these national-level factors as they relate to ‘mpox’ uptake, using GT or otherwise. Through this study, we hope to provide insights not only into national-level factors that were most conducive to the public’s adoption of the destigmatized name ‘mpox’ in 2022–2023—as reflected by the intensity of Google search queries—but also into improving the adoption of future changes in disease names.
Results
Over the study period, a majority of Google search users adopted the term ‘mpox’ in 9% (n = 17) of the 188 countries and territories considered in our analysis. Hereafter, we refer to these countries and territories as ‘majority adopters’. In other words, in these locations, the average Google search intensity for ‘mpox’ was higher than that for ‘monkeypox’ between November 1, 2022 and December 1, 2023. Among majority adopters, the median average search intensity for ‘mpox’ (on a scale from 0 to 100) over the study period was 9.1 (IQR: 5.9–14.5); in contrast, the median ‘mpox’ search intensity in other locations was 0.0 (IQR: 0.0–2.3). We analyzed how the intensity of Google searches for ‘mpox’ and ‘monkeypox’ correlated with a set of 18 covariates hypothesized to affect adoption of the term ‘mpox’. In doing so, we identified three consistent national-level factors significantly associated with the adoption of the term ‘mpox’: (1) any prior history of mpox in the country or territory, (2) greater LGBTQ + acceptance, and (3) no senior leader who recently propagated infectious disease misinformation. Importantly, our results were generally robust to the imputation strategy chosen. We present key summary statistics for all measured covariates, both pre- and post-imputation, in Table 1. Additionally, we compare the relative impact of three imputation strategies—as described in the Methods section—on the outcome and explanatory variables used in our models (Table 1, columns 2–4). Further, a visual representation of the imputation process is available in Appendix Figure S5.
Across all univariable logistic regression models, ten variables were significantly associated (p < 0.05) with our binary outcome, i.e., majority ‘mpox’ adoption (Fig. 1a). Meanwhile, in univariable linear regression models, we observed 18 significant associations (p < 0.05) with our continuous outcome, i.e., the fraction of searches attributable to ‘mpox’ (hereafter referred to as the ‘mpox’ search proportion) (Fig. 2a). When including only those variables with univariable p-values below our model inclusion threshold of 0.1 in the two corresponding multivariable models, we identified three consistently significant relationships across our two outcomes: two positive associations and one negative association. First, countries and territories that had ever reported an mpox case in humans or in mammals, either as part of the 2022–2023 epidemic or in a prior outbreak, were both more likely to adopt the new disease name and more likely to register a higher search proportion for ‘mpox’. Second, locations with a higher LGBTQ + Global Acceptance Index (GAI) score had both greater ‘mpox’ adoption and higher levels of the ‘mpox’ search proportion. Third, countries and territories where senior leaders had recently propagated misinformation about infectious diseases registered both lower ‘mpox’ adoption and lower levels of the ‘mpox’ search proportion (see Figs. 1b and 2b for the results of our multivariable logistic and linear regression models, respectively). More specifically, for every one-unit increase in the LGBTQ + GAI score of a given country or territory (on a scale from 0 to 10), we estimated 1.57 times higher odds [95% Uncertainty Interval (UI): 1.25;2.00] that a majority of Google search users in that location would adopt the term ‘mpox’, while accounting for other national-level covariates (Fig. 1b). Likewise, via our multivariable linear regression model, we estimated a 2.9 percentage point [95% UI: 1.7;4.9] increase in the ‘mpox’ search proportion for every one-unit increase in the LGBTQ + GAI score (Fig. 2b). Further, countries and territories with a prior history of mpox were 10.69 times more likely [95% UI: 1.54;15.62] to be majority adopters of the term ‘mpox’. In our continuous outcome model, we similarly found a 10.3 percentage point [95% UI: 6.6;13.6] increase in the ‘mpox’ search proportion in such locations. Lastly, countries and territories where senior leaders previously propagated misinformation regarding infectious diseases were 74% [95% UI: 53%;74%] less likely to adopt the new disease name. Likewise, through our multivariable linear regression model, we estimated a 11.0 percentage point [95% UI: -19.9;-1.7] decrease in the ‘mpox’ search proportion in such locations. Notably, the variable pertaining to misinformation propagation by senior leaders was present in only four of the ten imputation models for the binary outcome, but in all ten imputation models for the continuous outcome (Appendix Tables S3 and S4, respectively). While the absolute values of the point estimates obtained after fitting a logistic versus linear regression model differed in magnitude (i.e., OR versus \(\beta\)), their relative values were comparable. Indeed, in our binary outcome model, the OR for whether a country or territory ever had a case of mpox was approximately 6.8 times greater (10.69/1.57) than that for the LGBTQ + GAI score; similarly, in our continuous outcome model, the \(\beta\) coefficient for ever having had a case of mpox was approximately 3.6 times greater (10.3/2.9) than that for the LGBTQ + GAI score. In sum, regardless of the outcome measure (i.e., binary versus continuous), the same three key variables were identified through our statistical analyses: prior history of mpox, LGBTQ + acceptance, and prior history of misinformation propagation by senior leaders. Importantly, we observed highly consistent patterns across our sensitivity analyses for the binary and continuous outcome models: all showed a significant positive relationship with LGBTQ + acceptance; 7 of 10 eligible binary models and 11 of 11 eligible continuous models showed a significant positive relationship with prior history of mpox; and 6 of 12 binary and 11 of 12 continuous models showed a significant negative relationship with prior history of misinformation propagation by senior leaders (see Appendix Sect. 5 for more details).
National-level factors associated with ‘mpox’ majority adoption (binary outcome) in univariable (a) and multivariable (b) models.
Variables that had p-values below our inclusion threshold of 0.1 in the corresponding univariable logistic regression models (a) were included in the multivariable model (b). Highly collinear variables were removed before fitting the multivariable model. Models were all built using the fully imputed data set (i.e., Approach 3). On the figure, an asterisk (*) denotes a binary variable. Note that our multivariable model included a total of 8 variables (2 binary variables and 6 continuous variables). Notably, the logit-transformed variable ‘female access to mobile phones’ was included in only 4 of the 10 models (one for each of the 10 considered imputations); thus, the 95% uncertainty interval (UI) for the corresponding odds ratio (OR) is computed over these 4 models. Similarly, the binary variable ‘senior leaders used misinformation’ was included in only 3 of the 10 models; the 95% UI for the corresponding OR was also computed over these 3 models. The goodness-of-fit of the multivariable logistic regression model (b) was assessed using a pseudo R2 metric, namely the McFadden’s R2(ranging from 0 to 1), as well as the AIC, both averaged across all ten imputations. The average R2 was estimated at 0.184 and the average AIC was estimated at 107.8. Mean pseudo R2 values and AIC values corresponding to each univariable logistic regression model (a) appear in Appendix Table S3.
National-level factors associated with ‘mpox’ search proportion (continuous outcome) in univariable (a) and multivariable (b) models.
Variables that had p-values below our inclusion threshold of 0.1 in the corresponding univariable linear regression models (a) were included in the multivariable model (b). Highly collinear variables were removed before fitting the multivariable model. Models were all built using the fully imputed data set (i.e., Approach 3). An asterisk (*) denotes a binary variable in the model. Note that our multivariable model included a total of 12 variables (3 binary variables and 9 continuous variables). The goodness-of-fit of the multivariable linear regression model (b) was assessed using the adjusted R2 metric (ranging from 0 to 1) and the AIC, both averaged across all ten imputations. The average R2 was estimated at 0.291 and the average AIC was estimated at -129.9. Mean adjusted R2 and AIC values corresponding to each univariable linear regression model (a) appear in Appendix Table S4.
Beyond these three consistent associations emerging from both our binary and continuous outcome models, we identified two distinct significant positive associations, specific to each outcome. Through our multivariable logistic regression model for the binary outcome, we identified an additional relationship between a higher ratio of female-to-male access to the Internet and ‘mpox’ majority adoption (OR = 1.17 [95% UI: 1.01;1.41]). Through our multivariable linear regression model for the continuous outcome, we identified an additional relationship between a higher 2021 Global Health Security Index (GHSI) Overall Score and ‘mpox’ search proportion (\(\beta \hspace{0.17em}\)= 0.003 [95% UI: 0.001;0.005]). Notably, the strength of these two outcome-specific associations was less pronounced than that of the three consistent associations we uncovered and described above. Thus, caution is warranted when interpreting the role of greater female access to the Internet or higher GHSI overall scores in ‘mpox’ adoption; further research is needed to better understand the unique contribution of these factors at the national level.
Discussion
Our study suggests that countries and territories with higher LGBTQ + acceptance, as measured by the GAI, were more likely to adopt the term ‘mpox’ over the term ‘monkeypox’ in the year following the WHO recommendation of the name change. In other words, empirical evidence from the 2022–2023 epidemic indicates that locations where the public is more accepting of queer communities were also more likely to demonstrate a thoughtful use of destigmatizing language when conducting Google searches. Importantly, this finding was consistent across all sensitivity analyses we conducted, emphasizing its robustness to the choice of imputation strategy and model type. In contrast to historical mpox outbreaks that predominantly affected west and central Africa and had no reported sexual transmission, the 2022–2023 mpox epidemic has had a global reach and has largely been identified as a disease predominately afflicting the GBMSM community66,67. As a result, this community has faced new backlash and stigma, now related specifically to mpox26,27,68,69. Indeed, when interviewed, several members of the GBMSM community emphasized a fear not only of mpox itself, but also of the stigma they were likely to face69. Therefore, our study underscores the need to increase awareness and acceptance of marginalized populations. As exemplified in previous outbreaks—most notably HIV/AIDS— stigma associated with sexual transmission not only impacts a person’s quality of life, but can also limit their access to care and reduce their propensity to accurately report their behaviors. These negative repercussions can in turn lead to delayed treatment, poorer health outcomes, and sustained disease propagation70,71,72,73. More recently, a sentiment analysis of tweets suggested that mpox misinformation had contributed to heightened stigma of LGBTQ + populations, resulting in increased violence, harassment, and isolation74. Thus, in order to increase awareness and acceptance of marginalized populations going forward, strong risk communications combating bigotry, prejudice, and stigma are critical early in infectious disease crises75,76. Moreover, focused anti-bullying and anti-prejudice campaigns in schools may contribute to improving the acceptance of LGBTQ + communities and other stigmatized populations and, ultimately, to a more empathetic future generation.
Another strong correlate of ‘mpox’ search intensity—in terms of significance in both the binary and continuous models as well as in our sensitivity analyses—was countries or territories that had ever had a confirmed case of mpox, in humans and other mammals alike. We hypothesize that people living in places where cases had previously been identified had increased exposure to news and social media about mpox, thus increasing their awareness of the new disease name. This assumption aligns with prior research about the impact of news media on infectious diseases (e.g., mpox, H1N1, Ebola, COVID-19), which has demonstrated that increased exposure to quality information through news and social media can increase vaccine uptake75,76, reduce disease transmission77, and encourage protective behaviors78,79,80. Future research evaluating media communications regarding the change in disease name—from ‘monkeypox’ to ‘mpox’—may provide valuable insights into how different news sources referred to the disease over time; whether any political, geographical, or temporal trends manifested; and what messaging at what time point corresponded to higher uptake of the term ‘mpox’. In fact, the recent August 2024 mpox outbreak illustrates in near real-time the mixed adoption of the new disease name by mainstream news and social media38,39,40,41,42,43,44,45,81,82. Future investigations of the use of ‘mpox’ by the media throughout the 2024 outbreak and resulting PHEIC could provide meaningful insights into the adoption of this term—allowing researchers to generate more specific guidance for future media campaigns targeting changes to infectious disease nomenclature.
Finally, we identified a third strong correlate of ‘mpox’ adoption: in locations where senior leaders had recently propagated infectious disease misinformation, adoption of the new disease name by the general public was significantly lower. Such misinformation has been increasingly identified as a barrier to vaccination83,84,85,86, seeking timely care83,87,88, and positive behavioral change83,89; has eroded trust in governments and authorities90,91,92; and has ultimately worsened health outcomes88,91,93. During the COVID-19 pandemic, misinformation was employed by politicians in the US and abroad to promote the use of untested medicine such as ivermectin, azithromycin, chloroquine, and hydroxychloroquine94,95,96; the avoidance of many public health guidelines like vaccination or mask wearing97,98,99; and dangerous practices like ingesting or injecting bleach100,101, resulting in surges in product purchases, price hikes, and in some cases, serious injury or death94,102,103. Several strategies have been employed to combat the growing threat of misinformation: emphasizing quality sources of information; capitalizing on trusted leaders, officials, and scientists to convey accurate information; correcting and calling out misinformation; and increasing the frequency of both proactive, accurate messaging and correcting inaccurate information104. Currently, consequences incurred by leaders or politicians who spread misinformation vary by country or territory and by region. First, they may face financial and legal consequences, including penalties such as fines and, in some cases, imprisonment105. Second, social media platforms themselves may take action and either restrict user privileges (e.g., retweeting, sharing, or posting) or ban such leaders outright; yet many countries still struggle to walk the line between action and censorship106,107. While elected officials who propagate misinformation should undoubtedly be sanctioned, no one course of action is likely to suit every location. For example, countries where social media organizations are headquartered generally have the financial means to strengthen online governance efforts by supporting dedicated investments in content monitoring and corrections, provided the willingness exists. In contrast, journalists and media organizations based in countries with less democratic ideals do not have similar options105. In such contexts, the Center for International Media Assistance recommends that global health organizations instead invest in media and digital literacy efforts to protect the public from the threat posed by misinformation and thereby facilitate the adoption of appropriate disease-related terminologies105. In the future, researchers should—in collaboration with dedicated counter-misinformation oversight committees, when possible—seek to understand how effective each strategy is at reducing the spread of misinformation in a given country or territory and to provide evidence that guides local decision making. Given that sensationalized and stigmatizing naming of diseases has been weaponized in the past (most notably during COVID-19), focusing on combating misinformation, regarding both infectious diseases in general and the choice of naming conventions more specifically, will hopefully improve the adoption of carefully chosen, destigmatizing names for current and future pathogens.
Limitations
There are several limitations to this study. First, our main analysis relied on English as the lingua franca of scientific communication and our sensitivity analysis (Appendix Sect. 5.3, Figures S10 and S11) considered the five additional United Nations (UN)/WHO languages (i.e., Arabic, Chinese, French, Russian, Spanish). These five languages—while widely used—account for only a small fraction of all the languages spoken across the world; as such, we are likely missing search terms for ‘monkeypox’ expressed in other languages, resulting in an incomplete capture of Google search interest in the disease globally. While we acknowledge the diversity of languages worldwide and its importance in public health, around 4 billion people speak at least one of the 6 considered UN/WHO languages as a primary or secondary language, representing about half of the world’s population108,109.
Second, consistent with the WHO nomenclature announcement, we only searched the single terms ‘mpox’ and ‘monkeypox’ rather than more specifically for the composite terms ‘mpox virus’ and ‘monkeypox virus’, which remain the official taxonomic names. However, given the boolean methodology used by GT, search interest data collected for the terms ‘mpox’ and ‘monkeypox’ constitute a superset that would also account for the terms ‘mpox virus’ and ‘monkeypox virus’, respectively110.
Third, at the time of our analysis, foreign language data for two countries—Ecuador and Madagascar—were unavailable. To address this limitation, we attempted a counterfactual sensitivity analysis for the binary outcome of majority adoption. Specifically, we generated all possible permutations (i.e., assuming that each of Ecuador and Madagascar could have been either a majority adopter country or a non-adopter country) and re-fitted the logistic regression model for each scenario. The corresponding results were largely similar results to those from our main analyses, underscoring the robustness of our key findings (Appendix Sects. 3.8, 5.10, and Figure S24).
Fourth, this study is based exclusively on Internet searches conducted by users via Google Search alone, thus excluding those attributable to other search engines. Future research examining searches for ‘mpox’ and ‘monkeypox’ on other search engines (e.g., Bing, DuckDuckGo, Yahoo) would thus provide valuable comparative insights. Importantly, over 90% of Internet users worldwide use Google as their primary search engine—with the exception of China and Russia—suggesting that GT are capturing the majority of global Internet searches111,112.
Fifth, for the variable ‘ever had a case of mpox’, there were few countries and territories that had never reported an mpox case that were also ‘majority adopters’. As such, the uncertainty intervals for this variable were wide, especially in some of the sensitivity analyses regarding the chosen imputation strategy. Despite this imprecision in point estimates for odds ratios and beta coefficients, the variable appeared as significantly associated with ‘mpox’ adoption across many of our sensitivity analyses; as such, we believe this is a robust finding that warrants discussion and further exploration.
Sixth, this analysis is correlative in nature and not causative; as such, we cannot conclude that the societal factors we identified as significantly associated with ‘mpox’ adoption effectively lead to greater ‘mpox’ adoption. Indeed, the use of stigmatizing language is also a reflection of the state of tolerance in society, making it equally possible that greater LGBTQ + acceptance, for example, is rather an indication of a country with lower levels of existing stigma. Future research connecting survey data about tolerance in society with GT data would provide additional insights into the state of existing stigma in a given country or territory and thus help in disentangling this relationship.
Seventh, while GBMSM and LGBTQ + individuals were the most stigmatized during the 2022–2023 epidemic, the original name ‘monkeypox’ was more discriminatory towards Black individuals. Although our measure of LGBTQ + acceptance does not capture racial inequity, we hypothesize that many countries with higher social acceptance also have higher levels of racial acceptance57,113, though this may not be universally true; further research is needed to test this hypothesis.
Eighth, our main analysis employs a multivariate random normal model using the popular MASS package in R, which does not use random seeding—a critical feature to ensure that results can be reproduced114,115. As such, we acknowledge slight variability in our main model results across iterations. To address this limitation, we implemented sensitivity analyses using two alternate R packages, namely rockchalk114,116 and mvtnorm117,118. Notably, the use of the MASS package resulted in fewer statistically significant covariates and wider uncertainty intervals than analyses using the two other packages, leading us to view our main results as more conservative. Analyses using rockchalk and mvtnorm appear in Appendix Sect. 5.7, Figures S15-S18; the corresponding results largely corroborate our main conclusions using the MASS package.
Ninth, we implemented univariable screening as a preprocessing step and applied a variable inclusion threshold of 0.1. In other words, variables having a sufficiently strong association with the outcome, with a corresponding p-value < 0.1, were included into our multivariable models; others were not included. Notably, this preprocessing step was repeated for each of the ten imputations. As with any statistical threshold, a trade-off exists when choosing a relevant threshold for inclusion. Just as the statistical significance threshold “p-value < 0.05” is conventionally used as a compromise between type I and type II error rates, yet remains fundamentally context-dependent119,120, thresholds for variable inclusion used in the literature have varied. For instance, 0.05, 0.1, and 0.25 have all been previously considered121,122,123,124,125,126,127. In medical sciences in particular, the same p < 0.05 threshold is often used for both multivariable model inclusion and variable significance123,124,125. In this analysis, we chose a more conservative threshold of p < 0.1 for variable inclusion in the main models to avoid omitting variables deemed relevant by domain experts while simultaneously avoiding high multicollinearity and maintaining model parsimony. In order to assess the robustness of our regression models to the chosen variable inclusion threshold, we conducted three threshold-based sensitivity analyses: (1) we considered a lower (i.e., more stringent) inclusion threshold of 0.05, a conventional value used in univariable screening to favor parsimonious multivariable outcome models and match the level of statistical significance123,124,125, (2) we considered a higher (i.e., less stringent) inclusion threshold of 0.25, a value that has been frequently used in univariable screening to allow for the inclusion of a wider number of variables and thus balance model parsimony with the risk of omitting an essential covariate121,122, and (3) we ignored inclusion thresholding altogether, i.e., we included all non-collinear variables into our multivariable models. This third approach effectively corresponds to an inclusion threshold of p < 1.0. The results of these sensitivity analyses appear in Appendix Sect. 5.9, Figure S21 for p < 0.05, Figure S22 for p < 0.25, and Figure S23 for p < 1.0. We similarly considered the impact of using automated variable selection methods—namely, L1 regularization via LASSO regression—to fit parsimonious multivariable models. These additional sensitivity analyses were highly confirmatory of the results obtained in our main analyses; corresponding results appear in Appendix Figures S19 and S20.
Lastly, we employed a suite of 18 covariates in our regression models, with varying degrees of missingness. We used multiple imputation with as many complete variables as possible, but acknowledge differences in resulting sample sizes and covariate data distributions between the complete-case analysis (Approach 1, n = 154), the GAI-only imputation analysis (Approach 2, n = 166), and the full imputation analysis (Approach 3, n = 188). The most notable difference was observed for the gross domestic product (GDP) variable. Since this covariate was available for all 188 considered countries and territories, we could contrast its overall distribution to that in the analyses restricted to 154 (Approach 1) and 166 (Approach 2) countries and territories, respectively. Specifically, we observed a lower median GDP in both the GAI-only imputation analysis ($6609.60) and the complete-case analysis ($6609.60) than overall ($6991.30). This observation suggests that health system quality, pandemic preparedness, gender equitability, and social acceptance, which are all correlated with GDP, were likely not missing completely at random. Nonetheless, all of our sensitivity analyses regarding the chosen imputation strategy yielded similar results, reinforcing the robustness of our conclusions.
Conclusions
The first part of the twenty-first century has been marked by a surge of zoonotic disease transmission, outbreaks, and pandemics, including a recent mpox PHEIC declared in August 2024. Given increasing population connectivity and mobility, deforestation and development encroaching on zoonotic reservoirs, and climate change, this trend is likely to continue. As such, the potential for stigmatizing language and xenophobia, as well as their downstream repercussions, are becoming increasingly visible. We need to plan now for ways to counter this stigma. Learning from those countries and territories that have been most successful at instituting ‘mpox’ in lieu of ‘monkeypox’ offers a promising starting point. With focused efforts to improve social acceptance—particularly in schools—as well as more sanctions and targeted messaging to counteract misinformation—particularly on social media—we hope for greater acceptance not only of new disease nomenclature but also of stigmatized populations in future outbreak scenarios.
Methods
Data sourcing and preprocessing
Google Trends
We obtained the trajectory of Google search intensity for ‘mpox ‘ and ‘monkeypox ‘ over time through Google Trends (GT), a publicly-available online platform that provides anonymized, categorized, and aggregated data. We included 188 countries and territories with available GT data on either term throughout the study period (November 1, 2022 to December 1, 2023), out of 249 distinct countries and territories captured by the platform overall. Given that English is currently the lingua franca in science, thus serving as the language of disease communication and nomenclature, we relied on Google search queries made in English in our primary analysis. However, we acknowledge the inherent power imbalance of Anglocentrism128,129,130,131,132,133. Therefore, in an effort to also understand the dynamics of public interest in ‘mpox’ versus ‘monkeypox’ in countries and territories where English is not the primary language, we considered the five other official UN/WHO languages: Arabic, Chinese, French, Russian, and Spanish. We report the results of this sensitivity analysis, aimed at characterizing the intensity of Google searches that users made in their native language, in Appendix Sect. 5.3.
GT returns the proportion of Google search activity accounted for by each search term in a given location (e.g., metropolitan area, state, country or territory) at a given point in time (e.g., day, week). This quantity, ranging between 0 and 100%, is defined as the number of term-specific searches in that location at that time divided by the total number of searches over that same region and timespan134,135. By collecting data for both search terms simultaneously (i.e., ‘mpox’ and ‘monkeypox’), we were able to compare their respective search intensity on the same spatiotemporal scale for the period ranging from November 1, 2022 to December 1, 2023. In sum, using both search terms simultaneously allowed us to capture the comparative trajectories of ‘mpox’ and ‘monkeypox’ search intensity over thirteen months of Google searches performed by the general public following the WHO announcement of the name change made in November 2022.
Outcome variables
We derived two outcome variables defined at the national level. Both variables were based on the average Google search intensity for the terms ‘mpox’ and ‘monkeypox’ in a given country or territory over the study period. The first outcome, a binary variable, captured whether a country or territory had a greater average search intensity for ‘mpox’ than for ‘monkeypox’ (1 if yes, 0 otherwise), allowing us to distinguish ‘mpox’ majority adopters from non-adopters among the 188 countries and territories we considered. The second outcome, a continuous variable, was defined as the fraction of search intensity attributed to ‘mpox’ over the study period (i.e., the ratio of the search intensity for ‘mpox ‘ over the total search intensity for ‘mpox’ and ‘monkeypox’ combined, expressed as a percentage value); hereafter, we refer to this variable as the ‘mpox’ search proportion. While we defined two distinct outcomes to test the sensitivity of our findings to the use of metrics with different granularity (i.e., binary versus continuous), our overall analytic goal was to identify the consistent factors driving the adoption of the new name ‘mpox’; thus, our results are presented and interpreted jointly.
Covariates
In this study, we sought to identify national-level factors contributing to the adoption of the term ‘mpox’ globally. To achieve this goal, we compiled a suite of 18 publicly-available national-level covariates to model their associations with ‘mpox’ search intensity (Table 1). The considered national-level covariates span seven broad categories: LGBTQ + acceptance (1 variable), political context (4 variables), healthcare access and quality of care (1 variable), global health security (8 variables), country wealth (1 variable), average educational attainment (1 variable), relative timing of 2022–2023 mpox epidemic (1 variable), and prior history of mpox outbreaks (1 variable). In Appendix Sect. 3.9, we define each variable in greater detail and provide our rationale for its inclusion in the outcome models. Except where transformations were needed to achieve normality assumptions (4 variables), all covariates were kept in their original scale for ease of interpretation. All transformations are further described in Appendix Sect. 3.1.
Given varying patterns of data missingness across the 188 countries and territories included in the analysis (Appendix Figure S2), we used a multiple imputation approach with predictive mean matching over ten imputations. We considered three different combinations of data selection and imputation methods: (Approach 1) selecting countries and territories with no missing data and performing a complete-case analysis; (Approach 2) selecting countries and territories that either had complete data or were missing only the LGBTQ + GAI variable and imputing it where necessary; and (Approach 3) imputing all variables with missing data. Approaches 1, 2, and 3 resulted in the inclusion of 154, 166, and 188 distinct locations, respectively. Results presented in the main text capitalize on the fully imputed dataset (i.e., Approach 3), while results relying on the two other imputation strategies (i.e., Approaches 1 and 2) appear in Appendix Sects. 5.1 and 5.2, respectively.
Modeling
Following data extraction and preprocessing, we built univariable and multivariable regression models separately for each of our two outcomes. The binary outcome—whether a location was a majority adopter (1) or not (0)—was modeled using logistic regression models with time-invariant covariates. The continuous outcome—‘mpox’ search proportion—was modeled using generalized linear regression models with the same time-invariant covariates. First, we evaluated the strength of the univariable association between each of the two outcomes of interest (i.e., majority adoption, defined as a binary variable, and the proportion of searches attributable to ‘mpox’, defined as a continuous variable between 0 and 1) and each of the 18 considered covariates, using univariable logistic and linear regression models, respectively. Notably, the choice of these 18 covariates was guided by expert opinion. Multiple imputation was used to handle missing data; in total, ten imputation rounds were performed. For each imputed dataset, we built a separate logistic regression model and a separate linear regression model. We used individual univariable model p-values to decide whether to retain a given variable in the corresponding multivariable model, built upon that imputed dataset. For variable selection, we used an inclusion threshold of p < 0.1; this threshold was chosen to balance model parsimony and multicollinearity, while also allowing the inclusion of a broader set of variables deemed relevant by domain experts—namely, those that exhibited weaker associations than the classical cutoff of p < 0.05 used in medical sciences for both variable inclusion and significance123,124,125. Further, prior to model fitting, we removed variables that were highly collinear (even when they satisfied the inclusion threshold of p < 0.1). (Table 1) We subsequently implemented a multivariable regression model adjusted for the remaining subset of covariates. We then accounted for uncertainty across the ten iterations of the imputation process by drawing from a multivariate random normal distribution via the MASS package in R115. A variable was included in our final analysis if it was included in at least one of the ten multivariable models; details regarding the number of imputed datasets and multivariable models for which a variable was selected (out of ten in total) appear in Appendix Tables S3 and S4. To summarize our findings across all ten imputations, we used the median to derive a point estimate for the odds ratio (OR) or β coefficient as well as the 2.5th and 97.5th percentiles to obtain the corresponding 95% uncertainty interval (UI).
Sensitivity analyses
In addition to the analyses presented in the main text of this manuscript, we conducted eight types of sensitivity analyses to assess the robustness of our results to data and modeling decisions. First, we compared our main results with those emanating from the two alternative imputation strategies described above, namely the complete case analysis (Approach 1, see Appendix Figures S6 and S7, Sect. 5.1) and the imputation of only the LGBTQ + GAI variable (Approach 2, see Appendix Figures S8 and S9, Sect. 5.2). Descriptive statistics for these imputation approaches appear in Table 1 and Figure S5. Second, we evaluated Google searches made in languages other than English (Appendix Figures S10 and S11, Sect. 5.3). Third, we evaluated the impact of restricting our dataset to only the countries and territories that ever had a confirmed case of mpox in order to understand the role of prior exposure to this disease on the adoption of the term ‘mpox’ by the public (Appendix Figures S12 and S13, Sect. 5.4). Fourth, we implemented a Tobit model, which is particularly well-suited for zero-inflated data. In doing so, our goal was to address a large proportion of zeros in our continuous outcome measuring the ‘mpox’ search proportion (Appendix Figure S14, Sect. 5.5). Fifth, we used a Bonferroni correction to account for post-hoc testing of multiple hypotheses at once (Appendix Sect. 5.6). Sixth, we assessed the robustness of our findings to the use of different packages to draw from a multivariate random normal distribution, specifically rockchalk and mvtnorm (Appendix Figures S15-S18, Sect. 5.7). Seventh, we used LASSO regression for variable selection and model fitting in lieu of univariable screening (Appendix Figures S19 and S20, Sect. 5.8). Lastly, we varied the threshold for univariable screening from p < 0.1 to p < 0.05 (Appendix Figure S21, Sect. 5.9) and from p < 0.1 to p < 0.25 (Appendix Figure S22, Sect. 5.9). Additionally, we fitted the model with no screening at all (i.e. p < 1.0) (Appendix Figure S23, Sect. 5.9).
Role of the funder
All analyses were conducted using R version 4.3.1136. This research was supported in part by the National Institute of General Medical Sciences, National Institutes of Health (R35GM146974); the National Science Foundation (SES2200228, SES2230083, & IIS2229881); and the MIT-Harvard Broad Institute Eric & Wendy Schmidt Center. The funding sources had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Data availability
All data used in these analyses are openly available, and hosted, along with all analytic code, on Github at https://github.com/ehulland/mpox_GST.
References
Hoppe, T. Spanish Flu: When infectious disease names blur origins and stigmatize those infected. Am J Public Health 108, 1462–1464 (2018).
Karalis, N. T. Conflating culture with COVID-19: Xenophobic repercussions of a global pandemic. Soc Sci Humanit Open 2, 100044 (2020).
Champagne. Why Scientists Should Not Name Diseases Based On Location. Am. Soc. Microbiol. 2021. https://asm.org:443/Articles/2021/May/Why-Scientists-Should-Not-Name-Diseases-After-Plac (accessed Feb 21, 2025).
World Health Organization. Best Practices for the Naming of New Human Infectious Diseases. 2015; published online May. https://www.who.int/publications/i/item/WHO-HSE-FOS-15.1.
Scherr, S., Holthouse, D. Swine Flu Prompts Anti-Mexican Sentiment. South. Poverty Law Cent. 2009; published online Aug 30. https://www.splcenter.org/fighting-hate/intelligence-report/2009/swine-flu-prompts-anti-mexican-sentiment (accessed Nov 9, 2023).
Youssef, M. Egypt orders slaughter of all pigs over swine flu. The Independent. 2009; published online April 29. https://www.independent.co.uk/news/world/middle-east/egypt-orders-slaughter-of-all-pigs-over-swine-flu-1676090.html (accessed Nov 9, 2023).
Ronen, G. M. et al. From Eugenic euthanasia to habilitation of ‘disabled’ children: Andreas Rett’s contribution. J. Child Neurol. https://doi.org/10.1177/08830738083217 (2009).
Martini, M., Gazzaniga, V., Bragazzi, N. L. & Barberis, I. The Spanish influenza pandemic: a lesson from history 100 years after 1918. J Prev Med Hyg 60, E64–E67 (2019).
von Magnus, P., Andersen, EK., Petersen, K.B., Birch‐Andersen, A. A pox‐like disease in cynomolgus monkeys. (1959). https://doi.org/10.1111/j.1699-0463.1959.tb00328.x
Curaudeau, M. et al. Identifying the most probable mammal reservoir hosts for monkeypox virus based on ecological niche comparisons. Viruses 15, 727 (2023).
Le, T. K., Cha, L., Han, H.-R. & Tseng, W. Anti-Asian xenophobia and Asian American COVID-19 disparities. Am. J. Public Health 110, 1371–1373 (2020).
Lantz, B., Wenger, M. R. & Mills, J. M. Fear, political legitimization, and racism: Examining Anti-Asian xenophobia during the COVID-19 pandemic. Race Just. 13, 80–104 (2023).
Reja, M. Trump’s ‘Chinese Virus’ tweet helped lead to rise in racist anti-Asian Twitter content: Study. ABC News. 2021; published online March. https://abcnews.go.com/Health/trumps-chinese-virus-tweet-helped-lead-rise-racist/story?id=76530148 (accessed Dec 18, 2023).
Hswen, Y. et al. Association of “#covid19” versus “#chinesevirus” with anti-Asian sentiments on twitter: March 9–23, 2020. Am. J. Public Health 111, 956–964 (2021).
Yam, K. Anti-Asian hate crimes increased by nearly 150% in 2020, mostly in N.Y. and L.A., new report says. NBC News. 2021; published online March 9. https://www.nbcnews.com/news/asian-america/anti-asian-hate-crimes-increased-nearly-150-2020-mostly-n-n1260264 (accessed Dec 18, 2023).
Gover, A. R., Harper, S. B. & Langton, L. Anti-Asian hate crime during the COVID-19 pandemic: Exploring the reproduction of inequality. Am J Crim Justice 45, 647–667 (2020).
Jeung, R., Popovic, T., Lim, R., Lin, N. Anti-Chinese rhetoric employed by perpetrators of anti-Asian hate. Asian Pac Policy Plan Counc 2020; published online Oct 8. https://www.asianpacificpolicyandplanningcouncil.org/wp-content/uploads/REPORT_ANTI-CHINESE-RHETORIC_EMPLOYED_10_08_20.pdf.
Findling MG, Blendon RJ, Benson J, Koh H. COVID-19 Has Driven Racism and Violence Against Asian Americans: Perspectives From 12 National Polls. Health Aff Forefr 2022; published online April 12. https://doi.org/10.1377/forefront.20220411.655787.
Itkowitz, C. Trump again uses racially insensitive term to describe coronavirus. Wash. Post. 2020; published online June 24. https://www.washingtonpost.com/politics/trump-again-uses-kung-flu-to-describe-coronavirus/2020/06/23/0ab5a8d8-b5a9-11ea-aca5-ebb63d27e1ff_story.html (accessed Nov 9, 2023).
World Health Organization. Disease Outbreak News: Multi-country monkeypox outbreak. 2022; published online June 27. https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON396 (accessed Dec 18, 2023).
Centers for Disease Control and Prevention (CDC). Multistate outbreak of monkeypox--Illinois, Indiana, and Wisconsin. MMWR Morb. Mortal Wkly Rep. 52, 537–40 (2003)
Erez, N. et al. Diagnosis of imported monkeypox, Israel, 2018. Emerg Infect Dis 25, 980–983 (2019).
Vaughan, A. et al. Two cases of monkeypox imported to the United Kingdom. Eurosurveillance https://doi.org/10.2807/1560-7917.ES.2018.23.38.1800509 (2018).
Ponce, L. et al. Incubation period and serial interval of mpox in 2022 global outbreak compared with historical estimates. Emerg. Infect. Dis. 30, 1173. https://doi.org/10.3201/eid3006.231095 (2024).
Pollock, E.D. Potential for recurrent Mpox outbreaks among gay, bisexual, and other men who have sex with men––United States. MMWR Morb. Mortal Wkly Rep. 72. (2023) https://doi.org/10.15585/mmwr.mm7221a1.
März, J. W., Holm, S. & Biller-Andorno, N. Monkeypox, stigma and public health. Lancet Reg. Health - Eur 23, 100536 (2022).
Zimmermann, H. M. L. et al. Mpox stigma among men who have sex with men in the Netherlands: Underlying beliefs and comparisons across other commonly stigmatized infections. J Med Virol 95, e29091 (2023).
Jacobs, A. Why Experts Want to Rename Monkeypox. N. Y. Times. 2022; published online Aug 23. https://www.nytimes.com/2022/08/23/health/monkeypox-name-stigma.html (accessed Nov 9, 2023).
Chappell, B. Critics say ‘monkeypox’ is a racist name. But it’s not going away anytime soon. NPR. 2022; published online Aug 1. https://www.npr.org/sections/goatsandsoda/2022/08/01/1113908154/critics-say-monkeypox-is-a-racist-name-but-its-not-going-away-anytime-soon (accessed Dec 18, 2023).
Kaler, J., Hussain, A., Flores, G., Kheiri, S. & Desrosiers, D. Monkeypox: A comprehensive review of transmission, pathogenesis, and manifestation. Cureus 14, e26531 (2022).
Happi, C. et al. Urgent need for a non-discriminatory and non-stigmatizing nomenclature for monkeypox virus. PLOS Biol 20, e3001769 (2022).
World Health Organization. WHO recommends new name for monkeypox disease. 2022; published online Nov 28. https://www.who.int/news/item/28-11-2022-who-recommends-new-name-for-monkeypox-disease (accessed Oct 18, 2023).
Al Jazeera. WHO asks public for help with monkeypox name change. Al Jazeera. 2022; published online Aug 16. https://www.aljazeera.com/news/2022/8/16/who-asks-public-help-for-monkeypox-name-change (accessed Dec 18, 2023).
Ayala, G. & Spieldenner, A. HIV is a story first written on the bodies of gay and bisexual men. Am. J. Public Health 111, 1240–1242 (2021).
New Homosexual Disorder Worries Health Officials––The New York Times. https://www.nytimes.com/1982/05/11/science/new-homosexual-disorder-worries-health-officials.html (accessed Feb 2, 2024).
Current Trends First 100,000 Cases of Acquired Immunodeficiency Syndrome––United States. https://www.cdc.gov/mmwr/preview/mmwrhtml/00001442.htm (accessed April 15, 2024).
Babel, R. A., Wang, P., Alessi, E. J., Raymond, H. F. & Wei, C. Stigma, HIV risk, and access to HIV Prevention and treatment services among men who have sex with men (MSM) in the United States: A scoping review. AIDS Behav. 25, 3574–3604 (2021).
22 News WWLP. WHO declares global health emergency as cases of monkeypox spread rapidly. 22 News WWLP. 2024; published online Aug 19. https://www.wwlp.com/video/who-declares-global-health-emergency-as-cases-of-monkeypox-spread-rapidly/9967507/ (accessed Aug 20, 2024).
Ahmed, R.S. جدري القرود.. 5 طرق لانتقال العدوى من إنسان إلى آخر. Egypt Indep. 2024; published online Aug 19. https://www.almasryalyoum.com/news/details/3241580 (accessed Aug 20, 2024).
EU: ‘Monkeypox currently not a health emergency in Europe’. 2024; published online Aug 20. https://www.eunews.it/en/2024/08/20/eu-monkeypox-currently-not-a-health-emergency-in-europe/ (accessed Aug 20, 2024).
Belderbos, H. Monkeypox outbreak in Africa declared a global health emergency by the WHO. Open Access Gov. 2024; published online Aug 20. https://www.openaccessgovernment.org/monkeypox-outbreak-in-africa-declared-a-global-health-emergency-by-the-who/181313/ (accessed Aug 20, 2024).
Tianjin Center for Disease Control and Prevention. WHO declares monkeypox outbreak a ‘public health emergency of international concern’. Tianjin Cent. Dis. Control Prev. 2024; published online Aug 20. https://www.cdctj.com.cn/system/2024/08/20/030147331.shtml (accessed Aug 20, 2024).
Tadjer R. Variole du singe : l’Algérie où aucun cas n’a été enregistré prend des mesures. TSA Algeria. 2024; published online Aug 19. https://www.tsa-algerie.com/variole-du-singe-lalgerie-annonce-les-premieres-mesures/ (accessed Aug 20, 2024).
Ministerio de Salud Publica y Bienestar Social Paraguay. Medidas para prevenir la viruela símica. 2024; published online Aug 19. https://www.mspbs.gov.py/portal/30834/medidas-para-prevenir-la-viruela-simica.html (accessed Aug 20, 2024).
45Meduza. Пo миpy pacпpocтpaняeтcя нoвый oпacный вapиaнт виpyca oбeзьяньeй ocпы. Чтo пpo нeгo вaжнo знaть? И гpoзит ли нaм нoвaя пaндeмия? Meduza. 2024; published online Aug 16. https://meduza.io/feature/2024/08/17/po-miru-rasprostranyaetsya-novyy-opasnyy-variant-virusa-obezyaniey-ospy-chto-pro-nego-vazhno-znat-i-grozit-li-nam-novaya-pandemiya (accessed Aug 20, 2024).
Ma, S. & Yang, S. COVID-19 forecasts using internet search information in the United States. Sci. Rep. 12, 11539 (2022).
Van Kessel, R., Kyriopoulos, I., Wong, B. L. H. & Mossialos, E. The effect of the COVID-19 pandemic on digital health-seeking behavior: Big data interrupted time-series analysis of google trends. J. Med. Internet. Res. 25, e42401 (2023).
Wang, D., Lang, J. C. & Chen, Y.-H. Assessment of using google trends for real-time monitoring of infectious disease outbreaks: A measles case study. Sci. Rep. 14, 9470 (2024).
Husnayain, A., Fuad, A. & Su, E.C.-Y. Applications of google search trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan. Int. J. Infect. Dis. 95, 221–3 (2020).
Marty, R., Ramos-Maqueda, M., Khan, N. & Reichert, A. The evolution of the COVID-19 pandemic through the lens of google searches. Sci. Rep. 13, 19843 (2023).
Jimenez, A. J., Estevez-Reboredo, R. M., Santed, M. A. & Ramos, V. COVID-19 symptom-related google searches and local COVID-19 incidence in Spain: Correlational study. J. Med. Internet. Res. 22, e23518 (2020).
Kurian SJ, Bhatti A ur R, Alvi MA, et al. Correlations between COVID-19 cases and google trends data in the United States: A state-by-state analysis. Mayo Clin. Proc. 95, 2370–81 (2020).
Amusa, L. B., Twinomurinzi, H. & Okonkwo, C. W. Modeling COVID-19 incidence with google trends. Front. Res. Metr. Anal. https://doi.org/10.3389/frma.2022.1003972 (2022).
Yang, S., Santillana, M. & Kou, S. C. Accurate estimation of influenza epidemics using google search data via ARGO. Proc. Natl. Acad. Sci. 112, 14473–14478 (2015).
Dugas, A. F. et al. Influenza forecasting with google flu trends. PLoS One 8, e56176 (2013).
Majumder, M. S. et al. Utilizing nontraditional data sources for near real-time estimation of transmission dynamics during the 2015–2016 Colombian Zika virus disease outbreak. JMIR Public Health Surveill. 2, e5814 (2016).
Flores AR, Shaw A. Global Acceptance Index. UCLA School of Law Williams Institute, 2021 https://williamsinstitute.law.ucla.edu/projects/gai/ (accessed Nov 8, 2023).
Herre B, Ortiz-Ospina E, Rosen M. Our World In Data: Democracy. Retrieved from Github. GitHub. 2023. https://github.com/owid/notebooks/tree/main/BastianHerre/democracy (accessed Dec 13, 2023).
Transparency International. 2022 Corruption Perceptions Index: Explore the results. Transparency.org. 2023; published online Jan 31. https://www.transparency.org/en/cpi/2022 (accessed Nov 20, 2023).
Barber, R. M. et al. Healthcare access and quality index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: A novel analysis from the global burden of disease study 2015. Lancet 390, 231–266 (2017).
The Global Health Security Index. GHS Index. 2021. https://www.ghsindex.org/.
World Bank Open Data–GDP per capita (USD). World Bank Open Data. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD (accessed Nov 20, 2023).
Friedman, J. et al. Measuring and forecasting progress towards the education-related SDG targets. Nature 580, 636–639 (2020).
Mathieu, E., Spooner, F., Dattani, S., Ritchie, H., Roser, M. Mpox (monkeypox). Our World Data 2022; published online May 24. https://ourworldindata.org/monkeypox (accessed Dec 21, 2023).
Ramshaw, R.E., Henny, K., Hon. J., et al. Global monkeypox occurrences in the literature, 1972–2019: a geo-tagged database. Sci Data–Rev 2022; data upon request.
World Health Organization. Multi-country outbreak of mpox, External situation report #22. Geneva, Switzerland: World Health Organization, 2023 https://www.who.int/publications/m/item/multi-country-outbreak-of-mpox--external-situation-report--22---11-may-2023 (accessed April 18, 2024).
World Health Organization. 2022–23 Mpox (Monkeypox) Outbreak: Global Trends. 2024; published online March 20. https://worldhealthorg.shinyapps.io/mpx_global/ (accessed April 18, 2024).
Pasquini, G., Funk, C. Gay or bisexual men express concern about monkeypox, are critical of government’s response. Pew Res. Cent. 2022; published online Sept 22. https://www.pewresearch.org/short-reads/2022/09/22/gay-or-bisexual-men-express-concern-about-monkeypox-are-critical-of-governments-response/ (accessed April 18, 2024).
World Health Organization. Perceptions of monkeypox from those most at risk: men who have sex with men having multiple sexual partners. 2022; published online Autust. https://www.who.int/europe/news/item/26-08-2022-perceptions-of-monkeypox-from-those-most-at-risk--men-who-have-sex-with-men-having-multiple-sexual-partners (accessed April 18, 2024).
Stangl, A. L. et al. The health stigma and discrimination framework: A global, crosscutting framework to inform research, intervention development, and policy on health-related stigmas. BMC Med. 17, 31 (2019).
Mahajan, A. P. et al. Stigma in the HIV/AIDS epidemic: A review of the literature and recommendations for the way forward. AIDS Lond. Engl. 22, S67-79 (2008).
Fuentes, M., Shaw, A. The global impact of monkeypox on LGBTQ people. UCLA School of Law Williams Institute, 2022 https://williamsinstitute.law.ucla.edu/publications/monkeypox-lgbtq-people/ (accessed April 16, 2024).
Rueda, S. et al. Examining the associations between HIV-related stigma and health outcomes in people living with HIV/AIDS: A series of meta-analyses. BMJ Open 6, e011453 (2016).
Dsouza, V. S. et al. A sentiment and content analysis of tweets on monkeypox stigma among the LGBTQ+ community: A cue to risk communication plan. Dialog. Health 2, 100095 (2023).
Cheung, D. H. et al. Influences of mpox disease perceptions, sources and contents of information exposure on mpox vaccine uptake among gay, bisexual, and other men who have sex with men in Hong Kong China. Vaccine 42, 2337–2346 (2024).
Jung, M., Lin, L. & Viswanath, K. Associations between health communication behaviors, neighborhood social capital, vaccine knowledge, and parents’ H1N1 vaccination of their children. Vaccine 31, 4860–4866 (2013).
Majumder, M.S., Kluberg, S., Santillana, M., Mekaru, S., Brownstein, J.S. 2014 Ebola outbreak: Media events track changes in observed reproductive number. PLoS Curr. 7: ecurrents.outbreaks.e6659013c1d7f11bdab6a20705d1e865. (2015).
Scopelliti, M., Pacilli, M. G. & Aquino, A. TV news and COVID-19: Media influence on healthy behavior in public spaces. Int. J. Environ. Res. Public Health 18, 1879 (2021).
Suzuki, T., Yamamoto, H., Ogawa, Y. & Umetani, R. Effects of media on preventive behaviour during the COVID-19 pandemic. Humanit. Soc. Sci. Commun. 10, 1–8 (2023).
Liu, L., Xie, J., Li, K. & Ji, S. Exploring how media influence preventive behavior and excessive preventive intention during the COVID-19 pandemic in China. Int. J. Environ. Res. Public Health 17, 7990 (2020).
Van Kerkhove MD. Maria Van Kerkhove [@mvankerkhove] on X: ‘There is a pervasive myth that the DRC and other countries in Africa affected by the current #mpox outbreak lack capacity for the many elements that are needed for effective outbreak response. That is not true… my piece in @statnews https://t.co/LGluakA6ag’. X.com. 2024; published online Sept 19. https://x.com/mvankerkhove/status/1836842574293983590 (accessed Sept 24, 2024).
Jones, A. Alex Jones [@RealAlexJones] on X: ‘German Doctor Exposes Monkey Pox Scam: Says It’s a COVID-19 Vaccine Side Effect. From the German article titled: Doctor: Monkeypox is actually shingles, a side effect of the COVID vaccines The main symptom of monkeypox is the rash that can look like blisters and is accompanied https://t.co/yWum7TsO0N’. X.com. 2024; published online Aug 20. https://x.com/RealAlexJones/status/1825951267966845147 (accessed Sept 24, 2024).
Ferreira Caceres, M. M. et al. The impact of misinformation on the COVID-19 pandemic. AIMS Public Health 9, 262–277 (2022).
Benecke, O. & DeYoung, S. E. Anti-vaccine decision-making and measles resurgence in the United States. Glob. Pediatr. Health 6, 2333794X19862949 (2019).
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K. & Larson, H. J. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 5, 337–348 (2021).
Milondzo, T., Meyer, J. C., Dochez, C. & Burnett, R. J. Misinformation drives low human papillomavirus vaccination coverage in South African girls attending private schools. Front. Public Health 9, 598625 (2021).
Yamanis, T., Nolan, E. & Shepler, S. Fears and Misperceptions of the Ebola response system during the 2014–2015 outbreak in Sierra Leone. PLoS Negl. Trop. Dis. 10, e0005077 (2016).
Johnson, S. B. et al. Cancer misinformation and harmful information on facebook and other social media: A brief report. JNCI J. Natl. Cancer Inst. 114, 1036–1039 (2021).
Wang, S. et al. Hand hygiene and mask-wearing practices during COVID-19 among healthcare workers: Misinformation as a predictor. Am. J. Trop. Med. Hyg. 105, 1483–1489 (2021).
World Health Organization. Infodemic. World Health Organ. https://www.who.int/health-topics/infodemic (accessed April 23, 2024).
De Coninck, D. et al. Beliefs in conspiracy theories and misinformation about COVID-19: Comparative perspectives on the role of anxiety, depression and exposure to and trust in information sources. Front. Psychol. https://doi.org/10.3389/fpsyg.2021.646394 (2021).
Agley, J. & Xiao, Y. Misinformation about COVID-19: evidence for differential latent profiles and a strong association with trust in science. BMC Public Health 21, 89 (2021).
Perlis, R. H. et al. Association of major depressive symptoms with endorsement of COVID-19 vaccine misinformation among US adults. JAMA Netw. Open 5, e2145697 (2022).
Niburski, K. & Niburski, O. Impact of Trump’s promotion of unproven COVID-19 treatments and subsequent internet trends: Observational study. J. Med. Internet. Res. 22, e20044 (2020).
Cillizza, C. Rand Paul has a very wacky theory about ivermectin. CNN. 2021; published online Aug 31. https://www.cnn.com/2021/08/31/politics/rand-paul-covid-19-ivermectin/index.html (accessed April 24, 2024).
Cillizza, C. What the ivermectin debacle reveals about the hypocrisy of the anti-vaxxer crowd. CNN. 2021; published online Aug 25. https://www.cnn.com/2021/08/25/politics/ivermectin-covid-19-fox-news/index.html (accessed April 24, 2024).
Dwyer, C., Aubrey, A. CDC Now Recommends Americans Consider Wearing Cloth Face Coverings In Public. NPR. 2020; published online April 3. https://www.npr.org/sections/coronavirus-live-updates/2020/04/03/826219824/president-trump-says-cdc-now-recommends-americans-wear-cloth-masks-in-public (accessed April 24, 2024).
Raju, M. GOP’s no-mask caucus: ‘Can you smell through that mask?’ CNN. 2020; published online May 28. https://www.cnn.com/2020/05/28/politics/house-republicans-masks-debate-covid/index.html (accessed April 24, 2024).
LeBlanc, P. Trump mocks Biden for wearing a mask. CNN. 2020; published online Sept 3. https://www.cnn.com/2020/09/03/politics/trump-biden-coronavirus-mask/index.html (accessed April 24, 2024).
Rogers, K., Hauser, C., Yuhas, A., Haberman, M. Trump’s suggestion that disinfectants could be used to treat coronavirus prompts aggressive pushback. N. Y. Times. 2020; published online April 24. https://www.nytimes.com/2020/04/24/us/politics/trump-inject-disinfectant-bleach-coronavirus.html (accessed April 24, 2024).
Rivera, J. M. et al. Evaluating interest in off-label use of disinfectants for COVID-19. Lancet Digit. Health 2, e564–e566 (2020).
Adebayo, S.B. Bukola. Nigeria records chloroquine poisoning after Trump endorses it for coronavirus treatment. CNN. 2020; published online March 23. https://www.cnn.com/2020/03/23/africa/chloroquine-trump-nigeria-intl/index.html (accessed April 23, 2024).
Langreth, R. Ivermectin Poisonings Rise as Unproven Use for Covid Soars. Bloomberg. 2021; published online Oct 20. https://www.bloomberg.com/news/articles/2021-10-20/ivermectin-poisonings-rise-as-unproven-use-for-covid-soars (accessed April 24, 2024).
Swire-Thompson, B. & Lazer, D. Public health and online misinformation: Challenges and recommendations. Annu. Rev. Public Health 41, 433–451 (2020).
Lim, G., Bradshaw, S. Chilling Legislation: Tracking the Impact of “Fake News” Laws on Press Freedom Internationally. Cent Int Media Assist 2023; published online July.
Alba, D. Twitter Permanently Suspends Marjorie Taylor Greene’s Account. N. Y. Times. 2022; published online Jan 2. https://www.nytimes.com/2022/01/02/technology/marjorie-taylor-greene-twitter.html (accessed May 15, 2024).
Yadav, K., Siwakoti, S., Shapiro, J.N., Wanless, A. Countries have more than 100 laws on the books to combat misinformation. How well do they work? Bull. At. Sci. 2021; published online May 13. https://thebulletin.org/premium/2021-05/countries-have-more-than-100-laws-on-the-books-to-combat-misinformation-how-well-do-they-work/ (accessed May 15, 2024).
Ethnologue. What are the top 200 most spoken languages? Ethnologue. 2023. https://www.ethnologue.com/insights/ethnologue200/ (accessed May 15, 2024).
Central Intelligence Agency. The World Factbook 2023: World statistics. cia.gov. 2023. https://www.cia.gov/the-world-factbook/about/archives/2023/countries/world/#people-and-society (accessed June 19, 2024).
Google. Search tips for Trends - Trends Help. Google Search Trends. https://support.google.com/trends/answer/4359582?hl=en (accessed June 5, 2024).
China search engines market share 2024. Statista. 2024. https://www.statista.com/statistics/253340/market-share-of-search-engines-in-china-pageviews/ (accessed April 19, 2024).
Search Engine Market Share in 2024. Oberlo. 2024; published online March. https://www.oberlo.com/statistics/search-engine-market-share (accessed April 19, 2024).
World Population Review. Most Racist Countries. 2024. https://worldpopulationreview.com/country-rankings/most-racist-countries (accessed Feb 28, 2025).
mvrnorm function (rockchalk): RDocumentation. https://www.rdocumentation.org/packages/rockchalk/versions/1.8.129/topics/mvrnorm (accessed March 1, 2025).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).
Johnson, P. rockchalk: Regression estimation and presentation. 2022. https://CRAN.R-project.org/package=rockchalk.
Genz A, Bretz F. Mo. Heidelberg: Springer-Verlag, 2009.
mvnorm function (mvtnorm): RDocumentation. https://www.rdocumentation.org/packages/mvtnorm/versions/1.3-3/topics/Mvnorm (accessed March 1, 2025).
Benjamin, D. J. et al. Redefine statistical significance. Nat. Hum. Behav. 2, 6–10 (2018).
Hartig, F. & Barraquand, F. The evidence contained in the P-value is context dependent. Trends Ecol. Evol. 37, 569–570 (2022).
Chowdhury, M. Z. I. & Turin, T. C. Variable selection strategies and its importance in clinical prediction modelling. Fam. Med. Commun. Health 8, e000262 (2020).
Hosmer, Jr. D.W., Lemeshow, S., Sturdivant, R.X. Applied Logistic Regression, 3rd edition. (John Wiley & Sons, 2013).
Nor, A. M. et al. The recognition of stroke in the emergency room (ROSIER) scale: Development and validation of a stroke recognition instrument. Lancet Neurol. 4, 727–734 (2005).
Karcutskie, C. A. et al. Association of mechanism of injury with risk for venous thromboembolism after trauma. JAMA Surg. 152, 35–40 (2017).
Postlewait, L. M. et al. Association of preoperative risk factors with malignancy in pancreatic mucinous cystic neoplasms: A multicenter study. JAMA Surg. 152, 19–25 (2017).
Xu, H. et al. An exploratory model for the non-fatal drowning risks in children in Guangdong China. BMC Public Health 19, 599 (2019).
Miranda-Bautista, J. et al. Metabolic bone disease in patients diagnosed with inflammatory bowel disease from Spain. Ther. Adv. Gastroenterol. 12, 1756284819862152 (2019).
Amano, T. et al. The manifold costs of being a non-native English speaker in science. PLOS Biol. 21, e3002184 (2023).
Hanauer, D. I., Sheridan, C. L. & Englander, K. Linguistic injustice in the writing of research articles in English as a second language: Data from Taiwanese and Mexican researchers. Writ. Commun. 36, 136–154 (2019).
Politzer-Ahles, S., Girolamo, T. & Ghali, S. Preliminary evidence of linguistic bias in academic reviewing. J. Engl. Acad. Purp. 47, 100895 (2020).
Ramírez-Castañeda, V. Disadvantages in preparing and publishing scientific papers caused by the dominance of the English language in science: The case of Colombian researchers in biological sciences. PLoS One 15, e0238372 (2020).
Ferguson, G., Pérez-Llantada, C. & Plo, R. English as an international language of scientific publication: A study of attitudes. World Englishes 30, 41–59 (2011).
Gazzola M. English still rules the world, but that’s not necessarily OK. Is it time to curb its power? The Guardian. 2023; published online Dec 27. https://www.theguardian.com/commentisfree/2023/dec/27/english-world-power-language-linguistic-justice (accessed April 16, 2024).
Varian, H.R., Choi, H. Predicting the present with google trends. (2009) published online April 2. https://doi.org/10.2139/ssrn.1659302.
Nuti, S. V. et al. the use of google trends in health care research: A systematic review. PLoS One 9, e109583 (2014).
R Core Team. R: A language and environment for statistical computing. (2024). https://www.R-project.org/.
Acknowledgements
We would like to thank Paula Rodriguez Diaz and Robyn Correll for their assistance in understanding ‘mpox’ trends in South America and the translation of ‘monkeypox’ to Spanish; Nilufar Qahorova for her assistance in translating ‘monkeypox’ to Russian; and Zhanzhan Zhao in her assistance in translating ‘monkeypox’ to Chinese.
Funding
National Institutes of Health,R35GM146974, R35GM146974, R35GM146974, R35GM146974, R35GM146974, National Science Foundation, SES2230083, IIS2229881, SES2200228, SES2230083, MIT-Harvard Broad Institute Eric and Wendy Schmidt Center.
Author information
Authors and Affiliations
Contributions
Conceptualization (ENH, MLC, AND, MSM), data curation (ENH), formal analysis (ENH, MLC), methodology (ENH, MLC, MSM), validation (ENH, MLC, GYH, AND, MSM), visualization (ENH, MLC, GYH), original draft (ENH, MLC, GYH, AND, MSM), and review and editing (ENH, MLC, GYH, AND, MSM). All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
ENH reports contracts from the Global Listening Project and Council on Foreign Relations outside of the submitted work, as well as Google stock holdings at the time of the analysis, unrelated to the manuscript. In partial support of MLC’s work, MSM’s lab has recently been awarded funds for a postdoctoral fellowship from Moderna, Inc.; this funding did not influence the design, conduct, or reporting of the current study. All other authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Hulland, E.N., Charpignon, ML., Hayek, G.Y. et al. On the importance of communication, advocacy, and information accuracy in destigmatizing mpox disease. Sci Rep 15, 27064 (2025). https://doi.org/10.1038/s41598-025-06864-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-025-06864-3
