# The infodemic-pandemic nexus: Cross-national analysis of online misinformation's impact on population health

Yuxi Wang, Ugofilippo Basellini, Carlo Giovanni Camarda

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Yuxi Wang, Ugofilippo Basellini, Carlo Giovanni Camarda. The infodemic-pandemic nexus: Cross-national analysis of online misinformation's impact on population health. Social Science and Medicine, 2026, 390, pp.118881. 10.1016/j.socscimed.2025.118881. hal-05448123

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# The infodemic–pandemic nexus: Cross-national analysis of online misinformation’s impact on population health

Yuxi Wang<sup>a,c, D,*</sup>, Ugofilippo Basellini<sup>b, D</sup>, Carlo-Giovanni Camarda<sup>a, D</sup>

## ARTICLE INFO

JEL classification:

I1

J1

Keywords:

Misinformation

Pandemic

Protests

Population health

Excess mortality

## ABSTRACT

The COVID-19 pandemic has been parallelled by an “infodemic” — a surge in both accurate and misleading information circulating online. This study examines whether engagement with digital mis- and disinformation contributes to cross-national variation in population health outcomes. Drawing on data from 31 countries over a three-year period, we integrate indicators of online misinformation exposure and COVID-19 case rates, controlling for vaccination uptake, civil protests against public health measures and policy stringency. Using a mixed-effects regression framework that accounts for smoothed time trends and country-specific factors and random effects, we find that greater public engagement with unreliable online content predicts significant future increases in new infection rates. Our findings offer one of the first multinational assessments of the pathways through which digital misinformation shapes real-world health outcomes. We argue that unequal responsiveness to online misinformation can generate divergent epidemic trajectories, underscoring the urgent need for policy interventions targeting the digital information environment.

## 1. Introduction

Throughout history, infectious diseases have been the leading cause of human mortality. While modern medicine has made significant strides in controlling outbreaks — particularly through pharmaceutical innovations — the COVID-19 pandemic highlighted the continued reliance on non-pharmaceutical interventions (NPIs) such as social distancing, self-isolation, and vaccination. In an increasingly interconnected world, the ability to induce widespread behavioural change remains essential for effective epidemic control. Yet, human decision-making is not always well-adapted to the abstract and collective nature of global health threats. The invisible transmission of disease and the delayed effects of preventive action complicate risk perception, often leading individuals to make choices misaligned with public health goals.

Compounding this challenge is the rise of digital misinformation and disinformation, which can erode public trust and disrupt the adoption of life-saving interventions. The COVID-19 crisis has underscored how online narratives — particularly misleading or conspiratorial content — can undermine vaccination campaigns and adherence to NPIs. This “infodemic” is not a new phenomenon. Historically, misinformation has accompanied major disease outbreaks, from smallpox and cholera

to the 1918 influenza and the HIV/AIDS epidemic, often intensifying fear, stigma, and mistrust toward health authorities and scientific institutions (Wang et al., 2019; Jin et al., 2024).

Today, the proliferation of misleading information is further amplified by digital media ecosystems, where social platforms, partisan influencers, and populist actors can disseminate falsehoods rapidly and widely (Baumgaertner et al., 2018; Bennhold, 2020; Mejova and Kalimeri, 2020; World Health Organization, 2020). In such environments, misinformation does not merely confuse — it shapes public sentiment, fuels political unrest, and catalyses resistance to collective health measures (aljazeera, 2022; Barbieri and Bonini, 2021). Despite growing scholarly interest in the digital dynamics of the infodemic, little is known about the mechanisms through which misinformation transitions from online engagement to offline action. Even less is understood about how these dynamics vary across national contexts, contributing to differential population health outcomes during pandemics.

From a theoretical standpoint, an individual’s information intake and behaviour can be influenced by various sources, ranging from authoritative medical figures to diffused online networks. Physicians, who often embody the scientific expertise and institutional authority, have long been considered trusted informants guiding evidence-based

<footnote><sup>a</sup> French Institute for Demographic Studies, France</footnote>

<footnote><sup>b</sup> Max Planck Institute for Demographic Research, Germany</footnote>

<footnote><sup>c</sup> Covid Crisis Lab, Bocconi University, Italy</footnote>

<footnote>* Correspondence to: French Institute for Demographic Studies, 1004, 9 Cr des Humanités, 93300 Aubervilliers, France.
E-mail address: yuxi.wang@ined.fr (Y. Wang).</footnote>
decision-making in the healthcare context (Freed et al., 2011; Larson et al., 2015). Similarly, family and friends constitute one's immediate social network, which can exert substantial influence by shaping the perceptions and norms through repeated interpersonal interactions that are geographically and culturally anchored (Betsch and Sachse, 2012). In addition to these traditional vectors, the advent of digital and social media platforms has created novel and complex channels through which information — be it accurate or erroneous — flows rapidly across geographic borders. Such platforms can amplify the visibility and perceived credibility of official messages, often circumventing the gate-keeping processes that shape conventional news outlets (Lazer et al., 2018; Vosoughi et al., 2018; Burki, 2019; Lazard et al., 2025; Palmer and Gorman, 2025). While the influence of physicians and peer groups on health behaviours has been extensively studied (Nyhan and Reifler, 2015; Larson et al., 2015; Tustin et al., 2018), much less is known about how online information and misinformation shape real-world compliance with health guidelines at scale.

Prior research predating the COVID-19 pandemic demonstrated that digital indicators, such as online sentiment with Twitter posts, can mirror actual vaccine update trends (Salathé and Khandelwal, 2011) and that internet search queries captured by Google Trends can serve as proxies for tracking and predicting disease outbreaks (Carneiro and Mylonakis, 2009; Ginsberg et al., 2009). During the COVID-19 crisis, several small-scale studies have begun to elucidate how digital content, like vaccine-related memes, can influence intentions to vaccinate and how health misinformation dampens individuals' readiness to follow preventative measures (Pennycook et al., 2020; Bridgman et al., 2020; Wood et al., 2023). However, these explorations remain limited in scope and generalisability and do not explore the relationship at the population level. As new pathogens continue to emerge worldwide, the dissemination of misleading and potentially harmful claims regarding virus origins, modes of transmission, and vaccine efficacy persists online (Zarocostas, 2020). These evolving threats highlight the pressing need for a more systematic understanding of how digital mis/disinformation shapes epidemic trajectories (Cinelli et al., 2020). Ultimately, rigorous empirical evidence, grounded in a robust theoretical model, is needed to inform interventions, policymaking, and communication strategies designed to mitigate the harmful consequences of digital infodemics on public health. Our research sets forth a systematic, multifaceted investigation into whether health-related mis/disinformation can translate from online content streams to tangible, real-world outcomes. Instead of documenting the presence of misleading digital narratives, we seek to uncover the magnitudes of their influence on population-level health consequences.

## 1.1. Objectives

Our research questions are twofold: (i) Can engagement with digital mis/disinformation be a determinant of differential population health outcomes during a pandemic? (ii) How do these effects vary across countries?

By linking large-scale epidemiological and digital datasets, we seek to determine the extent to which populations experiencing higher levels of infodemic face elevated case rates, as well as whether this pattern correlates indirectly with higher excess mortality.

## 1.2. Literature overview

A growing body of research consistently finds that health-related mis/disinformation proliferates rapidly on social media platforms, frequently outstripping the reach and speed of credible sources (Acerbi, 2019; Wang et al., 2019). This is exacerbated by algorithmic recommendation systems that privilege emotionally engaging and sensationalist content over fact-based or nuanced reporting, thereby creating “echo chambers” that reinforce preexisting biases (Vosoughi et al.,

2018; Lazer et al., 2018; Cinelli et al., 2020). As platforms like Facebook, X (Twitter), YouTube, and TikTok incentivise clicks and shares, content that sparks strong reactions — even if misleading — tends to be widely disseminated with minimal friction or verification (Brennen et al., 2020; Zarocostas, 2020). Such an amplification mechanism allows falsehoods to permeate digital public spheres, facilitating large-scale exposure to health inaccuracies ranging from vaccine safety myths to conspiratorial beliefs about disease origins (Pennycook et al., 2020).

Compounding the problem, user-generated content often blurs the boundary between expert and non-expert voices, making it difficult for audiences to discern credible information from speculation or deliberate disinformation. Studies show that even individuals who are motivated to seek accurate information can fall prey to cognitive biases, such as confirmation bias and familiarity effects, which can lead to the uncritical acceptance or sharing of false claims (Pennycook and Rand, 2018; Scheufele and Krause, 2019). Despite the extensive prevalence of such mis/disinformation, most empirical studies rely on small-scale surveys or limited timeframes, lacking robust longitudinal or cross-cultural designs that could illuminate how falsehood evolves and impacts different demographics over time.

Evidence points to significant real-world impacts of health-related mis/disinformation at the national level, particularly in times of public health crisis. A wide range of studies has shown that exposure to misleading or false claims online can reduce vaccination intentions, undermine confidence in vaccine safety, and reduce willingness to adopt protective health behaviours (Lee et al., 2022; Loomba et al., 2021; Pierri et al., 2022). These findings are well-established in survey-based studies, experiments, and small-scale field analyses, which consistently demonstrate that misinformation can alter key behavioural responses at the individual level that are directly relevant to epidemic control.

Mis/disinformation also plays a central role in shaping broader patterns of non-compliance and resistance to public health measures. Scholars have documented a surge in civil protests against NPIs — such as lockdowns, mask mandates, and vaccination requirements — particularly in nations where political trust is low and anti-intellectual sentiments are more pronounced (Bethke and Wolff, 2023; Merkley and Loewen, 2021; Lange and Monscheuer, 2022; Roozenbeek et al., 2020). These movements often coalesce around digital communities, where shared grievances about government overreach intertwine with misinformation or conspiracy claims regarding the efficacy and safety of preventative measures (Forsyth, 2020; Janis, 1982). Psychology scholars often invoke the concept of groupthink — a process in which highly cohesive groups suppress dissent — which can intensify resistance to established scientific consensus, potentially undermining disease containment efforts (Forsyth, 2020). Moreover, misinformation can foster public confusion, heighten individual anxiety, and undermine coordinated pandemic responses (Lorenz-Sreen et al., 2022). This corrosive effect extends beyond health domains, infiltrating debates about climate change, political processes, and other collective social issues (Chan and Albarracín, 2023; Paoletti et al., 2024; Wahab et al., 2021; Van Der Linden, 2022).

Although extensive research documents how misinformation shapes individual attitudes, behaviours, and compliance — with clear implications for vaccination uptake and disease transmission — most existing studies examine only isolated links and are typically based on small samples or limited timeframes. As a result, it remains unclear whether these well-established micro-level mechanisms scale up to produce systematic, observable differences in epidemic trajectories across countries. This study addresses that gap by leveraging spatial and temporal variation in online misinformation to assess whether cross-national differences in the information environment predict subsequent COVID-19 case rates at the population level.
# 2. Materials and methods

## 2.1. Data collection

We construct the datasets from various sources. Information on the level of online infodemic from the COVID Infodemic Observatory collected by the Bruno Kessler Institute. The Observatory employs a comprehensive methodology to monitor and analyse the dissemination of COVID-19-related unreliable information across social media platforms, providing insights into misinformation trends during the pandemic. We obtained the daily Dynamic Infodemic Risk Index (IRI), which indicates the likelihood of a generic user directly endorsing (retweet) or engaging (reply) with online messages pointing to potentially unreliable sources on X/Twitter. When the dynamic IRI is high, it indicates that many Twitter users are interacting and sharing potentially misleading content. We describe the calculation method in section 1 of the supplementary material. The published article provides the technical explanation for the classification of information sources. We also provide a detailed table explaining each variable used for the main and sensitivity analysis in Section 4 in the supplementary material.

The World Health Organisation COVID-19 dashboard WHO provides global data on confirmed COVID-19 case rates (7-day smoothed), offering standardised and regularly updated information on infections across countries. This dataset includes daily case counts, allowing for time-series analysis of infection trends in relation to public behaviour. Additionally, data from the Human Mortality Database (HMD)'s Short-Term Mortality Fluctuations (STMF) dataset allows for the calculation of deviations from expected death rates from historical trends, providing more comprehensive measures of the pandemic impact beyond reported COVID-19 deaths. We fit a mortality smoothing and forecasting model to predict the expected deaths for the observed years, and the excess mortality is computed as the difference between observed and forecasted deaths. Further explanations can be found in section 2 of the supplementary material. We also integrated the reproduction rate, hospitalisation rate and ICU admission rate for the sensitivity analysis from the literature (Arroyo-Marioli et al., 2021), the European Centre for Disease Prevention and Control and national reports.

Finally, we include a comprehensive set of control variables at the country level to account for various behavioural, socio-economic and demographic factors that may influence our analysis. Vaccination data are drawn from the global COVID-19 vaccination database developed by Our World in Data, as first described in “A global database of COVID-19 vaccinations” (Mathieu et al., 2021). This dataset tracks the scale and pace of vaccine rollout across countries by aggregating official government reports and public health sources. We use the variable “new daily doses per population (smoothed over 7-day windows)” in the analysis. Another source of behavioural data is from the Armed Conflict Location & Event Data (ACLED), which offers comprehensive information on civil protest activities that are specifically targeted at COVID-19 NPIs. The dataset includes timestamps, locations, actors, event types, and specific COVID-19-related tags, allowing for more precise tracking of anti-NIP protests across the countries. We included a few examples in section 5 of the supplementary material for reference. We also include the policy stringency index over time from the Oxford COVID-19 Government Response Tracker, which captures the rigour of government policies implemented across different periods. For country-level time-invariant variables, we utilise the Human Development Index (HDI) from the United Nations Development Programme (UNDP) as a measure of overall well-being, encompassing health, education, and income dimensions. To provide a more nuanced understanding of economic conditions and demographic composition, we include GDP per capita data and the proportion of the population aged 65 and older sourced from the World Bank. The 31 countries included in our study (Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, the United

Kingdom, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Lithuania, Luxembourg, Latvia, Malta, the Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Sweden, and the United States) were primarily selected on the basis of data availability in the Infodemic Risk Index dataset, ensuring consistent coverage across countries. The study period extends from January 2020 to March 2023, allowing us to capture nearly the full course of the COVID-19 pandemic and maximise both longitudinal depth and cross-national variation. This design provides a robust framework for examining the interplay of infodemic, behaviour and health outcomes across diverse contexts.

## 2.2. Statistical model

To account for substantial cross-national variation, we employ a mixed-effects model with country-level random effects to analyse the relationship between epidemic outcomes and the prevalence of misinformation — termed the infodemic — alongside several control variables over time and across countries (see Eqs. (1) and (2)). The dependent variable is the log-transformed, smoothed rate of new cases ($C_{i,t}$). It is modelled as a function of the dynamic infodemic index ($I_{i,t-x_i}$), as well as control variables including protest events ($P_{i,t-x_p}$), new vaccination rates ($V_{i,t-x_v}$), country-level stringency measures ($S_{i,t}$), and a set of time-invariant covariates including GDP per capita, population density, and the Human Development Index (HDI), collectively denoted as $\mathbf{X}_i$. The infodemic, protest, and vaccination variables are lagged, with lag structures selected based on both prior literature and model performance (minimising AIC and BIC), through simulations evaluating all plausible lag combinations (see section 3 of supplementary material for details). To flexibly account for non-stationary temporal dynamics in the baseline case rate, we include a non-parametric time trend component ($T_t$), modelled using five B-spline basis functions with associated fixed coefficients $\eta$ as well as random coefficients $\eta_i$. Additional fixed effects include a country dummy ($CO_i$), a global intercept ($\alpha_0$), and shared slope coefficients ($\beta_1-\beta_4$, $\delta$), which capture the average effects of misinformation, protests, vaccination rates, policy stringency, and other country-level controls. Random effects account for unobserved heterogeneity across countries. These include a country-specific random intercept ($u_i$), and random slopes for the infodemic index ($b_{1i}$), protest events ($b_{2i}$), vaccination rates ($b_{3i}$), and time trends ($\eta_i$), allowing for country-specific deviations in baseline case rates and covariate effects. The random terms $u_i$ and $\eta_i$ follow a joint multivariate normal distribution, with a zero covariance constraint imposed during estimation. The residual term $\epsilon_{i,t}$ is assumed to be independent of the random effects and captures additional unobserved variability. This model structure allows us to estimate a global average relationship between misinformation and epidemic spread while also capturing country-specific variations in both baseline rates and behavioural responses. In doing so, it reveals how the infodemic and related social factors influence epidemic trajectories across diverse national contexts.

$$ \ln(C_{i,t}) = \alpha_0 + u_i + (\beta_1 + b_{1i})I_{i,t-x_i} + (\beta_2 + b_{2i})P_{i,t-x_p} \\ + (\beta_3 + b_{3i})V_{i,t-x_v} + \beta_4 S_{i,t} + \delta' \mathbf{X}_i + (\eta + \eta_i)^{\top} \mathbf{T}_t + \beta_5 CO_i + \epsilon_{i,t} \quad (1) $$

To understand a further step in the chain of events, we modelled the relationship between excess mortality and infodemic risks through the behavioural changes and case rates using weekly rolling averages since excess mortality is calculated weekly (see section 2 of the supplementary material). We collapse the data points by week and allow all main independent variables, including case rates, to have country-specific random effects. The rest of the model remains the same as Eq. (1) as follows:

$$ \ln(M_{i,t,w}) = \gamma_0 + u_{0i} + (\gamma_1 + u_{1i})I_{i,t-w} + (\gamma_2 + u_{2i})P_{i,t-w} \\ + (\gamma_3 + u_{3i})V_{i,t-w} + (\gamma_4 + u_{4i})C_{i,t-w} + \gamma_5 S_{i,t} \\ + \delta'_i \mathbf{X}_i + (\theta + \theta_i)^{\top} \mathbf{T}_w + \gamma_6 CO_i + \epsilon_{i,t,w} \quad (2) $$
<table><caption>**Table 1**

Descriptive statistics.</caption><thead><tr><th>Variables</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td>Main Variables</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dynamic Infodemic Index (Daily)</td><td>0.304</td><td>0.191</td><td>0</td><td>1</td><td>34,922</td></tr><tr><td>New Case Rate (per million, Daily)</td><td>358.825</td><td>702.903</td><td>0</td><td>7877</td><td>35,557</td></tr><tr><td>Excess Mortality Rate (Weekly)</td><td>9.941</td><td>17.923</td><td>-32.04</td><td>156.63</td><td>5084</td></tr><tr><td>Control Variables</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>New Vaccination Rate (Daily)</td><td>0.095</td><td>0.155</td><td>0</td><td>1.29</td><td>23,451</td></tr><tr><td>Protests (Daily)</td><td>0.135</td><td>0.115</td><td>0</td><td>1</td><td>35,557</td></tr><tr><td>Stringency Index (Daily)</td><td>41.148</td><td>23.763</td><td>0</td><td>96.3</td><td>33,325</td></tr><tr><td>Population density</td><td>166.143</td><td>258.524</td><td>3.404</td><td>1454.037</td><td>31</td></tr><tr><td>Proportion of 65 and older</td><td>18.52</td><td>2.377</td><td>13.416</td><td>23.021</td><td>31</td></tr><tr><td>GDP per capita</td><td>39006.42</td><td>15353.68</td><td>18563.31</td><td>94277.97</td><td>31</td></tr><tr><td>Hospital beds per thousand</td><td>4.727</td><td>1.72</td><td>2.22</td><td>8</td><td>31</td></tr><tr><td>Human development index</td><td>0.903</td><td>0.037</td><td>0.816</td><td>0.957</td><td>31</td></tr></tbody></table>

Where $ln(M_{i,t})$ is the weekly logarithmic excess mortality rate, all dependent variables are the weekly averages of Eq. (1), and $u_{0i}$ is the random intercept for each country.

To better characterise the mechanism underlying the association between misinformation and epidemic trajectories, we conducted two targeted analyses extending the main model. We first modified the infodemic measurement by examining distinct subcategories of misinformation and unreliable information. Within the infodemic dataset, daily tweets per country were classified by source credibility: reliable (scientific outlets and mainstream media), unreliable (satire, click-bait, politically biased content, fake/hoax claims, conspiracy theories, and junk science), and unknown (shadow domains and uncategorised sources). More details can be found in section 4 of the supplementary material. For each category, we calculated its proportion relative to the total COVID-related tweet volume and reapplied our model to these ratios. We further tested the sensitivity of our results to the general volumes of tweets to see whether there is a general effect of online engagement despite the domain source reliability rating. Second, to address potential biases in early-pandemic case reporting (e.g., limited testing capacity), we evaluated alternative outcome metrics, including reproduction rates, hospitalisation rates, and ICU admission rates, to determine whether results remained consistent across different measures of pandemic severity. The reproduction number reflects the underlying transmission dynamics of the epidemic and is less affected by reporting delays or testing intensity, while hospitalisation and ICU admission rates capture more severe manifestations of the epidemic.

## 2.3. Sensitivity analysis

We conducted multiple sensitivity analyses to further assess the robustness of our findings. First, we segment the observation period by the epidemic waves due to the different transmission patterns and vaccination rollouts. Second, we show the coefficients' sensitivity to the different time lag structures of the main variables, varying the lagged days by 5 in both directions. We plot the coefficients from the main mixed-effect model under different lag structures for the infodemic index, and we compare these model-based estimates to results from a traditional time-series approach by computing cross-correlation functions between the detrended infodemic index and case rates. To explore dynamic lead-lag patterns at a finer level, we also computed country-specific cross-correlation functions. Finally, we test our time-trend robustness by including a linear time trend, alternative spline counts, and removing the B-splines from the random effects.

# 3. Results

## 3.1. Descriptive statistics

We show the descriptive statistics in Table 1 for the main and control variables used in the analysis. The dynamic infodemic index

has a mean of 0.304 with a standard deviation of 0.191, which means that on average, 30% of the X/Twitter users in a country engage with unreliable information at a given time in the observed period. The daily new case rate (per million) has a mean of 358.825 and a high standard deviation of 702.903, reflecting considerable variation in case numbers across countries and time. The excess mortality rate is averaged at 9.941, with a wide standard deviation. Country-level controls include the new vaccination rate (daily), the protest frequency (daily), the stringency index (daily), population density, percentage of elderly people, GDP per capita, hospital bed density, and the Human Development Index (HDI).

The descriptive Figs. 1 and 2 show the temporal evolution of the key variables across all countries in the sample. Fig. 1 illustrates the dynamic infodemic risk index over the three-year period. While the overall temporal pattern is broadly similar across countries, there is substantial heterogeneity in the magnitude and volatility of the infodemic activity, with some countries exhibiting pronounced spikes and others displaying more stable but persistent fluctuations. Fig. 2 plots new COVID-19 case rates per million (grey) alongside excess mortality rates (pink). As expected, both indicators display marked wave-like dynamics, though the time and intensity of these waves vary considerably across countries. The descriptive plots do not reveal a simple visual correspondence between infodemic trends and epidemiological outcomes, motivating the need for more formal statistical analyses presented below.

## 3.2. Main result

Table 2 reports mixed-effects regression estimates examining the association between the dynamic infodemic index and new COVID-19 case rates (log-transformed) from Eq. (1), considering the period when vaccination is introduced. We observe that the coefficient for the infodemic index (lagged by 7 weeks) is positive and significant after including the behavioural and policy control variables.

The interpretation for the infodemic is that a 0.1-unit increase in the general engagement with online misinformation, after 7 weeks' time, will contribute to about a 6.25% increase in the daily new case rate. Notably, the random-effects components illustrate meaningful variability across countries, particularly in the slopes associated with the infodemic index, highlighting cross-national differences in the relationship between misinformation and infection rates.

Turning to the control variables, the coefficient for protest activity (lagged six weeks) is positive but not statistically significant. Substantively, the point estimate (0.186) suggests that higher levels of protest participation may correspond to increased case rates, consistent with the idea that mass gatherings heighten opportunities for viral transmission. However, the lack of statistical significance indicates that, once temporal trends and other covariates are accounted for, protest events do not exert a reliably detectable effect in this sample. This may reflect substantial cross-country variation in protest size, timing, mitigation
<figure><img src="image_2.png" /><figcaption>Fig. 1. Time series of dynamic infodemic index.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Time series of new case rate and excess mortality rate.</figcaption></figure>

measures, or reporting quality. The vaccination rate (logged and lagged five weeks) displays the expected strong and statistically significant negative association (−0.294***). This implies that, consistent with the literature, higher vaccination coverage is consistently linked to lower infection rates, even after adjusting for misinformation exposure, policy responses, and temporal trends. We further assess whether residual trends persist after controlling for time by including residual-fitted plots by country. Fig. S1 in the supplementary material shows that residuals are approximately centred around zero with no systematic patterns or curvatures. The consistency of residual distributions across countries indicates that the B-spline specification effectively removes within-country temporal trends.

We first graph the coefficients (fixed- and random-effects) for the infodemic variable by country, ranked by the effect size, as seen in Fig. 3. The y-axis shows the estimated mixed effects of each country, and the x-axis ranks these country-level effects. The plot shows that Spain (ESP), Portugal (PRT) and Italy (ITA) are the only countries that deviate significantly from the general effect, having a negative but insignificant effect of the infodemic on case rates. These countries show potential resilience effects against online misinformation. At the upper end, the Czech Republic (CZE), the United Kingdom (GBR), Sweden (SWE), Poland (POL), Austria (AUT) and Finland (FIN) exhibit the largest

positive effect, suggesting the highest impact of online misinformation on case rates. If we visualise the mixed effect on a map (Europe) as seen in Fig. 4, the geographic gradient becomes more obvious. We see strong and statistically significant positive effects of the infodemic on case rates for Northern, Central, and Eastern Europe, while Southern European countries exhibit weaker or negative effects. These cross-national differences likely reflect structural factors such as variations in institutional trust, media literacy, decentralised media ecosystems, and the strength of civil society autonomy. At the same time, we caution against overly simplistic interpretations. For instance, Sweden — where institutional trust is high but official responses were heterodox — illustrates how political context and policy design can shape the observed effects in ways not fully captured by aggregate indicators. Similarly, the concentration of misinformation effects during the Delta wave points to the importance of shifting social mechanisms: the rollout of vaccines introduced new arenas of contestation that altered how online narratives translated into epidemiological outcomes. Overall, these patterns suggest that our analysis identifies systematic associations across countries and waves, but that the underlying mechanisms require deeper investigation with individual-level and country-specific data.

The relationship between country-specific intercepts (baseline case rates) and slopes (effects of the Infodemic Index) is visualised in
<figure><img src="image_4.png" /><figcaption>Fig. 3. Caterpillar Plot (Mixed Effects) by Country.</figcaption></figure>

<table><caption>**Table 2**

Mixed-effect regression result for new case rates.</caption><thead><tr><th>Variables</th><th>(1)</th></tr><tr><th></th><th>Case Rate (log)</th></tr></thead><tbody><tr><td><strong>Fixed effects</strong></td><td></td></tr><tr><td>Infodemic Index (7 weeks lag)</td><td>0.625***<br>(0.123)</td></tr><tr><td>Protest (6 weeks lag)</td><td>0.186<br>(0.153)</td></tr><tr><td>Vaccination rate (log, 5 weeks lag)</td><td>-0.294***<br>(0.039)</td></tr><tr><td><strong>Random effects (variance)</strong></td><td></td></tr><tr><td>Country (intercept)</td><td>0.774</td></tr><tr><td>Infodemic Index (slope)</td><td>0.358</td></tr><tr><td>Protest (slope)</td><td>0.332</td></tr><tr><td>Vaccination rate (slope)</td><td>0.0045</td></tr><tr><td>Residual</td><td>1.431</td></tr><tr><td>No. countries</td><td>31</td></tr><tr><td>Country-level control variables</td><td>✓</td></tr><tr><td>Country FE</td><td>✓</td></tr><tr><td>Time Trends (B-Spline) FE and RE</td><td>✓</td></tr><tr><td>Observations</td><td>19,572</td></tr><tr><td>Log-Likelihood</td><td>-31552.78</td></tr><tr><td>AIC</td><td>63145.56</td></tr><tr><td>BIC</td><td>63303.2</td></tr></tbody></table>

Note: Optimal lags were determined from model fit and the literature. For all other covariates, see Table S6 in the supplementary material. FE: Fixed effects; RE: Random effects.

**Fig. 5.** Each data point represents a country, with the x-axis denoting the estimated slope of the Infodemic Index and the y-axis representing the country-specific intercept. The trend line suggests a slight negative correlation between the slope and intercept coefficients. This means that countries with higher baseline case rates (higher intercepts) tend to have weaker effects of misinformation on case growth (lower or negative slopes). Conversely, countries where misinformation has a stronger effect (higher slope) tend to have lower baseline case rates. Countries with similar baseline infection levels (intercepts) differed substantially in how misinformation translated into case growth (slopes). In stricter policy environments (e.g., Portugal, Spain), misinformation had little marginal effect because population behaviour was constrained. In more permissive or uncertain policy frameworks (e.g., Sweden, Poland, Czech Republic), misinformation translated into behaviour more directly, producing larger effects on case trajectories.

**Table 3** presents the results of the same modelling approach applied to excess mortality (log-transformed), with weekly data as in Eq. (2). In Model 1, which excludes new case rates, the infodemic index does not

<figure><img src="image_5.png" /><figcaption>Fig. 4. Map of Mixed-effect Coefficients Category (Europe).</figcaption></figure>

show significant direct effects on excess mortality. When new case rates are added (Model 2), they emerge as a strong and highly significant predictor ($\beta$ = 0.133), while the coefficients for the other variables remain small and nonsignificant. This pattern indicates that new case rates account for most of the explained variation in excess mortality, whereas the other predictors contribute little explanatory power.

To assess whether the epidemiological effects documented above are driven by specific types of information, we re-estimated the mixed-effects model using the share of the disaggregated infodemic category. Detailed descriptive statistics of each category can be found in Section 4 in the supplementary material. As shown in Fig. 6, the association between misinformation engagement and increased case rates was primarily driven by unreliable content such as satire, clickbait, conspiracy theories, junk science and fakes or hoaxes. By contrast, reliable information sources — including science and mainstream media domains
<figure><img src="image_6.png" /><figcaption>Fig. 5. Random Intercepts vs Slopes by Country.</figcaption></figure>

<table><caption>**Table 3**
Mixed-effect regression result for excess mortality rates.</caption><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>Mortality (log)</th><th>Mortality (log)</th></tr></thead><tbody><tr><td><strong>Fixed effects</strong></td><td></td><td></td></tr><tr><td>Infodemic Index (10 weeks lag)</td><td>0.237<br>(0.415)</td><td>0.159<br>(0.416)</td></tr><tr><td>Protest (9 weeks lag)</td><td>0.005<br>(0.031)</td><td>0.005<br>(0.031)</td></tr><tr><td>Vaccination rate (log, 8 weeks lag)</td><td>0.041<br>(0.032)</td><td>0.033<br>(0.032)</td></tr><tr><td>New case rate (log, 3 weeks lag)</td><td></td><td>0.133***<br>(0.039)</td></tr><tr><td><strong>Random effects (variance)</strong></td><td></td><td></td></tr><tr><td>Country (intercept)</td><td>0.019</td><td>0.025</td></tr><tr><td>Infodemic Index (slope)</td><td>0.251</td><td>0.241</td></tr><tr><td>Protest (slope)</td><td>3.84e-15</td><td>7.04e-13</td></tr><tr><td>Vaccination rate (slope)</td><td>5.84e-11</td><td>8.54e-16</td></tr><tr><td>New case rate (slope)</td><td></td><td>0.0001</td></tr><tr><td>Residual</td><td>0.414</td><td>0.412</td></tr><tr><td>No. countries</td><td>31</td><td>31</td></tr><tr><td>Country-level control variables</td><td>✓</td><td>✓</td></tr><tr><td>Country Fixed Effect</td><td>✓</td><td>✓</td></tr><tr><td>Time Trends (B-Spline) FE and RE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>1275</td><td>1,275</td></tr><tr><td>Log-Likelihood</td><td>-1360.473</td><td>-1357.454</td></tr><tr><td>AIC</td><td>2768.947</td><td>2766.908</td></tr><tr><td>BIC</td><td>2892.564</td><td>2900.826</td></tr></tbody></table>

Notes: For all other covariates check Table S7 in the supplementary material

— have negative effects on case rates. These findings further confirm our findings that the epidemiological impact of the infodemic is concentrated in engagement with unreliable information. The use of tweet volumes as alternative predictors (see Fig. S2 in the supplementary material) also showed that there is also a volume effect, as higher numbers of replies and original tweets *per se* also contribute to the case rate increase. This indicates a general infodemic effect despite the source reliability.

To examine whether our results depend on specific reporting artefacts in confirmed case data — especially during the early stages of the pandemic — we applied the model to three alternative epidemiological indicators: the estimated reproduction rate, hospitalisation rate, and ICU admission rate. These metrics capture transmission dynamics and clinical severity while being less sensitive to variation in testing capacity or reporting delays. Across all three measures, the coefficient for the

infodemic index remains positive and statistically significant, as seen in Table 4. This reinforces that the observed association is not exclusive to reported case counts. These mechanism testings complement the main model by confirming that (a) the epidemiological signal is specifically attributable to unreliable content, and (b) the effect is consistent across multiple measures of epidemic spread and severity.

### 3.3. Sensitivity analysis

To further evaluate the robustness of our findings, we conducted a series of sensitivity analyses. We first segmented the analysis by epidemic season to account for changes in dominant viral variants and associated public health responses. Because vaccination was only widely rolled out during the Delta Variant period, we only include the vaccination variable for that epidemic season. As shown in Table S3 in the supplementary material, the relationship between the Infodemic Index and infection rates varied markedly across phases. During the Delta wave, we observed a strong and statistically significant positive association (β=0.981), indicating that higher engagement with misinformation during this period was linked to increased infection rates. This effect was not present during the Alpha or Omicron phases, where coefficients were statistically insignificant. We also re-estimated the main model excluding the last weeks of 2021 and the first weeks of 2022 to address the early-2022 Omicron spike, with results similar to our main model. These findings underscore the dynamic nature of the infodemic's epidemiological impact, suggesting it may be particularly consequential in periods of heightened uncertainty or policy transition, such as during the Delta wave.

Second, we examined the sensitivity of our estimates to variations in the temporal lag structure of the independent variables. Shifting the time lag forward or backward by 5 days did not substantially alter the direction or statistical significance of the key coefficients, as seen in Table S4. This indicates robustness to assumptions about the temporal alignment of online engagement and health outcomes. We graphically examined the robustness of the lagged association between the infodemic index and case rates by comparing mixed-model coefficients across lags 0–50 with a detrended cross-lag analysis using the same covariates. As shown in Figures S3 and S4, the two approaches yield highly similar patterns, with coefficients increasing steadily at longer lags. We also computed country-specific partial cross-correlation functions between the infodemic index and detrended case rates, adjusting for time trends and covariates. As shown in Figure S5, these partial cross-correlations are generally positive and strengthen with longer lags, although the magnitude varies across countries.
<figure><img src="image_7.png" /><figcaption>Fig. 6. Sensitivity Test: By Information Source Classification.</figcaption></figure>

<table><caption>**Table 4**
Sensitivity analysis: Alternative outcomes.</caption><thead><tr><th>Variables</th><th>(1) Reproduction Rate</th><th>(2) Hospitalisation Rate(log)</th><th>(3) ICU Admission Rate (log)</th></tr></thead><tbody><tr><td>Infodemic Index (7 weeks lag)</td><td>0.075***<br>(0.021)</td><td>0.339**<br>(0.117)</td><td>0.325**<br>(0.113)</td></tr><tr><td>Protest (6 weeks lag)</td><td>-0.043<br>(0.024)</td><td>0.211*<br>(0.088)</td><td>0.173*<br>(0.072)</td></tr><tr><td>Vaccination Rate (5 weeks lag)</td><td>-0.003<br>(0.003)</td><td>-0.289***<br>(0.044)</td><td>-0.029<br>(0.040)</td></tr><tr><td>No. countries</td><td>31</td><td>31</td><td>31</td></tr><tr><td>Country-level control variables</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country Fixed Effect</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Time Trends (B-Spline) FE and RE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>19,580</td><td>13,354</td><td>11,464</td></tr><tr><td>Log-Likelihood</td><td>-1662.794</td><td>-14922.5</td><td>-11637.56</td></tr><tr><td>AIC</td><td>3365.588</td><td>29.889</td><td>23319.12</td></tr><tr><td>BIC</td><td>3523.234</td><td>30053.99</td><td>23480.76</td></tr></tbody></table>

Finally, we tested different specifications of the time trend smoothing technique. As seen in Table S5 in the supplementary material, model 1 shows the result of using date as time trends instead of B-splines; models 2 and 3 show the result of varying the number of B-splines, and in model 4 we remove the random effects of B-splines. We see that the coefficients for the infodemic are consistently positive and significant, with the magnitude reduced when including more B-splines, indicating that the infodemic effect was captured by the temporal dynamics, as expected, by allowing for greater flexibility.

## 4. Discussions

This study provides empirical evidence on how the digital infodemic has shaped the course of the COVID-19 pandemic across different countries. Our analysis shows that higher levels of population engagement with online misinformation, as measured by the dynamic infodemic index, are strong and consistent predictors of future increases in COVID-19 case rates — even after accounting for a range of behavioural and socio-economic factors as well as time trends. In contrast, although higher case rates are strongly associated with excess mortality, the infodemic index itself does not have a statistically significant direct effect on mortality once the role of infection rates is taken into account. The mechanism tests provide confirming evidence that only online engagement with unreliable content predicts higher case rates, while reliable sources do not contribute to future transmission, indicating that the epidemiological signal reflects misinformation specifically rather than general online activity. The association persists when using reproduction rates, hospitalisations, and ICU admissions,

demonstrating that the effect is not an artefact of case reporting but reflects broader epidemic severity.

The finding that higher engagement with online misinformation is associated with subsequent higher case rates underscores the critical role of digital information environments in influencing public health outcomes. This suggests that interventions aimed at mitigating the spread of misinformation — such as improving digital literacy, enhancing fact-checking mechanisms, and promoting scientific sources — may be essential for effective epidemic control. At the same time, it is important to acknowledge that health communication in a crisis cannot be reduced to a top-down transmission model. Uncertainty at the onset of the pandemic, including evolving or contradictory guidance from health authorities (e.g., early advice against mask use), also contributed to confusion and public contestation. Furthermore, in many countries, pandemic responses became politicised or militarised, and compliance measures imposed unequal burdens across social groups. In this light, protest activity cannot be dismissed solely as “anti-science” but should also be seen as part of broader struggles over authority, inequality, and public accountability. Our results therefore speak to one dimension of the infodemic–pandemic nexus, while future work should more fully integrate these complex social and political mechanisms.

In addition, the absence of a direct association between the infodemic index and excess mortality suggests that any impact of misinformation on mortality is likely to be indirect. Specifically, misinformation appears to contribute to higher case rates, which in turn drive excess deaths. This underscores the importance of controlling infection spread as a means to reduce mortality, and it indicates that while curbing mis/disinformation is critical, its effects on mortality may be contingent
upon broader public health responses and the resilience of health-
care systems. Moreover, in our model, we controlled for vaccination
rates because vaccine hesitancy represents a plausible pathway through
which misinformation could influence case rates. Since the association
between the infodemic index and case rates remains even after account-
ing for vaccination, this suggests that broader behavioural mechanisms
are likely at play. Although we cannot measure them directly, reduced
adherence to distancing, masking, isolation, and risk avoidance are
consistent candidates supported by prior evidence.

Our mixed-effects modelling reveals significant cross-national vari-
ability in the relationship between misinformation and COVID-19 in-
fection rates, particularly in the slopes associated with the infodemic
index. This heterogeneity likely reflects differences in national con-
texts, including variations in media ecosystems, public trust in in-
stitutions, healthcare capacity, and governmental responses. Compar-
isons of random components suggest that countries with high baseline
case rates may have faced structural healthcare vulnerabilities, pol-
icy inefficiencies, or early intense waves — conditions under which
misinformation had limited marginal influence. In Spain and Portu-
gal, stricter compliance and high epidemic pressures coincided with
negligible or even negative infodemic slopes, suggesting that misin-
formation had limited room to alter behaviours under strong insti-
tutional guidance. By contrast, countries with similar baseline case
levels but steeper misinformation-driven slopes — such as Sweden,
Poland, and the Czech Republic — show that misinformation was more
able to shape transmission-related behaviour where distancing and
mask adherence were voluntary or where risk communication became
politically contested, particularly in settings with fragmented infor-
mation ecosystems and fluctuating governmental credibility (Hallin
et al., 2024; Numerato et al., 2021). Regionally, Northern and Eastern
European countries exhibit a significantly stronger association between
misinformation exposure and infection rates, while this relationship
appears largely insignificant in Western and Southern Europe. South-
ern Europe, in particular, appears more resistant to the influence of
misinformation — potentially due to strong community-based health
networks, familial support systems, or greater exposure to diverse and
competing information sources that enhance societal resilience. Yet
the pronounced effect observed in some Nordic countries — despite
their proactive policies against online disinformation and generally
high institutional trust — suggests that civic autonomy may play a
complex role, potentially moderating or even amplifying the impact
of misinformation on public health outcomes. These patterns illustrate
that misinformation matters most where behavioural “space” is left
open by policy context: that is, where compliance is discretionary,
institutional authority is contested, or guidance is ambiguous.

These findings also highlight the difficulty of attributing cross-
national differences to any single factor. For example, while lower trust,
weaker health campaigns, or delayed responses may partly explain sus-
ceptibility, the Swedish case complicates such interpretations; Sweden's
heterodox strategy produced distinctive behavioural dynamics that may
have amplified the influence of online narratives despite high trust.
The apparent concentration of misinformation effects during the Delta
wave suggests that the relationship between digital misinformation
and epidemic spread is contingent on shifting social mechanisms. The
introduction of vaccines created new arenas of contestation — linking
misinformation not only to non-pharmaceutical interventions but also
to vaccination debates — which may explain why this period exhibited
particularly strong associations. Overall, these complexities reinforce
that our study identifies systematic associations across countries and
waves, but that the exact pathways are likely mediated by evolving po-
litical responses, communication environments, and public perceptions.
More fine-grained, multi-level analyses will be needed to unpack these
mechanisms in future research.

While our study leverages an extensive temporal span and robust
statistical models, several limitations merit consideration. First, the
reliance on aggregated country-level data may obscure intra-country

variations and local dynamics and may be subject to ecological fal-
lacy. Second, although we identified optimal lags based on model fit
and existing literature, alternative lag structures may capture addi-
tional nuances in the temporal relationships among misinformation,
behavioural responses, and epidemiological outcomes. These analy-
ses also do not identify causal mediation; they document temporally
ordered associations consistent with a hypothesised pathway. Third,
despite our comprehensive set of control variables to account for
confounding influences, unmeasured factors, particularly those related
to institutional trust, media consumption patterns, and cultural norms
around authority, may continue to shape the associations we observe.
Importantly, media consumption differs substantially across countries:
while in some contexts Twitter represents a central venue for political
debate and information exchange, in others, television, messaging apps,
or alternative platforms dominate public attention. In contexts such
as Poland or Hungary, where mainstream media are deeply politi-
cised, misinformation may circulate through institutional channels in
ways not fully reflected in Twitter-based indicators. This heterogeneity
means that our index — though capturing systematic engagement with
unreliable sources on Twitter — should be interpreted as a proxy
of broader misinformation dynamics, not an exhaustive measure of
all information pathways. Fourth, although the daily infodemic index
captures the scale of engagement with misinformation, it does not
render nuanced insights on the emotions of the population. Finally,
our daily vaccination rate should not be interpreted as a measure of
vaccine hesitancy but rather as a proxy of vaccination behaviour in
the population. Ideally, we would use daily data for vaccine refusal to
capture hesitancy more directly; however, such data is not available in
any of the observed countries.

Future research could also benefit from a multi-level approach that
combines individual-level data on media consumption and behavioural
responses with national-level indicators. Additionally, examining how
misinformation affects risk perception and adherence to public health
measures — potentially via structural equation modelling — could pro-
vide deeper insights into the causal pathways linking digital content to
health outcomes. We also note that future research should triangulate
such indices with survey-based measures of media consumption and
platform-specific misinformation metrics.

Overall, these findings contribute to the growing literature on the
infodemic by revealing its indirect yet significant impact on the spread
of COVID-19, as reflected in rising case rates. By demonstrating that
digital mis- and disinformation can exacerbate public health crises,
our study highlights the urgent need for coordinated strategies to
counter misinformation. The cross-national variation observed further
highlights the importance of context-specific strategies, emphasising
that mitigation efforts should be tailored to the unique media ecosystem
and socio-political conditions of each country.

CRediT authorship contribution statement

**Yuxi Wang:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Ugofilippo Basellini:** Writing – review & editing, Supervision. **Carlo-Giovanni Camarda:** Writing – review & editing, Supervision.

Ethics statement

This study used secondary data obtained from publicly available
sources. The data were fully anonymised prior to access and analysis,
and no identifiable personal information was used.

Declaration of competing interest

The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
## Acknowledgements

This research is funded by the EU's Horizon Europe Framework under grant agreement 101107454. The authors thank the participants of the Max Planck Institute for Demographic Research Seminar Series for their valuable comments. We also thank the discussion panellist, Prof. Sanyu Mojola, at the Population Association of America annual meeting. We also thank the audience at the CRIS seminar at Sciences Po for the valuable inputs. We further thank the participants at the PopDays conference in Cagliari, Sardegna for their constructive comments.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.socscimed.2025.118881.

## Data availability

Data will be made available on request.

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