Composition Vendor Newgen KnowledgeWorks (P) Ltd. PLOS Clim plos plosclimate PLOS Climate 2767-3200 Public Library of Science San Francisco, CA USA 10.1371/journal.pclm.0000542 PCLM-D-24-00249 Research Article Ecology and environmental sciencesWildfires Earth sciencesAtmospheric scienceAtmospheric chemistryAir quality Physical sciencesChemistryEnvironmental chemistryAtmospheric chemistryAir quality Ecology and environmental sciencesEnvironmental chemistryAtmospheric chemistryAir quality Medicine and health sciencesPublic and occupational health Medicine and health sciencesEpidemiologyMedical risk factors People and placesGeographical locationsNorth AmericaUnited StatesWashington Medicine and health sciencesEpidemiology Earth sciencesSeasons People and placesGeographical locationsNorth AmericaUnited StatesOregon Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens Disruption of outdoor activities caused by wildfires increases disease circulation https://orcid.org/0000-0002-5591-8367 Arregui-García Beatriz Formal analysis Visualization Writing – review & editing 1 Ascione Claudio Formal analysis Writing – review & editing 2 Pera Arianna Formal analysis 3 https://orcid.org/0000-0001-7276-370X Wang Boxuan Formal analysis 2 https://orcid.org/0000-0003-4910-3580 Stocco Davide Formal analysis 4 Carlson Colin J. Conceptualization Writing – review & editing 5 Bansal Shweta Conceptualization Funding acquisition Methodology Writing – review & editing 6 Valdano Eugenio Methodology Writing – review & editing 2 https://orcid.org/0000-0001-9980-0734 Pullano Giulia Conceptualization Formal analysis Investigation Methodology Project administration Supervision Writing – original draft 6 * Instituto de Fisica Interdisciplinary Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Paris, France Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark Department of Mathematics, Politecnico di Milano, Milan, Italy Department of Epidemiology of Microbial Diseases & Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut, United States of America Department of Biology, Georgetown University, Washington, DC, United States of America Chong Ka Chun Editor The Chinese University of Hong Kong, HONG KONG

The authors have declared that no competing interests exist.

 * E-mail: giulia.pullano@georgetown.edu 1862025 2025 4 6 e0000542 25102024 352025 2025 Arregui-García et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

As climate change accelerates, the frequency and severity of extreme weather events, such as wildfires, are increasing, with profound impacts on human health. While much attention has been paid to the direct health consequences of these events, such as chronic diseases from poor air quality, less is known about how behavioral shifts induced by such events can influence the transmission of infectious diseases. This study investigates how wildfire-induced changes in human behavior during the U.S. West Coast wildfires of 2020 may affect the spread of airborne diseases. Using a mobility data-driven indoor activity index, we find that the wildfire-induced deterioration of air quality led to a substantial increase in indoor activities, fostering conditions conducive to airborne disease transmission. Specifically, counties in Oregon and Washington experienced an average 10.8% and 14.3% increase in indoor activity, respectively, during the wildfire events, with major cities like Portland and Seattle experiencing increases of 11% and 16%, respectively. We quantify these behavioral changes and integrate them into an SIR epidemic model to characterize the increased indoor activity and disease dynamics. The model predicts the greatest impact on diseases with shorter generation times, such as RSV and influenza. Our results show that even a modest increase in indoor mask-wearing (as low as 10%) could significantly reduce the risk of disease spread in these settings, with higher compliance needed for more substantial reductions. As wildfires and other climate-related events become more frequent, integrating behavioral responses into public health policies will be crucial to mitigate the compounded risks of climate change and its secondary health impacts.

 Author summary

The effects of climate change on human health are becoming more evident, but we often overlook one crucial factor: how extreme weather events influence our behaviors and, in turn, the spread of infectious diseases. In this study, we explore the role of wildfire-induced behavioral changes on the transmission of airborne diseases, focusing on the U.S. West Coast wildfires of September 2020. Our findings show that wildfires led to a dramatic increase in indoor activities, creating the ideal conditions for respiratory diseases such as influenza to spread. But—by incorporating simple measures like indoor mask-wearing, we can reduce this risk. This research underscores the importance of considering human behavior responses when tackling health risks associated with climate change. As we face more frequent extreme events, public health strategies must evolve to address not just the environmental impact, but also the ways we adapt, react to the emergency. By understanding and planning for this behavioral response, we can better protect public health in a warming world.

 NIH R35GM153478 BansalShweta Fritz-Family Fellowship BansalShweta Research reported in this publication was supported by the Fritz-Family fellowship program to SB and GP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. GP received her salary from the Fritz-Family fellowship program. Data Availability The data generated in this study are publicly available in the GitHub repository (https://github.com/GiuliaPullano/wildfires_project). The indoor seasonality index we used is from [14] and is publicly available at https://github.com/bansallab/indoor_outdoor. The daily Air Quality Index (AQI) in all U.S. counties is collected by the Environmental Protection Agency (EPA) and is available on their website [18]. Population data were obtained from the U.S. Census Bureau (https://www.census.gov/data/) and represent the annual estimates of the resident population for each county in 2020. Main

Climate change is significantly increasing the frequency and intensity of extreme weather events, posing severe threats to both the environment and human health. As the planet continues to warm, we are witnessing an increase in the occurrence and severity of phenomena such as heatwaves, storms, floods, and wildfires. These events not only devastate ecosystems and economies but also pose significant risks to human health [1]. The health consequences are multifaceted, encompassing immediate and long-term effects. The repercussions include substantial loss of life, disability, and diminished well-being, driven by a complex interplay of physical injuries, the spread of infectious diseases, and the worsening of non-communicable diseases such as respiratory and cardiovascular conditions [2].

Extreme weather events also influence human behavior [3–4], which in turn might affect human health indirectly [5]. These behavioral shifts, often driven by the need to seek safety or comfort, has the potential to create compound risks for human health during ongoing public health emergencies [1]: for example, during the first three years of the Covid-19 pandemic, unprecedented heatwaves drove many people indoors, leading to a substantial number of excess Covid-19 cases and deaths [6–7]. This complex interplay between environmental stressors and behavioral responses highlights the potential for extreme weather events to amplify public health crises by creating conditions that facilitate the spread of infectious diseases. Despite these critical observations, the cascade effects of localized or permanent behavioral changes due to extreme weather events on the dynamics of infectious diseases remain largely underexplored. Addressing this gap is essential for enhancing our ability to predict and effectively respond to future epidemics in a world that has warmed approximately 1.3–1.5°C compared to the 1850–1900 average [8].

Wildfires are among the most disruptive events for human behavior, severely impacting air quality across vast regions [9] and posing significant concerns for public health. While much of the public health focus has been on the physiological effects of wildfires [10]—such as direct mortality due to short-term exposure to fine particulate matter (PM2.5) air pollution [11], and synergistic effects of PM2.5 exposure on respiratory infectious disease susceptibility and severity [12]—there has been less attention to how wildfire-induced changes in behavior might influence the spread of airborne diseases. However, perceptions of deteriorating air quality and government advisories [13] often lead individuals to reduce outdoor activities. This shift results in increased indoor activity, where conditions such as close social proximity, limited ventilation, and the accumulation of respiratory droplets can facilitate the transmission of airborne diseases [14,15]. From a public health perspective, with the increasing frequency and severity of wildfires, it has become crucial to understand how disruptions in outdoor activities during wildfires might indirectly impact disease dynamics in indoor settings.

To answer this question, we concentrated on the 2020 wildfire season, one of the most severe in recent history. In August 2020, thunderstorms sparked multiple wildfires across California, Oregon, and Washington [16]. By early September, a record-breaking heatwave and intense katabatic winds accelerated fire growth and spread smoke across Oregon and Washington state. To assess the fires’ impact on human behavior, we analyzed a weekly time series of the relative tendency for people to stay indoors versus outdoors across U.S. counties. This mobility metric, called the indoor activity seasonality index, was derived from mobile phone data [15]. While September is typically a period of low viral circulation for most airborne diseases, in Seattle (King County)—the only county with a substantial number of COVID-19 cases at the time—the effective reproduction number (R(t)) increased significantly approximately 10 days after peak wildfire-induced indoor activity (Fig A in S1 Text). This finding reinforces the plausibility of a link between increased indoor crowding and heightened transmission risk. Although multiple factors could have contributed to this trend, it underscores the need for a deeper understanding of the indirect epidemiological effects of extreme weather events.

By integrating mobility data with air quality indicators and a mechanistic epidemic model, we aimed to quantify the behavioral changes induced by wildfires and their implications for viral transmission, thereby informing public health strategies in response to wildfire events.

 Results Wildfires induce behavioral changes

During the 2020 wildfire season, numerous counties along the West Coast of the United States experienced significant deteriorations in air quality, as reflected by sharp increases in the Air Quality Index (AQI). These spikes were primarily due to smoke from extensive wildfires in California, Oregon, and Washington. The wildfire smoke was carried by atmospheric circulation, spreading poor air quality over vast regions, including areas far from the actual fires (Fig 1A).

10.1371/journal.pclm.0000542.g001Air Quality level during the West Coast wildfire season in 2020.

(a) Air Quality Index extracted for PM2.5 particle pollution for all counties in Oregon (OR), Washington (WA), and California (CA). Horizontal line at 150 indicates the Air Quality Index (AQI) threshold for health concerns; (b) The map highlights in dark red the most affected counties in Oregon (OR) and Washington (WA) that were included in our study. The orange counties represent unaffected areas used as a baseline. Both affected and unaffected counties are part of the Northern Indoor Seasonality Cluster (in dark grey). Base layer: https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_county_20m.zip.

Our analysis of AQI data revealed that counties in Oregon (OR) and Washington (WA) were particularly hard-hit, with AQI levels exceeding 150 during the 2020 wildfire season, as depicted in Fig 1A. Through much of September, indeed at least 8 large wildfires, each of 100,000 acres (400 km2) or more, were burning in Washington and Oregon, with 3 in Washington and 5 in Oregon. Although California also experienced elevated AQI levels over a more extended period, the specific air quality impact on September 10th was less severe compared to Oregon and Washington. We therefore focus our analysis on the most affected counties in OR and WA state (Fig 1B). In contrast, counties not affected by wildfire smoke generally maintained AQI levels below 50, indicative of normal air quality conditions.

Focusing on the counties with AQI levels exceeding 150 during the 2020 wildfire season, we examined the disruption in human behavior in such locations during the wildfires. As shown in Fig 2, our analysis revealed a sustained increase in indoor activity in the most affected counties for approximately four weeks during the wildfire events. In contrast, unaffected counties with healthy AQI levels exhibited no such increase during the same period (Fig B in S2 Text). The counties compared belong to the same Northern Indoor Seasonality Cluster (Fig 1B), meaning they share similar seasonal patterns of indoor and outdoor activity due to environmental aspects. Comparing counties within the same cluster is essential for isolating behavioral changes specifically related to the wildfire events, rather than broader seasonal trends based on exogenous factors. Refer to the Methods section for more details on county selection. Using a regression discontinuity approach (see Methods) [17], we found that Washington and Oregon states experienced an average increase in indoor activity of 10.8% and 14.3%, respectively. Notably, we observed a significant rise in indoor activity in all affected counties except for Marion County, Oregon (γ5% < 0), as shown in Table 1. Major cities were the most impacted, with indoor activities increasing by 11% in Portland (Multnomah County) and 16% in Seattle (King County).

10.1371/journal.pclm.0000542.t001Regression Discontinuity Coefficients by Affected U.S. County with 90% Confidence Intervals.
State County Name γ γ5% γ95%
OR Multnomah County 0.11 0.04 0.18
OR Washington County 0.09 0.03 0.14
OR Clackamas County 0.30 0.25 0.35
OR Lane County 0.07 0.02 0.13
OR Marion County 0.03 -0.03 0.07
WA King County 0.16 0.12 0.19
WA Spokane County 0.16 0.13 0.20
WA Clark County 0.09 0.03 0.15
WA Thurston County 0.11 0.06 0.16
WA Yakima County 0.02 0.00 0.04
10.1371/journal.pclm.0000542.g002Shift in human behaviors due to wildfire smoke.

The figure shows the indoor activity seasonality index between July 1, 2020, and November, 01, 2020 in the 10 selected affected counties: Multnomah County (Portland City), Washington County, Clackamas County, Lane County, Marion County in Oregon state, and King County (Seattle city), Spokane County, Yakima County, Clark County, Thurston County in Washington state. In each subplot, we also show in yellow the median and 95% CI of the indoor activity seasonality index for the unaffected counties that we used as a baseline. The violet curves represent the AQI of the affected counties during the studied period.

 Wildfire-response behavior shapes respiratory pathogen circulation

To assess how wildfire-induced changes in behavior impact the circulation of respiratory pathogens, we incorporated the indoor activity seasonality index—reflecting disruptions during wildfire events—into an infectious disease transmission model (described in the Methods section). This model enabled us to evaluate the effects of wildfires on various airborne respiratory diseases by examining a range of generation times, from 2 days (for flu-like illnesses) to 25 days (for pertussis-like diseases). We focused on Washington County, one of the areas most impacted by wildfire smoke, and compared the relative peak incidence of diseases in this area to that in unaffected counties. Our findings show that increased indoor activity due to wildfires significantly impacts disease spread, with this effect diminishing as the generation time lengthens (Fig 3A). For diseases with shorter generation times (less than one week), such as COVID-19 and influenza, we observed a notable increase in relative peak incidence (+2) and greater variability in disease spread between affected and unaffected counties. This suggests that the risk of increased epidemic activity is more pronounced when the disease dynamics has a temporal scale that is comparable with the duration of the human mobility disruptions.

10.1371/journal.pclm.0000542.g003The cascade impact of the shift in human behavior on airborne diseases.

A) Relative variation in peak incidence between Washington County, OR, and unaffected counties for different generation times [20] with R0 = 1.3. The boxplot median represents the median relative peak incidence across unaffected counties, and the blue vertical lines denote the average generation times for various airborne respiratory diseases. B) Relative variation in peak incidence for the selected 10 affected counties compared to the unaffected ones for three different reproduction ratios R0 = 1.3 (flu-like w/ interventions), R0 = 1.5 (flu-like). and, R0 = 3.0 (COVID-19-like). C) Relative variation in peak incidence when masking interventions are put in place, with R0 = 1.3.

By simulating the disease dynamics in any affected county, we also tested different reproduction ratios (R0) consistent with seasonal respiratory viruses and currently circulating strains of SARS-CoV-2 (Fig 3B). Results indicated that peak incidence in wildfire-affected counties surpassed that of unaffected counties, with the highest relative peak incidence in Washington County, OR (+1.98). This elevated peak incidence is attributed to the increased indoor activity and, in some counties, generally higher seasonal indoor behavior compared to baseline levels. The impact of increased indoor activities on disease spread diminished with higher R0 values. Similar results were observed for the relative attack rate (Fig C in S3 Text). We also examined a scenario incorporating 50% population immunity, representing an upper bound for seasonal influenza. Estimating population-level immunity is inherently challenging, as it depends on both vaccination coverage and prior infection—factors that fluctuate across regions and seasons. While adult influenza vaccination coverage in the U.S. typically falls just below 50%, actual immunity is further constrained by modest vaccine effectiveness (ranging from 20% to 60% over the past two decades) [18]. Even under this assumption of 50% immunity, wildfire-affected counties still experienced a relative peak incidence above 1, underscoring the persistent and measurable effect of wildfire-driven behavioral disruptions on respiratory disease transmission—even in populations with substantial pre-existing immunity (Fig D in S3 Text).

 Policy interventions

To tackle the increased exposure risk associated with wildfires, we thoroughly evaluated the impact of mask usage in indoor environments, considering a range of population coverage from 0.1% to 50%. As depicted in Fig 3C, our analysis reveals that even a modest 10% increase in mask usage can lead to a notable reduction in the rise of peak incidence driven by the surge in indoor activities during wildfires. However, achieving a substantial and meaningful reduction in exposure risk necessitates much higher levels of mask compliance. Specifically, masking rates approaching 50% are required to significantly lower the peak incidence and mitigate the elevated risk of virus transmission. In regions like Washington County, OR, which are particularly impacted, even more stringent masking adherence is essential to achieve a meaningful impact on disease spread. These results underscore the importance of strongly recommending indoor masking during air quality alerts. By promoting higher levels of mask usage, we can more effectively reduce the risk of virus transmission during air quality alerts and protect public health in extended and crowded indoor settings.

 Discussion

Our study provides novel insights into the indirect effects of climate change on public health by illustrating how wildfire-induced behavioral changes can exacerbate the spread of airborne diseases. The findings highlight the interconnectedness of environmental, behavioral, and epidemiological factors, emphasizing the need to consider the climate-driven behavioral shifts to characterize disease dynamics and design policies for disease prevention and control. Wildfires stand out among climate events for their profound disruption of human behavior, primarily due to the extensive reach of wildfire smoke. Carried over long distances by atmospheric currents, this smoke affects not only nearby areas but also distant communities, as was evident during the 2020 wildfire season [9,16]. While the direct health impacts of wildfire-related air pollution have been well-documented [11], there has been less attention given to the indirect effects—specifically, the increased time spent indoors during periods of smoke-induced poor air quality, which may enhance the transmission of airborne diseases in indoor settings. As global climate change is expected to intensify wildfire activity, leading to longer fire seasons and larger areas burned [19], it is crucial to understand these behaviorally driven risks. This knowledge is vital in shaping public health strategies and preparing for potential epidemic surges induced by extreme events.

To address this gap, we provided quantitative evidence on how the 2020 wildfire season altered human behavior, increasing the risk of pathogen transmission in indoor environments. Leveraging behavioral and air quality data, we first systematically assessed the disruption of seasonal outdoor activities across U.S. counties that experienced significant spikes in unhealthy Air Quality Index (AQI) levels. Our analysis reveals that declining air quality led to a reduction in outdoor activities, disrupting established behavioral patterns. Following the most intense wildfire events on September 10, indoor activity surged by 10.8% in Washington and up to 14.3% in Oregon. Even urban areas farther from the burn sites, such as Portland and Seattle, saw significant increases in indoor activity, with rises of 30% and 16%, respectively. These shifts in behavior were likely driven by concerns over unhealthy air quality, prompting self-isolation to avoid air pollution and adhere to public health guidelines.

By integrating the indoor seasonality index into an epidemic model, we show how behavioral shifts foster environments that facilitate the spread of airborne pathogens. By capturing the increased tendency of people to remain indoors versus outdoors during wildfires in the indoor activity index, we characterize broader variations in exposure risk by combining multiple transmission-related factors. These include elements that influence host contact, susceptibility, and transmissibility [15]. For instance, heightened indoor activity can lead to prolonged airborne exposure (e.g., spending extended time in confined spaces), increasing the risk of respiratory transmission. Higher indoor density can further enhance droplet transmission due to close-range interactions. Additionally, indoor environments with poor ventilation, elevated pollutant levels, limited sunlight, and low humidity can weaken immune responses, potentially increasing susceptibility. Climate-controlled spaces may also promote viral survival by maintaining low humidity and supporting viral spread. While the indoor activity index does not isolate these factors, it provides an aggregated measure of heightened exposure risk due to behavioral responses, based on indoor versus outdoor activities. This framework enables us to assess the impact of seasonal disruptions, such as those caused by wildfires, in comparison to typical seasonal exposure risks. By incorporating the seasonal index as a linear modulator of the force of infection in an SIR model, we can analyze the effects of these seasonal disruptions on disease dynamics. Our simulations indicate that the surge in indoor activity during wildfires significantly alters the transmission dynamics of airborne diseases, with varying effects depending on the pathogen’s intrinsic transmissibility and generation time. Diseases with shorter generation times, such as COVID-19 and influenza, exhibited more pronounced increases in peak incidence, as their rapid transmission aligns with the short-term mobility disruptions. In contrast, diseases like mumps and pertussis, which have longer infectious periods, were less impacted by these localized behavioral changes. Furthermore, variations in the pathogen’s basic reproduction number (R0) reveal that the degree to which increased indoor activity affects disease spread is closely tied to the pathogen’s transmissibility. It is important to note that our model assumes a fully susceptible population, which reflects a particular epidemiological scenario. This assumption is most applicable to the early stages of novel pathogen emergence, as seen with SARS-CoV-2, or to highly mutable viruses such as Influenza A virus subtype H3N2, for which antigenic drift can erode existing immunity, even among previously exposed or vaccinated individuals. To test the robustness of our findings, we also explored an extreme scenario with 50% population immunity and found that the increase in peak incidence during wildfires remained substantial, reinforcing the significance of behavior-driven risk even in partially immune populations. These findings underscore the importance of considering disease-specific characteristics when developing public health strategies for extreme climate events.

To mitigate the compound effects of wildfire on infectious disease dynamics for flu-like diseases, our study highlights the importance of indoor masking as a preventive measure. We find that even a modest increase in mask use (10%) can reduce the rise in peak incidence during wildfires. In heavily impacted areas like Washington County, OR, and Yakima County, WA, masking rates above 50% may be required to effectively mitigate disease transmission. These findings have immediate policy implications, suggesting that public health guidelines should integrate recommendations for mask use during wildfire events to reduce the increase of flu-like disease circulation. Future guidelines should address the dual challenges of poor air quality and increased risk of disease transmission, promoting strategies that balance the need for respiratory protection with the need to prevent airborne pathogen spread. As wildfires and other extreme weather events become more frequent with climate change, it is essential to develop comprehensive public health strategies that account for these interconnected risks.

While our study provides valuable insights, it is important to acknowledge several limitations. Our analysis is based on a specific wildfire season in the United States, which may limit the generalizability of the findings. As more behavioral data on indoor activities become available, future research should seek to validate these results across diverse geographic contexts and different wildfire seasons. Additionally, this work relies solely on the ratio of indoor-to-outdoor activities, neglecting indoor activities at home, which could also impact epidemic circulation. Nevertheless, our primary objective is to elucidate the relationship between shifts in indoor behavior and respiratory disease spread in crowded public settings, to inform control policies. Future research should explore the role of various indoor environments in disease transmission during wildfires, including the impact of residential settings on epidemic dynamics. Lastly, our model does not incorporate public health data on infection rates, which limits the ability to directly correlate behavioral changes with disease outcomes. Further studies incorporating such data are urgently needed to explore the broader implications of climate change on specific diseases more comprehensively.

Our research provides a foundation for future investigations into the indirect public health impacts of environmental disruptions on human behavior. It underscores the urgent need to address the secondary effects of climate change on public health, with a particular focus on infectious disease transmission. By revealing how behavioral shifts induced by wildfires can elevate disease spread, our study advocates for the inclusion of behavioral responses in public health strategies. As climate change continues to alter our environment, it is crucial for public health interventions to evolve accordingly, equipping us to tackle the multifaceted and interconnected risks of a warming world.

 Methods

Our analysis proceeded in two steps. First, we characterize weekly human behavior patterns during the 2020 wildfire season in the Western United States in both affected and unaffected areas to identify significant behavioral shifts. Second, we integrated these behavioral changes into a mechanistic model to assess their impact on many airborne diseases.

Detecting disruptions of indoor activity seasonality

Human behavior was quantified using the indoor seasonality index by county, as introduced in [15], which captures the tendency of individuals to engage in indoor versus outdoor activities. This index, derived from SafeGraph (now called Advan Patterns) Weekly Patterns mobility data spanning 2018–2021, tracks activity based on mobile phone users’ visits to over 4.6 million points of interest (POIs) in the United States, excluding home locations. POIs were categorized according to their primary indoor or outdoor nature using the six-digit North American Industry Classification System (NAICS) codes. For example, schools, hospitals, and grocery stores were classified as primarily indoor, while parks and cemeteries were classified as outdoor. Approximately 90% of the POIs were classified as indoor, 6.5% as outdoor, and 3.5% as unclear, as in the case of gas stations, which could have both indoor and outdoor features. To quantify the indoor seasonality index, a specific metric was defined as follows:

\[ σ{it}~= {Nitindoor} maxt {Nitoutdoor} \]

where Nitindoor and Nitoutdoor represents the number of visits to indoor and outdoor points of interest, respectively, in week t in the US county i.

This metric is then mean-centered to provide a relative measure of indoor activity seasonality that is comparable across all counties.

To detect shifts in indoor activity patterns, we applied a regression discontinuity (RD) approach [17]. RD is a statistical method that estimates the causal effect of an intervention by exploiting a sharp change in the relationship between a continuous assignment variable and an outcome variable. In our study, RD is based on the premise that indoor activity seasonality in each county remains consistent throughout the 8-week study period, except for the impact of the wildfire event. We employed a local linear regression model for the RD analysis, estimating the wildfire event’s effect, denoted as γ, which captures the difference in outcomes before and after the event. Additional details on the linear regression model are provided in S4 Text.

 Selecting counties affected and unaffected by 2020 wildfire season

Our study examines the 2020 wildfire season, with a specific focus on the severe wildfire event that occurred between September 10 and November 1, 2020 in California, Oregon, and Washington We used air quality data from the U.S. Environmental Protection Agency (EPA) to identify counties where the Air Quality Index (AQI) exceeded 150 for at least three days from September 10 to November 1,2020 signaling unhealthy air conditions. The AQI data was specifically extracted for PM2.5 particle pollution, a key pollutant associated with wildfire smoke [20]. These most affected counties were located in Oregon and Washington. From this subset, we selected the five most populous counties in each state, resulting in a final set of 10 counties significantly impacted by wildfire smoke: Multnomah County, Washington County (Portland), Clackamas County, Lane County, and Marion County in Oregon; and King County (Seattle), Spokane County, Yakima County, Clark County, and Thurston County in Washington. For populations, we used census data. For comparison, we identified unaffected counties which showed daily AQI levels consistently below 100 during the wildfire event. We selected a total of 51 unaffected counties all across the US and ensured they were within the top 25% in population distribution, using these as a baseline for our analyses (Fig E in S5 Text). To account for the significant geographical variability in seasonal indoor activity across U.S. counties [15], we refined our baseline selection process by incorporating the indoor seasonality index based on the mobile phone data tracking indoor and outdoor activity. This step ensured that the unaffected counties included in our analysis shared consistent seasonal behavior patterns with the affected ones, allowing for accurate comparisons of human behavior across impacted and unaffected regions. Additional details on county selection criteria for unaffected counties and the consistency of seasonal behavioral patterns are reported in S1 Text.

 Infectious disease model and scenario analysis

We assessed the impact of behavioral shifts on epidemic circulation using a deterministic Susceptible-Infected-Recovered (SIR) compartmental model for each county. The model does not depend on county population. The compartmental model simulated the daily spread of respiratory pathogens from seven days before the AQI alert to 14 days after. We defined the force of infection for a county ias λ=β0βi(t) [15]. β0 is a constant parameter that takes into account the overall transmissibility, while βi(t) is indoor seasonality index that accounts for season behavioral patterns in the county iand potential disruption of human mobility caused by wildfires. We calculated the relative variation in disease incidence by comparing the modeled peak incidence and attack rate in affected counties with that in unaffected ones.

The model explores various reproduction numbers (R0) equal to 1.3, 1.5, and 3 These values were selected because 1.3 and 1.5 are typical for seasonal respiratory viruses like influenza and SARS-CoV-2, while 3.0 approximates the basic reproduction number of the wild-type SARS-CoV-2. Additionally, we explored generation times ranging from 2 to 12 days to evaluate their impact on airborne diseases ranging from flu-like illnesses to pertussis-like illnesses [21].

To model the effect of mask-wearing, we incorporated a reduction factor m into the force of infection to account for masking. The reduced force of infected is defined as follow:

\[λ =mβ0βi(t)\]

Where m = σρ+(1 − ρ), with σ set to 0.6, representing the estimated reduction in respiratory infection due to mask-wearing [22], and ρ representing the fraction of the population wearing masks during the wildfire event. We explored various values for ρ ranging from 0.1% to 50%. Mask-wearing was considered only on days with unhealthy are quality (AQI > 150). Additional details on the model’s mathematical framework and simulation algorithm are provided in S6 Text.

 Supporting information Analysis of COVID-19 R(t) Trends in Wildfire-Affected vs. Unaffected Areas.

Fig A: Analysis of COVID-19 R(t) Trends in Wildfire-Affected vs. Unaffected Areas.

(DOCX)

 Indoor Seasonality Index for unaffected counties.

Fig B. Time series of the Indoor Seasonality Index in unaffected counties.

(DOCX)

 Sensitivity analysis.

Fig C: Analysis of Relative Attack Rate. Fig D: Sensitivity Analysis of the Proportion of Immune Population.

(DOCX)

 Regression Discontinuity of indoor activity seasonality.

(DOCX)

 County selection for unaffected counties.

Fig E: Comparison of Population Sizes Between Affected and Unaffected Counties.

(DOCX)

 Infectious disease model and simulation details.

(DOCX)

We gratefully acknowledge the Complexity72h workshop, held at IFISC in Palma, Spain, from 26 to 30 June 2023, where the initial design and a preliminary report of this study were developed.

 References Carlson CJ, Mitchell D, Gibb R, Stuart-Smith RF, Carleton TA, Lavelle TE, et al. Health losses attributable to anthropogenic climate change. medRxiv. doi: 10.1101/2024.08.07.24311640 Newman R, Noy I. The global costs of extreme weather that are attributable to climate change. Nat Commun. 2023;14(1):6103. doi: 10.1038/s41467-023-41888-1 37775690 Chen Z, Gong Z, Yang S, Ma Q, Kan C. Impact of extreme weather events on urban human flow: A perspective from location-based service data. Comput Environ Urban Syst. 2020;83:101520. doi: 10.1016/j.compenvurbsys.2020.101520 32834303 Bengtsson L, Gaudart J, Lu X, Moore S, Wetter E, Sallah K, et al. Using mobile phone data to predict the spatial spread of cholera. Sci Rep. 2015;5:8923. doi: 10.1038/srep08923 25747871 Thalheimer L. Compound impacts of extreme weather events and Covid-19 on climate mobilities. Area (Oxf). 2022. doi: 10.1111/area.12821 35941914 Lian X, Huang J, Li H, He Y, Ouyang Z, Fu S, et al. Heat waves accelerate the spread of infectious diseases. Environ Res. 2023;231(Pt 2):116090. doi: 10.1016/j.envres.2023.116090 37207737 Lo YTE, Mitchell DM, Gasparrini A. Compound mortality impacts from extreme temperatures and the COVID-19 pandemic. Nat Commun. 2024;15(1):4289. doi: 10.1038/s41467-024-48207-2 38782899 McCulloch MT, Winter A, Sherman CE, Trotter JA. 300 years of sclerosponge thermometry shows global warming has exceeded 1.5 °C. Nat Clim Chang. 2024;14(2):171–7. doi: 10.1038/s41558-023-01919-7 Salam E. US air quality as far south as Virginia affected by Nova Scotia wildfires. The Guardian. June 2023. Available from: https://www.theguardian.com/us-news/2023/jun/02/us-air-quality-virginia-maryland-nova-scotia-wildfires Romanello M, Di Napoli C, Drummond P, Green C, Kennard H, Lampard P, et al. The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. Lancet. 2022;400(10363):1619–54. doi: 10.1016/S0140-6736(22)01540-9 36306815 Park CY, Takahashi K, Fujimori S, Jansakoo T, Burton C, Huang H, et al. Attributing human mortality from fire PM2.5 to climate change. Nat Clim Chang. 2024;14(11):1193–200. doi: 10.1038/s41558-024-02149-1 Zhou X, Josey K, Kamareddine L, Caine MC, Liu T, Mickley LJ, et al. Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States. Sci Adv. 2021;7(33):eabi8789. doi: 10.1126/sciadv.abi8789 34389545 U.S. Environmental Protection Agency EPA. Wildfire Smoke Guide Publications. [cited 2023 October 1]. Available from: https://www.airnow.gov Nair AN, Anand P, George A, Mondal N. A review of strategies and their effectiveness in reducing indoor airborne transmission and improving indoor air quality. Environ Res. 2022;213:113579. doi: 10.1016/j.envres.2022.113579 35714688 Susswein Z, Rest EC, Bansal S. Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data. Elife. 2023;12:e80466. doi: 10.7554/eLife.80466 37014055 European Space Agency (ESA). West coast wildfires. Available from: https://www.esa.int/Applications/Observing_the_Earth/Copernicus/US_West_Coast_on_fire Van Der Klaauw W. Regression–Discontinuity Analysis: A Survey of Recent Developments in Economics. Labour. 2008;22(2):219–45. doi: 10.1111/j.1467-9914.2008.00419.x Centers for Disease Control and Prevention CDC. Seasonal Flu Vaccine Effectiveness Studies. [cited 2023 October 1]. Available from: https://www.cdc.gov/flu-vaccines-work/php/effectiveness-studies/index.html Reisen F, Duran SM, Flannigan M, Elliott C, Rideout K. Wildfire smoke and public health risk. Int J Wildland Fire. 2015;24(8):1029–44. doi: 10.1071/WF14204 U.S. Environmental Protection Agency EPA. Air Data: Air Quality Data Collected at Outdoor Monitors Across the US. [cited 2023 October 1]. Available from: https://www.epa.gov/outdoor-air-quality-data Vink MA, Bootsma MCJ, Wallinga J. Serial intervals of respiratory infectious diseases: a systematic review and analysis. Am J Epidemiol. 2014;180(9):865–75. doi: 10.1093/aje/kwu209 25294601 MacIntyre CR, Cauchemez S, Dwyer DE, Seale H, Cheung P, Browne G, et al. Face mask use and control of respiratory virus transmission in households. Emerg Infect Dis. 2009;15(2):233–41. doi: 10.3201/eid1502.081167 19193267 10.1371/journal.pclm.0000542.r001 Author response to Decision Letter 0 Submission Version 0

25 Oct 2024

 10.1371/journal.pclm.0000542.r002 Decision Letter 0 Chong Ka Chun Academic Editor 2025 Ka Chun Chong This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

PCLM-D-24-00249

Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens

PLOS Climate

Dear Dr. Pullano,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

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Ka Chun Chong

Academic Editor

PLOS Climate

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Please revise the submission, especially about the details of data.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria?>

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: N/A

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: This paper on wildfire smoke effects on behavioral changes and infectivity risk addresses an important public health question, in particular in light of continuing climate change impacts and emerging infectious diseases, and is an important early step in investigations in this area. I found the article generally well done and clearly presented.

I believe the article would benefit from additional description of the study population and some of the data used. The counties used are only from Oregon and Washington, but in the methods the population is described as the “western united states”. Please clarify if only WA and OR were used for both affected and baseline counties, or if counties from other states were included as well. Perhaps relatedly, it seems unlikely that the average population size of the unaffected counties is almost 1 million (Table S1), given that only one county in Washington state has a larger population than this. Examining the 10 most populous counties from these states, and excluding the 5 from each designated as affected, the average population is around 326,000. Including more counties from these states should only lower this number.

The supplemental materials on “county selection” do not actually provide additional details on selection, but only population averages. At least, the distribution of seasonal behavior patterns should be also presented.

The authors state that indoor activity data were incorporated in county selection, but do not give specifics of how this was done. Additionally, one can assume the indoor seasonality index mentioned in the following paragraph was used for this, however the metric is not explicitly stated.

The table in the main text is labeled (and referred to in text) as “Table S1” along with the table (showing county populations) in the supplemental material.

Check tenses and subject-verb agreement throughout:

e.g., “by simulation the disease synamics in any affected county…” should be “simulating”

“We focused on Washington County, one of the area most impacted by wildfire smoke,” should be “areas”

Possibly overly nuanced, but: the AQI is based on measurements but is itself not a measurement of air quality but an indicator of air quality.

Statement “…in a world that is already 1.3oC warmer” is incomplete. Warmer than what?

Reviewer #2: In this study, the authors explored the knock-on effect of indoor activity change driven by wildfires on the transmission of respiratory diseases. They first quantified the increase of indoor activities during and after wildfires in 2020 along the west coast of the US and then used an epidemic model to quantify the impact of mobility change on disease transmission under various scenarios. They further simulated the effect of mask wearing on reducing disease spread. While quantifying the cascading effect of wildfires is an important topic, there are several concerns in the study.

1. The impact of indoor activity on disease transmission was assumed to be linear. This is a strong assumption that needs proper validation. The indoor activity index was defined using the number of visits to indoor places. In an SIR model, the force of infection depends on three factors: the number of contacts for each person, the probability that a contact is infectious, and the probability of transmission given a contact with an infectious person. When the number of visitors in indoor settings increased, does the number of contacts increase linearly? Could the probability of transmission upon an exposure to infection increase due to closer inter-personal physical distance in indoor places? There may not be definitive answers to these questions, but the authors should explore these options to better capture these uncertainties. The current modeling setting seems overly simplified.

2. Sufficient technical details on the mobility data and indoor activity are lacking. For instance, how indoor places were defined? Did the author distinguish different types of places?

3. “The compartmental model simulated the daily spread of respiratory pathogens from seven days before the AQI alert to 14 days after.” The wildfires occurred in fall 2020, which is way earlier than the typical peak timing of seasonal respiratory diseases. I am wondering why simulations can produce outbreak peaks within a 3-week period, particularly for low R0 scenarios. What are the susceptible population and total population at the beginning of the simulation?

4. Putting modeling aside, are there any real-world data to show that respiratory disease activities increased after the wildfires in these locations? The study period is 2020, the first year of the pandemic. Could interventions and COVID-19 spread affect this analysis?

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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 10.1371/journal.pclm.0000542.r003 Author response to Decision Letter 1 Submission Version 1

5 Apr 2025

Submitted filename: Response_to_Reviewers.pdf

 10.1371/journal.pclm.0000542.r004 Decision Letter 1 Chong Ka Chun Academic Editor 2025 Ka Chun Chong This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

PCLM-D-24-00249R1

Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens

PLOS Climate

Dear Dr. Pullano,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 16 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Ka Chun Chong

Academic Editor

PLOS Climate

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

The authors have addressed all the comments well. Please address the last comment from the reviewer.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

publication criteria?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: (No Response)

Reviewer #2: I appreciate the authors' efforts to clarify some details and address my concerns. Before the publication of this study, I would strongly suggest the authors to add a discussion on the impact of initial susceptible population on the modeled increased infections. Setting the entire population as susceptible is a very special case (e.g. for a completely novel pathogen). For most endemic pathogens, a lower population susceptibility would damp the effect of mobility change on transmission. The modeled increase of infections will likely be overestimated for most currently circulating pathogens.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

**********

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 10.1371/journal.pclm.0000542.r005 Author response to Decision Letter 2 Submission Version 2

1 May 2025

Submitted filename: Response to Reviewers.pdf

 10.1371/journal.pclm.0000542.r006 Decision Letter 2 Chong Ka Chun Academic Editor 2025 Ka Chun Chong This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 2

Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens

PCLM-D-24-00249R2

Dear Mrs. Pullano,

We are pleased to inform you that your manuscript 'Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens' has been provisionally accepted for publication in PLOS Climate.

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Best regards,

Ka Chun Chong

Academic Editor

PLOS Climate

***********************************************************

Additional Editor Comments (if provided):

The authors have addressed all the comments well.

Reviewer Comments (if any, and for reference):