Individual-level noise exposure and its association with sleep quality and duration: A cross-sectional study using real-time data.

As urban populations grow, the problem of noise pollution becomes more significant. The limited number of epidemiological studies linking individual-level dynamic noise exposure to sleep highlights a gap in our understanding of how individual-level noise exposure impact sleep quality and duration. A cross-sectional survey was conducted in Hong Kong, and portable noise sensors were used to record participants' real-time noise exposure. The Pittsburgh Sleep Quality Index was used to assess their sleep quality. Logistic regression was used to examine the relationship of individual-level noise exposure with sleep quality and sleep duration. Among a total of 763 subjects included in the analysis, and a-weighted decibels [dB(A)] 24-h average exposure (L24) was 63.21 (58.85-67.85) dB(A) for workday and 63.52 (59.03-67.86) dB(A) for non-workday. A significant decrease in individual noise level was observed closer to bedtime (P < 0.05). Compared with the lowest noise exposure, exposure to the highest noise level was positively associated with the presence of poor sleep quality for L24, Ld and Ln, with an odds ratio (OR) and 95 % confidential interval (CI) of 1.53 (1.04-2.24), 1.65 (1.12-2.43), and 1.51 (1.03-2.21), respectively. Exposure to elevated noise level was associated with increased risk of shorter nocturnal sleep duration (< 7 h), the OR and 95 % CI was 1.87 (1.29-2.73) for L24, 1.58 (1.09-2.30) for Ld, 1.50 (1.03-2.18) for Le, and 1.60 (1.10-2.32) for Ln in the highest noise exposure group. Similar findings were observed both on workdays and non-workdays, respectively. Furthermore, the results showed that each 10 dB(A) increment in L24 and Ln was associated with a probable 29 % and 27 % increase in poor sleep quality and a 24 % and 24 % reduction in sleep duration. This study demonstrates that environmental noise exposure was associated with poor sleep quality and shorter sleep duration, posing a significant public health issue in Hong Kong.

The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures.

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Associations of smoking status with carotid atherosclerosis: Mediated role of blood indexes and blood pressure.

BACKGROUND AND AIMS: We aimed to assess the relationship between smoking status and carotid atherosclerosis (CA) with different manifestations and to explore the underlying mechanisms. METHODS AND RESULTS: A total of 60,655 middle-aged and elderly participants were recruited. Logistic regression models were used to examine the association of smoking, various blood indexes [i.e., blood pressure, blood lipid, glycated hemoglobin (HbA1c) and homocysteine (Hcy)] with CA in different manifestations including carotid intima-media thickness (cIMT), plaques, and stenosis. Compared with nonsmokers, current smokers were associated with a higher prevalence of CA (OR = 3.48, 95%CI: 3.21-3.77) and its specific manifestations including cIMT (OR = 2.66, 95%CI: 2.42-2.93), plaques (OR = 3.67, 95%CI: 3.35-4.02) and stenosis (OR = 2.04, 95%CI: 1.64-2.54), after adjusting for potential confounders. Former smokers were also associated with an increased prevalence of CA (OR = 1.82, 95%CI: 1.54-2.14), cIMT (OR = 1.39, 95%CI: 1.14-1.69), plaques (OR = 2.04, 95%CI: 1.71-2.44) and stenosis (OR = 1.72, 95%CI: 1.18-2.51), but the associations were consistently weaker than that of the current smokers. The prevalence of CA, cIMT, plaques and stenosis among current smokers was positively associated with pack-years of cigarette smoking, partially mediated by SBP, HbA1c, TG, HDL-C and Hcy with a mediated proportion of 1.12%, 3.28%, 6.33%, 6.01% and 12.7%, respectively. Stratified analysis by sex showed a significantly higher CA among the current male smokers than females. CONCLUSIONS: Current and former smoking was associated with increased carotid atherosclerosis, predominantly by current male smokers. A positive gradient between pack-years and carotid atherosclerosis among current smokers is partially mediated by SBP, HbA1c, TG, HDL-C and Hcy.

Assessing the affective quality of soundscape for individuals: Using third-party assessment combined with an artificial intelligence (TPA-AI) model.

When investigating the relationship between the acoustic environment and human wellbeing, there is a potential problem resulting from data source self-correlation. To address this data source self-correlation problem, we proposed a third-party assessment combined with an artificial intelligence (TPA-AI) model. The TPA-AI utilized acoustic spectrograms to assess the soundscape's affective quality. First, we collected data on public perceptions of urban sounds (i.e., inviting 100 volunteers to label the affective quality of 7051 10-s audios on a polar scale from annoying to pleasant). Second, we converted the labeled audios to acoustic spectrograms and used deep learning methods to train the TPA-AI model, achieving a 92.88 % predictive accuracy for binary classification. Third, geographic ecological momentary assessment (GEMA) was used to log momentary audios from 180 participants in their daily life context, and we employed the well-trained TPA-AI model to predict the affective quality of these momentary audios. Lastly, we compared the explanatory power of the three methods (i.e., sound level meters, sound questionnaires, and the TPA-AI model) when estimating the relationship between momentary stress level and the acoustic environment. Our results indicate that the TPA-AI's explanatory power outperformed the sound level meter, while using a sound questionnaire might overestimate the effect of the acoustic environment on momentary stress and underestimate other confounders.

Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts.

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

Mapping environmental suitability changes for arbovirus mosquitoes in Southeast Asia: 1960-2020.

Spatial epidemiology recognizes the impact of environmental factors on human infectious diseases through disease vectors. The expansion of Aedes aegypti and Aedes albopictus raises concerns about health risks due to their changing distribution. However, current mosquito mapping methods have low spatial resolution and limited focus on long-term trends and factors. This study develops a high-resolution framework (500 m) to map mosquito distribution in Southeast Asia from 1960 to 2020. It includes a species distribution model, a spatial autocorrelation model, and a geographical detector model. The study produces Southeast Asia's first 500 m resolution map of mosquito suitability, revealing significant increases in mosquito suitability in most cities over the past 60 years. The analysis indicates a shift in high-suitability areas from coastal to inland regions, with nighttime land surface temperature playing a key role. These findings are crucial for regional risk assessments and mitigation strategies related to vector-borne diseases.

Non-linear associations between noise level and people's short-term noise annoyance in different activity contexts.

Recent research has become increasingly interested in the on-linear associations between noise levels and people's short-term noise annoyance. However, there has been limited investigation into measuring short-term noise annoyance and how different activity contexts may affect these non-linear associations. To address this research gap, this study measured people's short-term noise annoyance using real-time Ecological Momentary Assessment (EMA) data and the Day Reconstruction Method's (DRM) recalled data. Corresponding noise levels were captured using Global Positioning Systems and portable noise sensors. Employing the Shapley additive explanations method, we examined the non-linear associations between noise level and people's real-time and recalled noise annoyance across different activity contexts. The results indicated that 1) People had greater sensitivity to noise levels in real-time annoyance (non-linear association threshold: 60 dB) compared to recalled annoyance, which had a higher non-linear association threshold of 70 dB. 2) The non-linear associations between noise level and people's real-time/recalled noise annoyance varied between different activity contexts. People tended to be more sensitive to noise in real-time annoyance than recalled annoyance on travel routes and at workplaces. 3) Among the factors examined, the contribution of noise level varied across activity contexts. Noise level contributed more significantly to people's real-time noise annoyance in outdoor recreational sites and on travel routes. These findings enhance our understanding of the non-linear association between noise level and people's short-term noise annoyance, moving beyond the linear paradigm. Policymakers should consider the non-linear relationships and different activity contexts when implementing noise control measures.

Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health.

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas, which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night (ALAN). Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations. This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms: population-level residence-based, individual-level residence-based, and individual-level mobility-oriented paradigms. We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis. We also used data from 940 participants in six representative communities for individual-level analyses. Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data. The total exposures were derived using the spatiotemporally weighted approaches. Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms, spatially non-stationary, and consistent among different socio-demographic groups. We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures. Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN, and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes, especially for those health outcomes impacted by the co-exposure to them.

Association of residential greenness with incident allergic rhinitis among adults: A prospective analysis of UK Biobank.

BACKGROUND: Limited studies have assessed the impact of residential greenness exposure on allergic rhinitis in adults, and its relationship with ambient air pollutants remains unknown. OBJECTIVE: To investigate the association of residential greenness with allergic rhinitis incidence and explore the mediation effects of ambient air pollutants in adults using data from a prospective cohort study in UK Biobank. METHODS: Greenness was defined as the area-weighted mean of greenness coverage based on the land used data from the Generalized Land Use Database for England (GLUD) 2005 in the UK Biobank. Multiple Cox proportional hazard models and a generalized additive model incorporating restricted cubic splines were used to model the potential nonlinear effect of residential greenness on allergic rhinitis incidence and the potential mediation effect of ambient air pollutants. RESULTS: Among the 281,699 subjects included in the analysis, 3260 allergic rhinitis incident cases occurred after a median follow-up of 14 years. With per 10% increase in residential greenness at a 300-m buffer, a 2.5% (95% CI: 1.0%, 4.0%) decreased risk of allergic rhinitis was observed. An L-shaped, non-linear dose-response relationship was indicated with a threshold of 54.9% greenness above which no excess allergic rhinitis risk was seen. PM10 partially mediated the relationship between greenness and allergic rhinitis incidence with a mediation effect of 26.9% (95% CI: 12.6%, 41.2%). A similar pattern of association was found at 1000-m buffer size. CONCLUSION: This study demonstrates a significant beneficial effect of residential greenness on reducing allergic rhinitis incidence. Greenness may erase air pollutants and mitigate the rhinitis risk from air pollution.

How mobility pattern shapes the association between static green space and dynamic green space exposure.

Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure.

How does urbanization affect the cognitive function among older adults: A geospatial analysis in China.

There has been a plethora of studies on urbanization and older adults, and more recent ones on how older adults adapt to this process with their cognitive competence. Yet it has been unclear about the relationship between them, like how the level and rate of urbanization affect the cognitive function among older adults. This study sourced, formed, and analyzed a set of geospatial big datasets from different sources, such as the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data, and the NPP/VIIRS nighttime light (NTL) data. Results showed a generally negative linear association between the rate of urbanization and cognitive performance among older adults in China. The "U" shaped non-linear relationship between urbanization level and cognitive function, as well as the tipping point, were identified. At the same time, it should be noted that mediators such as education, physical activity, social activity, and community elderly service might be able to mitigate these negative associations. Furthermore, older adults living in eastern regions or urban areas appeared to have better cognitive function than those living in mid-western regions or rural areas in China. The findings also pointed to the importance of focusing on older adults with poor cognitive health status in rapidly urbanizing areas.

Mobility census for monitoring rapid urban development.

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Mobility-oriented measurements of people's exposure to outdoor artificial light at night (ALAN) and the uncertain geographic context problem (UGCoP).

Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people's exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people's exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people's health outcomes. In this study, we systematically investigated outdoor ALAN exposure measurements in different geographic contexts using either residence-based or mobility-oriented measurements, different spatial scales, and multiple NTL data sources. Based on the GPS data collected from 208 participants in Hong Kong, outdoor ALAN exposures were measured from NTL imagery at 10 m, 130 m, and 500 m spatial resolutions using in-situ methods or 100 m, 300 m, and 500 m buffer zone averaging. Descriptive analysis, multiple t-tests, and logistic regression were employed to examine the differences between outdoor ALAN exposure measurements using various contextual settings and their effects on modeling people's overall health. Our results confirmed that different contextual settings may lead to significantly different outdoor ALAN exposure measurements. Our results also confirmed that contextual errors may lead to erroneous conclusions when using improper contextual settings to model people's overall health. Consequentially, we suggest measuring people's exposure to outdoor ALAN using the mobility-oriented approach, NTL representation with the high spatial resolution, and a very small buffer zone as a contextual unit to derive outdoor ALAN exposure. This study articulates essential methodological issues induced by uncertainties in outdoor ALAN exposure measurements and can provide essential implications and suggestions for a broad scope of studies that need accurate outdoor ALAN exposure measurements.

Dynamic greenspace exposure, individual mental health status and momentary stress level: A study using multiple greenspace measurements.

Previous research on the relationship between greenspace exposure and mental health has largely taken a residence-based approach to exposure assessment, ignoring the dynamic nature of people's daily movements. Moreover, most studies evaluated greenspace from an overhead perspective, whereas an eye-level perspective could potentially offer a more comprehensive understanding of individuals' encounters with greenspaces. Based on our survey in two communities in Hong Kong (Sham Shui Po and Tin Shui Wai), we captured people's eye-level greenspace exposure based on their travel routes and visited places using GPS trajectories, streetscape images, and deep learning methods. We then compared the results with those obtained with an overhead greenness exposure measure (the normalized difference vegetation index [NDVI]). The results indicate that these two greenspace measurements are not associated with each other, implying that they encompass distinct facets of greenspace, which may have different effects on mental health. Further, we examined the associations between various greenspace exposure measures and mental health using GPS trajectories and ecological momentary assessment data. The results reveal a negative association between eye-level greenspace exposure and momentary stress, while no similar association was observed when using the top-down NDVI as an indicator of greenspace exposure. Moreover, compared to the total volume of greenspace exposure, the distance-weighted average of greenspace exposure based on dynamic mobility contexts has a stronger association with individual overall mental health. Lastly, the relationship between greenspace exposure and mental health varies between the two communities with different socio-economic attributes. The study indicates that policymakers should focus not only on residential neighborhoods and overhead greenspace but also consider the dynamic environments and socio-economic contexts that people are embedded in.

Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure.

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536µg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608µg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

The interplay among individuals' distress, daily activities, and perceptions of COVID-19 and neighborhood cohesion: A study using network analysis.

The reduction of social interactions through non-pharmaceutical interventions (NPIs) has been shown to effectively curb COVID-19 transmission. However, these control measures were often accompanied by changes in people's daily routines and constraints on their activity space, which could lead to mental distress (i.e., anxiety and depression). This study examined the interplay among individuals' anxiety, depression, daily activities, and perceptions of COVID-19 and neighborhood cohesion. Taking Hong Kong as an example, an online survey (N = 376) was conducted to collect data from participants between March 14 to May 11, 2022. The data include respondents' self-reported anxiety and depressive symptoms, daily activities (e.g., smartphone use), perceptions of COVID-19 (e.g., the possibility of infecting COVID-19), and perceptions of neighborhood cohesion. Using network analysis, we found that excessive smartphone use, life disturbance by COVID-19, and a community with people getting along well with each other were significant factors associated with participants' anxiety and depression. Using critical path analysis, we observed that NPIs reduced human mobility, led to delayed bedtime, and increased smartphone use, which were associated with participants' mental distress. We also found that NPIs and COVID-19 were associated with people's perceptions of infection and the severity of COVID-19 and human mobility flexibility, which may further lead to mental distress. Our results also demonstrated that people with high education levels were vulnerable. These results provided important insights for designing appropriate interventions without generating deleterious impacts on people's mental health in the future.

People's political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data.

BACKGROUND: As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations. METHODS: We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. RESULTS: We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. CONCLUSIONS: Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism.

Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale.

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.