Fire is an abnormal event which can cause significant damage to lives and property. In this paper, we propose a deep learning-based fire detection method using a video sequence, which imitates the human fire detection process. The proposed method uses Faster Region-based Convolutional Neural Network (R-CNN) to detect the suspected regions of fire (SRoFs) and of non-fire based on their spatial features. Then, the summarized features within the bounding boxes in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and smoke are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by reducing both the false detections and the misdetections.

We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing, case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of neighborhood characteristics were independently associated with COVID-19 testing, case, and death rates. For example, the proportions of Blacks and Hispanics in a ZCTA were positively associated with COVID-19 case rate. Contrary to the conventional hypothesis, neighborhoods with low-density housing experienced higher COVID-19 case rates. In addition, demographic changes (e.g. out-migration) during the pandemic may bias the estimates of COVID-19 rates. Future research should further investigate these neighborhood-level factors and their interactions over time to better understand the mechanisms by which they affect COVID-19.

Objectives We examined the associations of statewide COVID-19 conditions (i.e., state-level case and death rates) with individual-level Generalized Anxiety Disorder (GAD) and Major Depression Disorder (MDD) focusing on the salient mediating roles of individual-level cognitive concerns and behavioral changes. Methods Using a national representative sample of adults in the United States ( n = 585,073), we fitted logistic regressions to examine the overall associations between the COVID-19 pandemic and GAD/MDD. We employed a causal mediation analysis with two mediators: cognitive concerns (i.e., concerns on going to the public, loss of income, food insufficiency, housing payment, and the economy) and behavioral changes (i.e., taking fewer trips, avoiding eating-out, more online-purchase, more curbside pick-up, and cancelling doctor's appointments). Results We found relationships of statewide COVID-19 cases with GAD (odds ratio [OR] = 1.06; 95% confidence interval [CI] = 1.05, 1.07) and MDD (OR = 1.08; 95% CI = 1.07, 1.09). The ORs were mediated by cognitive concerns for GAD (OR = 1.02, proportion mediated: 29%) and MDD (OR = 1.01, 17%). Another salient mediator was behavioral changes for GAD (OR = 1.02, 31%) and MDD (OR = 1.01, 15%). Similar associations were found with statewide COVID-19 death. Conclusions Our mediation analyses suggest that cognitive concerns and behavioral changes are important mediators of the relationships between statewide COVID-19 case/death rates and GAD/MDD. COVID-19 pandemic may involve individual-level concerns and behavior changes, and such experiences are likely to affect mental health outcomes. Public health approaches to alleviate adverse mental health consequences should take into account the mediating factors. Supplementary Information The online version contains supplementary material available at 10.1007/s00127-022-02265-3.

Noise is one of the most frequently complained nuisances and public health hazards. While traffic-related noise has been studied extensively, research on construction noise has been lacking. In this study, we examined the relationship between construction activities and noise annoyance and tested whether this relationship is stronger during after-hours. Data were drawn from a historical inventory of major development projects and crowdsourced citizen complaints data (311 calls) in Vancouver, Canada from 2011 to 2016. Mixed effects models were developed with an interaction between construction activities and after-hours report. Results show that neighborhood noise complaints were significantly associated with major constructions (IRR = 1.062, 95% CI = 1.024-1.097). A significant interaction effect was also found between construction activities and after-hours reporting (IRR = 1.050 CI = 1.012-1.087). To our knowledge, this is one of the first studies to empirically show adverse effects of urban development on noise annoyance. Results imply that existing noise bylaws may not be effective in restricting construction activities at night and during sleeping hours that may cause adverse health effects.

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