Predicting peak inundation depths with a physics informed machine learning model.

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

Network diffusion model reveals recovery multipliers and heterogeneous spatial effects in post-disaster community recovery.

Community recovery from hazards occurs through various diffusion processes within social and spatial networks of communities. Existing knowledge regarding the diffusion of recovery in community socio-spatial networks, however, is rather limited. To bridge this gap, we created a network diffusion model to characterize the unfolding of population activity recovery in spatial networks of communities. In particular, this study aims to answer the research question "To what extent can the diffusion model capture the spatial patterns of recovery?" Using population activity recovery data derived from location-based information associated with 2017 Hurricane Harvey in the Houston area, we parameterized the threshold-based network diffusion model using the genetic algorithm and then simulated the recovery diffusion process. The results show that the spatial effects of recovery are rather heterogeneous across different areas; some spatial areas demonstrate a greater spatial effect in their recovery. Also, the results show that low-income and minority areas are community recovery multipliers; with faster recovery in these areas corresponding to accelerated recovery for the entire community. Hence, prioritizing these areas in resource allocation during recovery has the potential to accelerate could expedite the recovery of the entire community's recovery process while promoting recovery equality and equity.

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards.

Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' hazard exposures due to activities outside home neighborhood and underestimating peoples' overall hazard exposures. To address this limitation, this study proposes a novel mobility-based index for the hazard exposure evaluation. Using large-scale human mobility data, we quantify the extent of population dwell time in high environmental hazard places in 239 US counties for three environmental hazards. We explore how human mobility extends the reach of environmental hazards and leads to the emergence of latent exposure for populations living outside high-hazard areas. Notably, neglect of mobility can lead to over 10% underestimation of hazard exposures. The interplay of spatial clustering in high-hazard regions and human movement trends creates "environmental hazard traps." Poor and ethnic minority residents disproportionately face multiple types of environmental hazards. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents' home areas, revealing millions of the population having 5 to 10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices.

Human mobility networks manifest dissimilar resilience characteristics at macroscopic, substructure, and microscopic scales.

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such as motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks.

Disparities in access and association between access to critical facilities during day-to-day and disrupted access as a result of storm extreme weather events.

This study examines the relationship between households' access to critical facilities day-to-day and during weather-related extreme events. Despite a robust understanding of both day-to-day access and access during disasters, the interplay between the two remains unclear. To bridge this knowledge gap, we propose a novel empirical approach, using a Texas statewide household survey (N = 810). The survey evaluates day-to-day and past events access, exploring the experiences of respondents during multiple recent disasters, rather than focusing on a specific hazard. Using correlation analysis, we examined various access-related factors such as day-to-day trip duration, alternative trip duration, and loss of access during past events. Additionally, we evaluated the association between access-related factors and sociodemographic characteristics such as income, ethnicity, and urban status. The results indicate: (1) daily trip duration to critical facilities is associated with disrupted access during storm events, and (2) disparities persist during both day-to-day times and during extreme events. These results bring new insights to the existing body of knowledge on day-to-day access and access during disasters. The findings provide scientifically grounded evidence to city managers and planners, emphasizing the need for equitable distribution of facilities to enhance access to essential facilities both in daily life and during extreme weather-related events.

Latent sub-structural resilience mechanisms in temporal human mobility networks during urban flooding.

In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topological network properties indicate that the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities.

A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features.

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.

Revealing hazard-exposure heterophily as a latent characteristic of community resilience in social-spatial networks.

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with dissimilar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in hazard-prone spatial areas with significant hazard-exposure similarity, homophily, would lack sufficient resourcefulness to aid each other to lessen the impact of hazards. In the context of the Houston metropolitan area, we use Meta's Social Connectedness data to construct a socio-spatial network in juxtaposition with flood exposure data from National Flood Hazard Layer to analyze flood hazard exposure of spatial areas. The results reveal the extent and spatial variation of hazard-exposure heterophily in the study area. Notably, the results show that lower-income areas have lower hazard-exposure heterophily possibly caused by income segregation and the tendency of affordable housing development to be located in flood zones. Less resourceful social ties in hazard-prone areas due to their high-hazard-exposure homophily may inhibit low-income areas from better coping with hazard impacts and could contribute to their slower recovery. Overall, the results underscore the significance of characterizing hazard-exposure heterophily in socio-spatial networks to reveal community vulnerability and resilience to hazards.

Characterizing equitable access to grocery stores during disasters using location-based data.

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents' ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage.

Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities.

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Location intelligence reveals the extent, timing, and spatial variation of hurricane preparedness.

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public's hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents' preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents' hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents' disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner.

Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data.

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.

Electrochemical enzyme-based blood uric acid biosensor: new insight into the enzyme immobilization on the surface of electrode via poly-histidine tag.

In a new approach, we considered the special affinity between Ni and poly-histidine tags of recombinant urate oxidase to utilize Ni-MOF for immobilizing the enzyme. In this study, a carbon paste electrode (CPE) was modified by histidine-tailed urate oxidase (H-UOX) and nickel-metal-organic framework (Ni-MOF) to construct H-UOX/Ni-MOF/CPE, which is a rapid, sensitive, and simple electrochemical biosensor for UA detection. The use of carboxy-terminal histidine-tailed urate oxidase in the construction of the electrode allows the urate oxidase enzyme to be positioned correctly in the electrode. This, in turn, enhances the efficiency of the biosensor. Characterization was carried out by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and field emission scanning electron microscopy (FE-SEM). At optimum conditions, the biosensor provided a short response time, linear response within 0.3-10 µM and 10-140 µM for UA with a detection limit of 0.084 µM, repeatability of 3.06%, and reproducibility of 4.9%. Furthermore, the biosensor revealed acceptable stability and selectivity of UA detection in the presence of the commonly coexisted ascorbic acid, dopamine, L-cysteine, urea, and glucose. The detection potential was at 0.4 V vs. Ag/AgCl.

Tracing app technology: an ethical review in the COVID-19 era and directions for post-COVID-19.

We conducted a systematic literature review on the ethical considerations of the use of contact tracing app technology, which was extensively implemented during the COVID-19 pandemic. The rapid and extensive use of this technology during the COVID-19 pandemic, while benefiting the public well-being by providing information about people's mobility and movements to control the spread of the virus, raised several ethical concerns for the post-COVID-19 era. To investigate these concerns for the post-pandemic situation and provide direction for future events, we analyzed the current ethical frameworks, research, and case studies about the ethical usage of tracing app technology. The results suggest there are seven essential ethical considerations-privacy, security, acceptability, government surveillance, transparency, justice, and voluntariness-in the ethical use of contact tracing technology. In this paper, we explain and discuss these considerations and how they are needed for the ethical usage of this technology. The findings also highlight the importance of developing integrated guidelines and frameworks for implementation of such technology in the post- COVID-19 world. Supplementary Information: The online version contains supplementary material available at 10.1007/s10676-022-09659-6.

Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies.

While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread.

Epidemiology of Asthma in Patients with COVID-19: Investigation of Respiratory Allergy as a Risk Factor for COVID-19 Severity.

Background: The outcome of coronavirus disease 2019 (COVID-19) is complicated by various comorbidities; asthma, a common chronic disease, may be considered one of these conditions. This study aimed to investigate the effect of asthma as a potential comorbid condition on the COVID-19 prognosis. Materials and Methods: This retrospective study included all RT-PCR confirmed COVID-19 cases recorded on the Shiraz health department's electronic database from January to May 2020. A questionnaire was designed to collect information about patients' demographics, their history of asthma and other comorbidities, and the severity of COVID-19 by contacting them by phone. Results: Of 3163 COVID-19 patients, 109 (3.4%) had self-reported asthma with a mean age of 42.7 ± 19.1 years. Most patients (98%) had mild-to-moderate asthma, while 2% had severe disease. Among asthmatic patients, fourteen (12.8%) were admitted to the hospital, and five (4.6%) died. Univariate logistic regression results showed that asthma had no significant effect on hospitalization (OR 0.95, 95% CI: 0.54-1.63) and mortality (OR 1.18, 95% CI: 0.48-2.94) in patients with COVID-19. Compared living and deceased patients with COVID-19, the pooled OR was 18.2 (95% CI: 7.3-40.1) for cancer, 13.5 (95% CI: 8.2-22.5) for age 40-70 years, 3.1 (95% CI: 2-4.8) for hypertension, 3.1 (95% CI: 1.8-5.3) for cardiac disease and 2.1 (95% CI: 1.3-3.5) for diabetes mellitus. Conclusion: This study showed that asthma is not associated with an increased risk of hospitalization and mortality in patients with COVID-19. Further studies are needed to investigate the risk of different asthma phenotypes on the severity of COVID-19 disease.

Compound hazards: An examination of how hurricane protective actions could increase transmission risk of COVID-19.

Hurricane season brings new and complex challenges as we continue to battle the COVID-19 pandemic. In May 2020, the National Oceanic and Atmospheric Administration has predicted nearly twice the normal number of tropical storms and hurricanes this season, while projections of COVID-19 models continue to rise in the United States as the Atlantic hurricane season progresses. Our research examines the critical intersection of hurricane response and public health in Harris County, Texas. We examine a hypothetical case of the 2017 Hurricane Harvey occurring amid the current pandemic. This research uses point of interest visitations as location intelligence data provided by SafeGraph together with Social Vulnerability Index and historical flood data to examine the critical intersection of natural hazard planning and response and the COVID-19 pandemic to assess the risks of a compound hazard situation. COVID-19 transmission hotspots and businesses in a community due to storm preparation activity were identified. The main drivers of transmission risk arise from overall pandemic exposure and increased interpersonal contact during hurricane preparation. Residents of health-risk areas will need to make logistical arrangements to visit alternative medical facilities for treatments related to either COVID-19 or physical impacts, such as injuries, due to the hurricane risks. Points of interest needed for disaster preparation are more likely to be situated in high-risk areas, therefore making cross-community spread more likely. Moreover, greater susceptibility could arise from social vulnerability (socioeconomic status and demographic factors) and disrupted access to healthcare facilities. Results from this study can be used to identify high-risk areas for COVID-19 transmission for prioritization in planning for temporary healthcare centers and other essential services in low-risk areas. Understanding the interplay between disaster preparation and the restrictive environment laid out by the pandemic is critical for community leaders and public health officials for ensuring the population has sufficient access to essential infrastructure services. The findings from this study can help guide the direction of disaster planning and pandemic response strategies and policies.

Fine-grained data reveal segregated mobility networks and opportunities for local containment of COVID-19.

Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced.

Silver-coated magnetic nanoparticles as an efficient delivery system for the antibiotics trimethoprim and sulfamethoxazole against E. Coli and S. aureus: release kinetics and antimicrobial activity.

Trimethoprim and sulfamethoxazole are prescribed for a broad spectrum of bacteria. However, the use of these medicines is restricted due to the risk of microbial resistance in the body. Nanotechnology is a strategy for overcoming this problem by helping develop novel drug delivery systems. This study aims to assess the ability of Fe3O4/Ag and Fe3O4@SiO2/Ag nanoparticles to improve efficiency of the traditional formulation of trimethoprim and sulfamethoxazole. Fe3O4/Ag and Fe3O4@SiO2/Ag were found to have sphere-like morphologies with average sizes of 33.2 and 35.1 nm, respectively. The values of the zeta potential for the pure sulfamethoxazole and trimethoprim were -30.6 and -10.0 mV, respectively, which increased to zero or even larger positive values after being conjugated with the NPs. The study of the release kinetics showed that 64.7% of the medicines were released from the carriers within 40 days. The values of MIC for sulfamethoxazole, trimethoprim, Fe3O4/Ag/sulfamethoxazole, Fe3O4/Ag/trimethoprim, Fe3O4@SiO2/Ag/sulfamethoxazole, and Fe3O4@SiO2/Ag/trimethoprim against Escherichia coli were calculated to be 12, 9, 4, 4, 4, and 4 µg/mL, respectively. Besides, the relevant values against Staphylococcus aureus were measured to be 12, 9, 4, 4, 3, and 2 µg/mL, respectively. The use of synthesized nanomaterials for the delivery of these antibiotics leads to smaller doses compared to their traditional forms.