Urbanization and Infectious Diseases: General Principles, Historical Perspectives, and Contemporary Challenges

Synergistic antimicrobial nanofiber membranes based on metal incorporated silica nanoparticles as advanced antimicrobial layers

In this post-new-normal era, the public prioritizes preventive measures over curing, which is a constructive approach to staying healthy. In this study, an innovative antimicrobial membrane material has been developed, showcasing the promising potential for various applications. The metal-doped silica nanoparticles (Ag, Cu, and Co) were incorporated into a cellulose acetate (CA) polymer-based nanofiber membrane using the electrospinning technique. The metal nanoparticles were doped into a silanol network of silica nanoparticles. The fabricated membranes underwent detailed characterization using a wide range of techniques including PXRD, FTIR, Raman, SEM, TEM, TGA, and tensile testing. These analyses provided compelling evidence confirming the successful incorporation of metal-doped silica nanoparticles (Ag, Cu, and Co) into cellulose-based nanofibers. The band gap energies of the fabricated CA mats lie below 3.00 eV, confirming that they are visible light active. The trimetallic silica nanohybrid exhibited the lowest band gap energy of 2.84 eV, proving the self-sterilizing ability of the CA mats. The DPPH assay further confirmed the best radical scavenging activity by the trimetallic silica nanohybrid incorporated nanofiber mat (91.77 ± 0.88%). The antimicrobial activity was assessed by using the bacterial ATCC strains of Staphylococcus aureus, Streptococcus pneumoniae, MRSA (Methicillin-resistant Staphylococcus aureus), Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa and fungal strains; quality control samples of Trichophyton rubrum, Microsporum gypsium, and Aspergillus niger, as well as the ATCC strain of Candida albicans. The trimetallic silica nanohybrid-incorporated CA membranes demonstrated the most significant inhibition zones. The reported findings substantiate the self-sterilizing mat's viability, affordability, efficacy against a broad spectrum of microbial strains, cost-effectiveness, and biodegradability. Furthermore, the mat serves as a dual-purpose physical and biological barrier against microbes, affirming its potential impact.

Viral diseases and the environment relationship

Viral diseases have been present throughout human history, with early examples including influenza (1500 B.C.), smallpox (1000 B.C.), and measles (200 B.C.). The term "virus" was first used in the late 1800s to describe microorganisms smaller than bacteria, and significant milestones include the discovery of the polio virus and the development of its vaccine in the mid-1900s, and the identification of HIV/AIDS in the latter part of the 20th century. The 21st century has seen the emergence of new viral diseases such as West Nile Virus, Zika, SARS, MERS, and COVID-19. Human activities, including crowding, travel, poor sanitation, and environmental changes like deforestation and climate change, significantly influence the spread of these diseases. Conversely, viral diseases can impact the environment by polluting water resources, contributing to deforestation, and reducing biodiversity. These environmental impacts are exacerbated by disruptions in global supply chains and increased demands for resources. This review highlights the intricate relationship between viral diseases and environmental factors, emphasizing how human activities and viral disease progression influence each other. The findings underscore the need for integrated approaches to address the environmental determinants of viral diseases and mitigate their impacts on both health and ecosystems.

Long-term variations of urban-Rural disparities in infectious disease burden of over 8.44 million children, adolescents, and youth in China from 2013 to 2021: An observational study

BACKGROUND

An accelerated epidemiological transition, spurred by economic development and urbanization, has led to a rapid transformation of the disease spectrum. However, this transition has resulted in a divergent change in the burden of infectious diseases between urban and rural areas. The objective of our study was to evaluate the long-term urban-rural disparities in infectious diseases among children, adolescents, and youths in China, while also examining the specific diseases driving these disparities.

METHODS AND FINDINGS

This observational study examined data on 43 notifiable infectious diseases from 8,442,956 cases from individuals aged 4 to 24 years, with 4,487,043 cases in urban areas and 3,955,913 in rural areas. The data from 2013 to 2021 were obtained from China's Notifiable Infectious Disease Surveillance System. The 43 infectious diseases were categorized into 7 categories: vaccine-preventable, bacterial, gastrointestinal and enterovirus, sexually transmitted and bloodborne, vectorborne, zoonotic, and quarantinable diseases. The calculation of infectious disease incidence was stratified by urban and rural areas. We used the index of incidence rate ratio (IRR), calculated by dividing the urban incidence rate by the rural incidence rate for each disease category, to assess the urban-rural disparity. During the nine-year study period, most notifiable infectious diseases in both urban and rural areas exhibited either a decreased or stable pattern. However, a significant and progressively widening urban-rural disparity in notifiable infectious diseases was observed. Children, adolescents, and youths in urban areas experienced a higher average yearly incidence compared to their rural counterparts, with rates of 439 per 100,000 compared to 211 per 100,000, respectively (IRR: 2.078, 95% CI [2.075, 2.081]; p < 0.001). From 2013 to 2021, this disparity was primarily driven by higher incidences of pertussis (IRR: 1.782, 95% CI [1.705, 1.862]; p < 0.001) and seasonal influenza (IRR: 3.213, 95% CI [3.205, 3.220]; p < 0.001) among vaccine-preventable diseases, tuberculosis (IRR: 1.011, 95% CI [1.006, 1.015]; p < 0.001), and scarlet fever (IRR: 2.942, 95% CI [2.918, 2.966]; p < 0.001) among bacterial diseases, infectious diarrhea (IRR: 1.932, 95% CI [1.924, 1.939]; p < 0.001), and hand, foot, and mouth disease (IRR: 2.501, 95% CI [2.491, 2.510]; p < 0.001) among gastrointestinal and enterovirus diseases, dengue (IRR: 11.952, 95% CI [11.313, 12.628]; p < 0.001) among vectorborne diseases, and 4 sexually transmitted and bloodborne diseases (syphilis: IRR 1.743, 95% CI [1.731, 1.755], p < 0.001; gonorrhea: IRR 2.658, 95% CI [2.635, 2.682], p < 0.001; HIV/AIDS: IRR 2.269, 95% CI [2.239, 2.299], p < 0.001; hepatitis C: IRR 1.540, 95% CI [1.506, 1.575], p < 0.001), but was partially offset by lower incidences of most zoonotic and quarantinable diseases in urban areas (for example, brucellosis among zoonotic: IRR 0.516, 95% CI [0.498, 0.534], p < 0.001; hemorrhagic fever among quarantinable: IRR 0.930, 95% CI [0.881, 0.981], p = 0.008). Additionally, the overall urban-rural disparity was particularly pronounced in the middle (IRR: 1.704, 95% CI [1.699, 1.708]; p < 0.001) and northeastern regions (IRR: 1.713, 95% CI [1.700, 1.726]; p < 0.001) of China. A primary limitation of our study is that the incidence was calculated based on annual average population data without accounting for population mobility.

CONCLUSIONS

A significant urban-rural disparity in notifiable infectious diseases among children, adolescents, and youths was evident from our study. The burden in urban areas exceeded that in rural areas by more than 2-fold, and this gap appears to be widening, particularly influenced by tuberculosis, scarlet fever, infectious diarrhea, and typhus. These findings underscore the urgent need for interventions to mitigate infectious diseases and address the growing urban-rural disparity.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

Emerging and Re-Emerging Viral Infections: An Indian Perspective

Emerging and re-emerging viral infections pose a constant threat, especially in healthcare settings. Viral infections can be thought of as an ecological system, like a forest or a pond, with different species competing for resources. Pandemics tend to occur when there is a disruption to this ecosystem, such as introducing a strain of virus into humans or animals that they have no immunity against. Around 60% of human infectious diseases and 75% of emerging infections are zoonotic, with two-thirds originating in wildlife. There is an ongoing risk of viral diseases as the human population continues to grow and the rate of urbanization increases. The emergence and re-emergence of viral diseases are influenced by a variety of virologic and environmental factors. These factors can be roughly categorized as affecting humans, the environment and/or ecology, and viruses. The spread of zoonotic diseases among humans can be prevented by reducing the transmission risk associated with wildlife and exotic pets through education, legislation, and behavioral change programs that target individuals at risk for exposure.

A mathematical model for simulating the spread of a disease through a country divided into geographical regions with different population densities

The SIR (susceptible-infectious-recovered) model is a well known method for predicting the number of people (or animals) in a population who become infected by and then recover from a disease. Modifications can include categories such people who have been exposed to the disease but are not yet infectious or those who die from the disease. However, the model has nearly always been applied to the entire population of a country or state but there is considerable observational evidence that diseases can spread at different rates in densely populated urban regions and sparsely populated rural areas. This work presents a new approach that applies a SIR type model to a country or state that has been divided into a number of geographical regions, and uses different infection rates in each region which depend on the population density in that region. Further, the model contains a simple matrix based method for simulating the movement of people between different regions. The model is applied to the spread of disease in the United Kingdom and the state of Rio Grande do Sul in Brazil.

Fifty years of struggle to control cutaneous leishmaniasis in the highest endemic county in Iran: A longitudinal observation inferred with interrupted time series model

Negligible data are available following major social activities and environmental changes on leishmaniasis. Therefore, how interactions between these events influence cutaneous leishmaniasis (CL) risk is not well-known. This longitudinal study was undertaken to explore the impact of interventions conducted between 1971 and 2020 in Bam county, which has had the highest disease burden in Iran. Only confirmed CL cases during this period were taken into account. Data were analyzed by SPSS 22 using the X² test to assess the significance of the difference between proportions. Moreover, we used interrupted time series (ITS) to assess the impact of three environmental events during this period. Overall, 40,164 cases of CL occurred in the past five decades. Multiple complex factors were among the leading causes that synergistically induced the emergence/re-emergence of CL outbreaks in Bam. The main factors attributed negatively to CL control were cessation of malaria spraying activity, expansion of the city spaces, and a massive earthquake creating new breeding potentials for the vectors. The highest impact on CL incidence during these years was related to the earthquake [coefficient = 17.8 (95% CI: 11.3, 22.7); p-value < 0.001]. Many factors can contribute to CL outbreaks in endemic foci. They also can cause new foci in new areas. Since humans are the single reservoir for CL in this area, early detection and effective management significantly contribute to controlling CL to reduce the disease burden. However, essential evidence gaps remain, and new tools are crucial before the disease can ultimately be controlled. Nevertheless, sustained funding and more trained task forces are essential to strengthen surveillance and case management and monitor the interventions’ impact.

We aimed to conduct this longitudinal observation to assess the impact of interventions applied between 1971 and 2020 in Bam county, Iran’s highest anthroponotic cutaneous leishmaniasis burden. Only confirmed CL cases were taken into account. Overall, 40,164 cases of CL occurred in the past five decades. Multiple complex factors were among the leading causes that synergistically induced the emergence/re-emergence of CL outbreaks in Bam. The main factors attributed negatively to CL control were cessation of malaria spraying activity, expansion of the city spaces, and a massive earthquake creating new breeding potentials for the vectors. Since humans are the single reservoir host for CL in this area, early detection and effective management significantly contribute to controlling CL to reduce the disease burden. However, continuous funding and more trained forces are critical to strengthening surveillance and case management and monitoring the interventions’ impact.

Tracing the connections between international business and communicable diseases

We posit that international business and the emergence and spread of communicable diseases are intrinsically connected. To support our arguments, we first start with a historical timeline that traces the connections between international business and communicable diseases back to the sixth century. Second, following the epidemiology of communicable diseases, we identify two crucial transitions related to international business: the emergence of epidemics within a host country and the shift from epidemics to global pandemics. Third, we highlight international business contextual factors (host country regulatory quality, urbanization, trade barriers, global migration) and multinationals' activities (foreign direct investment, corporate political activity, global supply chain management, international travel) that could accelerate each transition. Finally, building on public health insights, we suggest research implications for business scholars on how to integrate human health challenges into their studies and practical implications for global managers on how to help prevent the emergence and spread of communicable diseases.

Socio-economic factors associated with the incidence of Shiga-toxin producing Escherichia coli (STEC) enteritis and cryptosporidiosis in the Republic of Ireland, 2008–2017

The Republic of Ireland (ROI) currently reports the highest incidence rates of Shiga-toxin producing Escherichia coli (STEC) enteritis and cryptosporidiosis in Europe, with the spatial distribution of both infections exhibiting a clear urban/rural divide. To date, no investigation of the role of socio-demographic profile on the incidence of either infection in the ROI has been undertaken. The current study employed bivariate analyses and Random Forest classification to identify associations between individual components of a national deprivation index and spatially aggregated cases of STEC enteritis and cryptosporidiosis. Classification accuracies ranged from 78.2% (STEC, urban) to 90.6% (cryptosporidiosis, rural). STEC incidence was (negatively) associated with a mean number of persons per room and percentage of local authority housing in both urban and rural areas, addition to lower levels of education in rural areas, while lower unemployment rates were associated with both infections, irrespective of settlement type. Lower levels of third-level education were associated with cryptosporidiosis in rural areas only. This study highlights settlement-specific disparities with respect to education, unemployment and household composition, associated with the incidence of enteric infection. Study findings may be employed for improved risk communication and surveillance to safeguard public health across socio-demographic profiles.

Biological Biosensors for Monitoring and Diagnosis

Quantification and detection of various contaminants in the ecosystem have become critically important in the past few decades due to their exhaustive use in soil and aquatic ecosystems. The contamination by both organic and inorganic contaminants in the ecosystem has drawn attention due to their persistence, biological accumulation, and toxicity. Organic contaminants reach the air, water, food, soil, and other systems through drift mechanism and have detrimental effect on various life systems after entering the food chain, thus interfering the normal biological process of the ecosystem. Inorganic contaminants have less solubility, primarily get adsorbed, and accumulate on lower sediments. The sources of both organic and inorganic contaminants include anthropogenic activities which dispose industrial and sewage effluent directly into water bodies. Most of the contaminants are very much toxic and have tumorigenic, carcinogenic, and mutagenic effect on various life-forms. Biosensors have various prospective and existing applications in the detection of these compounds in the environment by transducing a signal. It also has immense applications in the detection of different contaminants in the food industry, environmental monitoring, disease diagnosis, etc. where reliable and precise analyses are required. This chapter points out a comprehensive glimpse on different biosensors and their characteristics, operating principles, and their designs, based on transduction types and biological components. Efforts have been made to summarize various applications of biosensors in food industry, environmental monitoring, drug delivery systems, and clinical diagnostics etc.