Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt.

Objective: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods. Method: The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE). Result: For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model's robustness. Conclusion: GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number R 0 < 1 . For R 0 > 1 , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

A risk-induced dispersal strategy of the infected population for a disease-free state in the SIS epidemic model.

This article proposes a dispersal strategy for infected individuals in a spatial susceptible-infected-susceptible (SIS) epidemic model. The presence of spatial heterogeneity and the movement of individuals play crucial roles in determining the persistence and eradication of infectious diseases. To capture these dynamics, we introduce a moving strategy called risk-induced dispersal (RID) for infected individuals in a continuous-time patch model of the SIS epidemic. First, we establish a continuous-time n-patch model and verify that the RID strategy is an effective approach for attaining a disease-free state. This is substantiated through simulations conducted on 7-patch models and analytical results derived from 2-patch models. Second, we extend our analysis by adapting the patch model into a diffusive epidemic model. This extension allows us to explore further the impact of the RID movement strategy on disease transmission and control. We validate our results through simulations, which provide the effects of the RID dispersal strategy.

Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic, along with the implementation of public health and social measures (PHSMs), have markedly reshaped infectious disease transmission dynamics. We analysed the impact of PHSMs on 24 notifiable infectious diseases (NIDs) in the Chinese mainland, using time series models to forecast transmission trends without PHSMs or pandemic. Our findings revealed distinct seasonal patterns in NID incidence, with respiratory diseases showing the greatest response to PHSMs, while bloodborne and sexually transmitted diseases responded more moderately. 8 NIDs were identified as susceptible to PHSMs, including hand, foot, and mouth disease, dengue fever, rubella, scarlet fever, pertussis, mumps, malaria, and Japanese encephalitis. The termination of PHSMs did not cause NIDs resurgence immediately, except for pertussis, which experienced its highest peak in December 2023 since January 2008. Our findings highlight the varied impact of PHSMs on different NIDs and the importance of sustainable, long-term strategies, like vaccine development.

Social contacts patterns relevant to the transmission of infectious diseases in Suzhou, China following the COVID-19 epidemic.

BACKGROUND: The COVID-19 pandemic has profoundly affected human social contact patterns, but there is limited understanding regarding the post-pandemic social contact patterns. Our objective is to quantitatively assess social contact patterns in Suzhou post-COVID-19. METHODS: We employed a diary design and conducted social contact surveys from June to October 2023, utilizing paper questionnaires. A generalized linear model was utilized to analyze the relationship between individual contacts and covariates. We examined the proportions of contact type, location, duration, and frequency. Additionally, age-related mixed matrices were established. RESULTS: The participants reported an average of 11.51 (SD 5.96) contact numbers and a total of 19.78 (SD 20.94) contact numbers per day, respectively. The number of contacts was significantly associated with age, household size, and the type of week. Compared to the 0-9 age group, those in the 10-19 age group reported a higher number of contacts (IRR = 1.12, CI: 1.01-1.24), while participants aged 20 and older reported fewer (IRR range: 0.54-0.67). Larger households (5 or more) reported more contacts (IRR = 1.09, CI: 1.01-1.18) and fewer contacts were reported on weekends (IRR = 0.95, CI: 0.90-0.99). School had the highest proportion of contact durations exceeding 4 h (49.5%) and daily frequencies (90.4%), followed by home and workplace. The contact patterns exhibited clear age-assortative mixing, with Q indices of 0.27 and 0.28. CONCLUSIONS: We assessed the characteristics of social contact patterns in Suzhou, which are essential for parameterizing models of infectious disease transmission. The high frequency and intensity of contacts among school-aged children should be given special attention, making school intervention policies a crucial component in controlling infectious disease transmission.

Mortality Associated with Priority Diseases in Flood-Affected Areas Using District Health Information System (DHIS2) During September-December 2022: Pakistan Experience.

OBJECTIVES: To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan. METHODS: The study's design is a descriptive trend analysis. The study utilized the disease data reported to District Health Information System (DHIS2) for the 12 most frequently reported priority diseases under the Integrated Disease Surveillance and Response (IDSR) system in Pakistan. RESULTS: In total, there were 1,532,963 suspected cases during August to December 2022 in flood-affected districts (n = 75) across Pakistan; Sindh Province reported the highest number of cases (n = 692,673) from 23 districts, followed by Khyber Pakhtunkhwa (KP) (n = 568,682) from 17 districts, Balochistan (n = 167,215) from 32 districts, and Punjab (n = 104,393) from 3 districts. High positivity was reported for malaria (79,622/201,901; 39.4%), followed by acute diarrhea (non-cholera) (23/62; 37.1%), hepatitis A and E (47/252; 18.7%), and dengue (603/3245; 18.6%). The crude mortality rate was 11.9 per 10 000 population (1824/1,532,963 [deaths/cases]). CONCLUSION: The study identified acute respiratory infection, acute diarrhea, malaria, and skin diseases as the most prevalent diseases. This suggests that preparedness efforts and interventions targeting these diseases should be prioritized in future flood response plans. The study highlights the importance of strengthening the IDSR as a Disease Early Warning System through the implementation of the DHIS2.

Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness.

The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.

The burden of infectious diseases throughout and after the COVID-19 pandemic (2020-2023) and Russo-Ukrainian war migration.

Understanding how the infectious disease burden was affected throughout the COVID-19 pandemic is pivotal to identifying potential hot spots and guiding future mitigation measures. Therefore, our study aimed to analyze the changes in the rate of new cases of Poland's most frequent infectious diseases during the entire COVID-19 pandemic and after the influx of war refugees from Ukraine. We performed a registry-based population-wide study in Poland to analyze the changes in the rate of 24 infectious disease cases from 2020 to 2023 and compared them to the prepandemic period (2016-2019). Data were collected from publicly archived datasets of the Epimeld database published by national epidemiological authority institutions. The rate of most of the studied diseases (66.6%) revealed significantly negative correlations with the rate of SARS-CoV-2 infections. For the majority of infectious diseases, it substantially decreased in 2020 (in case of 83%) and 2021 (63%), following which it mostly rebounded to the prepandemic levels and, in some cases, exceeded them in 2023 when the exceptionally high annual rates of new cases of scarlet fever, Streptococcus pneumoniae infections, HIV infections, syphilis, gonococcal infections, and tick-borne encephalitis were noted. The rate of Clostridioides difficile enterocolitis was two-fold higher than before the pandemic from 2021 onward. The rate of Legionnaires' disease in 2023 also exceeded the prepandemic threshold, although this was due to a local outbreak unrelated to lifted COVID-19 pandemic restrictions or migration of war refugees. The influx of war migrants from Ukraine could impact the epidemiology of sexually transmitted diseases. The present analysis indicates that continued efforts are needed to prevent COVID-19 from overwhelming healthcare systems again and decreasing the control over the burden of other infectious diseases. It also identifies the potential tipping points that require additional mitigation measures, which are also discussed in the paper, to avoid escalation in the future.

Obesity is a communicable disease.

Obesity has multiple causes and correlates. Usually studied as a metabolic and endocrine disease, with mechanical and musculoskeletal comorbidities, obesity also has a communicable angle to it. Obesity can be considered a communicable disease from the conventional point of view, as it is associated with viral etiology in animal and human models. It is also associated with increased prevalence and worse prognosis of infectious diseases. Not only that, obesity is a 'socially communicable' disease, as it 'spreads' amongst people living in similar environments.

Infectious diseases in Poland in 2021. / Choroby zakazne w Polsce w 2021 r.

OBJECTIVE OF THE WORK. As 2021 was the second year of COVID-19 pandemic we expect the continuous impact of the pandemic on other infectious diseases. We aimed at reviewing the national infectious surveillance data based on available surveillance reports (Epidemiological Chronicle) to summarize the infectious disease situation in 2021. MATERIAL AND METHODS. National infectious disease surveillance system collects mandatory notifications from physicians and laboratories as well as epidemiological investigation reports prepared by State Sanitary Inspection, where relevant. We also include mortality data based on the reports of Statistics Poland office. RESULTS AND DISCUSSION. In 2021, there were 2,852,789 cases of COVID-19 reported, corresponding to the incidence of 7475.4 per 100,000 and 90,126 deaths related to COVID-19. For most of diseases the incidence remained lower than before the pandemic. This included influenzea and influenzea-like illness incidence (- 5.4% vs 2020 and - 37.6% vs median 2015-2019) and tuberculosis incidence (+9.3% vs 2020 and -35.9% vs median 2015-2019). The incidence was lower than in 2020 for: pertussis (-75.7%), measles (-54.9%), rubella (48.7%), mumps (-16.4%), chickenpox (-19.0%) or H. influenzea invasive disease (-33.0%). A notable exception to these trends was Clostridium difficile intestinal infections incidence, which was higher by 88.2% from the 2015-2019 median with 21,157 case and 1,120 fatalities reported in 2021. There was also an almost 4-fold increase in norovirus infections incidence. The number of chronic hepatitis infections diagnoses were substantially lower than median for 2015-2019 (-53.7% for HBV and - 68.8% for HCV). The COVID-19 pandemic still played the crucial role as a public health problem, but its impact on other infectious diseases was less clear than in 2020. The reduction in the number of registered cases was with likely attributable to non-pharmaceutic interventions and to delays in registration due to reduced public health resources.

Geography and health: role of human translocation and access to care.

Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today's interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled "Geography and health: role of human translocation and access to care" document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era.

Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance.

Background: At the beginning of the year 2023, the Chatbot Generative Pre-Trained Transformer (ChatGPT) gained remarkable attention from the public. There is a great discussion about ChatGPT and its knowledge in medical sciences, however, literature is lacking to evaluate the ChatGPT knowledge level in public health. Therefore, this study investigates the knowledge of ChatGPT in public health, infectious diseases, the COVID-19 pandemic, and its vaccines. Methods: Multiple Choice Questions (MCQs) bank was established. The question's contents were reviewed and confirmed that the questions were appropriate to the contents. The MCQs were based on the case scenario, with four sub-stems, with a single correct answer. From the MCQs bank, 60 MCQs we selected, 30 MCQs were from public health, and infectious diseases topics, 17 MCQs were from the COVID-19 pandemic, and 13 MCQs were on COVID-19 vaccines. Each MCQ was manually entered, and tasks were given to determine the knowledge level of ChatGPT on MCQs. Results: Out of a total of 60 MCQs in public health, infectious diseases, the COVID-19 pandemic, and vaccines, ChatGPT attempted all the MCQs and obtained 17/30 (56.66%) marks in public health, infectious diseases, 15/17 (88.23%) in COVID-19, and 12/13 (92.30%) marks in COVID-19 vaccines MCQs, with an overall score of 44/60 (73.33%). The observed results of the correct answers in each section were significantly higher (p = 0.001). The ChatGPT obtained satisfactory grades in all three domains of public health, infectious diseases, and COVID-19 pandemic-allied examination. Conclusion: ChatGPT has satisfactory knowledge of public health, infectious diseases, the COVID-19 pandemic, and its vaccines. In future, ChatGPT may assist medical educators, academicians, and healthcare professionals in providing a better understanding of public health, infectious diseases, the COVID-19 pandemic, and vaccines.

Trends in Nationally Notifiable Infectious Diseases in Humans and Animals during COVID-19 Pandemic, South Korea.

We investigated trends in notifiable infectious diseases in both humans and animals during the COVID-19 pandemic in South Korea and compared those data against expected trends had nonpharmaceutical interventions (NPIs) not been implemented. We found that human respiratory infectious diseases other than COVID-19 decreased by an average of 54.7% after NPIs were introduced. On the basis of that trend, we estimated that annual medical expenses associated with respiratory infections other than COVID-19 also decreased by 3.8% in 2020 and 18.9% in 2021. However, human gastrointestinal infectious diseases and livestock diseases exhibited similar or even higher incidence rates after NPIs were instituted. Our investigation revealed that the preventive effect of NPIs varied among diseases and that NPIs might have had limited effectiveness in reducing the spread of certain types of infectious diseases. These findings suggest the need for future, novel public health interventions to compensate for such limitations.

A meta-analysis on global change drivers and the risk of infectious disease.

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors1. Studies have shown that infectious disease risk is modified by changes to biodiversity2-6, climate change7-11, chemical pollution12-14, landscape transformations15-20 and species introductions21. However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host-parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

Persistence and Variation of the Indirect Effects of COVID-19 Restrictions on the Spectrum of Notifiable Infectious Diseases in China: Analysis of National Surveillance Among Children and Adolescents From 2018 to 2021.

BACKGROUND: Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown. OBJECTIVE: This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels. METHODS: The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference. RESULTS: A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6% during 2020 but rebounded by 56.9% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95% CI 0.53-0.87), but rebounded in 2021. CONCLUSIONS: The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections.

Diagnostics for Public Health - Infectious Disease Surveillance and Control.

AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.

Spatial Interaction Analysis of Infectious Disease Import and Export between Regions.

Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.

Scenario-based assessment of emergency management of urban infectious disease outbreaks.

Infectious diseases pose a severe threat to human health and are accompanied by significant economic losses. Studies of urban outbreaks of infectious diseases are diverse. However, previous studies have neglected the identification of critical events and the evaluation of scenario-based modeling of urban infectious disease outbreak emergency management mechanisms. In this paper, we aim to conduct an empirical analysis and scenario extrapolation using a questionnaire survey of 18 experts, based on the CIA-ISM method and scenario theory, to identify the key factors influencing urban infectious disease outbreaks. Subsequently, we evaluate the effectiveness of urban infectious disease outbreak emergency management mechanisms. Finally, we compare and verify the actual situation of COVID-19 in China, drawing the following conclusions and recommendations. (1) The scenario-based urban infectious disease emergency management model can effectively replicate the development of urban infectious diseases. (2) The establishment of an emergency command center and the isolation and observation of individuals exposed to infectious diseases are crucial factors in the emergency management of urban outbreaks of infectious disease.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention.

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.