Disease Early Warning System Research Articles

Satellite-Derived, Smartphone-Delivered Geospatial Cholera Risk Information for Vulnerable Populations.

Cholera, an acute waterborne diarrheal disease, remains a major global health challenge. Despite being curable and preventable, it can be fatal if left untreated, especially for children. Bangladesh, a cholera-endemic country with a high disease burden, experiences two peaks annually, during the dry pre-monsoon spring and the wet post-monsoon fall seasons. An early warning system for disseminating cholera risk, which has potential to reduce the disease burden, currently does not exist in Bangladesh. Such systems can raise timely awareness and allow households in rural, riverine areas like Matlab to make behavioral adjustments with water usage and around water resources to reduce contracting and transmitting cholera. Current dissemination approaches typically target local government and public health organizations; however, the vulnerable rural populations largely remain outside the information chain. Here, we develop and evaluate the accuracy of an early warning system-CholeraMap that uses high-resolution earth observations to forecast cholera risk and disseminate geocoded risk maps directly to Matlab's population via a mobile smartphone application. Instead of relying on difficult to obtain station-based environmental and hydroclimatological data, this study offers a new opportunity to use remote sensing data sets for designing and operating a disease early warning system. CholeraMap delivers monthly, color-coded geospatial maps (1km×1km spatial resolution) with household and community cholera risk information. Our results demonstrate that the satellite-derived local-scale risk model satisfactorily captured the seasonal cholera pattern for the Matlab region, and a detailed high-resolution picture of the spatial progression of at-risk areas during outbreak months.

Establishment and application of infectious disease monitoring, early warning and disposal system

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Expert consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases

An effective infectious disease surveillance and early warning system is a crucial component of public health safety and is essential for preventing and controlling outbreaks of infectious diseases. Enhancing surveillance and early warning capabilities is an urgent priority for advancing high-quality disease prevention and control efforts. Combining the research findings and practical experiences of experts in epidemiology, clinical medicine, disease prevention and control, data science, and computer science, and following multiple rounds of expert discussions, we have developed a consensus on the key technologies for a multi-point trigger intelligent surveillance and early warning system for infectious diseases. This consensus primarily covers the related concepts and definitions of the multi-point trigger intelligent surveillance and early warning system for infectious diseases, the key technical framework, sources, acquisition, and governance of multi-channel warning data, classification of early warning methods, multi-point trigger intelligent surveillance and early warning paths, multi-point trigger warning and comprehensive assessment, response to warning signals, and evaluation of early warning effectiveness. It aims to provide technical references for the construction and application of a multi-point trigger intelligent surveillance and early warning system for infectious diseases.

Unveiling wheat rust epidemics: A long‐term surveillance study in Ethiopia's North Western Amhara region

AbstractWheat is one of the most significant food security crops in Ethiopia. However, the production and productivity of wheat is far below its potential due to many challenges. Wheat disease epidemics, typified by wheat rusts, are among the most important challenges, causing large‐scale production losses that put farmers’ income and food security at risk. We analysed and presented the long‐term and comprehensive wheat rust surveillance results to better understand the past rust disease occurrences, ultimately contributing to future wheat production and rust control strategies. A total of 1140 wheat fields have been surveyed from 2009 to 2021 throughout four administrative zones of the North Western Amhara region. We identified spatial and temporal trends in the distribution and status of wheat stem rust (Sr), yellow rust (Yr) and leaf rust (Lr) using a combination of different statistical tools and data‐analysis methods. The results of the study indicated that 52.5%, 11.3% and 3.4% of the wheat fields were infected with yellow, stem and leaf rust, respectively. Even so, the distribution and intensity of wheat rust disease vary across locations, elevations and cultivated varieties. The associations between altitude and disease prevalence show a linear correlation. We also identified hotspots of yellow and stem rust, which will be used by the country's wheat rust disease early warning system. In order to reverse low varietal diversification in the farmer's field, it is necessary to work with all stakeholders involved in the wheat production sector in a better synergy.

Effects of weather and air pollution on outpatient visits for insect-and-mite-caused dermatitis: an empirical and predictive analysis

BackgroundDermatitis caused by insects and mites, diagnosed as papular urticaria or scabies, is a common skin disease. However, there is still a lack of studies about the effects of weather and air pollution on outpatient visits for this disease. This study aims to explore the impacts of meteorological and environmental factors on daily visits of dermatitis outpatients.MethodsAnalyses are conducted on a total of 43,101 outpatient visiting records during the years 2015–2020 from the largest dermatology specialist hospital in Guangzhou, China. Hierarchical cluster models based on Pearson correlation between risk factors are utilized to select regression variables. Linear regression models are fitted to identify the statistically significant associations between the risk factors and daily visits, taking into account the short-term effects of temperatures. Permutation importance is adopted to evaluate the predictive ability of these factors.ResultsShort-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. In terms of total outpatients, the one-day lagged temperature not only has a significant impact on daily visits, but also has the highest median value of permutation importance. This conclusion is robust across most subgroups except for subgroups of summer and scabies, wherein the three-day lagged temperature has a negative effect. By contrast, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. Moreover, weekdays, holidays and trends have significant impacts on daily visits, but with weak predictive abilities.ConclusionsOur study suggests that short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. Nevertheless, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

The Intelligent Infectious Disease Active Surveillance and early warning system in China: An application of dengue prevention and control

Utilizing advanced information technologies such as big data and artificial intelligence (AI), China has established and implemented the Intelligent Infectious Disease Active Surveillance and Early Warning System. It provides new tools for the surveillance, early warning, and response to infectious diseases, enhancing the timeliness, scientific basis, and efficiency of epidemic control efforts. The system comprises four functional modules including multi-channel active surveillance, intelligent early warning, data-driven risk assessment, and smart emergency response. This paper provides a detailed overview of the structure and functions of the Intelligent Infectious Disease Active Surveillance and Early Warning System in China, with a specific focus on its application in dengue prevention and control in Hainan Province from February to May 2024. Firstly, the system can proactively capture and integrate heterogeneous surveillance data from multiple sources. Based on these multi-channel data, users can select appropriate warning indicators and AI models to automatically trigger early warnings. Using vast amounts of surveillance data, the system can construct machine learning models to accurately assess the transmission risk of infectious diseases. In terms of emergency response, the system offers powerful tools for early diagnosis, smart epidemiological investigation, digital contact tracing, vaccine and drug development, and evaluation of intervention measures. This system facilitates early detection, reporting, and management of outbreaks, serving as a valuable reference for other countries and regions. Nevertheless, continuous efforts are needed to strengthen scientific research and multidisciplinary collaboration, establish reliable data collection mechanisms, enhance continuous model monitoring and adjustments, and leverage the latest large language models. In the future, the system will be further optimized to help control emerging and major infectious diseases more effectively.

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

To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan. 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. 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]). 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.

Strengthening medical facility responses to respiratory infectious diseases: Global trends, challenges, and innovations post-COVID-19.

Respiratory infectious diseases have long been a serious public health problem. This study explores the significance of respiratory infectious disease prevention and control systems worldwide, particularly during and after the COVID-19 pandemic. The epidemiology of many respiratory diseases such as influenza changed over the past two years, and the incidence of pathogens such as Mycoplasma pneumoniae and Bordetella pertussis has also increased. Based on influenza surveillance data in China, the influenza season in 2023 was notably delayed and extended, with A(H1N1) pdm09 being the predominant strain. This editorial also reviewed the gradual establishment of China's infectious disease prevention and control system following the 2003 SARS outbreak, highlighting the role of medical facilities in managing public health emergencies, conducting infectious disease pre-screening, and reporting cases to networks in real time. In the future, China will further develop an intelligent, multi-trigger infectious disease surveillance and early warning system to increase the early detection of unknown infectious diseases and optimize the allocation of medical resources. A robust infectious disease control system is crucial to addressing future pandemic threats.

A Lightweight Neural Network-Based Method for Identifying Early-Blight and Late-Blight Leaves of Potato

Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system.

A novel multi-head CNN design to identify plant diseases using the fusion of RGB images

Plant diseases and insect pests cause a significant threat to agricultural production. Early detection and diagnosis of these diseases are critical and can reduce economic losses. The recent development of deep learning (DL) benefits various fields, such as image processing, remote sensing, medical diagnosis, and agriculture. This work proposed a novel approach based on DL for plant disease detection by fusing RGB and segmented images. A multi-headed DenseNet-based architecture was developed, considering two images as input. We evaluated the model on a public dataset, PlantVillage, consisting of 54183 images with 38 classes. The fivefold cross-validation technique achieved an average accuracy, recall, precision, and f1-score of 98.17%, 98.17%, 98.16%, and 98.12%, respectively. The proposed approach can distinguish various plant diseases with different characteristics by image fusion. The high success rate with low standard deviation proves the robustness of the model, and the model can be integrated into plant disease detection and early warning system.

A Review of Alzheimer’s Disease and Inflammation: Pathogenesis, Inflammatory Processes, and Novel Insights from the Artificial Intelligence Perspective

Alzheimer’s disease is a progressive neurodegenerative disease which is characterised by the increased deposition and spread of extracellular β-amyloid plaques and intracellular hyperphospho-rylated tau as neurofibrillary tangles. This is thought to be driven by the sustained activation of brain microglia and astrocytes. In this review, we will provide an overview of the current understanding of the pathogenesis of Alzheimer’s disease, the role of inflammation and associated factors in disease progression as well as current treatments including those in late-stage clinical trials. We will also discuss how machine learning has been previously used to create Alzheimer’s disease risk metrics and the potential for blood-based inflammatory factors to be used to create an artificial intelligence-based Alzheimer’s disease early warning system. The development of an Alzheimer’s disease-based early warning system would enable the improved use of existing and future disease-modifying agents and thereby help to slow or halt disease progression.

2021–2022 monitoring, early warning, and forecasting of global infectious diseases

BackgroundCOVID-19 has had a considerable impact on society since 2019, and the disease has high mortality and infection rates. There has been a particular focus on how to best manage COVID-19 and how to analyze and predict the epidemic status of infectious diseases in general.MethodsThe present study analyzed the COVID-19 epidemic patterns and made predictions of future trends based on the statistics obtained from a global infectious disease network data monitoring and early warning system (OBN, http://27.115.41.130:8888/OBN/). The development trends of other major infectious diseases were also examined.ResultsThe global COVID-19 pandemic showed periodic increases throughout 2021. At present, there is a high incidence in European countries, especially in Eastern Europe, followed by in Africa. The risk of contracting COVID-19 was divided into high, medium–high, medium, medium–low, and low grades depending on the stage of the epidemic in each examined region over the current period. The occurrence and prevalence of major infectious diseases throughout the world did not significantly change in 2021.ConclusionsThe COVID-19 pandemic has strongly impacted people’s lives and the economy. The effects of global infectious diseases can be ameliorated by strengthening monitoring and early warning systems and by facilitating the international exchange of information.

Relationship between meteorological factors, air pollutants and hand, foot and mouth disease from 2014 to 2020

BackgroundMeteorological factors and air pollutants have been reported to be associated with hand, foot, and mouth disease (HFMD) epidemics before the introduction of vaccine. However, there is limited evidence for studies with long-term dimensions.MethodsWe collected the daily HFMD counts, weather and air pollution data from 2014 to 2020 in Chengdu. Distributed lag non-linear models (DLNM) were used to assess the associations of meteorological factors and air pollutants on HFMD cases.ResultsFrom 2014–2020, high relative humidity and precipitation and extremely high and low levels of PM10, O3, SO2 and CO increased the risk of HFMD. In pre-vaccination period, extreme high and low temperatures, PM10 and NO2, low precipitation and high concentrations of PM2.5 and O3 significantly increase the risk of HFMD; In post-vaccination period, high relative humidity and low level of CO can significantly increase the incidence of HFMD; During the period of COVID-19, only low temperature will significantly increase the risk of HFMD; Low concentration of air pollutants has the greatest impact on the 6–14 age group, while the high concentration of air pollutants has the greatest impact on the 0–1 age group.ConclusionsOur study suggest that high relative humidity and precipitation and extremely high and low levels of PM10, O3, SO2 and CO increased the risk of HFMD from 2014 to 2020. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

Progress in the practice of surveillance and early warning of infectious diseases in major countries and regions

The COVID-19 pandemic is yet another reminder that the threat of infectious disease has never really gone away. As the cornerstone of preventing and controlling infectious diseases, effective surveillance and early warning are of great significance in understanding the outbreak and epidemic of specific infectious diseases and putting forward effective prevention and control measures. Therefore, we must continue strengthening the construction of infectious disease surveillance and early warning system. We reviewed the surveillance and early warning practices of infectious diseases in major countries and regions, then discussed the development direction in the field of surveillance and early warning of infectious diseases to provide the reference for strengthening the construction and capacity of infectious disease surveillance and early warning system in China.

Application and reflection of laboratory-based monitoring in early warning of infectious diseases

Despite the fact that our cognition towards infectious disease prevention, the advanced technology and the economic status of the whole society has made a great progress in the last decade, the outbreak of COVID-19 pneumonia has again enabled the public to acquire more about super-challenges of infectious diseases, epidemics and the relevant preventive measurements. In order to identify the epidemic signals in early stage or even before the onset of epidemic, the data research and utilization of a series of factors related to the occurrence and transmission of infectious diseases have played a significant role in research of prevention and control during the whole period of surveillance and early warning. Laboratory-based monitoring for the etiology has always been an important part of infectious disease warning system due to pathogens as the direct cause of such diseases. China has initially established a laboratory-based monitoring and early warning system for bacterial infectious diseases based on the Chinese Pathogen Identification Network with an aim to identify pathogens, outbreaks and sources. This network has played an essential role in early detection, tracking and precise prevention and control of bacterial infectious diseases, such as plague, cholera, and epidemic cerebrospinal meningitis. This issue focuses on the function of laboratory-based monitoring during the period of early warning, prevention, and control of bacterial infectious diseases, and conducted a wide range of researches based on the analysis of the epidemic and outbreak isolates, together with field epidemiological studies and normal monitoring systems. All of these could illustrate the effect of laboratory surveillance in the infectious disease risk assessment and epidemic investigation. At the same time, we have put forward our review and expectation of scenarios about laboratory-based monitoring and early warning technologies to provide innovative thoughts for promoting a leapfrog development of infectious disease monitoring and early warning system in China.

Electronic Integrated Disease Early Warning System Surveillance System Evaluation, Sana'a Capital, Yemen, 2021

Background The Electronic Integrated Disease Early Warning System (eIDEWS) is an essential system; it contributes to the better prevention and management of epidemics. Through the collection of complete, accurate, and timely data, countries are able to determine the priorities for suitable interventions that save the lives of communities. Regardless of the conflict in Yemen, the system is still functioning and is expanding to be the most effective epidemiological surveillance program. Objective We aimed to determine the usefulness of the eIDEWS, assess its performance, and identify the strengths and weaknesses of its implementation. Methods The usefulness and performance attributes of the eIDEWS were evaluated using the Centers for Disease Control and Prevention’s updated guidelines for evaluating public health surveillance systems. The evaluation was carried out in Sana’a capital from January to March 2021 by interviewing 25 stakeholders at 3 levels—the central, governorate, and health district levels—and using a semistructured questionnaire. Attributes of the system were ranked as poor (<60%), average (60% to <80%), good (80% to <90%), and excellent (≥90%) on the basis of indicators to calculate the final scores. Results The eIDEWS’ overall usefulness and performance score was 90%—an excellent rank. The mean score of system attributes was 100% for acceptability, completeness, and timeliness. The flexibility was good (83%), since the change in reporting method was applied difficultly. The system depends completely on foreign funds; thus, the system’s stability was average (75%). However, the eIDEWS was expanded recently to add new health facilities; its representativeness was average (76%). Conclusions The system is working effectively at evaluated sites. The overall system performance was excellent; however, flexibility and stability were good due to the negative adaptation of the system with regard to the reporting method and the absence of other fund resources. Therefore, evaluating the newly upgraded system, strengthening its stability by finding other supporting resources, and further expanding coverage to include all public and private health care facilities are recommended.

Diphtheria resurgence in Sada'a-Yemen, 2017\u20132020

BackgroundDiphtheria is a contagious vaccine-preventable disease that contributes to the high morbidity and mortality among under 5 children, especially in Yemen. As a consequence of war and collapse of the health system, a fatal epidemic occurred at the end of 2017. This study aims to describe the epidemiology of diphtheria by time, place, and person and vaccination status of affected children.MethodsA study was conducted in Sada'a governorate by using accumulative line list of diphtheria from November 2017 to September 2020 at electronic Integrated Disease Early Warning System (eIDEWS). The case definition of WHO was adopted. Data was analyzed by Microsoft Excel and Epi info- version 7.2 and multivariable logistic analysis used for identifying significant associated factors.Results747 cases were met of WHO case definition. The annual peak of cases started during week 31 and weak 49. Males were slightly more than females (51% vs 49%) and about 35% of cases involved children aged 10 to < 15 years. The overall incidence of diphtheria and case fatality rate (CFR) were 69/ 100,000 and 6.4%, respectively. The highest CFR was among age groups under 5 years 11% (P < 0.001) and among females was 8%. Dysphagia and swollen lymph nodes were the predominant symptoms 98%, 92%, respectively. Based on the Vaccination status, the percentage of unvaccinated and unknown were 53% and 41% respectively, with CFR 11% among cases who received one dose. Furthermore, the most case were from Sahar 40% with case fatality rate 8% and the highest CFR was significantly higher among cases in border and ongoing conflict district (P < 0.05).ConclusionsThe findings highlight that diphtheria is still an ongoing cause of morbidity and mortality among under 5 children in Sada'a that is rising with the low diphtheria immunization coverage. Therefore, concomitant efforts should now focus on improving and monitoring routine immunization across all age groups and healthcare services, especially in borders and continuing conflict districts.

Development of a Muga Disease Early Warning System – A Mobile-Based Service for Seri Farmers

Development of a Muga Disease Early Warning System – A Mobile-Based Service for Seri Farmers

Using Text Mining to Track Outbreak Trends in Global Surveillance of Emerging Diseases: ProMED-mail

Abstract ProMED-mail (Program for Monitoring Emerging Disease) is an international disease outbreak monitoring and early warning system. Every year, users contribute thousands of reports that include reference to infectious diseases and toxins. However, due to the uneven distribution of the reports for each disease, traditional statistics-based text mining techniques, represented by term frequency-related algorithm, are not suitable. Thus, we conducted a study in three steps (i) report filtering, (ii) keyword extraction from reports and finally (iii) word co-occurrence network analysis to fill the gap between ProMED and its utilization. The keyword extraction was performed with the TextRank algorithm, keywords co-occurrence networks were then produced using the top keywords from each document and multiple network centrality measures were computed to analyse the co-occurrence networks. We used two major outbreaks in recent years, Ebola, 2014 and Zika 2015, as cases to illustrate and validate the process. We found that the extracted information structures are consistent with World Health Organisation description of the timeline and phases of the epidemics. Our research presents a pipeline that can extract and organize the information to characterize the evolution of epidemic outbreaks. It also highlights the potential for ProMED to be utilized in monitoring, evaluating and improving responses to outbreaks.

Comparative Study on Early Warning Methods of Infectious Diseases

Major epidemics of infectious diseases will not only endanger people’s lives and property, but also cause panic and social unrest. Therefore, it is particularly important to establish an infectious disease early warning system and take effective measures in time to prevent infectious disease outbreaks. The article summarizes the relevant definitions of infectious disease early warning system, domestic and foreign development status, infectious disease early warning models and methods, and aims to provide references for the establishment of infectious disease early warning systems.