Weekly Forecasting Model for Dengue Hemorrhagic Fever Outbreak in Thailand

A dengue virus causes diseases, including dengue hemorrhagic fever (DHF) which induces several sicknesses and deaths in Thailand. DHF is categorized as one of the most dangerous communicable diseases by the Ministry of Public Health Thailand (MoPH); moreover, the MoPH also sets strict protocols and encourages forecasting techniques for monitoring and dealing with the outbreaks. This research aims to utilize the data that were gathered from external sources, e.g. Google Trends data and meteorology data, to forecast the number of cases that will occur within the 7 day-interval in the next 1–4 weeks. Six provinces—including Chiang Rai, Mukdahan, Pattani, Phichit, Ayutthaya, and Ratchaburi—were selected as they represent the unique patterns of dengue outbreaks in Thailand. The machine learning models—including Random Forest, AdaBoost, Extra-Trees, and Regularized Regressions—were used to forecast the number of the cases. The performances of these models were compared to the performances of the traditional time series model including Naïve model and Moving Average. The proposed machine learning models for Chiang Rai, Mukdahan, and Pattani yield better results than those of the traditional models

Review on Nowcasting using Least Absolute Shrinkage Selector Operator (LASSO) to Predict Dengue Occurrence in San Juan and Iquitos as Part of Disease Surveillance System

Dengue which was first detected mainly in South East Asia during 1940s is now a serious public health concern across the subtropical and temperate regions of Americas, Europe and China due to the change in global climate and international travel. Hence, 3.9 billion people in 128 countries are exposed to the danger of potentially fatal dengue infection. This is a review paper of various dengue forecasting methodology to identify suitable models for predicting the disease occurrence in San Juan, Puerto Rico and Iquitos, Peru. Least Absolute Shrinkage Selector Operator (LASSO) model using climatic variables and Google Trends search terms as predictors was proposed to forecast dengue cases four weeks in advance. LASSO’s flexibility in incorporating a variety of predictors and its ease of interpretation present LASSO as a compelling case against the general predictive models. Public health regulators could make use of such nowcasting model to facilitate the timing of vector control and public health campaigns along with the medical resource allocation to cope with potential dengue outbreaks

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

© 2013 IEEE. The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985-2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis

Trending ticks: using Google Trends data to understand tickborne disease prevention

IntroductionTicks and pathogens they carry seriously impact human and animal health, with some diseases like Lyme and Alpha-gal syndrome posing risks. Searching for health information online can change people’s health and preventive behaviors, allowing them to face the tick risks. This study aimed to predict the potential risks of tickborne diseases by examining individuals’ online search behavior.MethodsBy scrutinizing the search trends across various geographical areas and timeframes within the United States, we determined outdoor activities associated with potential risks of tick-related diseases. Google Trends was used as the data collection and analysis tool due to its accessibility to big data on people’s online searching behaviors. We interact with vast amounts of population search data and provide inferences between population behavior and health-related phenomena. Data were collected in the United States from April 2022 to March 2023, with some terms about outdoor activities and tick risks.Results and DiscussionResults highlighted the public’s risk susceptibility and severity when participating in activities. Our results found that searches for terms related to tick risk were associated with the five-year average Lyme Disease incidence rates by state, reflecting the predictability of online health searching for tickborne disease risks. Geographically, the results revealed that the states with the highest relative search volumes for tick-related terms were predominantly located in the Eastern region. Periodically, terms can be found to have higher search records during summer. In addition, the results showed that terms related to outdoor activities, such as “corn maze,” “hunting,” “u-pick,” and “park,” have moderate associations with tick-related terms. This study provided recommendations for effective communication strategies to encourage the public’s adoption of health-promoting behaviors. Displaying warnings in the online search results of individuals who are at high risk for tick exposure or collaborating with outdoor activity locations to disseminate physical preventive messages may help mitigate the risks associated with tickborne diseases

Incorporating Connectivity among Internet Search Data for Enhanced Influenza-like Illness Tracking

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends

Terveydenhuollon ammattilaisten internetin tiedonhaku infektioepidemioiden seurannassa

The aim of this study was to analyze online information seeking by healthcare professionals (HCPs) in order to both evaluate its extent and assess whether it can be used in clinically relevant settings, such as epidemiology. HCPs need reliable medical information to be used in daily clinical work. Physician’s Databases (PD) serve as online medical sources that are available throughout the Finnish healthcare system and provide medical information for HCPs performing the searches. Every query is included in the log files of PD. To analyze information needs among various HCPs, the queries in different healthcare sectors (primary care, specialized care, pharmacies, and private care) showed the known characteristics of each sector in terms of the time of day, weekdays, weekends, seasons, and quantities of HCPs working in a specific healthcare sector nationwide. To detect infectious disease epidemics, similar patterns were found between the diagnoses and queries of Lyme borreliosis (LB) performed by both HCPs and the general public. The media publications on LB only occasionally related to queries. HCPs’ queries on oseltamivir and influenza showed similar patterns annually compared with the diagnoses and laboratory reports on influenza. When detecting influenza epidemics, the queries on oseltamivir preceded influenza diagnoses by -0.80 weeks (95% CI: -1.0, 0.0, p = 0.000) with high correlation (tau = 0.943); and the queries on influenza preceded oseltamivir queries by -0.80 weeks (95% CI: -1.2, 0.0, p = 0.015) with high correlation (tau = 0.738) and influenza diagnoses by -1.60 weeks (95% CI: -1.8, -1.0, p = 0.000) with high correlation (tau = 0.894). Assessing the log files of PD, and comparing them with epidemiological registers on infectious diseases, heralds a new approach for using HCPs’ online queries from real-time databases as an additional source of information for disease surveillance when detecting epidemics.Väitöskirjan tavoitteena oli tutkia terveydenhuollon ammattilaisten tiedonhakua ja sen yhteyttä infektioepidemioihin. Duodecimin Terveysportin Lääkärin tietokannat on internetpohjainen tietolähde terveydenhuollon ammattilaisille, jotka hakevat luotettavaa lääketieteellistä tietoa potilaiden hoitoon. Jokainen haku tallentuu tietokannan lokitietoihin. Tutkimuksen tarkoituksena oli arvioida sekä tiedonhaun laajuutta että sen hyödynnettävyyttä esimerkiksi infektioepidemiologiassa. Tutkimuksessa (1) arvioitiin terveydenhuollon ammattilaisten tiedontarvetta tutkimalla eri terveydenhuollon sektoreilla (perusterveydenhuolto, erikoissairaanhoito, apteekit ja yksityissektori) tapahtuvaa tiedonhakua Lääkärin tietokannoista. Niin haun vuorokaudenajan, viikonpäivän, vuodenajan kuin sektorilla työskentelevien ammattilaisten määrän todettiin olevan ominaisia kullekin sektorille. Tämän jälkeen (2) verrattiin Lääkärin tietokantojen Lymen borrelioosi -hakuja ja Terveyden ja hyvinvoinnin laitoksen rekisterin borrelioosidiagnooseja toisiinsa. Niillä havaittiin ajallinen yhteys: haut ja diagnoosit ilmenevät samaan aikaan. Tämä tarkoittaa, että ammattilaisten hakuja voitaisiin hyödyntää epidemioiden seurannassa perinteisten rekistereiden rinnalla. Tutkimuksessa myös (3) verrattiin ammattilaisten Lääkärin tietokantojen Lymen borrelioosi -hakuja ja maallikoiden Terveyskirjaston Lymen borrelioosi -hakuja toisiinsa. Niissäkin toteutui samanlainen ajallinen yhteys, joka noudatti perinteistä infektioepidemiologista rekisteriä borrelioosista. Suurimpien suomalaisten medioiden verkkosivuilta kerättiin borrelioosiin liittyvät mediajulkaisut, ja ne olivat yhteydessä Terveyskirjaston Lymen borrelioosi -hakuihin vain ajoittain. Borrelioosin medianäkyvyys saattaa kuitenkin vaikuttaa sekä ammattilaisten että maallikoiden internetin tiedonhakuun. Lopuksi (4) tutkittiin terveydenhuollon ammattilaisten Lääkärin tietokantojen influenssahakuja ja Duodecimin lääketietokannan oseltamiviirihakuja. Niillä todettiin yhteys Terveyden ja hyvinvoinnin laitoksen influenssadiagnooseihin ja laboratoriolöydöksiin. Tämä tarkoittaa, että kun oseltamiviirihaut edelsivät ajallisesti influenssadiagnooseja ja kun influenssahaut edelsivät sekä oseltamiviirihakuja että influenssadiagnooseja, niin ammattilaisten hakuja tietokannasta voitaisiin hyödyntää influenssaepidemioiden seurannassa. Lokitietojen vertaaminen infektioepidemiologisiin rekistereihin tuo uutta tietoa terveydenhuollon ammattilaisten internetin tiedonhausta. Hakutietoa on mahdollista hyödyntää perinteisten rekistereiden rinnalla infektiotautien ennakoinnissa ja seurannassa

Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure