Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors

Development and validation of a potential risk area identification model for hand, foot, and mouth disease in metropolitan China

Maximum Entropy model (MaxEnt), as a machine learning algorithm, is widely used to identify potential risk areas for emerging infectious diseases. However, MaxEnt usually overlooks the influence of the optimal selection of spatial grid scale and the optimal combination of factor information on identification accuracy. Furthermore, the internal level information of factors is closely related to the potential risk of disease occurrence but is rarely applied to enhance MaxEnt's accuracy. In this study, the Optimal Parameters-based Geographical Detectors-Information Value-MaxEnt (OPGD-IV-MaxEnt) was first proposed to identify the potential risk areas of hand, foot, and mouth disease (HFMD) in Shenzhen and compared its identification accuracy with that of OPGD-MaxEnt and MaxEnt. Firstly, the optimal grid scale and optimal combination of factor information were determined by OPGD. Secondly, the contributions of factors' internal level information to the potential risk of HFMD occurrence were quantified and incorporated by IV. Lastly, the spatial patterns of potential risk areas and their main driving factors were elucidated. Results showed that: (i) Area under the curve (AUC) of single MaxEnt were 0.638, 0.688, 0.763, 0.796, and 0.757 at 100 m, 250 m, 500 m, 750 m, and 1000 m scale, respectively, and 750 m were deemed the optimal scale. (ii) At the optimal scale, OPGD-IV-MaxEnt (AUC = 0.868) identified potential risk areas more accurately than MaxEnt (AUC = 0.796) and OPGD-MaxEnt (AUC = 0.827). (iii) Resident (r = 0.61, q = 0.39) and Market (r = 0.61, q = 0.36) were the primary factors affecting the identification of potential risk areas. (iv) Potential high-risk areas of HFMD were mainly distributed in northwestern, southwestern, and central Shenzhen, with dense resident and market distribution. Such insights are instrumental in devising targeted infection prevention and control measures for emerging infectious diseases and provide references for improving the identification accuracy of similar machine learning algorithms.

Exploring the spatiotemporal effects of meteorological factors on hand, foot and mouth disease: a multiscale geographically and temporally weighted regression study

The influence of meteorological factors on hand, foot, and mouth disease (HFMD) is not on the same scale, it's rare for previous studies to measure and recognize the independent regression relationship between each variable in space and time scale. This study used a multiscale geographically and temporally weighted regression (MGTWR) model to explore the relationship between the incidence of HFMD and related meteorological factors in Shandong Province, China, during 2015-2019 and attempted to quantify the influence of meteorological factors on HFMD under different spatiotemporal effects. Meanwhile, we used the Global Moran's I statistic and Local Moran's I statistic to test the spatial autocorrelation of the incidence of HFMD. HFMD had spatial autocorrelation at the county level in Shandong Province. The MGTWR model outperformed the OLS and GTWR models in determining the relationship between meteorological factors and HFMD. The study highlights significant spatiotemporal non-stationarity in the relationship between meteorological factors and HFMD. Temperature was predominantly positively correlated with HFMD, especially in the peninsula region during spring and summer. Humidity exhibited a predominantly positive correlation, especially in the Shandong Peninsula. Precipitation also showed a positive correlation with HFMD, particularly in western regions and during the winter months. Wind speed had a predominantly negative correlation with HFMD in the central and southwestern regions. The results might help public health authorities set priorities for targeted prevention and control measures in different regions and weather conditions, and provide guidance for the government to rationally allocate public health resources.

Precision public health, the key for future outbreak management: A scoping review

Background

Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.

Objective

We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.

Methods and analysis

Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.

Results

A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).

Conclusions

PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

Public Concern about Haze and Ozone in the Era of Their Coordinated Control in China

In China, due to the implementation of the Action Plan for Prevention and Control of Air Pollution (APPCAP), the concentrations of PM_2.5 (fine particulate matter) and severe haze in most cities have decreased significantly. However, at present, haze pollution in China has not been completely mitigated, and the problem of O₃ (ozone) has become prominent. Therefore, the prevention and control of haze and O₃ pollution have become important and noticeable issues in the field of atmospheric management. We used the Baidu search indices of "haze" and "ozone" to reflect public concerns about air quality and uncover different correlations between level of concern and level of pollution, and then we identified regions in China that require public attention. The results showed that (1) over the last decade, the search index of haze had a rapid trend of variation in line with changes in haze pollution, but that of O₃ had a relatively slowly increasing trend; (2) the lag days between the peaks of public concern and the peaks of air pollution became increasingly shorter according to daily data analysis; and (3) 96 polluted cities did not receive sufficient public attention. Although periods of heavily haze-polluted weather, which affects visibility, have generated much public concern, periods of slight pollution have not received enough public attention. Public health protection and environmental participation regarding these periods of slight pollution in China deserve appropriate levels of attention.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.

Short-Term Impacts of Meteorology, Air Pollution, and Internet Search Data on Viral Diarrhea Infection among Children in Jilin Province, China

The influence of natural environmental factors and social factors on children’s viral diarrhea remains inconclusive. This study aimed to evaluate the short-term effects of temperature, precipitation, air quality, and social attention on children’s viral diarrhea in temperate regions of China by using the distribution lag nonlinear model (DLNM). We found that low temperature affected the increase in children’s viral diarrhea infection for about 1 week, while high temperature and heavy precipitation affected the increase in children’s viral diarrhea infection risk for at least 3 weeks. As the increase of the air pollution index may change the daily life of the public, the infection of children’s viral diarrhea can be restrained within 10 days, but the risk of infection will increase after 2 weeks. The extreme network search may reflect the local outbreak of viral diarrhea, which will significantly improve the infection risk. The above factors can help the departments of epidemic prevention and control create early warnings of high-risk outbreaks in time and assist the public to deal with the outbreak of children’s viral diarrhea.

Establishment of epidemic early warning index system and optimization of infectious disease model: Analysis on monitoring data of public health emergencies

The ability to mitigate the damages caused by emergencies is an important symbol of the modernization of an emergency capability. When responding to emergencies, government agencies and decision makers need more information sources to estimate the possible evolution of the disaster in a more efficient manner. In this paper, an optimization model for predicting the dynamic evolution of COVID-19 is presented by combining the propagation algorithm of system dynamics with the warning indicators. By adding new parameters and taking the country as the research object, the epidemic situation in countries such as China, Japan, Korea, the United States and the United Kingdom was simulated and predicted, the impact of prevention and control measures such as effective contact coefficient on the epidemic situation was analyzed, and the effective contact coefficient of the country was analyzed. The paper strives to provide early warning of emergencies scientifically and effectively through the combination of these two technologies, and put forward feasible references for the implementation of various countermeasures. Judging from the conclusion, this study reaffirmed the importance of responding quickly to public health emergencies and formulating prevention and control policies to reduce population exposure and prevent the spread of the pandemic.

Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania

The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.

Does public concern over haze pollution matter? Evidence from Beijing-Tianjin-Hebei region, China

Chinese residents are becoming more and more concerned about the living environment especially under the situation of environmental degradation caused by the unbalanced and inadequate economic development. The widespread of internet use provide a new way for public to express the dissatisfaction on environmental pollution. Although the public is the main body of society, the public concern over environmental issues are rarely studied. In this paper, the impact of public concern over haze on haze pollution is quantitatively examined by the utilization of econometric model. Specifically, the Baidu search index (BSI) is utilized as indicators for public concern. Using the panel data consisting of 13 cities in Beijing-Tianjin-Hebei region from the period from January 2014 to December 2019, estimation results showed a significant improvement effect of public concern on haze pollution. In general, the public concern can improve the air quality in a short turn. However, this improvement effect varies with different economic development levels. These findings can help policy makers to better understand the role of public in social governance and improve the air quality in China with the inclusion of public participation.

The relationship between cyclonic weather regimes and seasonal influenza over the Eastern Mediterranean

The prediction of the occurrence of infectious diseases is of crucial importance for public health, as clearly seen in the ongoing COVID-19 pandemic. Here, we analyze the relationship between the occurrence of a winter low-pressure weather regime - Cyprus Lows - and the seasonal Influenza in the Eastern Mediterranean. We find that the weekly occurrence of Cyprus Lows is significantly correlated with clinical seasonal Influenza in Israel in recent years (R = 0.91; p < .05). This result remains robust when considering a complementary analysis based on Google Trends data for Israel, the Palestinian Authority and Jordan. The weekly occurrence of Cyprus Lows precedes the onset and maximum of Influenza occurrence by about one to two weeks (R = 0.88; p < .05 for the maximum occurrence), and closely follows their timing in eight out of ten years (2008-2017). Since weather regimes such as Cyprus Lows are more robustly predicted in weather and climate models than individual climate variables, we conclude that the weather regime approach can be used to develop tools for estimating the compatibility of the transmission environment for Influenza occurrence in a warming world. Furthermore, this approach may be applied to other regions and climate sensitive diseases. This study is a new cross-border inter-disciplinary regional collaboration for appropriate adaptation to climate change in the Eastern Mediterranean.

Short-term effects of rainfall on childhood hand, foot and mouth disease and related spatial heterogeneity: evidence from 143 cities in mainland China

BACKGROUND

Numerous studies have demonstrated the potential association between rainfall and hand, foot and mouth disease (HFMD), but the results are inconsistent. This study aimed to quantify the relationship between rainfall and HFMD based on a multicity study and explore the potential sources of spatial heterogeneity.

METHODS

We retrieved the daily counts of childhood HFMD and the meteorological variables of the 143 cities in mainland China between 2009 and 2014. A common time series regression model was applied to quantify the association between rainfall and HFMD for each of the 143 cities. Then, we adopted the meta-regression model to pool the city-specific estimates and explore the sources of heterogeneity by incorporating city-specific characteristics.

RESULTS

The overall pooled estimation suggested a nonlinear exposure-response relationship between rainfall and HFMD. Once rainfall exceeded 15 mm, the HFMD risk stopped increasing linearly and began to plateau with the excessive risk ratio (ERR) peaking at 21 mm of rainfall (ERR = 3.46, 95% CI: 2.05, 4.88). We also found significant heterogeneity in the rainfall-HFMD relationships (I² = 52.75%, P < 0.001). By incorporating the city-specific characteristics into the meta-regression model, temperature and student density can explain a substantial proportion of spatial heterogeneity with I² statistics that decreased by 5.29 and 6.80% at most, respectively.

CONCLUSIONS

Our findings verified the nonlinear association between rainfall and HFMD. The rainfall-HFMD relationship also varies depending on locations. Therefore, the estimation of the rain-HFMD relationship of one location should not be generalized to another location.

When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018

BACKGROUND

Social media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking.

OBJECTIVE

This study aims to examine research themes, the role of social media, and research methods in social media-based public health research published from 2000 to 2018.

METHODS

A dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed.

RESULTS

This study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role.

CONCLUSIONS

Social media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Age-standardized mortality, disability-adjusted life-years and healthy life expectancy in different cultural regions of Guangdong, China: a population-based study of 2005-2015

BACKGROUND

Guangdong province is dominated by three cultural regions: Canton, Hakka and Hoklo. However, little is known about the disease burden within these regions, particularly because different population,environmental and socioeconomic risk factors might cause different patterns of mortality, disability-adjusted life-years (DALY), life expectancy and healthy life expectancy (HALE). We aimed to compare the patterns of disease burden in Canton, Hakka and Hoklo regions between 2005 and 2015.

METHOD

We calculated the mortality, YLL, YLD for 116 diseases for different cultural regions between 2005 and 2015. We calculated the DALYs for 116 causes as the sum of YLLs and YLDs. We estimated the life expectancy and HALE by using sex-specific mortality rates and YLDs for the three cultural regions.

RESULTS

With a respective reduction of 22.3, 15.8 and 17.8% in 2015 compared with 2005, the age-standardized DALY rates in 2015 was 19,988.0, 14,396.5 and 20,436.6 in Hakka, Canton and Hoklo region. Canton region had a significantly lower mortality and DALYs in most diseases, followed by Hoklo and Hakka regions. The life expectancy and HALE at birth were highest in Canton region in both 2005 and 2015, than in Hoklo and Hakka region.

CONCLUSIONS

Our findings call for improved public health care via the refinement of policy and effective measures for disease prevention. Understanding the environmental and culture-related risk factors of diseases in Hoklo and Hakka regions may help inform public health sectors to reduce the disease burden and the between-region inequality.

Syndromic surveillance using web data: a systematic review

During the recent years, a lot of debate is taken place about the evolution of Smart Healthcare systems. Particularly, how these systems can help people improve human conditions of health, by taking advantages of the new Information and Communication Technologies (ICT), regarding early prediction and efficient treatment. The purpose of this study is to provide a systematic review of the current literature available that focuses on information systems on syndromic surveillance using web data. All published items concern articles, books, reviews, reports, conference announcements, and dissertations. We used a variation of PRISMA Statements methodology to conduct a systematic review. The review identifies the relevant published papers from the year 2004 to 2018, systematically includes and explores them to extract similarities, gaps, and conclusions on the research that has been done so far. The results presented concern the year, the examined disease, the web data source, the geographic location/country, and the data analysis method used. The results show that influenza is the most examined infectious disease. The internet tools most used are Twitter and Google. Regarding the geographical areas explored in the published papers, the most examined country is the United States, since many scientists come from this country. There is a significant growth of articles since 2009. There are also various statistical methods used to correlate the data retrieved from the internet to the data from national authorities. The conclusion of all researches is that the Web can be a useful tool for the detection of serious epidemics and for a creation of a syndromic surveillance system using the Web, since we can predict epidemics from web data before they are officially detected in population. With the advance of ICT, Smart Healthcare can benefit from the monitoring of epidemics and the early prediction of such a system, improving national or international health strategies and policy decision. This can be achieved through the provision of new technology tools to enhance health monitoring systems toward the new innovations of Smart Health or eHealth, even with the emerging technologies of Internet of Things. The challenges and impacts of an electronic system based on internet data include the social, medical, and technological disciplines. These can be further extended to Smart Healthcare, as the data streaming can provide with real-time information, awareness on epidemics and alerts for both patients or medical scientists. Finally, these new systems can help improve the standards of human life.

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases.

The application of meteorological data and search index data in improving the prediction of HFMD: A study of two cities in Guangdong Province, China

Hand, foot and mouth disease (HFMD) is a public health issue in China, and its incidence in Guangdong Province is higher than the national average. Previous studies have found climatic factors have an influential role in the transmission of HFMD. Internet search technology has been shown to predict some infectious disease epidemics and is a potential resource in tracking epidemics in countries where the use of Internet search index data is prevalent. This study aims to improve the prediction of HFMD in two Chinese cities, Shantou and Shenzhen in Guangdong Province, applying both meteorological data and Baidu search indices to create a HFMD forecasting model. To this end, the relationship between meteorological factors and HFMD was found to be linear in both cities, while the relationship between search engine data and HFMD was not consistent. The results of our study suggest that using both Internet search and meteorological data can improve the prediction of HFMD incidence. Using comparative analysis of both cities, we posit that improved quality search indices enhance prediction of HFMD.

Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China

BACKGROUND

Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence.

METHODS

We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan, China from 2009 to 2011 for our experiments.

RESULTS

Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (68.27%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan, China. Among the six representative climate variables, temperature (OR = 2.59), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD.

CONCLUSION

Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics.

Molecular epidemiology and clinical features of hand, foot and mouth disease in northern Thailand in 2016: a prospective cohort study

Background

Hand, foot and mouth disease (HFMD) is a major communicable disease in children ≤6 years old, particularly in several countries in the Asia-Pacific Region, including Thailand. HFMD impacts public health and the economy, especially in northern Thailand.

Methods

A prospective cohort study was conducted to estimate the incidence rate and to identify the serotype and clinical features of HFMD among children in northern Thailand. A validated questionnaire and throat swab were used for data collection. Polymerase chain reaction (PCR) was used to detect human enterovirus and identify its serotypes. Participants were recruited from 14 hospitals in two provinces in northern Thailand, specifically, Chiang Rai and Pha Yao Province, between January 1, 2016, and December 31, 2016. Chi-square or Fisher’s exact test was used to detect the associations of signs and symptoms with HFMD serotype. Logistic regression was used to detect the associations of variables with a positive enterovirus at alpha = 0.05.

Result

In total, 612 children aged ≤6 years from Chiang Rai and Pha Yao Province who were diagnosed with HFMD by a throat swab were recruited for the analysis. Approximately half of the cohort was male (57.2%), 57.5% was aged < 2 years, and 57.5% lived in rural areas. The incidence rate was 279.72/100,000 person-years in Chiang Rai Province and 321.24 per 100,000 person-years in Pha Yao Province. Additionally, 42.5% of children were positive for human enterovirus; among these children, 56.1% were positive for enterovirus-A (EV-A), 17.7% were positive for coxsackievirus (CV), and 26.2% were positive for other human RNA enteroviruses. During the study period, 21 distinct outbreaks of HFMD were recognized. Four to five patients (total 92 patients) were selected from each outbreak for identifying its serotype; enterovirus-A71 (EV-A71) was detected in 34.8% of HFMD cases, coxsackievirus-A16 (CV-A16) in 26.1%, coxsackivirus-A6 (CV-A6) in 15.2%, coxsackievirus-A10 (CV-A10) in 10.9%, coxsackievirus-A4 (CV-A4) in 2.2%, coxsackievirus-B2 (CV-B2) in 2.2%, human rhinovirus in 2.2%, and unknown serotype in 6.4%. Multivariable analysis demonstrated that a history of breastfeeding for ≤6 months was associated with a higher chance of enterovirus infection than a history of breastfeeding > 6 months, and children who had mother who worked as farmers, daily wage employees, and unprofessional skilled jobs had a greater chance of enterovirus infection than those who had unemployed mothers. Coxsackievirus-infected children had a higher rate of rashes on the buttocks, knee, and elbow and fever but a lower rate of lethargy and malaise than EV-A71-infected children.

Conclusions

EV-A71 is a major cause of HFMD in children < 6 years old in northern Thailand, but rash, fever, and mouth ulcers are mostly found in participants with coxsackievirus infection. Breastfeeding should be promoted during early childhood for at least 6 months to prevent HFMD particularly those mother who are working in unprofessional skill jobs.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3560-4) contains supplementary material, which is available to authorized users.

Characteristics of public concern on haze in China and its relationship with air quality in urban areas

Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China; (2) according to daily data analysis, the search index of haze has increased progressively with increased PM_2.5 concentration with a time lag of 0-4 days and the lag time has a declining tendency from 2013 to 2017; (3) according to annual data analysis, the public concern showed a weak correlation with air quality and they showed an opposite temporal trend. However, when the long-term annual trend was removed, the strong positive correlation emerges between the fluctuation parts of the search index of haze and monitoring data of air quality. This indicates the public is more sensitive to the short-term fluctuation of air quality. The results of this paper provide statistical evidence on the evolution of public concern on air quality from 2013 to 2017. This study will help policy makers to better understand the patterns of the public's perception of environmental problems and consequently improve the government's capability to deal with these challenges.

Temperature and humidity affect the incidence of hand, foot, and mouth disease: a systematic review of the literature - a report from the International Society of Dermatology Climate Change Committee

Hand, foot, and mouth disease (HFMD) is an enterovirus-mediated condition that predominantly affects children under 5 years of age. The tendency for outbreaks to peak in warmer summer months suggests a relationship between HFMD and weather patterns. We reviewed the English-language literature for articles describing a relationship between meteorological variables and HFMD. Seventy-two studies meeting criteria were identified. A positive, statistically significant relationship was identified between HFMD cases and both temperature (61 of 67 studies, or 91.0%, reported a positive relationship) [CI 81.8-95.8%, P = 0.0001] and relative humidity (41 of 54 studies, or 75.9%) [CI 63.1-85.4%, P = 0.0001]. No significant relationship was identified between HFMD and precipitation, wind speed, and/or sunshine. Most countries reported a single peak of disease each year (most commonly early Summer), but subtropical and tropical climate zones were significantly more likely to experience a bimodal distribution of cases throughout the year (two peaks a year; most commonly late spring/early summer, with a smaller peak in autumn). The rising global incidence of HFMD, particularly in Pacific Asia, may be related to climate change. Weather forecasting might be used effectively in the future to indicate the risk of HFMD outbreaks and the need for targeted public health interventions.

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.