Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Scaling features in high-concentrations PM2.5 evolution: the Ignored factor affecting scarlet fever incidence

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM2.5 (HC-PM2.5) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM2.5. It has been found that index D can quantify the time distribution of HC-PM2.5, and lower D values indicate more cluster distribution of HC-PM2.5. Moreover, scale-invariance in HC-PM2.5 at different time scales has been discovered, which indicates that HC-PM2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM2.5. This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM2.5 evolution.

Epidemiological characteristics and spatiotemporal clustering of scarlet fever in Liaoning Province, China, 2010-2019

BACKGROUND

To explore the epidemiological characteristics and spatiotemporal distribution of scarlet fever in Liaoning Province, which could provide scientific evidence for the formulation and improvement of prevention and control strategies and measures.

METHODS

Data on scarlet fever cases and population were obtained from the China Information System for Disease Control and Prevention in Liaoning Province between 2010 and 2019. We examined the spatial and spatiotemporal clusters of scarlet fever across Liaoning Province using the Moran's I, local indicators of spatial association, local Gi* hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis.

RESULTS

Between 1st January 2010 and 31st December 2019, 46,652 cases of scarlet fever were reported in Liaoning Province, with an annual average incidence of 10.67 per 100,000. The incidence of scarlet fever had obvious seasonality with high incidence in early summer June and early winter December. The male-to-female ratio was 1.53:1. The highest incidence of cases occurred in 3-9 year old children. The most likely spatiotemporal cluster and the secondary clusters were detected in urban regions of Shenyang and Dalian, Liaoning Province.

CONCLUSIONS

The incidence of scarlet fever has obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in urban area of Shenyang and Dalian, Liaoning Province. Control strategies need to focus on high-risk season, high-risk areas and high-risk populations in order to reduce the incidence of scarlet fever.

Climate and human health: a review of publication trends in the International Journal of Biometeorology

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020-March 2022

Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days).

Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R²) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)₁₂. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

Association of children wheezing diseases with meteorological and environmental factors in Suzhou, China

Wheezing diseases are one of the major chronic respiratory diseases in children. To explore the effects of meteorological and environmental factors on the prevalence of children wheezing diseases, clinical data of children hospitalized with wheezing diseases in Suzhou, China from 2013 to 2017 were collected. Meteorological and environmental factors from 2013 to 2017 were obtained from the local Meteorological Bureau and Environmental Protection Bureau. Relationships between wheezing diseases and meteorological and environmental factors were evaluated using Pearson's correlation and multivariate regression analysis. An autoregressive integrated moving average (ARIMA) model was used to estimate the effects of meteorological and environmental variables on children wheezing diseases. Children wheezing diseases were frequently presented in infants less than 12 months old (1897/2655, 58.28%), and the hospitalization rate was highest in winter (1024/3255, 31.46%). In pathogen-positive specimens, the top three pathogens were respiratory syncytial virus (21.35%), human rhinovirus (16.28%) and mycoplasma pneumoniae (10.47%). The seasonality of wheezing children number showed a distinctive winter peak. Children wheezing diseases were negatively correlated with average temperature (P < 0.001, r =  - 0.598). The ARIMA (1,0,0)(0,0,0)₁₂ model could be used to predict temperature changes associated wheezing diseases. Meteorological and environmental factors were associated with the number of hospitalized children with wheezing diseases and can be used as early warning indicators for the occurrence of wheezing diseases and prevalence of virus.

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Background

We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014–2018.

Methods

A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders.

Results

A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038–1.323 in mild air pollution; 1.374, 95% CI 1.078–1.749 in moderate air pollution; 1.610, 95% CI 1.163–2.314 in severe air pollution; 1.887, 95% CI 1.163–3.061 in most severe air pollution].

Conclusions

Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06674-8.

Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017

OBJECTIVE

To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease.

METHODS

The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord G_i^⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence.

RESULTS

Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively.

CONCLUSION

The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.

Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

A review of the impact of outdoor and indoor environmental factors on human health in China

The Intergovernmental Panel on Climate Change (IPCC) reported that global climate change has led to the increased occurrence of extreme weather events. In the context of global climate change, more evidence indicates that abnormal meteorological conditions could increase the risk of epidemiological mortality and morbidity. In this study, using a systematic review, we evaluated a total of 175 studies (including 158 studies on outdoor environment and 17 studies on indoor environment) to summarize the impact of outdoor and indoor environment on human health in China using the database of PubMed, Web of Science, the Cochrane Library, and Embase. In particular, we focused on studies about cardiovascular and respiratory mortality and morbidity, the prevalence of digestive system diseases, infectious diseases, and preterm birth. Most of the studies we reviewed were conducted in three of the metropolises of China, including Beijing, Guangzhou, and Shanghai. For the outdoor environment, we summarized the effects of climate change-related phenomena on health, including ambient air temperature, diurnal temperature range (DTR), temperature extremes, and so on. Studies on the associations between temperature and human health accounted for 79.7% of the total studies reviewed. We also screened out 19 articles to explore the effect of air temperature on cardiovascular diseases in different cities in the final meta-analysis. Besides, modern lifestyle involves a large amount of time spent indoors; therefore, indoor environment also plays an important role in human health. Nevertheless, studies on the impact of indoor environment on human health are rarely reported in China. According to the limited reports, adverse indoor environment could impose a high health risk on children.

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018

Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004-2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746-6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960-6.330%), 19.496% (95% CI 2.368-39.490%), and 3.812 (95% CI 1.243-11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

Exposure to air pollution and scarlet fever resurgence in China: a six-year surveillance study

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011-2018 was twice that in 2004-2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29-2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO₂ (r = 0.21) and O₃ (r = 0.11). A 10 μg/m³ increase of NO₂ and O₃ was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02-1.10) and 1.04 (95% CI: 1.01-1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO₂ and O₃ may be associated with an increased risk of scarlet fever incidence, but direct causality is not established.

Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models

Objective

The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS.

Methods

Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period.

Results

Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)₁₂ model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS.

Conclusion

This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.

Increase of emm1 isolates among group A Streptococcus strains causing scarlet fever in Shanghai, China

OBJECTIVE

Scarlet fever epidemics caused by group A Streptococcus (GAS) have been ongoing in China since 2011. However, limited data are available on the dynamic molecular characterizations of the epidemic strains.

METHOD

Epidemiological data of scarlet fever in Shanghai were obtained from the National Notifiable Infectious Disease Surveillance System. Throat swabs of patients with scarlet fever and asymptomatic school-age children were cultured. Illumina sequencing was performed on 39emm1 isolates.

RESULTS

The annual incidence of scarlet fever was 7.5-19.4/100,000 persons in Shanghai during 2011-2015, with an average GAS carriage rate being 7.6% in school-age children. The proportion ofemm1 GAS strains increased from 3.8% in 2011 to 48.6% in 2014; they harbored a superantigen profile similar to emm12 isolates, except for the speA gene. Two predominant clones, SH001-emm12, and SH002-emm1, circulated in 66.9% of scarlet fever cases and 44.8% of carriers. Genomic analysis showed emm1 isolates throughout China constituted distinct clades, enriched by the presence of mobile genetic elements carrying the multidrug-resistant determinants ermB and tetM and virulence genes speA, speC, and spd1.

CONCLUSION

A significant increase in the proportion ofemm1 strains occurred in the GAS population, causing scarlet fever in China. Ongoing surveillance is warranted to monitor the dynamic changes of GAS clones.

Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017

This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.

An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

Purpose

Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR).

Methods

We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate .

Results

During 2004–2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)₁₂, NNNAR(7,1,4)₁₂ and SARIMA(2,0,2)(1,1,0)₁₂-NNNAR(3,1,7)₁₂, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results.

Conclusion

The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

Relationship of meteorological factors and human brucellosis in Hebei province, China

BACKGROUND

Brucellosis has always been one of the major public health problems in China. Investigating the influencing factors of brucellosis is conducive to its prevention and control. The incidence trend of brucellosis shows an obvious seasonality, suggesting that there may be a correlation between brucellosis and meteorological factors, but related studies were few. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological factors and brucellosis.

METHODS

The data of monthly incidence of brucellosis and meteorological factors in Hebei province from January 2004 to December 2015 were collected from the Chinese Public Health Science Data Center and Chinese meteorological data website. An ARIMA model incorporated with covariables was conducted to estimate the effects of meteorological variables on brucellosis.

RESULTS

There was a highest peak from May to July every year and an upward trend during the study period. Atmospheric pressure, wind speed, mean temperature, and relative humidity had significant effects on brucellosis. The ARIMA(1,0,0)(1,1,0)₁₂ model with the covariates of atmospheric pressure, wind speed and mean temperature was the optimal model. The results showed that the atmospheric pressure with a 2-month lag (β = -0.004, p = 0.037), the wind speed with a 1-month lag (β = 0.030, p = 0.035), and the mean temperature with a 2-month lag (β = -0.003, p = 0.034) were significant predictors.

CONCLUSION

Our study suggests that atmospheric pressure, wind speed, mean temperature, and relative humidity have a significant impact on brucellosis. Further understanding of its mechanism would help facilitate the monitoring and early warning of brucellosis.

A Bayesian Spatiotemporal Analysis of Pediatric Group A Streptococcal Infections

Background

Pharyngitis due to group A Streptococcus (GAS) is a common pediatric infection. Physicians might diagnose GAS pharyngitis more accurately when given biosurveillance information about GAS activity. The availability of geographic GAS testing data may be able to assist with real-time clinical decision-making for children with throat infections.

Methods

GAS rapid antigen testing data were obtained from the records of 6086 children at Boston Children's Hospital and 8648 children at Duke University Medical Center. Records included children tested in outpatient, primary care settings. We constructed Bayesian generalized additive models, in which the outcome variable was the binary result of GAS testing, and predictor variables included smoothed functions of patient location data and both cyclic and longitudinal time data.

Results

We observed a small degree of geographic heterogeneity, but no convincing clusters of high risk. The probability of a positive test declined during the summer months.

Conclusions

Future work should include geographic data about school catchments to identify whether GAS transmission clusters within schools.

Analysis of Epidemiological Characteristics of Scarlet Fever in Zhejiang Province, China, 2004-2018

Objective: The aim of this study was to analyze the trends and epidemiological characteristics of scarlet fever in Zhejiang Province in 2004–2018, intending to provide a basis for targeted prevention and control of this disease. Method: We collated the epidemiological data for cases of scarlet fever from the China Information System for Disease Control and Prevention (CISDCP) in Zhejiang province between 1 January 2004 and 31 December 2018. Descriptive statistical analysis was used to analyze epidemiological characteristics of scarlet fever, whereas the Getis-Ord Gi* statistic was used to determine the hotspot incidence of scarlet fever. Results: In 2004–2018, a total of 22,194 cases of scarlet fever were reported in Zhejiang Province, with no death reports. The annual average of scarlet fever incidence was 2.82/100,000 (range,1.12 to 6.34/100,000). The male incidence was higher than that among female (χ² = 999.834, p < 0.05), and a majority of the cases (86.42%) occurred in children aged 3–9 years. Each year, the incidence of scarlet fever in Zhejiang Province appeared two seasonal peaks: the first peak occurred from March to June (the constituent ratio was 49.06%), the second peak was lower than the first one during November and the following January (the constituent ratio was 28.67%). The two peaks were almost in accordance with the school spring semester and autumn–winter semester, respectively. The incidence in the northern regions of the province was generally higher than that in the southern regions. High-value clusters were detected in the central and northern regions, while low-value clusters occurred in the southern regions via the Getis-Ord Gi* statistical analysis. Conclusions: The prevalence of scarlet fever in Zhejiang Province showed a marked seasonality variation and mainly clustered in the central and northern regions in 2004–2018. Children under 15 years of age were most susceptible to scarlet fever. Kindergartens and primary schools should be the focus of prevention and control, and targeted strategies and measures should be taken to reduce the incidence.

Environmental Determinants of Hemorrhagic Fever with Renal Syndrome in High-Risk Counties in China: A Time Series Analysis (2002-2012)

The transmission pattern of hemorrhagic fever with renal syndrome (HFRS) is associated with environmental conditions, including meteorological factors and land-cover. In the present study, the association between HFRS and environmental factors (including maximum temperature, relative humidity, rainfall, and normalized difference vegetation index) were explored in two typical counties in Northeast and two counties in Northwest China with severe HFRS outbreaks by using seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). The results showed that rainfall with 3- to 4-month lag was closely associated with HFRS in the two counties in Northeast China, whereas relative humidity with 1- or 5-month lag significantly impacts HFRS transmission in the two counties in Northwest China. Moreover, the SARIMAX models exhibit accurate forecasting ability of HFRS cases. Our findings provide scientific support for local HFRS monitoring and control, and the development of a HFRS early warning system.

Time series analysis of mumps and meteorological factors in Beijing, China

Background

Over the past decades there have been outbreaks of mumps in many countries, even in populations that were vaccinated. Some studies suggest that the incidence of mumps is related to meteorological changes, but the results of these studies vary in different regions. To date there is no reported study on correlations between mumps incidence and meteorological parameters in Beijing, China.

Methods

A time series analysis incorporating selected weather factors and the number of mumps cases from 1990 to 2012 in Beijing was performed. First, correlations between meteorological variables and the number of mumps cases were assessed. A seasonal autoregressive integrated moving average model with explanatory variables (SARIMAX) was then constructed to predict mumps cases.

Results

Mean temperature, rainfall, relative humidity, vapor pressure, and wind speed were significantly associated with mumps incidence. After constructing the SARIMAX model, mean temperature at lag 0 (β = 0.016, p < 0.05, 95% confidence interval 0.001 to 0.032) was positively associated with mumps incidence, while vapor pressure at lag 2 (β = ˗0.018, p < 0.05, 95% confidence interval ˗0.038 to ˗0.002) was negatively associated. SARIMAX (1, 1, 1) (0, 1, 1)₁₂ with temperature at lag 0 was the best predictive construct.

Conclusions

The incidence of mumps in Beijing from 1990 to 2012 was significantly correlated with meteorological variables. Combining meteorological variables, a predictive SARIMAX model that could be used to preemptively estimate the incidence of mumps in Beijing was established.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4011-6) contains supplementary material, which is available to authorized users.

Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017

OBJECTIVE

To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures.

METHODS

Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou.

RESULTS

We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 °C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity.

CONCLUSION

Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rainfall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future.

Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

A resurgence of scarlet fever has caused many pediatric infections in East Asia and the United Kingdom. Although scarlet fever in Taiwan has not been a notifiable infectious disease since 2007, the comprehensive national health insurance data can still track its trend. Here, we used data from the open data portal of the Taiwan Centers for Disease Control. The scarlet fever trend was measured by outpatient and hospitalization rates from 2009 to 2017. In order to elucidate the spatio-temporal hotspots, we developed a new method named the spatio-temporal Gi* statistic, and applied Joinpoint regression to compute the annual percentage change (APC). The overall APCs in outpatient and hospitalization were 15.1% (95% CI: 10.3%-20.2%) and 7.7% (95%CI: 4.5% -10.9%). The major two infected groups were children aged 5-9 (outpatient: 0.138 scarlet fever diagnoses per 1,000 visits; inpatient: 2.579 per 1,000 visits) and aged 3-4 (outpatient: 0.084 per 1,000 visits; inpatient: 1.469 per 1,000 visits). We found the counties in eastern Taiwan and offshore counties had the most hotspots in the outpatient setting. In terms of hospitalization, the hotspots mostly occurred in offshore counties close to China. With the help of the spatio-temporal statistic, health workers can set up enhanced laboratory surveillance in those hotspots.

Impact of heat on mortality and morbidity in low and middle income countries: A review of the epidemiological evidence and considerations for future research

Heat waves and high air temperature are associated with increased morbidity and mortality. However, the majority of research conducted on this topic is focused on high income areas of the world. Although heat waves have the most severe impacts on vulnerable populations, relatively few studies have studied their impacts in low and middle income countries (LMICs). The aim of this paper is to review the existing evidence in the literature on the impact of heat on human health in LMICs. We identified peer-reviewed epidemiologic studies published in English between January 1980 and August 2018 investigating potential associations between high ambient temperature or heat waves and mortality or morbidity. We selected studies according to the following criteria: quantitative studies that used primary and/or secondary data and report effect estimates where ambient temperature or heat waves are the main exposure of interest in relation to human morbidity or mortality within LMICs. Of the total 146 studies selected, eighty-two were conducted in China, nine in other countries of East Asia and the Pacific, twelve in South Asia, ten in Sub-Saharan Africa, eight in the Middle East and North Africa, and seven in each of Latin America and Europe. The majority of studies (92.9%) found positive associations between heat and human morbidity/mortality. Additionally, while outcome variables and study design differed greatly, most utilized a time-series study design and examined overall heath related morbidity/mortality impacts in an entire population, although it is notable that the selected studies generally found that the elderly, women, and individuals within the low socioeconomic brackets were the most vulnerable to the effects of high temperature. By highlighting the existing evidence on the impact of extreme heat on health in LMICs, we hope to determine data needs and help direct future studies in addressing this knowledge gap. The focus on LMICs is justified by the lack of studies and data studying the health burden of higher temperatures in these regions even though LMICs have a lower capacity to adapt to high temperatures and thus an increased risk.

Hopf bifurcation of a delay SIRS epidemic model with novel nonlinear incidence: Application to scarlet fever

An [Formula: see text] epidemic model incorporating incubation time delay and novel nonlinear incidence is proposed and analyzed to seek for the control strategies of scarlet fever, where the contact rate which can reflect the regular behavior and habit changes of children is non-monotonic with respect to the number of susceptible. The model without delay may exhibit backward bifurcation and bistable states even though the basic reproduction number is less than unit. Furthermore, we derive the conditions for occurrence of Hopf bifurcation when the time delay is considered as a bifurcation parameter. The data of scarlet fever of China are simulated to verify our theoretical results. In the end, several effective preventive and intervention measures of scarlet fever are found out.

The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015

The influence of meteorological determinants on tuberculosis (TB) incidence remains severely under-discussed, especially through the perspective of time series analysis. In the current study, we used a distributed lag nonlinear model (DLNM) to analyze a 10-year series of consecutive surveillance data. We found that, after effectively controlling for autocorrelation, the changes in meteorological factors related to temperature, humidity, wind and sunshine were significantly associated with subsequent fluctuations in TB incidence: average temperature was inversely associated with TB incidence at a lag period of 2 months; total precipitation and minimum relative humidity were also inversely associated with TB incidence at lag periods of 3 and 4 months, respectively; average wind velocity and total sunshine hours exhibited an instant rather than lagged influence on TB incidence. Our study results suggest that preceding meteorological factors may have a noticeable effect on future TB incidence; informed prevention and preparedness measures for TB can therefore be constructed on the basis of meteorological variations.

Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012)

Objectives

Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model.

Methods

A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970–2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE).

Results

Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004–0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009–0.037) displayed the highest prediction accuracy.

Conclusions

The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity.

Effects of meteorological factors on incidence of scarlet fever during different periods in different districts of China

OBJECTIVE

To reveal the difference of meteorological effect on scarlet fever in Beijing and Hong Kong, China, during different periods among 2004-2014.

METHODS

The data of monthly incidence of scarlet fever and meteorological variables from 2004 to 2014 in Beijing and Hong Kong were collected from Chinese science data center of public health, meteorological data website and Hong Kong observatory website. The whole study period was separated into two periods by the outbreak year 2011 (Jan 2004-Dec 2010 and Jan 2011-Dec 2014). A generalized additive Poisson model was conducted to estimate the effect of meteorological variables on monthly incidence of scarlet fever during two periods in Beijing and Hong Kong, China.

RESULTS

Incidence of scarlet fever in two districts were compared and found the average incidence during period of 2004-2010 were significantly different (Z=203.973, P<0.001) while average incidence became generally equal during 2011-2014 (Z=2.125, P>0.05). There was also significant difference in meteorological variables between Beijing and Hong Kong during whole study period, except air pressure (Z=0.165, P=0.869). After fitting GAM model, it could be found monthly mean temperature showed a negative effect (RR=0.962, 95%CI: 0.933, 0.992) on scarlet fever in Hong Kong during the period of 2004-2010. By comparison, for data in Beijing during the period of 2011-2014, the RRs of monthly mean temperature range growing 1°C and monthly sunshine duration growing 1h was equal to 1.196(1.022, 1.399) and 1.006(1.001, 1.012), respectively. The changes of meteorological effect on scarlet fever over time were not significant both in Beijing and Hong Kong.

CONCLUSION

This study suggests that meteorological variables were important factors for incidence of scarlet fever during different period in Beijing and Hong Kong. It also support that some meteorological effects were opposite in different period although these differences might not completely statistically significant.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.