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

Investigation of the Correlation between Enterovirus Infection and the Climate Factor Complex Including the Ping-Year Factor and El Niño-Southern Oscillation in Taiwan

Enterovirus infection and enterovirus infection with severe complications (EVSC) are critical issues in several aspects. However, there is no suitable predictive tool for these infections. A climate factor complex (CFC) containing several climate factors could provide more effective predictions. The ping-year factor (PYF) and El Niño-Southern Oscillation (ENSO) are possible CFCs. This study aimed to determine the relationship between these two CFCs and the incidence of enterovirus infection. Children aged 15 years and younger with enterovirus infection and/or EVSC were enrolled between 2007 and 2022. Each year was categorized into a ping-year or non-ping-year according to the PYF. Poisson regression was used to evaluate the associations between the PYF, ENSO, and the incidence of enterovirus infection. Compared to the ping-year group, the incidence rate of enterovirus infection, the incidence rate of EVSC, and the ratio of EVSC in the non-ping-year group were 1.24, 3.38, and 2.73 times higher, respectively (p < 0.001). For every one-unit increase in La Niña, the incidence rate of enterovirus infection decreased to 0.96 times (p < 0.001). Our study indicated that CFCs could be potential predictors for enterovirus infection, and the PYF was more suitable than ENSO. Further research is needed to improve the predictive model.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study

Background

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China.

Methods

HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD.

Results

HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53).

Conclusion

Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.

Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study

BACKGROUND

Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted.

METHODS

Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives.

RESULTS

From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92).

CONCLUSIONS

There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China

BACKGROUND

This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.

METHOD

A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.

RESULTS

Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.

CONCLUSION

This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

Spatial correlated incidence modeling with zero inflation

Disease mapping models have been popularly used to model disease incidence with spatial correlation. In disease mapping models, zero inflation is an important issue, which often occurs in disease incidence datasets with high proportions of zero disease count. It is originated from limited survey coverage or unadvanced testing equipment, which makes some regions have no observed patients. Then excessive zeros recorded in the disease incidence dataset would mess up the true distributions of disease incidence and lead to inaccurate estimates. To address this issue, a zero-inflated disease mapping model is developed in this work. In this model, a zero-inflated process using Bernoulli indicators is assumed to characterize whether the zero inflation occurs for each region. For regions without zero inflation, a coherent and generative disease mapping model is applied for mapping the spatially correlated disease incidence. Independent spatial random effects are incorporated in both processes to account for the spatial patterns of zero inflation and disease incidence. External covariates are also considered in both processes to better explain the disease count data. To estimate the model, a Markov chain Monte Carlo algorithm is proposed. We evaluate model performance via a variety of simulation experiments. Finally, a Lyme disease dataset of Virginia is analyzed to illustrate the application of the proposed model.

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review

Background

The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.

Methods

We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.

Conclusions

Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07669-9.

Analysis of HFMD Transmissibility Among the Whole Population and Age Groups in a Large City of China

Background

Hand-Foot-and-Mouth-Disease (HFMD) has been widely spread in Asia, and has result in a high disease burden for children in many countries. However, the dissemination characteristics intergroup and between different age groups are still not clear. In this study, we aim to analyze the differences in the transmissibility of HFMD, in the whole population and among age groups in Shenzhen city, by utilizing mathematical models.

Methods

A database that reports HFMD cases in Shenzhen city from January 2010 to December 2017 was collected. In the first stage, a Susceptive-Infected-Recovered (SIR) model was built to fit data of Shenzhen city and its districts, and R_eff was used to assess transmissibility in each district. In the second stage, a cross-age groups SIR model was constructed to calculate the difference in transmissibility of reported cases among three age groups of EV71 virus: 0–3 years, 3–5 years, and over 5 years which was denoted as age group 1, 2, and 3, respectively.

Results

From 2010 to 2017, 345,807 cases of HFMD were reported in Shenzhen city, with peak incidence in spring and autumn in Shenzhen city and most of its districts each year. Analysis of the EV71 incidence data by age group revealed that age Group 1 have the highest incidence (3.13 ×10⁻⁷–2.31 ×10⁻⁴) while age group 3 had the lowest incidence (0–3.54 ×10⁻⁵). The differences in weekly incidence of EV71 between age groups were statistically significant (t₁₂ = 7.563, P < 0.0001; t₂₃ = 12.420, P < 0.0001; t₁₃ = 16.996, P < 0.0001). The R² of the SIR model Shenzhen city population-wide HFMD fit for each region was >0.5, and P < 0.001. R_eff values were >1 for the vast majority of time and regions, indicating that the HFMD virus has the ability to spread in Shenzhen city over the long-term. Differences in R_eff values between regions were judged by using analysis of variance (ANOVA) (F = 0.541, P = 0.744). S_iI_iR_i-S_jI_jR_j models between age groups had R² over 0.7 for all age groups and P <0.001. The R_eff values between groups show that the 0–2 years old group had the strongest transmissibility (median: 2.881, range: 0.017–9.897), followed by the over 5 years old group (median: 1.758, range: 1.005–5.279), while the 3–5 years old group (median: 1.300, range: 0.005–1.005) had the weakest transmissibility of the three groups. Intra-group transmissibility was strongest in the 0–2 years age group (median: 1.787, range: 0–9.146), followed by Group 1 to Group 2 (median: 0.287, range: 0–1.988) and finally Group 1 to Group 3 (median: 0.287, range: 0–1.988).

Conclusion

The incidence rate of HFMD is high in Shenzhen city. In the data on the incidence of EV71 in each age group, the highest incidence was in the 0–2 years age group, and the lowest incidence was in the over 5 years age group. The differences in weekly incidence rate of EV71 among age groups were statistically significant. Children with the age of 0–2 years had the highest transmissibility.

China's ecosystem service value in 1992-2018: Pattern and anthropogenic driving factors detection using Bayesian spatiotemporal hierarchy model

Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies.

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents, including malaria, cholera, dengue fever, hand, foot, and mouth disease (HFMD), and the recent Corona-virus pandemic. HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s. The outbreaks may result from a combination of rapid population growth, climate change, socioeconomic changes, and other lifestyle changes. However, the modeling of climate variability and HFMD remains unclear, particularly in statistical theory development. The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling. When dealing with time-series data with clustered variables such as HFMD with clustered states, the independence principle of both modeling approaches may be violated. Thus, a Generalized Additive Mixed Model (GAMM) is used to investigate the relationship between HFMD and climate factors in Malaysia. The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect. This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation. The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia. The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm. Besides, a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances. The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon. The study's outcomes can be used by public health officials and the general public to raise awareness, and thus, implement effective preventive measures.

Epidemiological analysis of cystic echinococcosis and alveolar echinococcosis in an extremely high prevalence region: Population-based survey and host animal monitoring in Shiqu County, China

Echinococcosis is the most common parasitic disease in the Tibetan Plateau, placing a large disease burden on the local population. Shiqu County, located in western Sichuan Tibetan region, had a particularly high prevalence rate of cystic echinococcosis (CE) and alveolar echinococcosis (AE) in previous surveys. From 2015 to 2018, a population-based field survey was conducted to explore the epidemic situation. Epidemiological features were examined through demographic analysis and mapping. In addition, the infection prevalence of Echinococcus in dogs was also investigated and mapped by spatial autoregression. A total of 84,768 people were screened by abdominal ultrasound inspection, and 2,341 CE and 3,930 AE cases were detected, with a corresponding prevalence of 2.76% and 4.64%, respectively. This made Shiqu County one of the most severe echinococcosis endemic regions in the world, especially with regard to AE. The cases were not evenly distributed among towns: particularly, the AE rates in the northwest towns were very high and closely related to the infection rate of neighboring host animals. Simultaneously, a comprehensive prevention project including patient treatment and host management was conducted and achieved preliminary success in source control. According to feces monitoring findings, the infection rate of dogs declined from 26.38% in early 2016 to 3.71% in 2018. Combined with host animal distribution data at the town level, the predicted risk ranks were categorized by risk index and mapped to guide further control operations.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Their Influencing Factors in Urumqi, China

Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang.

METHODS

HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors.

RESULTS

A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD.

CONCLUSIONS

HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China

Background: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. Methods: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). Results: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15–35 mm, and MAAP showed a positive correlation in the range of 925–945 hPa. HFMD incidence increased with MAT in the range of 15–23 °C, and the effective value of MAWS was in the range of 1.3–1.7 m/s, which was positively correlated with incidences of HFMD. Conclusions: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Spatial distribution of leptospirosis incidence in the Upper Yangtze and Pearl River Basin, China: Tools to support intervention and elimination

BACKGROUND

Since 2011 human leptospirosis incidence in China has remained steadily low with persistent pockets of notifications reported in communities within the Upper Yangtze River Basin (UYRB) and Pearl River Basin (PRB). To help guide health authorities within these residual areas to identify communities where interventions should be targeted, this study quantified the local effect of socioeconomic and environmental factors on the spatial distribution of leptospirosis incidence and developed predictive maps of leptospirosis incidence for UYRB and PRB.

METHODS

Data on all human leptospirosis cases reported during 2005-2016 across the UYRB and PRB regions were geolocated at the county-level and included in the analysis. Bayesian conditional autoregressive (CAR) models with zero-inflated Poisson link for leptospirosis incidence were developed after adjustment of environmental and socioeconomic factors such as precipitation, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), land surface temperature (LST), elevation, slope, land cover, crop production, livestock density, gross domestic product and population density.

RESULTS

The relationship of environmental and socioeconomic variables with human leptospirosis incidence varied between both regions. While across UYRB incidence of human leptospirosis was associated with MNDWI and elevation, in PRB human leptospirosis incidence was significantly associated with NDVI, livestock density and land cover. Precipitation was significantly and positively associated with the spatial variation of incidence of leptospirosis in both regions. After accounting for the effect of environmental and socioeconomic factors, the predicted distribution of residual high-incidence county is potentially more widespread both in the UYRB and PRB compared to the observed distribution. In the UYRB, the highest predicted incidence was found along the border of Chongqing and Guizhou towards Sichuan basin and northwest Yunnan. The highest predicted incidence was also identified in counties in the central and lower reaches of the PRB.

CONCLUSIONS

This study demonstrated significant geographical heterogeneity in leptospirosis incidence within UYRB and PRB, providing an evidence base for prioritising targeted interventions in counties identified with the highest predicted incidence. Furthermore, environmental drivers of leptospirosis incidence were highly specific to each of the regions, emphasizing the importance of localized control measures. The findings also suggested the need to expand interventional coverage and to support surveillance and diagnostic capacity on the predicted high-risk areas.

Spatial-temporal heterogeneity of hand, foot and mouth disease and impact of meteorological factors in arid/ semi-arid regions: a case study in Ningxia, China

Background

The incidence of hand, foot and mouth disease (HFMD) varies over space and time and this variability is related to climate and social-economic factors. Majority of studies on HFMD were carried out in humid regions while few have focused on the disease in arid/semi-arid regions, more research in such climates would potentially make the mechanism of HFMD transmission clearer under different climate conditions.

Methods

In this paper, we explore spatial-temporal distribution of HFMD in Ningxia province, which has an arid/semi-arid climate in northwest China. We first employed a Bayesian space-time hierarchy model (BSTHM) to assess the spatial-temporal heterogeneity of the HFMD cases and its relationship with meteorological factors in Ningxia from 2009 to 2013, then used a novel spatial statistical software package GeoDetector to test the spatial-temporal heterogeneity of HFMD risk.

Results

The results showed that the spatial relative risks in northern part of Ningxia were higher than those in the south. The highest temporal risk of HFMD incidence was in fall season, with a secondary peak in spring. Meteorological factors, such as average temperature, relative humidity, and wind speed played significant roles in the spatial-temporal distribution of HFMD risk.

Conclusions

The study provide valuable information on HFMD distribution in arid/semi-arid areas in northwest China and facilitate understanding of the concentration of HFMD.

Spatio-Temporal Analysis of Infectious Diseases

Epidemiological research on the pathogenesis, diagnosis, and treatment of infectious diseases is a broad field of study with renewed validity in the face of social changes and new threats [...].

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.