The scrub typhus in mainland China: spatiotemporal expansion and risk prediction underpinned by complex factors

Associations of meteorological factors and dynamics of scrub typhus incidence in South Korea: A nationwide time-series study

Scrub typhus, also known as Tsutsugamushi disease, is a climate-sensitive vector-borne disease that poses a growing public health threat. However, studies on the association between scrub typhus epidemics and meteorological factors in South Korea need to be complemented. Therefore, we aimed to analyze the association among ambient temperature, precipitation, and the incidence of scrub typhus in South Korea. First, we obtained data on the weekly number of scrub typhus cases and concurrent meteorological variables at the city-county level (Si-Gun) in South Korea between 2001 and 2019. Subsequently, a two-stage meta-regression analysis was conducted. In the first stage, we conducted time-series regression analyses using a distributed lag nonlinear model (DLNM) to investigate the association between temperature, precipitation, and scrub typhus incidence at each location. In the second stage, we employed a multivariate meta-regression model to combine the association estimates from all municipalities, considering regional indicators, such as mite species distribution, Normalized Difference Vegetation Index (NDVI), and urban-rural classification. Weekly mean temperature and weekly total precipitation exhibited a reversed U-shaped nonlinear association with the incidence of scrub typhus. The overall cumulative association with scrub typhus incidence peaked at 18.7 C° (with RRs of 9.73, 95% CI: 5.54-17.10) of ambient temperature (reference 9.7 C°) and 162.0 mm (with RRs of 1.87, 95% CI: 1.02-3.83) of precipitation (reference 2.8 mm), respectively. These findings suggest that meteorological factors contribute to scrub typhus epidemics by interacting with vectors, reservoir hosts, and human behaviors. This information serves as a reference for future public health policies and epidemiological research aimed at controlling scrub typhus infections.

Changing epidemiologic patterns of typhus group rickettsiosis and scrub typhus in China, 1950-2022

OBJECTIVES

We conducted a systematic analysis of the notifiable rickettsial diseases in humans in China during 1950-2022.

METHODS

We utilized descriptive statistics to analyze the epidemiological characteristics, clinical manifestations, and diagnostic characteristics of typhus group rickettsiosis (TGR) and scrub typhus (ST) cases.

RESULTS

Since the 1950s, there have been variations in the incidence rate of TGR and ST in China, with a downtrend for TGR and an uptrend for ST. The South became a high-incidence area of TGR, whereas the North was previously the high-incidence area. ST cases were concentrated in the South and the geographic area of ST spread northward and westward. The seasonality of TGR and ST were similar in the South but distinct in the North. Most TGR and ST cases were reported by county-level medical institutions, whereas primary institutions reported the least. Delayed diagnosis was associated with fatal outcomes of TGR and ST. Cases in low-incidence provinces, confirmed by laboratory tests and reported from county/municipal-level institutions had higher odds of delayed diagnoses.

CONCLUSIONS

Our study revealed significant changes in the epidemiological characteristics of TGR and ST in China, which can provide useful information to enhance the control and prevention strategies of rickettsial diseases in China.

Spatiotemporal heterogeneity and long-term impact of meteorological, environmental, and socio-economic factors on scrub typhus in China from 2006 to 2018

BACKGROUND

Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control.

METHODS

This study utilized a Bayesian space-time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions.

RESULTS

Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882.

CONCLUSIONS

These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus.

Spatiotemporal epidemiology and risk factors of scrub typhus in Hainan Province, China, 2011-2020

Background

The re-emergence of scrub typhus in the southern provinces of China in recent decades has been validated, thereby attracting the attention of public health authorities. There has been a spatial and temporal expansion of scrub typhus in Hainan Province, but the epidemiological characteristics, environmental drivers, and potential high-risk areas for scrub typhus have not yet been investigated.

Objective

The aims of this study were to characterize the spatiotemporal epidemiology of scrub typhus, identify dominant environmental risk factors, and map potential risk areas in Hainan Province from 2011 to 2020.

Methods

The spatiotemporal dynamics of scrub typhus in Hainan Province between 2011 and 2020 were analyzed using spatial analyses and seasonal-trend decomposition using regression (STR). The maximum entropy (MaxEnt) model was applied to determine the key environmental predictors and environmentally suitable areas for scrub typhus, and the demographic diversity of the predicted suitable zones was evaluated.

Results

During 2011-2020, 3260 scrub typhus cases were recorded in Hainan Province. The number of scrub typhus cases increased continuously each year, particularly among farmers (67.61%) and individuals aged 50-59 years (23.25%) who were identified as high-risk groups. A dual epidemic peak was detected, emerging annually from April to June and from July to October. The MaxEnt-based risk map illustrated that highly suitable areas, accounting for 25.36% of the total area, were mainly distributed in the northeastern part of Hainan Province, where 75.43% of the total population lived. Jackknife tests revealed that ground surface temperature, elevation, cumulative precipitation, evaporation, land cover, population density, and ratio of dependents were the most significant environmental factors.

Conclusion

In this study, we gained insights into the spatiotemporal epidemiological dynamics, pivotal environmental drivers, and potential risk map of scrub typhus in Hainan Province. These results have important implications for researchers and public health officials in guiding future prevention and control strategies for scrub typhus.

Potential distribution of Leptotrombidium scutellare in Yunnan and Sichuan Provinces, China, and its association with mite-borne disease transmission

BACKGROUND

Leptotrombidium scutellare is one of the six main vectors of scrub typhus in China and is a putative vector of hemorrhagic fever with renal syndrome (HFRS). This mite constitutes a large portion of the chigger mite community in southwest China. Although empirical data on its distribution are available for several investigated sites, knowledge of the species' association with human well-being and involvement in the prevalence of mite-borne diseases remains scarce.

METHODS

Occurrence data on the chigger mite were obtained from 21 years (2001-2021) of field sampling. Using boosted regression tree (BRT) ecological models based on climate, land cover and elevation variables, we predicted the environmental suitability for L. scutellare in Yunnan and Sichuan Provinces. The potential distribution range and shifts in the study area for near-current and future scenarios were mapped and the scale of L. scutellare interacting with human activities was evaluated. We tested the explanatory power of the occurrence probability of L. scutellare on incidences of mite-borne diseases.

RESULTS

Elevation and climate factors were the most important factors contributing to the prediction of the occurrence pattern of L. scutellare. The most suitable habitats for this mite species were mainly concentrated around high-elevation areas, with predictions for the future showing a trend towards a reduction. Human activity was negatively correlated with the environmental suitability of L. scutellare. The occurrence probability of L. scutellare in Yunnan Province had a strong explanatory power on the epidemic pattern of HFRS but not scrub typhus.

CONCLUSIONS

Our results emphasize the exposure risks introduced by L. scutellare in the high-elevation areas of southwest China. Climate change may lead to a range contraction of this species towards areas of higher elevation and lessen the associated exposure risk. A comprehensive understanding of the transmission risk requires more surveillance efforts.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China

BACKGROUND

Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.

METHODS

A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.

RESULTS

The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.

CONCLUSIONS

These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Impacts of meteorological factors on the risk of scrub typhus in China, from 2006 to 2020: A multicenter retrospective study

Scrub typhus is emerging as a global public health threat owing to its increased prevalence and remarkable geographic expansion. However, it remains a neglected disease, and possible influences of meteorological factors on its risk are poorly understood. We conducted the largest-scale research to assess the impact of meteorological factors on scrub typhus in China. Weekly data on scrub typhus cases and meteorological factors were collected across 59 prefecture-level administrative regions from 2006 to 2020. First, we divided these regions into 3 regions and analyzed the epidemiological characteristics of scrub typhus. We then applied the distributed lag nonlinear model, combined with multivariate meta-analysis, to examine the associations between meteorological factors and scrub typhus incidence at the total and regional levels. Subsequently, we identified the critical meteorological predictors of scrub typhus incidence and extracted climate risk windows. We observed distinct epidemiological characteristics across regions, featuring obvious clustering in the East and Southwest with more even distribution and longer epidemic duration in the South. The mean temperature and relative humidity had profound effects on scrub typhus with initial-elevated-descendent patterns. Weather conditions of weekly mean temperatures of 25-33°C and weekly relative humidity of 60-95% were risk windows for scrub typhus. Additionally, the heavy rainfall was associated with sharp increase in scrub typhus incidence. We identified specific climatic signals to detect the epidemic of scrub typhus, which were easily monitored to generalize. Regional heterogeneity should be considered for targeted monitoring and disease control strategies.

Climate drives the spatiotemporal dynamics of scrub typhus in China

Scrub typhus is a climate-sensitive and life-threatening vector-borne disease that poses a growing public health threat. Although the climate-epidemic associations of many vector-borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming. Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2-month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change.

How meteorological factors impacting on scrub typhus incidences in the main epidemic areas of 10 provinces, China, 2006-2018

Scrub typhus, caused by Orientia tsutsugamushi, is a serious public health problem in the Asia-Pacific region, threatening the health of more than one billion people. China is one of the countries with the most serious disease burden of scrub typhus. Previous epidemiological evidence indicated that meteorological factors may affect the incidence of scrub typhus, but there was limited evidence for the correlation between local natural environment factors dominated by meteorological factors and scrub typhus. This study aimed to evaluate the correlation between monthly scrub typhus incidence and meteorological factors in areas with high scrub typhus prevalence using a distributed lag non-linear model (DLNM). The monthly data on scrub typhus cases in ten provinces from 2006 to 2018 and meteorological parameters were obtained from the Public Health Science Data Center and the National Meteorological Data Sharing Center. The results of the single-variable and multiple-variable models showed a non-linear relationship between incidence and meteorological factors of mean temperature (Tmean), rainfall (RF), sunshine hours (SH), and relative humidity (RH). Taking the median of meteorological factors as the reference value, the relative risks (RRs) of monthly Tmean at 0°C, RH at 46%, and RF at 800 mm were most significant, with RRs of 2.28 (95% CI: 0.95-5.43), 1.71 (95% CI: 1.39-2.09), and 3.33 (95% CI: 1.89-5.86). In conclusion, relatively high temperature, high humidity, and favorable rainfall were associated with an increased risk of scrub typhus.

Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis

Background

Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health.

Methods

We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012-2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST.

Results

Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods.

Conclusion

The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.

Epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province during 2012–2020

Background

Scrub typhus has become a serious public health concern in the Asia-Pacific region including China. There were new natural foci continuously recognized and dramatically increased reported cases in mainland China. However, the epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province have yet to be investigated.

Objective

This study proposes to explore demographic characteristics and spatiotemporal dynamics of scrub typhus cases in Fujian province, and to detect high-risk regions between January 2012 and December 2020 at county/district scale and thereby help in devising public health strategies to improve scrub typhus prevention and control measures.

Method

Monthly cases of scrub typhus reported at the county level in Fujian province during 2012–2020 were collected from the National Notifiable Disease Surveillance System. Time-series analyses, spatial autocorrelation analyses and space-time scan statistics were applied to identify and visualize the spatiotemporal patterns of scrub typhus cases in Fujian province. The demographic differences of scrub typhus cases from high-risk and low-risk counties in Fujian province were also compared.

Results

A total of 11,859 scrub typhus cases reported in 87 counties from Fujian province were analyzed and the incidence showed an increasing trend from 2012 (2.31 per 100,000) to 2020 (3.20 per 100,000) with a peak in 2018 (4.59 per 100,000). There existed two seasonal peaks in June-July and September-October every year in Fujian province. A significant positive spatial autocorrelation of scrub typhus incidence in Fujian province was observed with Moran’s I values ranging from 0.258 to 0.471 (P<0.001). Several distinct spatiotemporal clusters mainly concentrated in north and southern parts of Fujian province. Compared to low-risk regions, a greater proportion of cases were female, farmer, and older residents in high-risk counties.

Conclusions

These results demonstrate a clear spatiotemporal heterogeneity of scrub typhus cases in Fujian province, and provide the evidence in directing future researches on risk factors and effectively assist local health authorities in the refinement of public health interventions against scrub typhus transmission in the high risk regions.

Scrub typhus is a vector-borne zoonotic disease caused by Orientia tsutsugamushi and is popular in the Asia-Pacific area. Nowadays scrub typhus has been recognized as a considerable burden on public health in Fujian province. We explored the epidemiological characteristics, spatiotemporal patterns and diffusion characteristics of scrub typhus, and detected high-risk regions at the county level in Fujian province between January 2012 and December 2020. Our results indicated that the majority of cases were reported in June-July and September-October and that that middle aged and elderly people were more prone to infection every year in Fujian province. The spatial autocorrelation analysis revealed clustering in geographic distribution of cases and several distinct spatiotemporal clusters were identified in north and southern parts of Fujian province. Compared with cases from low-risk areas, a higher proportion of cases were female, farmer, and older residents in high-risk counties.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021

Background

Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government.

Method

In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis.

Results

We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter.

Conclusion

Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China. It utilizes long-term surveillance data on HB during 2008–2021 and investigation data during 2014–2021. A total of 1989 HB cases were reported in Xi'an, consisting of 505 local cases, i.e., those located in Xi'an and 1,484 non-local cases, i.e., those located in other cities. Significantly epidemiological heterogeneity was observed between them, mainly owing to differences in the gender, occupation, diagnostic delays, and reporting institutions. Serological investigations suggested that 59 people and 1,822 animals (sheep, cattle, and cows) tested positive for brucellosis from 2014 to 2021, with the annual average seroprevalence rates were 1.38 and 1.54%, respectively. The annual animal seroprevalence rate was positively correlated with the annual incidence of non-local HB cases. Multivariate boosted regression tree models revealed that gross domestic product, population density, length of township roads, number of farms, and nighttime lights substantially contributed to the spatial distribution of local HB. Approximately 7.84 million people inhabited the potential infection risk zones in Xi'an. Our study highlights the reemergence of HB in non-epidemic areas and provides a baseline for large and medium-sized cities to identify regions, where prevention and control efforts should be prioritized in the future.

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease

Background

Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.

Methodology/principal findings

We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty.

Conclusions/significance

The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.

Metrics such as the per susceptible rate of infection acquisition (Force-of-Infection) are crucial to understand the current epidemiological situation and the impact of control interventions for long-lasting diseases in which the infection event might have occurred many years previously, such as Chagas disease. FoI values are estimated from serological age profiles, often obtained in a few locations. However, when using predictive models to estimate the FoI over time and space (including areas where serosurveys had not been conducted), methods able to handle and propagate uncertainty must be implemented; otherwise, overconfident predictions may be obtained. Although Machine Learning (ML) methods are powerful tools, they may not be able to entirely handle this challenge. Therefore, the use of ML must be considered in relation to the aims of the analyses. ML will be more relevant to characterise the central trends of the estimates while Linear Models will help identify areas where further serosurveys should be conducted to improve the reliability of the predictions. Our approaches can be used to generate FoI predictions in other Chagas disease-endemic countries as well as in other diseases for which serological surveillance data are collected.

Mapping the distributions of blood-sucking mites and mite-borne agents in China: a modeling study

Background

Emerging mite-borne pathogens and associated disease burdens in recent decades are raising serious public health concerns, yet their distributions and ecology remain under-investigated. We aim to describe the geographical distributions of blood-sucking mites and mite-borne agents and to assess their ecological niches in China.

Methods

We mapped 549 species of blood-sucking mites belonging to 100 genera at the county level and eight mite-associated agents detected from 36 species of blood-sucking mites in China during 1978–2020. Impacts of climatic and environmental factors on the ecology of 21 predominant vector mites and a leading pathogen, Orientia tsutsugamushi, were assessed using boosted regression tree (BRT) models, and model-predicted risks were mapped. We also estimated the model-predicted number, area and population size of affected counties for each of the 21 mite species in China.

Results

Laelaps echidninus is the leading mite species that potentially affects 744 million people, followed by La. jettmari (517 million) and Eulaelaps stabularis (452 million). Leptotrombidium scutellare is the mite species harboring the highest variety of mite-borne agents including four Rickettsia species and two viruses, followed by Eu. stabularis (2 agents), L. palpale (2) and La. echidninus (2). The top two agents that parasitize the largest number of mite species are O. tsutsugamushi (28 species) and hantavirus (8). Mammalian richness, annual mean temperature and precipitation of the driest quarter jointly determine the ecology of the mites, forming four clusters of major mite species with distinct geographic distributions. High-risk areas of O. tsutsugamushi are mainly distributed in southern and eastern coastal provinces where 71.5 million people live.

Conclusions

Ecological niches of major mite species and mite-borne pathogens are much more extensive than what have been observed, necessitating expansion of current filed surveillance.

Graphic Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00966-0.

Mapping the Distributions of Mosquitoes and Mosquito-Borne Arboviruses in China

The geographic expansion of mosquitos is associated with a rising frequency of outbreaks of mosquito-borne diseases (MBD) worldwide. We collected occurrence locations and times of mosquito species, mosquito-borne arboviruses, and MBDs in the mainland of China in 1954−2020. We mapped the spatial distributions of mosquitoes and arboviruses at the county level, and we used machine learning algorithms to assess contributions of ecoclimatic, socioenvironmental, and biological factors to the spatial distributions of 26 predominant mosquito species and two MBDs associated with high disease burden. Altogether, 339 mosquito species and 35 arboviruses were mapped at the county level. Culex tritaeniorhynchus is found to harbor the highest variety of arboviruses (19 species), followed by Anopheles sinensis (11) and Culex pipiens quinquefasciatus (9). Temperature seasonality, annual precipitation, and mammalian richness were the three most important contributors to the spatial distributions of most of the 26 predominant mosquito species. The model-predicted suitable habitats are 60–664% larger in size than what have been observed, indicating the possibility of severe under-detection. The spatial distribution of major mosquito species in China is likely to be under-estimated by current field observations. More active surveillance is needed to investigate the mosquito species in specific areas where investigation is missing but model-predicted probability is high.

Spatiotemporal Dynamics of Scrub Typhus in Jiangxi Province, China, from 2006 to 2018

Background: Scrub typhus (ST) has become a significant potential threat to public health in Jiangxi. Further investigation is essential for the control and management of the spatiotemporal patterns of the disease. Methods: Time-series analyses, spatial distribution analyses, spatial autocorrelation analysis, and space-time scan statistics were performed to detect spatiotemporal dynamics distribution of the incidence of ST. Results: From 2006 to 2018, a total of 5508 ST cases occurred in Jiangxi, covering 79 counties. The number of ST cases increased continuously from 2006 to 2018, and there was obvious seasonality during the variation process in each year, with a primary peak in autumn (September to October) and a smaller peak in summer (June to August). From 2007 to 2018, the spatial distribution of the ST epidemic was significant heterogeneity, and Nanfeng, Huichang, Xunwu, Anyuan, Longnan, and Xinfeng were hotspots. Seven spatiotemporal clusters were observed using Kulldorff's space-time scan statistic, and the most likely cluster only included one county, Nanfeng county. The high-risk areas of the disease were in the mountainous, hilly region of Wuyi and the southern mountainous region of Jiangxi. Conclusions: Targeted interventions should be executed in high-risk regions for the precise prevention and control of ST.

Mapping ticks and tick-borne pathogens in China

Understanding ecological niches of major tick species and prevalent tick-borne pathogens is crucial for efficient surveillance and control of tick-borne diseases. Here we provide an up-to-date review on the spatial distributions of ticks and tick-borne pathogens in China. We map at the county level 124 tick species, 103 tick-borne agents, and human cases infected with 29 species (subspecies) of tick-borne pathogens that were reported in China during 1950-2018. Haemaphysalis longicornis is found to harbor the highest variety of tick-borne agents, followed by Ixodes persulcatus, Dermacentor nutalli and Rhipicephalus microplus. Using a machine learning algorithm, we assess ecoclimatic and socioenvironmental drivers for the distributions of 19 predominant vector ticks and two tick-borne pathogens associated with the highest disease burden. The model-predicted suitable habitats for the 19 tick species are 14‒476% larger in size than the geographic areas where these species were detected, indicating severe under-detection. Tick species harboring pathogens of imminent threats to public health should be prioritized for more active field surveillance.