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

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          Abstract

          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.

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          Most cited references43

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          A spatial scan statistic

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            Spatial Epidemiology: Current Approaches and Future Challenges

            Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.
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              Scrub typhus: the geographic distribution of phenotypic and genotypic variants of Orientia tsutsugamushi.

              Orientia tsutsugamushi is the etiological agent of scrub typhus, an acute, mite-borne, febrile illness that occurs in the Asia-Pacific region. Historically, strain characterization used serological analysis and revealed dramatic antigenic diversity. Eyeing a recommendation of potential vaccine candidates for broad protection, we review geographic diversity and serological and DNA prevalences. DNA analysis together with immunological analysis suggest that the prototype Karp strain and closely related strains are the most common throughout the region of endemicity. According to serological analysis, approximately 50% of isolates are seroreactive to Karp antisera, and approximately one-quarter of isolates are seroreactive to antisera against the prototype Gilliam strain. Molecular methods reveal greater diversity. By molecular methods, strains phylogenetically similar to Karp make up approximately 40% of all genotyped isolates, followed by the JG genotype group (Japan strains serotypically similar to the Gilliam strain but genetically non-Gilliam; 18% of all genotyped isolates). Three other genotype groups (Kato-related, Kawasaki-like, and TA763-like) each represent approximately 10% of genotyped isolates. Strains genetically similar to the Gilliam strain make up only 5% of isolates. Strains from these groups should be included in any potential vaccine.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                26 April 2021
                May 2021
                : 18
                : 9
                : 4599
                Affiliations
                [1 ]The Collaboration Unit for Field Epidemiology of State Key Laboratory of Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang 330038, China; younsoo@ 123456163.com (S.Y.); zhengweiqing2001@ 123456163.com (W.Z.); renlongzh0829@ 123456163.com (R.F.); younsoo@ 123456126.com (C.Y.)
                [2 ]State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China; liuxiaobo@ 123456icdc.cn (X.L.); gaoyuancdc@ 123456126.com (Y.G.); luliang@ 123456icdc.cn (L.L.); liuqiyong@ 123456icdc.cn (Q.L.)
                [3 ]School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China; wuhancbz@ 123456tom.com
                Author notes
                [* ]Correspondence: liguichang@ 123456icdc.cn (G.L.); nccdcchy@ 123456126.com (H.C.)
                [†]

                These authors also contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-1600-6517
                Article
                ijerph-18-04599
                10.3390/ijerph18094599
                8123664
                33926106
                80117ac9-9d1f-4467-8636-cb772accdd94
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 04 March 2021
                : 20 April 2021
                Categories
                Article

                Public health
                scrub typhus,spatiotemporal analysis
                Public health
                scrub typhus, spatiotemporal analysis

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