Objective　To identify the temporal-spatial distribution patterns and changing of hotspot areas of malaria importations, and high-risk importation areas for imported malaria in Jiangsu Province, in order to provide the scientific evidence for prevention of malaria reintroduction in China. Methods　Cases with imported malaria in Jiangsu Province from 2016 to 2022 were accessed from the National Notifiable Disease Report System and the Information Management System for Parasitic Disease Control in China. The county-level vector map of Jiangsu Province was obtained from the National Fundamental Geographic Information System, China. ArcGIS 10.7 software was utilized to create a thematic map depicting the distribution of imported malaria cases in Jiangsu Province at the county level. Global and local autocorrelation analysis was then conducted to investigate the spatiotemporal changes in malaria import hotspot counties. Results　There were a total of 1 189 cases with imported malaria reported in 77 counties (81.05%, 77/95) of Jiangsu Province from 2016 to 2022. The global spatial autocorrelation analysis showed that a global spatial cluster of imported malaria in Jiangsu was only identified in 2020 ( Moran's I =0.46, Z=4.37, P<0.01), but local spatial autocorrelation analysis found that a total 60 hotspot counties existed from 2016 to 2022. There are 23 counties in central Jiangsu (38.33%), and 20 counties in southern Jiangsu (33.33%), 17 counties in northern Jiangsu (28.33%). The distribution of hotspot counties exhibits continuity. For instance, Chongchuan District, which falls under the jurisdiction of Nantong City, has consistently emerged as a hotspot county for 2016-2021. Since 2020, two recurring hotspot counties emerged in northern Jiangsu and southern Jiangsu. These counties are Ganyu District, under the jurisdiction of Lianyungang City, and Lishui District, under the jurisdiction of Nanjing City. Conclusions　The spatial-temporal cluster of cases with imported malaria was identified at the county level in Jiangsu, that hotspot counties were consistently detected. It is essential to maintain the sustainability of malaria surveillance and response in hotspot counties which were new detected, and strengthen the capacity of surveillance and response in hotspot counties which were continually detected based on the spatial-temporal distribution characteristics and changing rules of imported malaria.

Objective To quantitatively analyze the risk indicators of re-introduction of imported malaria in China and their weighting coefficients, so as to investigate the difference in the contribution of risk indicators included in the current risk assessment framework for re-introduction of imported malaria in China to the risk assessment of re-introduction of imported malaria. Methods Publications pertaining to the risk assessment framework for re-introduction of imported malaria in China that reported the risk indicators and their weighting coefficients were retrieved in PubMed, Web of Science, CNKI, Wanfang Data, and VIP with terms of “malaria”, “re-introduction/re-transmission/re-establishment”, “risk assessment/risk evaluation/risk prediction” from the inception of the database through 3 August 2023, and literature search was performed in Google Scholar to ensure the comprehensiveness of the retrieval. Basic characteristics of included studies were extracted using pre-designed information extraction forms by two investigators, and data pertaining to risk indicators of re-introduction of imported malaria were cross-checked by these two investigators. The risk indicators included in the risk assessment framework for re-introduction of imported malaria in China and their weighting coefficients were visualized with the Nightingale’s rose diagrams using the software R 4.2.1, and the importance of risk indictors was evaluated with the frequency of risk indicators included in the risk assessment framework and the ranking of weighting coefficients of risk indicators. In addition, the capability of risk indicators screened by different weighting methods was compared by calculating the ratio of the maximum to the minimum of the weighting coefficients of the risk indicators screened by different weighting methods. Results A total of 2 138 publications were retrieved, and following removal of duplications and screening, a total of 8 publications were included in the final analysis. In these 8 studies, 8 risk assessment frameworks for re-introduction of imported malaria in China and 52 risk indicators of re-introduction of imported malaria were reported, in which number of imported malaria cases (n = 8) and species of malaria vectors were more frequently included in the risk assessment frameworks (n = 8), followed by species of imported malaria parasites (n = 6) and population density of local malaria vectors (n = 6), and species of local malaria vectors (n = 6), number of imported malaria cases (n = 5) and species of imported malaria parasites had the three highest weighting coefficients (n = 4). The weighting methods included expert scoring method, combination of expert scoring method and analytic hierarchy process, and combination of expert scoring method and entropy weight method in these 8 studies, and the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by the expert scoring method were 1.143 to 2.241, while the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by combination of the expert scoring method and analytic hierarchy process were 34.970 to 162.000. Conclusions Number of imported malaria cases, species of imported malaria parasites, species of local malaria vectors and population density of local malaria vectors are core indicators in the current risk assessment framework for re-introduction of imported malaria in China. Combination of the expert scoring method and analytic hierarchy process is superior to the expert scoring method alone for weighting the risk indicators.