The rapid rise and fast development of artificial intelligence (AI) has brought unprecedented opportunities and challenges to all sectors, including disease prevention control. Malaria is one of the world’s most devastating infectious diseases. This article summarizes the advances in the research and application of AI-empowered malaria control programmes, analyzes key challenges during the implementation of malaria control programmes, and proposes future development directions and research proprieties, so as to provide insights into facilitating the translation of AI-driven strategies in global infectious disease control efforts.

The insecticide resistance is becoming increasingly severe in malaria vectors and has become one of the most important threats to global malaria elimination. Currently, malaria vectors not only have developed high resistance to conventional insecticides, including organochlorine, organophosphates, carbamates, and pyrethroids, but also have been resistant to recently used neonicotinoids and pyrrole insecticides. This article describes the current status of global insecticide resistance in malaria vectors and global insecticide resistance management strategies, analyzes the possible major challenges in the insecticide resistance management, and proposes the response actions, so as to provide insights into global insecticide resistance management and contributions to global malaria elimination.

Objective To investigate the development and dynamic changes of cysts in the brain of mice following infection with different forms of Toxoplasma gondii, so as to provide insights into for toxoplasmosis prevention and control. Methods ICR mice at ages of 6 to 8 weeks, each weighing 20 to 25 g, were intraperitoneally injected with tachyzoites of the T. gondii PRU strain at a dose of 1 × 10⁵ tachyzoites per mouse, orally administered with cysts at a dose of 20 oocysts per mouse or oocysts at a dose of 200 oocysts per mouse for modeling chronic T. gondii infection in mice, and the clinical symptoms and survival of mice were observed post-infection. Mice were orally infected with T. gondii cysts at doses of 10 (low-dose group), 20 (medium-dose group), 40 cysts per mouse (high-dose group), and the effect of different doses of T. gondii infections on the number of cysts was examined in the mouse brain. Mice were orally administered with T. gondii cysts at a dose of 20 cysts per mouse, and grouped according to gender (female and male) and time points of infections (20, 30, 60, 90, 120, 150, 180 days post-infection), and the effects of gender and time points of infections on the number of cysts was examined in the mouse brain. In addition, mice were divided into the tachyzoite group (Group T), the first-generation cyst group (Group C1), the second-generation cyst group (Group C2), the third-generation cyst (Group C3) and the fourth-generation cyst group (Group C4). Mice in the Group T were intraperitoneally injected with T. gondii tachyzoites at a dose of 1 × 10⁵ tachyzoites per mouse, and the cysts were collected from the mouse brain tissues 30 days post-infection, while mice in the Group C1 were orally infected with the collected cysts at a dose of 30 cysts per mouse. Continuous passage was performed by oral administration with cysts produced by the previous generation in mice, and the effect of continuous passage on the number of cysts was examined in the mouse brain. Results Following infection with T. gondii tachyzoites, cysts and oocysts in mice, obvious clinical symptoms were observed on days 6 to 13 and mice frequently died on days 7 to 12. The survival rates of mice were 67.0%, 87.0% and 53.0%, and the mean numbers of cysts were (516.0 ± 257.2), (1 203.0 ± 502.0) and (581.0 ± 183.1) in the mouse brain (F = 11.94, P < 0.01) on day 30 post-infection with T. gondii tachyzoites, cysts and oocysts, respectively, and the numbers of cysts in the brain tissues were significantly lower in mice infected with T. gondii tachyzoites and oocysts than in those infected with cysts (all P values < 0.01). The survival rates of mice were 87.0%, 87.0% and 60.0%, and the mean numbers of cysts were (953.0 ± 355.5), (1 084.0 ± 474.3) and (1 113.0 ± 546.0) in the mouse brain in the low-, medium- and high-dose groups on day 30 post-infection, respectively (F = 0.42, P > 0.05). The survival rates of male and female mice were 73.0% and 80.0%, and the mean numbers of cysts were (946.4 ± 411.4) and (932.1 ± 322.4) in the brain tissues of male and female mice, respectively (F = 1.63, P > 0.05). Following continuous passage, the mean numbers of cysts were (516.0 ± 257.2), (1 203.0 ± 502.0), (896.8 ± 332.3), (782.5 ± 423.9) and (829.2 ± 306.0) in the brain tissues of mice in the T, C1, C2, C3 and C4 groups, respectively (F = 4.82, P < 0.01), and the number of cysts was higher in the mouse brain in Group 1 than in Group T (P < 0.01). Following oral administration of 20 T. gondii cysts in mice, cysts were found in the moues brain for the first time on day 20 post-infection, and the number of cysts gradually increased over time, peaked on days 30 and 90 post-infection and then gradually decreased; however, the cysts were still found in the mouse brain on day 180 post-infection. Conclusions There is a higher possibility of developing chronic T. gondii infection in mice following infection with cysts than with oocysts or tachyzoites and the most severe chronic infection is seen following infection with cysts. The number of cysts does not correlate with the severity of chronic T. gondii infection, and the number of cysts peaks in the mouse brain on days 30 and 90 post-infection.

Objective To collect data on imported malaria cases in Jiangsu Province from 2019 to 2023 after malaria elimination and to analyze the current epidemic situation and prevention and control measures of imported malaria, discussing future prevention and control strategies. Methods Malaria case information for Jiangsu Province from 2019 to 2023 was extracted and downloaded from the China Information System for Disease Control and Prevention (CISDCP) as well as the Jiangsu Provincial malaria epidemic database. Statistical analysis was conducted using Stata 12.0 and SPSS 16.0 software. Results From 2019 to 2023, a total of 534 cases of malaria were directly reported online in Jiangsu Province, with annual cases numbering 244, 90, 32, 36, and 132 respectively, all being laboratory-confirmed imported malaria cases from abroad. During the COVID-19 pandemic from 2020 to 2022, the number of imported malaria cases significantly decreased, with several months reporting zero cases. Among the 534 malaria cases, the vast majority were individuals who had traveled to countries in sub-Saharan Africa and Southeast Asia for work, business, international studies, or tourism. Over the five years, the median, minimum, and maximum days for patients from onset of illness to health-seeking were 1(0,12), 1(0,8), 0(0,6), 0(0,10), and 1(0,18) days, with a statistically significant difference in health-seeking time among patients (Fisher's exact test, P=0.03). Over the past three years of the COVID-19 pandemic, compared to outside centralized isolation stations, malaria cases within centralized isolation stations were diagnosed in a shorter time (Fisher exact test, P=0.007). A total of 24 severe malaria cases were reported, with no deaths, including 23 cases of P. falciparum and 1 case of P. ovale. Conclusions After the elimination of malaria, imported malaria cases in Jiangsu Province have sharply decreased due to the impact of the COVID-19 pandemic. Malaria cases in centralized isolation stations (CIS) for COVID-19 control of Jiangsu Province are more likely to be promptly diagnosed, and the timeliness from onset to health-seeking among malaria patients returning from high-malaria areas improved. As COVID-19 prevention and control policies adjusted, there has been a sharp increase in imported malaria cases in 2023. It's still necessary to strengthen measures for malaria prevention and control and maintain the capacity to prevent malaria re-transmission in Jiangsu Province.

Abstract: Objective To conduct genotyping and molecular tracing analysis on Plasmodium vivax samples from a cluster of P. vivax malaria outbreak in order to provide a reference for case geographical origin determination. Methods Blood samples from 4 patients in a vivax malaria cluster in Longhui County, Hunan Province from June to July 2018 were collected for species identification by qPCR, and 9 microsatellite molecular markers were used to genotype the parasite strains from four samples. The population genetic STRUCTURE analysis was performed based on the VivaxGEN-MS microsatellite genotype database of P. vivax in the Asia Pacific Malaria Elimination Network, to determine the genetic subgroups and geographical origin of the strains. Results By qPCR, all 4 cases were identified as Plasmodium vivax infection, and 9 microsatellite loci of the 4 cases were successfully typed, and the four samples had different genetic haplotypes, among which case 1, case 3, and case 4 were infected by a single clonal strain, and case 2 was infected by a polyclonal strain. When all P. vivax samples were divided into 2 subpopulations (K=2) by STURCTURE analysis, 4 Hunan samples were classified into tropical genetic subpopulations (comprising strains from Ethiopia, Iran, Bhutan, Malaysia, Indonesia, and southern China). When the samples were divided into 4 subgroups by STURCTURE analysis (K=4), the 4 Hunan samples were classified as South Asian/Southeast Asian genetic subgroups (originating from Bhutan, Malaysia, Indonesia, and southern China). Conclusions The results of molecular tracing do not support that the 4 P. vivax strains in this outbreak originated from the population of central China. The technology of molecular tracing of P. vivax can provide objective evidence for determining the source of infection in malaria cases during the stage of malaria elimination and post-elimination.

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.

After achieving malaria elimination, preventing re-establishment from imported malaria and consolidating malaria elimination achievements are top priorities of the national malaria control program in China. Due to the long-term existence of overseas imported malaria cases and incomplete eradication of local epidemic conditions, there are multiple challenges for prevention of re-establishment from imported malaria in China. Hereby, we propose that regular assessment is an effective approach to maintaining the capability of prevention of re-establishment from imported malaria, and describe the purpose, significance, management and implementation of the capability assessment for prevention of re-establishment from imported malaria, so as to provide insights into the formulation and adjustment of malaria control strategies during the post-elimination phase.

Objective To investigate the seasonal Aedes population fluctuation and the resistance of Aedes populations to common insecticides in Jiangsu Province in 2020, so as to provide insights into vector-borne infectious diseases control.. Methods One village was randomly sampled from each of Xinbei District of Changzhou City and Zhangjiagang County of Suzhou City in southern Jiangsu Province, Hai’an County of Nantong City and Yandu District of Yancheng City in Central Jiangsu Province, and Suining County of Xuzhou City and Sihong County of Suqian City in northern Jiangsu Province during the period between May and October, 2020. A small ponding container was sampled, and larval Aedes mosquitoes were collected using straws once each in early and late stages of each month. All larvae were bred in laboratory to adults for population identification. In addition, larval breeding were observed in all small ponding containers in and out of 30 households that were randomly sampled from six surveillance sites, and the larval mosquito density was estimated using Breteau index. Larval A. albopictus mosquitoes were sampled around Cuiyuan New Village in Jintan District of Changzhou City, and bred in laboratory to the first offspring generation, and the susceptibility of adult female mosquitoes to deltamethrin, lambda-cyhalothrin, malathion, and propoxur was tested using the filter-paper bioassay recommended by WHO. Results A total of 1 165 larval Aedes mosquitoes were captured from small ponding containers in six surveillance sites of Jiangsu Province in 2020, and all were identified as A. albopictus following eclosion. The largest number of Aedes larvae captured was found in July. A total of 1 152 households were investigated in six surveillance sites, and the mean Breteau indexes were 9.58, 13.20, 13.71, 13.20, 12.18 and 5.58 from May to October, respectively, while a high Aedes transmission risk was seen in Xinbei District of Changzhou City, with a higher Breteau index than in Suining (H = 23.667, P_adjusted = 0.001) and Sihong (H = 22.500, P_adjusted = 0.003) counties. The field-captured A. albopictus from Cuiyuan New Village in Jintan District of Changzhou City remained sensitive to malathion, but was resistant to propoxur, and developed high-level resistance to deltamethrin and lambda-cyhalothrin. Conclusions A. albopictus was present in southern, central and northern Jiangsu Province in 2020, and the larval density peaked in July. A. albopictus captured from Cuiyuan New Village in Jintan District of Changzhou City has developed high-level resistance to pyrethroid pesticides.

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Objective: To investigate the current status of malaria rapid diagnostic test (RDT) strips application and malaria laboratory technicians' evaluation about them at primary healthcare provider level in Jiangsu Province. Methods: From November to December 2016, 878 medical institutions and 118 CDCs of city, county and township/community level in Jiangsu Province were selected as study samples using stratified random sampling method. Self-designed questionnaire was distributed to investigate the institution's malaria work task, RDT strips application and evaluation status in 2015. We also investigated the socio-demographic information and collected the RDT strips evaluation score from the malaria laboratory technicians selected from the institutions investigated (one technician from each institution). Rank sum test was performed to compare the RDT strips evaluation scores between medical institutions and CDCs, and among different medical institutions and CDCs. Results: In 2015, 405 cases of malaria were reported, 362 200 person-time of malaria blood testing task was conducted, and 100 000 RDT strips were procured and provided for healthcare providers in Jiangsu province for free. Of the 996 healthcare institutions investigated, 628 used RDT strips in the year 2015 and the median (P₂₅, P₇₅) of RDT strips volume used in these institutions was 10 (2, 25). The volume of RDT strips used in CDCs (15 (5, 52)) was significantly higher than that in medical institutions (10 (2, 25), (Z=3.42, P=0.001)). The investigated CDCs gave higher score on RDT strips' testing time per operation (10 (8.5, 10)) than medical institutions (9(8, 10), (Z=-2.20, P=0.028)). The employers of 614 investigated malaria laboratory technicians used RDT strips in 2015. The median of the scores given by CDC malaria laboratory technicians for RDT strips in terms of testing time per operation, testing operation and results judgement difficulties were 10 (9, 10), 10 (9, 10) and 10 (9, 10), respectively, which were significantly higher than those from technicians of medical institutions (9 (8, 10), 9 (8, 10), 9 (8, 10), (Z values were -2.55, -2.97 and -2.96, respectively; P values were all less than 0.05)). Conclusion RDT strips had been widely performed in health institutions in Jiangsu Province. The amount of RDT strips used in CDCs was significantly higher than that in medical institutions. Primary-level institutions and malaria laboratory technicians generally recognized RDT strips' advantage for application in terms of testing time and operational procedure. CDCs and malaria laboratory technicians from them gave higher regards on RDT strips in terms of testing time per operation, testing operation and results judgement difficulties compared with that of medical institutions.