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Acute lung injury (ALI) is a severe respiratory disorder occurring in critical care medicine, with high rates of mortality and morbidity. This study aims to screen the potential biomarkers for ALI.

ObjectiveTo explore the changing trend of negative predictive value and number of false negatives in screening tests under the condition of low infection rate of infectious diseases.MethodsAssuming that the population is 20 million， to calculate the negative predictive value， numbers of true negatives and false negatives of the combination of different sensitivity （75.0%， 80.0%， 85.0%， 90.0%， 95.0%， 99.0%） and specificity （90.0%， 95.0%， 99.0%， 99.9%） when the disease infection rate of the population is 0.10%， 1.0% and 5.0% respectively.ResultsWhen the population infection rate is 0.1%， with the screening test sensitivity ≥75.0% and specificity ≥90.0%， the number of true negatives in 20 million people is about 17.98‒19.96 million. When the sensitivity is 75.0%， the negative predictive value is 99.972%‒99.975%， and the number of false negatives is 5 000； When the sensitivity increases to 99.0%， the negative predictive value is 99.999%， and the number of false negatives decreases to 200. When the population infection rate is 1.0%， a screening test with sensitivity ≥75.0% and specificity ≥90.0% can detect about 17.82‒19.78 million true negatives in 20 million population. When the sensitivity is 75.0%， the negative predictive value is 99.720%‒99.748%， and the number of false negatives is 50 000； When the sensitivity increases to 99.0%， the negative predictive value increases to 99.990%， and the number of false negatives decreases to 2 000. When the population infection rate is 5.0%， with sensitivity ≥75.0% and specificity ≥90.0%， the number of true negatives in 20 million people is about 17.10‒18.98 million； when the sensitivity is 75.0%， the negative predictive value is 98.559%‒98.700%， and the number of false negatives can reach 250 000； When the sensitivity is 99.0%， the negative predictive value increases to 99.942%‒99.947%， and the number of false negatives decreases to 10 000. The lower the infection rate of the population， the fewer false negatives will appear in the screening.ConclusionThe number of false negatives in large-scale screenings increases exponentially with the increase of infection rate. Screenings should be carried out as early as possible in a pandemic of infectious diseases， so as to control the spread of the pandemic as soon as possible.

ObjectiveTo explore the positive predictive value （PPV） and false positive （FP） number of screening test in mass testing when the prevalence of infection is low.MethodsAssuming a population of 20 million with the prevalence of disease infection ranging from 0.1% to 5.0%， PPV， true positive （TP） and FP numbers were calculated under different scenarios of combination of sensitivity （99.0%， 99.5%， and 100.0%） with specificity （97.0%， 97.5%， 98.0%， 98.5%， 99.0%， 99.5%， and 99.9%）.ResultsFor low infection prevalence （≤5.0%）， specificity has a greater impact on PPV than sensitivity； with the decrease of infection prevalence， the increase in PPV elevates when the specificity increases. When the infection prevalence is >1.0%， the closer the specificity is to 99.9%， the closer the PPV is to 100.0%. However， when the infection prevalence is

Background: Schistosomiasis is a global public health issue. In China, while the seroprevalence of Schistosomiasis japonica has currently reduced to a relatively low level, risk of infection still exists in certain areas. However, there has been a lack of comprehensive research on the long-term trends of national seroprevalence, changes across age groups, and characteristics in spatial distribution, which is crucial for effectively targeting interventions and achieving the goal of eliminating schistosomiasis by 2030. Our study aimed to address this gap by analyzing the long-term trends of Schistosomiasis japonica seroprevalence in China from 1982 to 2020 based on the data from diverse sources spanning a period of 39 years. Methodology: Seroprevalence data were collected from literature databases and national schistosomiasis surveillance system. Meta-analysis was conducted to estimate the seroprevalence. Joinpoint model was used to identify changing trend and inflection point. Inverse distance weighted interpolation was used to determine the spatial distribution of seroprevalence. Principal findings: The seroprevalence decreased from 34.8% in 1982 to 2.4% in 2020 in China. Before 2006, the seroprevalence was higher in the middle age group, and a pattern of increasing with age was observed afterwards. The areas with high seroprevalence existed in Dongting Lake, Poyang Lake, Jianghan Plain, the Anhui branch of the Yangtze River and some localized mountainous regions in Sichuan and Yunnan provinces. Conclusions/Significance: There was a significant decline in the seroprevalence of Schistosomiasis japonica from 1982 to 2020 in China. Nevertheless, schistosomiasis has not been eradicated; thus, implementing precise and personalized monitoring measures is crucial for the elimination of schistosomiasis, especially in endemic areas and with a particular focus on the elderly. Author summary: Serological immunological testing is a crucial means of monitoring schistosomiasis. In China, while the seroprevalence of Schistosomiasis japonica has currently reduced to a relatively low level, risk of infection still exists in certain areas. Achieving the goal of eliminating schistosomiasis by 2030 necessitates more precise and effective prevention and control measures. Our study intended to figure out the long-term trends of Schistosomiasis japonica seroprevalence in China from 1982 to 2020, focusing on age and spatial variations to identify high-risk areas and populations. We expect that the results can inform the development of targeted measures for schistosomiasis elimination. [ABSTRACT FROM AUTHOR]

A local COVID-19 outbreak with two community clusters occurred in a large industrial city, Shaoxing, China, in December 2021 after serial interventions were imposed. We aimed to understand the reason by analysing the characteristics of the outbreak and evaluating the effects of phase-adjusted interventions. Publicly available data from 7 December 2021 to 25 January 2022 were collected to analyse the epidemiological characteristics of this outbreak. The incubation period was estimated using Hamiltonian Monte Carlo method. A well-fitted extended susceptible-exposed-infectious-recovered model was used to simulate the impact of different interventions under various combination of scenarios. There were 387 SARS-CoV-2-infected cases identified, and 8.3% of them were initially diagnosed as asymptomatic cases. The estimated incubation period was 5.4 (95% CI 5.2–5.7) days for all patients. Strengthened measures of comprehensive quarantine based on tracing led to less infections and a shorter duration of epidemic. With a same period of incubation, comprehensive quarantine was more effective in containing the transmission than other interventions. Our findings reveal an important role of tracing and comprehensive quarantine in blocking community spread when a cluster occurred. Regions with tense resources can adopt home quarantine as a relatively affordable and low-impact intervention measure compared with centralized quarantine. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space. Methodology: The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data. Principal/Findings: MGWR was the best-fitting model (R2: 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water. Conclusions/Significance: Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control. Author summary: The elimination of schistosomiasis depends critically on the identification of a set of determinants. A site-specific analysis of the determinants allows organizers to adjust and clarify the focus areas for the next round. This analysis, based on anecdotal evidence and investigations specific to a narrow geographic area at a fine scale, would enhance our understanding of the determinants. Especially for areas where elimination goals are to be achieved, local spatial variation in determinants is worth exploring in depth. In this analysis we included the seropositive rates of schistosomiasis from 1714 villages or communities and established six spatial regression models. Benefiting from determinants with different bandwidths, MGWR was good at simulating schistosomiasis. We also explored the spatial variability of multiple determinants and mapped variability for some key determinants at the village level. We hope the findings will be used for developing precision intervention measure for schistosomiasis control. [ABSTRACT FROM AUTHOR]