Evaluation of phase-adjusted interventions for COVID-19 using an improved SEIR model.

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.

Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models.

BACKGROUND: Oncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control. METHODS: Data describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585). RESULTS: The RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change. CONCLUSIONS: This study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.

Mechanically sensitive HSF1 is a key regulator of left-right symmetry breaking in zebrafish embryos.

The left-right symmetry breaking of vertebrate embryos requires nodal flow. However, the molecular mechanisms that mediate the asymmetric gene expression regulation under nodal flow remain elusive. Here, we report that heat shock factor 1 (HSF1) is asymmetrically activated in the Kupffer's vesicle of zebrafish embryos in the presence of nodal flow. Deficiency in HSF1 expression caused a significant situs inversus and disrupted gene expression asymmetry of nodal signaling proteins in zebrafish embryos. Further studies demonstrated that HSF1 is a mechanosensitive protein. The mechanical sensation ability of HSF1 is conserved in a variety of mechanical stimuli in different cell types. Moreover, cilia and Ca2+-Akt signaling axis are essential for the activation of HSF1 under mechanical stress in vitro and in vivo. Considering the conserved expression of HSF1 in organisms, these findings unveil a fundamental mechanism of gene expression regulation by mechanical clues during embryonic development and other physiological and pathological transformations.

Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model.

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.

Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study.

BACKGROUND: Snail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial-temporal effects of these changes. METHODS: A snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial-temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial-temporal effects of the change. RESULTS: Volatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted "U" curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope. CONCLUSIONS: This study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.

Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019.

BACKGROUND: The area of Oncomelania hupensis snail remains around 3.6 billion m2, with newly emerging and reemergent habitats continuing to appear in recent years. This study aimed to explore the long-term dynamics of snail density before and after the operation of Three Gorges Dam (TGD). METHODS: Data of snail survey between 1990 and 2019 were collected from electronic databases and national schistosomiasis surveillance. Meta-analysis was conducted to estimate the snail density. Joinpoint model was used to identify the changing trend and inflection point. Inverse distance weighted interpolation (IDW) was used to determine the spatial distribution of recent snail density. RESULTS: A total of 3777 snail survey sites with a precise location of village or beach were identified. For the downstream area, snail density peaked in 1998 (1.635/0.11 m2, 95% CI: 1.220, 2.189) and fluctuated at a relatively high level before 2003, then declined steadily from 2003 to 2012. Snail density maintained lower than 0.150/0.11 m2 between 2012 and 2019. Joinpoint model identified the inflection of 2003, and a significant decreasing trend from 2003 to 2012 with an annual percentage change (APC) being - 20.56% (95% CI: - 24.15, - 16.80). For the upstream area, snail density peaked in 2005 (0.760/0.11 m2, 95% CI: 0.479, 1.207) and was generally greater than 0.300/0.11 m2 before 2005. Snail density was generally lower than 0.150/0.11 m2 after 2011. Snail density showed a significant decreasing trend from 1990 to 2019 with an APC being - 6.05% (95% CI: - 7.97, - 7.09), and no inflection was identified. IDW showed the areas with a high snail density existed in Poyang Lake, Dongting Lake, Jianghan Plain, and the Anhui branch of the Yangtze River between 2015 and 2019. CONCLUSIONS: Snail density exhibited a fluctuating downward trend in the Yangtze River basin. In the downstream area, the operation of TGD accelerated the decline of snail density during the first decade period, then snail density fluctuated at a relatively low level. There still exists local areas with a high snail density. Long-term control and monitoring of snails need to be insisted on and strengthened.

Global Distribution of Culex tritaeniorhynchus and Impact Factors.

Culex tritaeniorhynchus is the primary vector of Japanese encephalitis (JE) and has a wide global distribution. However, the current and future geographic distribution maps of Cx. tritaeniorhynchus in global are still incomplete. Our study aims to predict the potential distribution of Cx. tritaeniorhynchus in current and future conditions to provide a guideline for the formation and implementation of vector control strategies all over the world. We collected and screened the information on the occurrence of Cx. tritaeniorhynchus by searching the literature and online databases and used ten algorithms to investigate its global distribution and impact factors. Cx. tritaeniorhynchus had been detected in 41 countries from 5 continents. The final ensemble model (TSS = 0.864 and AUC = 0.982) indicated that human footprint was the most important factor for the occurrence of Cx. tritaeniorhynchus. The tropics and subtropics, including southeastern Asia, Central Africa, southeastern North America and eastern South America, showed high habitat suitability for Cx. tritaeniorhynchus. Cx. tritaeniorhynchus is predicted to have a wider distribution in all the continents, especially in Western Europe and South America in the future under two extreme emission scenarios (SSP5-8.5 and SSP1-2.6). Targeted strategies for the control and prevention of Cx. tritaeniorhynchus should be further strengthened.

Life expectancy and healthy life expectancy of patients with advanced schistosomiasis in Hunan Province, China.

BACKGROUND: Few studies have investigated the change in life expectancy (LE) and the healthy lifespan among patients with advanced schistosomiasis. This study was to evaluate the LE and healthy life expectancy (HLE) for patients and assess the mechanism responsible for the LE inequality. METHODS: We utilized data from a dynamic advanced schistosomiasis cohort (10,362 patients) for the period from January 2008 to December 2019 in Hunan Province, China, to calculate the LEs of patients, and made a comparison with that of general population (19,642 schistosomiasis-free individuals) in the schistosomiasis endemic areas. LEs were estimated from 15 years of age by constructing period life tables. Arriaga's decomposition method was applied to quantify the influence of the age structure on the difference in LE. HLE for advanced schistosomiasis patients was calculated by using Sullivan method with age-specific disability weight. The LE and HLE were calculated for both males and females to perform further analyses on gender gap. RESULTS: The estimated LE for advanced schistosomiasis patients aged 15-19 was 49.51 years (48.86 years for males and 51.07 years for females), which was 20.14 years lower compared with general population (69.65 years), and the LE gap between patients and general population decreased with age. The largest age-specific mortality contribution to the gap (32.06%) occurred at age 80-84 years. Women had a lower LE and HLE than men at age ≥ 60 years (both gender gaps in LE and HLE < 0). For advanced schistosomiasis patients, the gender gap in LE was largely attributed to the difference in mortality among those under the age of 55; the age-specific mortality in women exerted positive influence on the gap at age 25-64 and 75-79 years, with the contribution rate ranging from 0.59% to 57.02%, and made the negative contribution at other age groups. CONCLUSIONS: The LE of advanced schistosomiasis patients was still much lower compared with general population. Strengthened prevention strategies and targeted treatments are needed to reduce morbidity and mortality due to advanced schistosomiasis, especially for younger population and elderly female patients.

Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China.

Malaria is a parasitic disease caused by Plasmodium, and Anopheles sinensis is a vector of malaria. Although malaria is no longer indigenous to China, a high risk remains for local transmission of imported malaria. This study aimed to identify the risk distribution of vector An. sinensis and malaria transmission. Using data collected from routine monitoring in Shanghai from 2010 to 2020, online databases for An. sinensis and malaria, and environmental variables including climate, geography, vegetation, and hosts, we constructed 10 algorithms and developed ensemble models. The ensemble models combining multiple algorithms (An. sinensis: area under the curve [AUC] = 0.981, kappa = 0.920; malaria: AUC = 0.959, kappa = 0.800), with the best out-of-sample performance, were used to identify important environmental predictors for the risk distributions of An. sinensis and malaria transmission. For An. sinensis, the most important predictor in the ensemble model was moisture index, which reflected degree of wetness; the risk of An. sinensis decreased with higher degrees of wetness. For malaria transmission, the most important predictor in the ensemble model was the normalized differential vegetation index, which reflected vegetation cover; the risk of malaria transmission decreased with more vegetation cover. Risk levels for An. sinensis and malaria transmission for each district of Shanghai were presented; however, there was a mismatch between the risk classification maps of An. sinensis and malaria transmission. Facing the challenge of malaria transmission in Shanghai, in addition to precise An. sinensis monitoring in risk areas of malaria transmission, malaria surveillance should occur even in low-risk areas for An. sinensis.

Changing trend of sickness absenteeism among students during 2010-2017 academic years and its correlation with the incidence of acute infectious diseases in Jiading District，Shanghai / 中国学校卫生

Objective@#To describe the sickness absenteeism among primary and secondary school students in Jiading District and to analyze its correlation with the incidence of infectious diseases and associated lead or lag times, so as,to provide a scientific basis for the health and education departments to take measures.@*Methods@#The regularity and symptom composition of school sickness absenteeism among primary and secondary school students in Jiading District from the 2010-2017 were described. The Joinpoint model was applied to analyze the changing trend of absenteeism. And the rate of school sickness absenteeism and incidences of 7 acute infectious diseases were compared through visual analysis. The time lagged correlation coefficients and Granger causality tests were applied to quantitatively analyze the correlation and lead/lag patterns.@*Results@#The average rate of school sickness absenteeism among primary and secondary school students in Jiading District, Shanghai was 0.27%. Since the 2012 academic year, the overall rate of sickness absenteeism has been on the rise. Fever was the most common symptom of absenteeism (47.49%). The trend of sickness absenteeism rate was mainly consistent with the incidence of influenza, and showed a one way Granger causality. The maximum time lagged correlation coefficient was obtained in the second semester of the 2017 academic year with a value of 0.43 (95% CI =0.25-0.58). The maximum time lagged correlation coefficient between the rate of sickness absenteeism with fever and other respiratory symptoms and the incidence of influenza was also obtained in the second semester of the 2017 academic year and the maximum correlation coefficient was 0.50(95% CI =0.33-0.65).@*Conclusion@#The sickness absenteeism rate in primary and secondary school students is correlated to influenza incidence, and it has potential application value in supplementing early warning of influenza activities in the community.

Association of Daily Exposure to Air Pollutants with the Risk of Tuberculosis in Xuhui District of Shanghai, China.

Previous studies have suggested that air pollutant exposure is related to tuberculosis (TB) risk, but results have not been consistent. This study evaluated the relation between daily air pollutant exposure and TB incidence in Shanghai from 2014 to 2019. Overall, there were four pollutants that were positively related to the risk of new TB cases. After a 5 µg/m3 increase, the maximum lag-specific and cumulative relative risk (RR) of SO2 were 1.081, (95% CI: 1.035-1.129, lag: 3 days) and 1.616 (95% CI: 1.119-2.333, lag: 0-13 days), while for NO2, they were 1.061 (95% CI: 1.015-1.11, lag: 4 days) and 1.8 (95% CI: 1.113-2.91, lag: 0-15 days). As for PM2.5, with a 50 µg/m3 increase, the lag-specific and cumulative RR were 1.064 (95% CI: 1-1.132, lag: 6 days) and 3.101 (95% CI: 1.096-8.777, lag: 0-21 days), while for CO, the lag-specific RR was 1.03 (95% CI: 1.005-1.057, lag: 8 days) and the cumulative RR was 1.436 (95% CI: 1.004-2.053, lag: 0-16 days) with a 100 µg/m3 increase. The associations tended to be stronger in male and elderly patients and differed with seasons. Air pollutant exposure may be a risk factor for TB incidence.