Surveillance, Epidemiology and Impact of EV-A71 Vaccination on Hand, Foot, and Mouth Disease in Nanchang, China, 2010–2019

After the first national-scale outbreak of Hand, foot, and mouth disease (HFMD) in China, a national surveillance network was established. Here we described the epidemiology and pathogenic profile of HFMD and the impact of EV-A71 vaccination on pathogen spectrum of enteroviruses in the southeastern Chinese city of Nanchang during 2010–2019. A total of 7,951 HFMD cases from sentinel hospitals were included, of which 4,800 EV-positive cases (60.4%) were identified by real-time RT-PCR. During 2010–2012, enterovirus 71 (EV-A71) was the main causative agent of HFMD, causing 63.1% of cases, followed by 19.3% cases associated with coxsackievirus A16 (CV-A16). Since 2013, the proportion of other enteroviruses has increased dramatically, with the sub genotype D3 strain of Coxsackievirus A6 (CV-A6) replacing the dominance of EV-A71. These genetically diverse native strains of CV-A6 have co-transmitted and co-evolved in Nanchang. Unlike EV-A71 and CV-A16, most CV-A6 infections were concentrated in autumn and winter. The incidence of EV-A71 infection negatively correlated with EV-A71 vaccination (r = −0.990, p = 0.01). And severe cases sharply declined as the promotion of EV-A71 vaccines. After 2-year implementation of EV-A71 vaccination, EV-A71 is no longer detected from the reported HFMD cases in Nanchang. In conclusion, EV-A71 vaccination changed the pattern of HFMD epidemic, and CV-A6 replaced the dominance of EV-A71 over time.

Clinical Profile and Characterization of Rotavirus Strains in Under-Five Children with Diarrhea: A Hospital-Based Study from Eastern Odisha, India

Objective Diarrheal diseases are one of the most common causes of hospitalization in children under five. Rotavirus is the most common cause of acute diarrhea in younger children, and the prevalence decreases rapidly with increasing age. The objective of the study was to estimate the burden of rotavirus infection in acute gastroenteritis among under-five children admitted to a tertiary care hospital in eastern Odisha, for the clinical profile and identity of the prevalent strains. Methods This was a prospective observational study linked to the National Rotavirus Surveillance Network (NRSN), where 720 under-five children with diarrhea were enrolled. In total, 675 stool samples of eligible candidates were sent for rotavirus isolation, and identification of strains was done by identifying VP7 (G-type) and VP4 (P-type) genes by reverse transcription polymerase chain reaction. Results Categorical variables were presented as frequency and percentage, and continuous variables were expressed as mean ± standard deviation. Rotavirus was detected in 256 (37.92%) samples. Males outnumbered females. The most common affected age group was 7 to 12 months, followed by 13 to 18 months. G3P[8] was the most prevalent strain in this study. Conclusion Children between the age of 7 and 18 months were most vulnerable to rotavirus infection. The most prevalent strain varies from one region to another and continuous surveillance is needed.

Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism

In order to improve the ability to detect network attacks, traditional intrusion detection models often used convolutional neural networks to encode spatial information or recurrent neural networks to obtain temporal features of the data. Some models combined the two methods to extract spatio-temporal features. However, these approaches used separate models and learned features insufficiently. This paper presented an improved model based on temporal convolutional networks (TCN) and attention mechanism. The causal and dilation convolution can capture the spatio-temporal dependencies of the data. The residual blocks allow the network to transfer information in a cross-layered manner, enabling in-depth network learning. Meanwhile, attention mechanism can enhance the model's attention to the relevant anomalous features of different attacks. Finally, this paper compared models results on the KDD CUP99 and UNSW-NB15 datasets. Besides, the authors apply the model to video surveillance network attack detection scenarios. The result shows that the model has advantages in evaluation metrics.

Are People Optimistically Biased about the Risk of COVID-19 Infection? Lessons from the First Wave of the Pandemic in Europe

Unrealistic optimism, the underestimation of one’s risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network (n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries—France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

Detection of Rift Valley Fever Virus Lineage H from South Africa through Syndromic Sentinel Surveillance Network in Senegal

Background Rift Valley fever virus (RVFV) is an arbovirus that causes epizootics and epidemics among livestock population and humans. Our surveillance system has revealed multiple emergences and re-emergences of RVFV in West Africa over the last decade. Methods In Senegal a sentinel syndromic surveillance network (4S) has been implemented since 2011. Samples from human suspected arbovirus infection in 4S sentinel sites were sent to Institut Pasteur de Dakar (IPD) where arbovirus diagnosis by enzyme-linked immunosorbent assay (ELISA), real-time reverse transcription polymerase chain reaction (RT-PCR), and virus isolation were performed. Overall, IPD has received a total of 1,149 samples from arboviral suspected patients through the 4S network from January to December 2020. These samples were screened for seven arboviruses including RVFV. Whole genome sequencing of positive RVFV samples by RT-PCR were performed using Illumina Miseq platform followed by genome assembly. Phylogenetic analysis were performed using MEGA X. Results Out of the 1,149 arbovirus suspected cases, four RVFV positive samples were detected with RT-PCR while five RVFV positive samples were detected by ELISA. Complete genome sequences were obtained for three strains among the four positive samples by RT-PCR. Phylogenetic analyses indicated an emergence of a virus first described in South Africa during a major outbreak. Conclusion Strong surveillance system allowed the detection of RVFV outbreak in Senegal in 2020. The obtained genomes clustered with strains from South Africa belonging to lineage H. This calls for an implementation of a strong surveillance system in wild animals, humans, and livestock simultaneously in all African Countrries.

Divergent evolution of Corynebacterium diphtheriae in India: An update from National Diphtheria Surveillance network

Diphtheria is caused by a toxigenic bacterium Corynebacterium diphtheria which is being an emerging pathogen in India. Since diphtheria morbidity and mortality continues to be high in the country, the present study aimed to study the molecular epidemiology of C. diphtheriae strains from India. A total of 441 diphtheria suspected specimens collected as part of the surveillance programme between 2015 and 2020 were studied. All the isolates were confirmed as C. diphtheriae with standard biochemical tests, ELEK’s test, and real-time PCR. Antimicrobial susceptibility testing for the subset of isolates showed intermediate susceptibility to penicillin and complete susceptible to erythromycin and cefotaxime. Isolates were characterized using multi locus sequence typing method. MLST analysis for the 216 C. diphtheriae isolates revealed major diversity among the sequence types. A total of 34 STs were assigned with majority of the isolates belonged to ST466 (30%). The second most common ST identified was ST405 that was present in 14% of the isolates. The international clone ST50 was also seen. The identified STs were grouped into 8 different clonal complexes (CC). The majority belongs to CC5 followed by CC466, CC574 and CC209, however a single non-toxigenic strain belongs to CC42. This epidemiological analysis revealed the emergence of novel STs and the clones with better dissemination properties. This study has also provided information on the circulating strains of C. diphtheriae among the different regions of India. The molecular data generated through surveillance system can be utilized for further actions in concern.

Characteristics and Factors Associated with SARS-CoV-2 Infections in Individuals That Attended Referral Hospitals from Southern Region of Bahia State, Brazil: A Surveillance Network Retrospective Study

In December 2019, a novel coronavirus was detected in Wuhan, China, and rapidly spread worldwide. In Brazil, to date, there have been more than 20,000,000 confirmed cases of COVID-19 and more than 550,000 deaths. The purpose of the current study was to determine the clinical and epidemiological profile of the population affected by COVID-19 that have attended referral hospitals in Southern region of Bahia State, to better understand the disease and its risk factors in order to enable more appropriate conduct for patients. An observational, descriptive, cross-sectional, exploratory study was conducted using secondary data collected from the Laboratório de Farmacogenômica e Epidemiologia Molecular, Universidade Estadual de Santa Cruz (LAFEM/UESC). Chi-squared and Fisher’s exact tests were applied to determine the association between clinical symptoms and laboratory results, and to identify risk factors associated with SARS-CoV-2 infection. A total of 3135 individuals with suspected severe respiratory illness were analyzed and 41.4% of them tested positive for SARS-CoV-2 infection. Male individuals and having comorbidities were risk factors significantly associated with SARS-CoV-2 infection (OR = 1.17 and OR = 1.37, respectively). Interestingly, being a healthcare professional was a significantly protective factor (OR = 0.81, p < 0.001). Our findings highlight the importance of routinely testing the population for early identification of infected individuals, and also provide important information to health authorities and police makers to improve control measures, management, and screening protocols.

COVID-19 testing, timeliness and positivity from ICMR’s laboratory surveillance network in India: Profile of 176 million individuals tested and 188 million tests, March 2020 to January 2021

Background The Indian Council of Medical Research set up a pan-national laboratory network to diagnose and monitor Coronavirus disease 2019 (COVID-19). Based on these data, we describe the epidemiology of the pandemic at national and sub-national levels and the performance of the laboratory network. Methods We included surveillance data for individuals tested and the number of tests from March 2020 to January 2021. We calculated the incidence of COVID-19 by age, gender and state and tests per 100,000 population, the proportion of symptomatic individuals among those tested, the proportion of repeat tests and test positivity. We computed median (Interquartile range—IQR) days needed for selected surveillance activities to describe timeliness. Results The analysis included 176 million individuals and 188 million tests. The overall incidence of COVID-19 was 0.8%, and 12,584 persons per 100,000 population were tested. 6.1% of individuals tested returned a positive result. Ten of the 37 Indian States and Union Territories accounted for about 75.6% of the total cases. Daily testing scaled up from 40,000 initially to nearly one million in March 2021. The median duration between symptom onset and sample collection was two (IQR = 0,3) days, median duration between both sample collection and testing and between testing and data entry were less than or equal to one day. Missing or invalid entries ranged from 0.01% for age to 0.7% for test outcome. Conclusion The laboratory network set-up by ICMR was scaled up massively over a short period, which enabled testing a large section of the population. Although all states and territories were affected, most cases were concentrated in a few large states. Timeliness between the various surveillance activities was acceptable, indicating good responsiveness of the surveillance system.

The Impact of COVID-19 on Mortality in Spain: Monitoring Excess Mortality (MoMo) and the Surveillance of Confirmed COVID-19 Deaths

Measuring mortality has been a challenge during the COVID-19 pandemic. Here, we compared the results from the Spanish daily mortality surveillance system (MoMo) of excess mortality estimates, using a time series analysis, with those obtained for the confirmed COVID-19 deaths reported to the National Epidemiological Surveillance Network (RENAVE). The excess mortality estimated at the beginning of March 2020 was much greater than what has been observed in previous years, and clustered in a very short time. The cumulated excess mortality increased with age. In the first epidemic wave, the excess mortality estimated by MoMo was 1.5 times higher than the confirmed COVID-19 deaths reported to RENAVE, but both estimates were similar in the following pandemic waves. Estimated excess mortality and confirmed COVID-19 mortality rates were geographically distributed in a very heterogeneous way. The greatest increase in mortality that has taken place in Spain in recent years was detected early by MoMo, coinciding with the spread of the COVID-19 pandemic. MoMo is able to identify risk situations for public health in a timely manner, relying on mortality in general as an indirect indicator of various important public health problems.

Multi-center prospective study on central line-associated bloodstream infections in 79 ICUs of China

Background China has not yet established a national surveillance network such as NHSN from America, so there is still no large-scale investigations on central line-associated bloodstream infection (CLABSI) incidence. Several retrospective studies in China reported that the incidence of CLABSI varied due to inconsistent diagnostic criteria. We performed a nationwide survey to investigate the utilization rate of central venous catheters (CVCs) and the incidence of CLABSI in ICUs of different areas of China. Methods This is a prospective multi-center study. Patients admitted to ICUs with the use of CVCs between January 1, 2014 and December 31, 2018 were enrolled in this study. Hospitals were given the definition of catheter-related bloodstream infection as: a laboratory-confirmed bloodstream infection where CVC was in place on the date of event or the day before. The characteristics of patients, information of catheterization, implementation rates of precautions, and CLABSIs were collected. The statistical analysis was performed by SPSS 25.0 software and website of Open Source Epidemiologic Statistics for Public Health. Results A total of 38,212 patients and 466,585 catheter days were involved in surveillance. The average CLABSI incidence in a thousand catheter days was 1.50, the lowest incidence unit was in pediatric ICU (0/1000 catheter days), and the lowest incidence area was in Northeast China (0.77/1000 catheter days), while the highest incidence unit was in cardiac ICU (2.48/1000 catheter days) and the highest incidence area was in Eastern China (1.62/1000 catheter days). The average utilization rate of CVC was 42.85%, the lowest utilization rate was in pediatric ICU (5.85%) and in Central China (38.05%), while the highest utilization rate was in surgical ICU (64.92%) and in Western China (51.57%). Among the 702 CLABSI cases reported, a total of 735 strains of pathogens were cultured. Staphylococcus spp. was the most common organism isolated (27.07%), followed by Enterobacteriaceae (22.31%). The implementation rates of all precautions showed an upward trend during the study period (P ≤ 0.001). Conclusion The average incidence of CLABSI in ICUs in China is 1.5/1000 catheter days, similar to the rates reported in developed countries but lower than previous reports in China. CLABSI incidence showed regional differences in China. It is necessary to implement targeted surveillance of CLABSI cases by using standardized CLABSI surveillance definitions and methodologies.