Epidemiology and Infection最新文献

Since the outbreak of the COVID-19 epidemic, it has posed a great crisis to the health and economy of the world. The objective is to provide a simple deep-learning approach for predicting, modelling, and evaluating the time evolutions of the COVID-19 epidemic. The Dove Swarm Search (DSS) algorithm is integrated with the echo state network (ESN) to optimize the weight. The ESN-DSS model is constructed to predict the evolution of the COVID-19 time series. Specifically, the self-driven ESN-DSS is created to form a closed feedback loop by replacing the input with the output. The prediction results, which involve COVID-19 temporal evolutions of multiple countries worldwide, indicate the excellent prediction performances of our model compared with several artificial intelligence prediction methods from the literature (e.g., recurrent neural network, long short-term memory, gated recurrent units, variational auto encoder) at the same time scale. Moreover, the model parameters of the self-driven ESN-DSS are determined which acts as a significant impact on the prediction performance. As a result, the network parameters are adjusted to improve the prediction accuracy. The prediction results can be used as proposals to help governments and medical institutions formulate pertinent precautionary measures to prevent further spread. In addition, this study is not only limited to COVID-19 time series forecasting but also applicable to other nonlinear time series prediction problems.

Lassa fever (LF) virus (LASV) is endemic in Sierra Leone (SL) and poses a significant public health threat to the region; however, no risk factors for clinical LF have been reported in SL. The objective of this study was to identify the risk factors for clinical LF in an endemic community in SL. We conducted a case-control study by enrolling 37 laboratory-confirmed LF cases identified through the national LF surveillance system in SL and 140 controls resided within a one-kilometre radius of the case household. We performed a conditional multiple logistic regression analysis to identify the risk factors for clinical LF. Of the 37 cases enrolled, 23 died (62% case fatality rate). Cases were younger than controls (19.5 years vs 28.9 years, p < 0.05) and more frequently female (64.8% vs 52.8%). Compared to the controls, clinical LF cases had higher contact with rodents (rats or mice) in their households in the preceding three weeks (83.8% vs 47.8%). Households with a cat reported a lower presence of rodents (73% vs 38%, p < 0.01) and contributed to a lower rate of clinical LF (48.6% vs 55.7%) although not statistically significant (p = 0.56). The presence of rodents in the households (matched adjusted odds ratio (mAOR): 11.1) and younger age (mAOR: 0.99) were independently associated with clinical LF.Rodent access to households and younger age were independently associated with clinical LF. Rodent access to households is likely a key risk factor for clinical LF in rural SL and potentially in other countries within the West African region. Implementing measures to control rodents and their access to households could potentially decrease the number of clinical LF cases in rural SL and West Africa.

SARS-CoV-2 transmission dynamics within households involving children are complex. We examined the association between paediatric index case (PIC) age and subsequent household SARS-CoV-2 transmission among cases reported to the Minnesota Department of Health between March 2021 and February 2022. In our primary analysis, we used logistic regression to estimate odds ratios adjusted for race/ethnicity, sex, geographic region, and disease severity among households with an unvaccinated PIC. We performed a secondary analysis among households where the PIC was eligible for vaccination adjusting for the same covariates plus time since the last vaccination. Both analyses were stratified by variant wave. During the Alpha wave, PICs of all age groups had similar odds of subsequent transmission. During Delta and Omicron waves, PICs aged 16-17 had higher odds of subsequent transmission than PICs aged 0-4 (Delta OR, 1.32; [95% CI, 1.16-1.51], Omicron OR, 4.21; [95% CI, 3.25-5.45]). In the secondary analysis, unvaccinated PICs had higher odds of subsequent transmission than vaccinated PICs (Delta OR 2.89 [95% CI, 2.18-3.84], Omicron OR 1.35 [95% CI, 1.21-1.50]). Enhanced preventative measures, especially for 12-17-year-olds, may limit SARS-CoV-2 transmission within households involving children.

Foodborne diseases are ongoing and significant public health concerns. This study analysed data obtained from the Foodborne Outbreaks Surveillance System of Wenzhou to comprehensively summarise the characteristics of foodborne outbreaks from 2012 to 2022. A total of 198 outbreaks were reported, resulting in 2,216 cases, 208 hospitalisations, and eight deaths over 11 years. The findings suggested that foodborne outbreaks were more prevalent in the third quarter, with most cases occurring in households (30.8%). Outbreaks were primarily associated with aquatic products (17.7%) as sources of contamination. The primary transmission pathways were accidental ingestion (20.2%) and multi-pathway transmission (12.1%). Microbiological aetiologies (46.0%), including Vibrio parahaemolyticus, Salmonella ssp., and Staphylococcus aureus, were identified as the main causes of foodborne outbreaks. Furthermore, mushroom toxins (75.0%), poisonous animals (12.5%), and poisonous plants (12.5%) were responsible for deaths from accidental ingestion. This study identified crucial settings and aetiologies that require the attention of both individuals and governments, thereby enabling the development of effective preventive measures to mitigate foodborne outbreaks, particularly in coastal cities.

Our study aims to enhance future pandemic preparedness by leveraging insights from historical pandemics, focusing on the multidimensional analysis of past outbreaks. In this study, we digitised and analysed for the first time aggregated mortality and morbidity data series from the Russian flu in Switzerland in 1889/1890 and subsequent years to assess its comprehensive impact. The strongest effects were observed in January 1890, showing significant monthly excess mortality from all causes compared to the preceding five years (58.9%, 95% CI 36.6 to 81.0). Even though the whole of Switzerland was affected, the impact varied regionally due to ecological variations. Deaths from other conditions such as tuberculosis and heart disease also increased during this period. A significant drop in birth occurred 9 months later, in the autumn of 1890. Morbidity estimates by physicians suggest that around 60% of the Swiss population fell ill, with regional discrepancies and earlier outbreaks among postal workers (1-2 weeks earlier than the rest of the population). A subsequent spike in all-cause excess and influenza mortality was recorded in January 1894 but more localized than in 1890. Our findings show no cross-protection between the 1890 and 1894 outbreaks.

Residual blood specimens provide a sample repository that could be analyzed to estimate and track changes in seroprevalence with fewer resources than household-based surveys. We conducted parallel facility and community-based cross-sectional serological surveys in two districts in India, Kanpur Nagar District, Uttar Pradesh, and Palghar District, Maharashtra, before and after a measles-rubella supplemental immunization activity (MR-SIA) from 2018 to 2019. Anonymized residual specimens from children 9 months to younger than 15 years of age were collected from public and private diagnostic laboratories and public hospitals and tested for IgG antibodies to measles and rubella viruses. Significant increases in seroprevalence were observed following the MR SIA using the facility-based specimens. Younger children whose specimens were tested at a public facility in Kanpur Nagar District had significantly lower rubella seroprevalence prior to the SIA compared to those attending a private hospital, but this difference was not observed following the SIA. Similar increases in rubella seroprevalence were observed in facility-based and community-based serosurveys following the MR SIA, but trends in measles seroprevalence were inconsistent between the two specimen sources. Despite challenges with representativeness and limited metadata, residual specimens can be useful in estimating seroprevalence and assessing trends through facility-based sentinel surveillance.

By coupling long-range polymerase chain reaction, wastewater-based epidemiology, and pathogen sequencing, we show that adenovirus type 41 hexon-sequence lineages, described in children with hepatitis of unknown origin in the United States in 2021, were already circulating within the country in 2019. We also observed other lineages in the wastewater, whose complete genomes have yet to be documented from clinical samples.

We report a foodborne outbreak of the previously undetected Cryptosporidium parvum gp60 subtype IIγA11. In December 2023, notifications of cryptosporidiosis cases increased in Sweden, prompting the initiation of a national outbreak investigation, and a case-control study was performed to identify the source. We identified 60 cases between 15 December 2023 and 1 January 2024. The median age was 44 years (range: 16-81), and 73% were women. Controls were recruited from a national random pool; frequency was matched by age group and sex. Compared to controls, cases were more likely to have consumed items from salad bars in grocery stores (8% vs. 85%; adjusted odds ratios [aOR]: 58; 95% confidence interval [CI]: 22-186). In regards to food items from the salad bars, cases were more likely to have consumed kale mix salad compared to controls (62% vs. 32%; aOR: 3.6; 95%CI: 1.2-12). Trace-back investigations identified kale producers from Sweden, Belgium, and Spain, but no particular grower was identified, and no food samples were available for microbiological analysis. Our investigation indicates that leafy greens such as kale may contain Cryptosporidium spp. and cause outbreaks and it is important to understand how the contamination occurs to prevent future outbreaks and apply adequate preventive measures.

Since the beginning of mass vaccination campaign for COVID-19 in Italy (December 2020) and following the rapidly increasing vaccine administration, sex differences have been emphasized. Nevertheless, incomplete and frequently incoherent sex-disaggregated data for COVID-19 vaccinations are currently available, and vaccines clinical studies generally do not include sex-specific analyses for safety and efficacy. We looked at sex variations in the COVID-19 vaccine's effectiveness against infection and severe disease outcomes. We conducted a nationwide retrospective cohort study on Italian population, linking information on COVID-19 vaccine administrations obtained through the Italian National Vaccination Registry, with the COVID-19 integrated surveillance system, held by the Istituto Superiore di Sanità. The results showed that, in all age groups, vaccine effectiveness (VE) was higher in the time-interval ≤120 days post-vaccination. In terms of the sex difference in vaccination effectiveness, men and women were protected against serious illness by vaccination in a comparable way, while men were protected against infection to a somewhat greater extent than women. To fully understand the mechanisms underlying the sex difference in vaccine response and its consequences for vaccine effectiveness and development, further research is required. The sex-related analysis of vaccine response may contribute to adjust vaccination strategies, improving overall public health programmes.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.