Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries

Many countries implemented measures to control the COVID-19 pandemic, but the effects of these measures have varied greatly. We evaluated the effects of different policies, the prevalence of dominant variants (e.g., Delta), and vaccination on the characteristics of the COVID-19 pandemic in eight countries. We quantified the lag times of different non-pharmaceutical interventions (NPIs) and vaccination using a distributed lag non-linear model (DLNM). We also tested whether these lag times were reasonable by analyzing changes in daily cases and the effective reproductive number (Rt)over time. Our results indicated that the response to vaccination in countries with continuous vaccination programs lagged by at least 40 days, and the lag time for a response to NPIs was at least 14 days. A rebound was most likely to occur during the 40 days after the first vaccine dose. We also found that the combination of school closure, workplace closure, restrictions on mass gatherings, and stay-at-home requirements were successful in containing the pandemic. Our results thus demonstrated that vaccination was effective, although some regions were adversely affected by new variants and low vaccination coverage. Importantly, relaxation of NPIs soon after implementation of a vaccination program may lead to a rebound.

The Impact of Pre- and Probiotic Product Combinations on Ex vivo Growth of Avian Pathogenic Escherichia coli and Salmonella Enteritidis

Due to the global spread of antibiotic resistance, there is a strong demand to replace antimicrobial growth promotors in livestock. To identify suitable additives that inhibit the growth of avian pathogenic Escherichia coli O1/O18 and Salmonella enterica serotype Enteritidis strains, an ex vivo screening was performed. Inulin and fructooligosaccharides (FOS) were investigated as prebiotics. Enterococcus faecium and Bacillus coagulans served as probiotic strains. Firstly, the pathogen was anaerobically incubated in caecal digesta from different broiler breeder flocks with the addition of feed additives. Secondly, subsamples of these suspensions were incubated in an antibiotic medium for selective growth of the pathogen. During this step, turbidity was recorded, and lag times were calculated for each pathogen as readout of growth inhibition. Combinations of E. faecium with inulin or FOS significantly extended the lag time for E. coli compared to control. Moreover, older age was a significant factor to enhance this inhibitory effect. In contrast, the combination of FOS and B. coagulans showed shorter lag times for S. Enteritidis. Our results indicate that the E. faecium strain with prebiotics may inhibit the pathogen proliferation in the studied poultry flocks. Furthermore, our results suggest that prophylactic treatments should be assigned by feed additive, age and animal origin.

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

Family violence homicide rates: a state-wide comparison of three data sources in Victoria, Australia

Background Family violence homicide (FVH) is a major public health and social problem in Australia. FVH trend rates are key outcomes that determine the effectiveness of current management practices and policy directions. Data source–related methodological problems affect FVH research and policy and the reliable measurement of homicide trends. Objective This study aimed to determine data reliability and temporal trends of Victorian FVH rates and sex and relationship patterns. Method FVH rates per 100,000 persons in Victoria were compared between the National Coronial Information System (NCIS), Coroners Court of Victoria (CCoV) Homicide Register, and the National Homicide Monitoring Program (NHMP). Trends for 2001–2017 were analysed using Joinpoint regression. Crude rates were determined by sex and relationship categories using annual frequencies and Australian Bureau of Statistics population estimates. Results NCIS closed FVH cases totalled 360, and an apparent downward trend in the FVH rate was identified. However, CCoV and NHMP rates trended upwards. While NCIS and CCoV were case-based, NHMP was incident-based, contributing to rate variations. The NCIS-derived trend was particularly impacted by unavailable case data, potential coding errors and entry backlog. Neither CCoV nor NHMP provided victim-age in their public domain data to enable age-adjusted rate comparison. Conclusion Current datasets have limitations for FVH trend determination; most notably lag times for NCIS data. Implications This study identified an indicative upward trend in FVH rates in Victoria, suggesting insufficiency of current management and policy settings for its prevention and control.

PP118 A Survival Analysis Of The Lag Times In The Publication Of Network Meta-Analyses

IntroductionThe use of inconsistent and outdated information may significantly compromise healthcare decision-making. We aimed to assess the extent of lag times in the publication and indexing of network meta-analyses (NMAs).MethodsSearches for NMAs on drug interventions were performed in PubMed (May 2020). Lag times were measured as the time between the last systematic search and the date of the article's submission, acceptance, online publication, indexing, and Medical Subject Heading (MeSH) allocation. Correlations between lag times and time trends were calculated by means of Spearman's rank correlation coefficient. Time-to-event analyses were performed considering independent variables such as geographical origin, journal impact factor, Scopus CiteScore, and open access status.ResultsWe included 1,245 NMAs. The median time from last search to article submission and publication was 6.8 months and 11.6 months, respectively. Only five percent of authors updated their literature searches after submission. There was a very slight decreasing historical trend for acceptance (r =−0.087; p = 0.01), online publication (r =−0.08; p = 0.008), and indexing lag times (r =−0.080; p = 0.007). Journal impact factor influenced the MeSH allocation process (log-rank p = 0.02). Slight differences were observed for acceptance, online publication, and indexing lag times when comparing open access and subscription journals.ConclusionsAuthors need to update their literature searches before submission to reduce evidence production time. Peer reviewers and editors should ensure that authors comply with NMA standards and encourage the development of living meta-analyses.

Are We Missing the Opportunity of Low-Carbon Lifestyles? International Climate Policy Commitments and Demand-Side Gaps

Current commitments in nationally determined contributions (NDCs) are insufficient to remain within the 2-degree climate change limit agreed to in the Paris Agreement. The Intergovernmental Panel on Climate Change (IPCC) states that lifestyle changes are now necessary to stay within the limit. We reviewed a range of NDCs and national climate change strategies to identify inclusion of low-carbon lifestyles. We found that most NDCs and national climate change strategies do not yet include the full range of necessary mitigation measures targeting lifestyle change, particularly those that could reduce indirect emissions. Some exceptional NDCs, such as those of Austria, Slovakia, Portugal and the Netherlands, do include lifestyle changes, such as low-carbon diets, reduced material consumption, and low-carbon mobility. Most countries focus on supply-side measures with long lag times and might miss the window of opportunity to shape low-carbon lifestyle patterns, particularly those at early stages of development trajectories. Systemic barriers exist that should be corrected before new NDCs are released, including changing the accounting and reporting methodology, accounting for extraterritorial emissions, providing guidance on NDC scope to include the menu of options identified by the IPCC, and increasing support for national level studies to design demand-side policies.

An alternative index to NINO3.4 for predicting monthly rainfall anomaly in East Java

ENSO and NINO3.4 index are known to have some relation with Indonesian monthly rainfall anomaly. There is a gap between scientific studies on one hand and forecasting operational problems on the other hand since previous studies are not giving enough attention to the N+1,2,3 concept. The concept is about giving three next month rainfall anomaly prediction rather than connecting ENSO index with three-monthly rainfall anomaly. Here we propose an alternative index for ENSO. The median of categorical gridded rainfall anomaly of East Java is used as a general representation. Plots of correlation between the median and anomaly sea surface temperature from ReynSmithOIv2 are used to determine locus candidate to be compared with NINO3.4. The Near Maritime Continent (NMC) index is selected and proven to have a significant average difference in correlation between based on bootstrap technique. Verification of prediction used in this study is simulation-based and only uses binary hit-miss final result. Prediction is generated by simple linear regression with three lag times (2,3, and 4). Verification based on three categories shows that NMC’s hits are higher than NINO.34 in lag-2 and lag-3. In lag-2, NMC’s verification is 57.5% compared to only 38.7% for NINO3.4. However, NINO3.4 is a still better predictor in lag-4. Radar charts of monthly verifications are also developed.