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Purpose: To minimize the impact of the COVID-19 pandemic, local public health authorities are often required to make prompt and informed decisions on anticipated case-loads, resource allocation for surveillance and testing, and public health intervention appropriateness. The objective of this research was to develop a near-term forecasting model to predict COVID-19 cases using real-time human mobility information in Ontario, Canada to assist public health authorities with outbreak response. Methods & Materials: We utilized a deep neural network model to generate a short-term forecast of new COVID-19 cases by two weeks from May to August 2021. Variable selection was informed by a recent literature review and our ongoing research associating COVID-19 cases with human mobility, demographic and socio-economic factors. A real-time human mobility statistics consisting of a weekly summary of short and long-distance movement, demographic characteristics, weather, vaccination coverage, geo-location, and reported COVID-19 cases two weeks prior were included as predictors. We considered weeks as temporal and health regions as geographic units to account for population-level variabilities. We used a holdout method for model validation of over 300 iterations. Average mean squared error (MSE) and 95% confidence interval (CI) along with overlaying forecasted COVID-19 cases over the reported were used to evaluate the overall model fit. The model predictions were summarized as means and 95% CIs. Results: Our best forecasting model had a mean MSE of 0.53 (95% CI: 0.49 – 0.56). Since May 2021, the overall trend of the reported COVID-19 cases in Ontario closely followed the forecasted cases, about 89% of the reported cases were within 1.5 times the interquartile range (IQR) and all were within the entire range of the distribution of the predictions. Forecasting accuracy also varied by health region characteristics, such a population size and density, remoteness, and reported COVID-19 case volume during the most recent weeks. Conclusion: A near-term prediction of new COVID-19 cases with real-time population-level data could help public health authorities anticipate, plan and monitor disease burden in a population. Such predictions also allow the assessment of population-level health interventions to minimize new COVID-19 cases on a real-time basis and inform prompt decision making.

Background: Mosquito-borne orthobunyaviruses are a growing priority for public and animal health in Canada. It is anticipated that disease incidence will increase due to a warming climate, given that habitats are expanding for reservoir hosts and vectors, particularly in Canada. Little is known about the ecology of primary vectors that perpetuate these orthobunyaviruses, including the viral transmission cycle and the impact of climatic and landscape factors. Methods: A scoping review was conducted to describe the current state of knowledge on the epidemiology of orthobunyaviruses relevant to Canada. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines was used to characterize studies focused on vector species. A literature search was conducted in six databases and gray literature. Eligible studies characterized orthobunyavirus epidemiology related to vector species, including viral competency, geospatial distributions, seasonal trends, and/or risk factors. Results: A total of 1734 unique citations were identified. Screening of these citations revealed 172 relevant studies, from which 87 studies presented primary data related to vectors. The orthobunyaviruses included Cache Valley virus (CVV), Jamestown Canyon virus (JCV), Snowshoe Hare virus (SHV), and La Crosse virus (LACV). Surveillance was the predominant study focus, with most citations representing the United States, specifically, LACV surveillance in Tennessee, followed by CVV and JCV in Connecticut. Orthobunyaviruses were detected in many mosquito species across multiple genera, with high vector specificity only being reported for LACV, which included Aedes triseriatus , Aedes albopictus , and Aedes japonicus. Peridomestic areas were positively associated with infected mosquitoes compared with dense forests. Orthobunyavirus infections, coinfections, and gut microbiota affected mosquito feeding and breeding behavior. Conclusion: Knowledge gaps included Canadian surveillance data, disease modeling, and risk projections. Further research in these areas, especially accounting for climate change, is needed to guide health policy for prevention of orthobunyaviral disease.

Background: Mosquito-borne orthobunyaviruses in Canada are a growing public health concern. Orthobunyaviral diseases are commonly underdiagnosed and in Canada, likely underreported as surveillance is passive. No vaccines or specific treatments exist for these disease agents. Further, climate change is facilitating habitat expansion for relevant reservoirs and vectors, and it is likely that the majority of the Canadian population is susceptible to these viruses. Methods: A scoping review was conducted to describe the current state of knowledge on orthobunyavirus epidemiology in Canada. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guideline was used. Literature searches were conducted in six databases and in gray literature. The epidemiology of orthobunyaviruses was characterized for studies focusing on host species, including spatiotemporal patterns, risk factors, and climate change impact. Results: A total of 172 relevant studies were identified from 1734 citations from which 95 addressed host species, including humans, wildlife, and domestic animals including livestock. The orthobunyaviruses-Cache Valley virus (CVV), Jamestown Canyon virus (JCV), Snowshoe Hare virus (SHV), and La Crosse virus (LACV)-were identified, and prevalence was widespread across vertebrate species. CVV, JCV, and SHV were detected across Canada and the United States. LACV was reported only in the United States, predominantly the Mid-Atlantic and Appalachian regions. Disease varied by orthobunyavirus and was associated with age, environment, preexisting compromised immune systems, or livestock breeding schedule. Conclusion: Knowledge gaps included seroprevalence data in Canada, risk factor analyses, particularly for livestock, and disease projections in the context of climate change. Additional surveillance and mitigation strategies, especially accounting for climate change, are needed to guide future public health efforts to prevent orthobunyavirus exposure and disease.

Introduction: Zika virus (ZIKV) is primarily transmitted byAedes aegypti and Aedes albopictus mosquitoes between humans and non-human primates. Climate change may enhance virus reproduction in Aedes spp. mosquito populations, resulting in intensified ZIKV outbreaks. The study objective was to explore how an outbreak similar to the 2016 ZIKV outbreak in Brazil might unfold with projected climate change., Methods: A compartmental infectious disease model that included compartments for humans and mosquitoes was developed to fit the 2016 ZIKV outbreak data from Brazil using least squares optimization. To explore the impact of climate change, published polynomial relationships between temperature and temperature-sensitive mosquito population and virus transmission parameters (mosquito mortality, development rate, and ZIKV extrinsic incubation period) were used. Projections for future outbreaks were obtained by simulating transmission with effects of projected average monthly temperatures on temperature-sensitive model parameters at each of three future time periods: 2011-2040, 2041-2070, and 2071-2100. The projected future climate was obtained from an ensemble of regional climate models (RCMs) obtained from the Co-Ordinated Regional Downscaling Experiment (CORDEX) that used Representative Concentration Pathways (RCP) with two radiative forcing values, RCP4.5 and RCP8.5. A sensitivity analysis was performed to explore the impact of temperature-dependent parameters on the model outcomes., Results: Climate change scenarios impacted the model outcomes, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the duration of the ZIKV outbreak. Comparing 2070-2100 to 2016, using RCP4.5, the peak incidence was 22,030 compared to 10,473; the time to epidemic peak was 12 compared to 9 weeks, and the outbreak duration was 52 compared to 41 weeks. Comparing 2070-2100 to 2016, using RCP8.5, the peak incidence was 21,786 compared to 10,473; the time to epidemic peak was 11 compared to 9 weeks, and the outbreak duration was 50 compared to 41weeks. The increases are due to optimal climate conditions for mosquitoes, with the mean temperature reaching 28 °C in the warmest months. Under a high emission scenario (RCP8.5), mean temperatures extend above optimal for mosquito survival in the warmest months., Conclusion: Outbreaks of ZIKV in locations similar to Brazil are expected to be more intense with a warming climate. As climate change impacts are becoming increasingly apparent on human health, it is important to quantify the effect and use this knowledge to inform decisions on prevention and control strategies., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Yellow fever (YF) is primarily transmitted by Haemagogus species of mosquitoes. Under climate change, mosquitoes and the pathogens that they carry are expected to develop faster, potentially impacting the case count and duration of YF outbreaks. The aim of this study was to determine how YF virus outbreaks in Brazil may change under future climate, using ensemble simulations from regional climate models under RCP4.5 and RCP8.5 scenarios for three time periods: 2011-2040 (short-term), 2041-2070 (mid-term), and 2071-2100 (long-term)., Methods: A compartmental model was developed to fit the 2017/18 YF outbreak data in Brazil using least squares optimization. To explore the impact of climate change, temperature-sensitive mosquito parameters were set to change over projected time periods using polynomial equations fitted to their relationship with temperature according to the average temperature for years 2011-2040, 2041-2070, and 2071-2100 for climate change scenarios using RCP4.5 and RCP8.5, where RCP4.5/RCP8.5 corresponds to intermediate/high radiative forcing values and to moderate/higher warming trends. A sensitivity analysis was conducted to determine how the temperature-sensitive parameters impacted model results, and to determine how vaccination could play a role in reducing YF in Brazil., Results: Yellow fever case projections for Brazil from the models varied when climate change scenarios were applied, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the outbreak duration. Overall, a decrease in YF cases and outbreak duration was observed. Comparing the observed incidence in 2017/18 to the projected incidence in 2070-2100, for RCP4.5, the cumulative case incidence decreased from 184 to 161, and the outbreak duration decreased from 21 to 20 weeks. For RCP8.5, the peak case incidence decreased from 184 to 147, and the outbreak duration decreased from 21 to 17 weeks. The observed decrease was primarily due to temperature increasing beyond that suitable for Haemagogus mosquito survival., Conclusions: Climate change is anticipated to have an impact on mosquito-borne diseases. We found outbreaks of YF may reduce in intensity as temperatures increase in Brazil; however, temperature is not the only factor involved with disease transmission. Other factors must be explored to determine the attributable impact of climate change on mosquito-borne diseases., Competing Interests: There are no conflicts of interest to disclose. This research required no ethics approval as it only used publicly available unidentifiable data., (© 2021 The Authors.)

Background: As globalization and climate change progress, the expansion and introduction of vector-borne diseases (VBD) from endemic regions to non-endemic regions is expected to occur. Mathematical and statistical models can be useful in predicting when and where these changes in distribution may happen. Our objective was to conduct a scoping review to identify and characterize predictive and importation models related to vector-borne diseases that exist in the global literature., Methods: A literature search was conducted to identify publications published between 1999 and 2016 from five scientific databases using relevant keywords. All publications had to be in English or French, and include a predictive or importation model on VBDs, pathogens, reservoirs and/or vectors. Relevance screening and data characterization were performed by two reviewers using pretested forms. The data were analyzed using descriptive statistics., Results: The search initially identified 19 710 unique articles, reports, and conference abstracts. This was reduced to 428 relevant documents after relevance screening and data charting. About half of the models used mathematical techniques, and the remainder were statistical. Most of the models were predictive (87%), rather than importation (5%). The most commonly investigated diseases were malaria and dengue fever. Around 12% of the publications did not report all the parameters used in their model. Only 29% of the models incorporated the impacts of climate change., Conclusions: A wide variety of mathematical and statistical models on vector-borne diseases exist. Researchers creating their own mathematical and/or statistical models may be able to use this scoping review to be informed about the diseases and/or regions, parameters, model types, and methodologies used in published models., Competing Interests: The authors have declared that no competing interests exist.

Objective: The CALIPER program has established a comprehensive database of pediatric reference intervals using largely the Abbott ARCHITECT biochemical assays. To expand clinical application of CALIPER reference standards, the present study is aimed at transferring CALIPER reference intervals from the Abbott ARCHITECT to Beckman Coulter AU assays., Design and Methods: Transference of CALIPER reference intervals was performed based on the CLSI guidelines C28-A3 and EP9-A2. The new reference intervals were directly verified using up to 100 reference samples from the healthy CALIPER cohort., Results: We found a strong correlation between Abbott ARCHITECT and Beckman Coulter AU biochemical assays, allowing the transference of the vast majority (94%; 30 out of 32 assays) of CALIPER reference intervals previously established using Abbott assays. Transferred reference intervals were, in general, similar to previously published CALIPER reference intervals, with some exceptions. Most of the transferred reference intervals were sex-specific and were verified using healthy reference samples from the CALIPER biobank based on CLSI criteria. It is important to note that the comparisons performed between the Abbott and Beckman Coulter assays make no assumptions as to assay accuracy or which system is more correct/accurate., Conclusion: The majority of CALIPER reference intervals were transferrable to Beckman Coulter AU assays, allowing the establishment of a new database of pediatric reference intervals. This further expands the utility of the CALIPER database to clinical laboratories using the AU assays; however, each laboratory should validate these intervals for their analytical platform and local population as recommended by the CLSI., (Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Background: The CALIPER program has established a comprehensive database of age- and sex-stratified pediatric reference intervals for over 85 common biochemical markers, largely using the Abbott ARCHITECT assays. To allow a broader application of the CALIPER database, we examined transference to 36 Beckman Coulter Synchron Unicel DxC800 assays, based on the CLSI C28-A3/EP9-A3 guidelines., Methods: Patient sample comparisons were performed for 36 biochemical assays using 200 serum specimens obtained from pediatric patients on the Abbott ARCHITECT ci8200 and the Beckman Coulter DxC800. For each analyte, R(2) values were calculated to assess the quality of correlation between the platforms. Statistical criteria used to assess transferability included a) regression analysis to create the equation of the line of best fit, b) standardized residual, c) Bland-Altman, and d) quantile-quantile plots. Transferred reference intervals were further verified by analyzing serum samples from 100 healthy children from the CALIPER cohort on the Beckman Coulter system., Results: The reference intervals for most of the assessed analytes were transferable to Beckman Coulter assays (31 out of 36 studied) and the newly calculated reference intervals were verified through analysis of CALIPER reference samples (28 out of 31). Eighteen assays demonstrated excellent correlation (R(2)≥0.95), and 13 assays showed strong correlation (0.77≤R2≤0.94)., Conclusion: The current study allowed successful transference of a large number of biochemical markers from the CALIPER database to assays on the Beckman Coulter DxC800 platform. Transference should facilitate broader application of CALIPER reference intervals at pediatric centers using DxC biochemical assays., (Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Peer review has been defined as a process of subjecting an author's scholarly work, research or ideas to the scrutiny of others who are experts in the same field. It functions to encourage authors to meet the accepted high standards of their discipline and to control the dissemination of research data to ensure that unwarranted claims, unacceptable interpretations or personal views are not published without prior expert review. Despite its wide-spread use by most journals, the peer review process has also been widely criticised due to the slowness of the process to publish new findings and due to perceived bias by the editors and/or reviewers. Within the scientific community, peer review has become an essential component of the academic writing process. It helps ensure that papers published in scientific journals answer meaningful research questions and draw accurate conclusions based on professionally executed experimentation. Submission of low quality manuscripts has become increasingly prevalent, and peer review acts as a filter to prevent this work from reaching the scientific community. The major advantage of a peer review process is that peer-reviewed articles provide a trusted form of scientific communication. Since scientific knowledge is cumulative and builds on itself, this trust is particularly important. Despite the positive impacts of peer review, critics argue that the peer review process stifles innovation in experimentation, and acts as a poor screen against plagiarism. Despite its downfalls, there has not yet been a foolproof system developed to take the place of peer review, however, researchers have been looking into electronic means of improving the peer review process. Unfortunately, the recent explosion in online only/electronic journals has led to mass publication of a large number of scientific articles with little or no peer review. This poses significant risk to advances in scientific knowledge and its future potential. The current article summarizes the peer review process, highlights the pros and cons associated with different types of peer review, and describes new methods for improving peer review.