Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada

Background Limitations in laboratory diagnostic capacity impact population surveillance of COVID-19. It is currently unknown whether participatory surveillance tools for COVID-19 correspond to government-reported case trends longitudinally and if it can be used as an adjunct to laboratory testing. The primary objective of this study was to determine whether self-reported COVID-19-like illness reflected laboratory-confirmed COVID-19 case trends in Ontario Canada. Methods We retrospectively analyzed longitudinal self-reported symptoms data collected using an online tool–Outbreaks Near Me (ONM)–from April 20th, 2020, to March 7th, 2021 in Ontario, Canada. We measured the correlation between COVID-like illness among respondents and the weekly number of PCR-confirmed COVID-19 cases and provincial test positivity. We explored contemporaneous changes in other respiratory viruses, as well as the demographic characteristics of respondents to provide context for our findings. Results Between 3,849–11,185 individuals responded to the symptom survey each week. No correlations were seen been self-reported CLI and either cases or test positivity. Strong positive correlations were seen between CLI and both cases and test positivity before a previously documented rise in rhinovirus/enterovirus in fall 2020. Compared to participatory surveillance respondents, a higher proportion of COVID-19 cases in Ontario consistently came from low-income, racialized and immigrant areas of the province- these groups were less well represented among survey respondents. Interpretation Although digital surveillance systems are low-cost tools that have been useful to signal the onset of viral outbreaks, in this longitudinal comparison of self-reported COVID-like illness to Ontario COVID-19 case data we did not find this to be the case. Seasonal respiratory virus transmission and population coverage may explain this discrepancy.

Use of chatbots during the coronavirus pandemic: A scoping review and case study (Preprint)

BACKGROUND Digital technologies such as chatbots have been widely used during the pandemic. However, the use of such technologies has both benefits and risks. OBJECTIVE An updated review of COVID-19 chatbots is needed to determine how such technologies can be used to provide maximum health benefits, especially during a pandemic METHODS In this study, we reviewed the literature on the use of chatbots during the COVID-19 pandemic, and identifies any issues and gaps in the literature, so the results can inform future scholars on chatbot and emergency response design and evaluation. RESULTS : The results indicate that chatbots have been widely used at both small and national levels across countries. Known uses of chatbots during the pandemic were in population surveillance, case identification, contact tracing, disease management, and general public communication. Although chatbots have offered ease of use and scalability, concerns have been raised, particularly about how chatbots will ensure data privacy and cybersecurity, bias due to limited user representation, and the risks of misinformation. Thus, we highlight both the benefits and risks of chatbots that provide COVID-related information. We found that chatbots offer speed, scalability, accessibility, personalization, and quickness support for self-care while also reducing hospital load and stigma. Despite these benefits, chatbots have some risks and issues to address, including issues pertaining to the actual effectiveness of chatbots, data privacy, cybersecurity, safety risks, and misinformation. There is also a need for a solid infrastructure, guidance, and representative user groups and engagement. Beyond outlining the key benefits and risks of using chatbots during the COVID-19 pandemic, we also highlighted the best practices from the literature and strategies recommended by the World Health Organization (WHO) and other international organizations to address the key issues in the use of chatbots for pandemic preparedness and response. We also determined the different chatbot strategies used by the WHO to address COVID-19 that are critical in guiding future pandemic preparedness and response efforts worldwide. We found specific use cases showing how interfaces across various sectors, as well as support from different funding sources, are critical to reaching and engaging additional chatbot users and ensuring high chatbot quality. CONCLUSIONS Health systems in the future will likely become digital. Additional guidelines and research must be done in the evaluation and use of new technologies, such as chatbots, in emergency preparedness and response. Collaborations across multiple actors are needed to the ensure efficiency and effectiveness of the use of chatbots in the healthcare system.

Attraction of the Mosquitoes Aedes aegypti and Aedes albopictus (Diptera: Culicidae) to a 3-Part Phytochemical Blend in a Mesocosm

Aedes aegypti (L.) and Aedes albopictus (Skuse) mosquitoes of both sexes were attracted to a 3-part volatile synthetic phytochemical blend but differed according to their component ratios, 7:3:2 or 1:1:1, and their initial concentrations. These arbovirus vectors were presented with the blends as baits in paired baited and blank CFG traps in a large greenhouse mesocosm. Ae. aegypti attraction was highest at a 7:3:2 blend ratio, but at a concentration half that found most effective for an anopheline mosquito species in outdoor screenhouses. Both lower and higher concentrations yielded substantially lower attraction scores for Ae. aegypti. By contrast, the few tests conducted on Ae. albopictus showed that it was not as sensitive to concentration, but again it was more responsive to the 7:3:2 ratio of components than to the 1:1:1 ratio. The two sexes of both species were represented equally in the trap catches, indicating the potential value of this and similar attractive blends for population surveillance and control of Aedes mosquitoes.

Application of SARS-CoV-2 Serology to Address Public Health Priorities

Background: Antibodies against SARS-CoV-2 can be detected by various testing platforms, but a detailed understanding of assay performance is critical.Methods: We developed and validated a simple enzyme-linked immunosorbent assay (ELISA) to detect IgG binding to the receptor-binding domain (RBD) of SARS-CoV-2, which was then applied for surveillance. ELISA results were compared to a set of complimentary serologic assays using a large panel of clinical research samples.Results: The RBD ELISA exhibited robust performance in ROC curve analysis (AUC> 0.99; Se = 89%, Sp = 99.3%). Antibodies were detected in 23/353 (6.5%) healthcare workers, 6/9 RT-PCR-confirmed mild COVID-19 cases, and 0/30 non-COVID-19 cases from an ambulatory site. RBD ELISA showed a positive correlation with neutralizing activity (p = <0.0001, R2 = 0.26).Conclusions: We applied a validated SARS-CoV-2-specific IgG ELISA in multiple contexts and performed orthogonal testing on samples. This study demonstrates the utility of a simple serologic assay for detecting prior SARS-CoV-2 infection, particularly as a tool for efficiently testing large numbers of samples as in population surveillance. Our work also highlights that precise understanding of SARS-CoV-2 infection and immunity at the individual level, particularly with wide availability of vaccination, may be improved by orthogonal testing and/or more complex assays such as multiplex bead assays.

A Multiplex Noninvasive Salivary Antibody Assay for SARS-CoV-2 Infection and Its Application in a Population-Based Survey by Mail

Given the enormous impacts of the COVID-19 pandemic, developing tools for population surveillance of infection is of paramount importance. This article describes the development of a multiplex immunoassay on a Luminex platform to measure salivary immunoglobulin G responses to the spike protein, its two subunits and receptor binding domain, and the nucleocapsid protein of SARS-CoV-2.

Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.

COVID RADAR app: Description and validation of population surveillance of symptoms and behavior in relation to COVID-19

Background Monitoring of symptoms and behavior may enable prediction of emerging COVID-19 hotspots. The COVID Radar smartphone app, active in the Netherlands, allows users to self-report symptoms, social distancing behaviors, and COVID-19 status daily. The objective of this study is to describe the validation of the COVID Radar. Methods COVID Radar users are asked to complete a daily questionnaire consisting of 20 questions assessing their symptoms, social distancing behavior, and COVID-19 status. We describe the internal and external validation of symptoms, behavior, and both user-reported COVID-19 status and state-reported COVID-19 case numbers. Results Since April 2nd, 2020, over 6 million observations from over 250,000 users have been collected using the COVID Radar app. Almost 2,000 users reported having tested positive for SARS-CoV-2. Amongst users testing positive for SARS-CoV-2, the proportion of observations reporting symptoms was higher than that of the cohort as a whole in the week prior to a positive SARS-CoV-2 test. Likewise, users who tested positive for SARS-CoV-2 showed above average risk social-distancing behavior. Per-capita user-reported SARS-CoV-2 positive tests closely matched government-reported per-capita case counts in provinces with high user engagement. Discussion The COVID Radar app allows voluntarily self-reporting of COVID-19 related symptoms and social distancing behaviors. Symptoms and risk behavior increase prior to a positive SARS-CoV-2 test, and user-reported case counts match closely with nationally-reported case counts in regions with high user engagement. These results suggest the COVID Radar may be a valid instrument for future surveillance and potential predictive analytics to identify emerging hotspots.

Global prevalence of physical activity for children and adolescents; inconsistencies, research gaps, and recommendations: a narrative review

Background One of the strategic actions identified in the Global Action Plan on Physical Activity (PA) 2018–2030 is the enhancement of data systems and capabilities at national levels to support regular population surveillance of PA. Although national and international standardized surveillance of PA among children and adolescents has increased in recent years, challenges for the global surveillance of PA persist. The aims of this paper were to: (i) review, compare, and discuss the methodological inconsistencies in children and adolescents’ physical activity prevalence estimates from intercontinental physical activity surveillance initiatives; (ii) identify methodological limitations, surveillance and research gaps. Methods Intercontinental physical activity surveillance initiatives for children and adolescents were identified by experts and through non-systematic literature searches. Prevalence of meeting PA guidelines by country, gender, and age were extracted when available. A tool was created to assess the quality of the included initiatives. Methods and PA prevalence were compared across data/studies and against the methodological/validity/translation differences. Results Eight intercontinental initiatives were identified as meeting the selection criteria. Methods and PA definition inconsistencies across and within included initiatives were observed, resulting in different estimated national prevalence of PA, and initiatives contradicting each other’s cross-country comparisons. Three findings were consistent across all eight initiatives: insufficient level of PA of children and adolescents across the world; lower levels of PA among girls; and attenuation of PA levels with age. Resource-limited countries, younger children, children and adolescents not attending school, with disability or chronic conditions, and from rural areas were generally under/not represented. Conclusions There are substantial inconsistencies across/within included initiatives, resulting in varying estimates of the PA situation of children and adolescents at the global, regional and national levels. The development of a new PA measurement instrument that would be globally accepted and harmonized is a global health priority to help improve the accuracy and reliability of global surveillance.

Population surveillance approach to detect and respond to new clusters of COVID-19

Background: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. Methods: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. Results: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. Conclusion: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.