Facilitating Public Health Action through Surveillance Dashboards

ObjectiveTo address the limitations of traditional static surveillancereporting by developing in-house infrastructure to create and maintaininteractive surveillance dashboards.IntroductionTraditionally, public health surveillance departments collect,analyze, interpret, and package information into static surveillancereports for distribution to stakeholders. This resource-intensiveproduction and dissemination process has major shortcomings thatimpede end users from optimally utilizing this information for publichealth action. Often, by the time traditional reports are ready fordissemination they are outdated. Information can be difficult to findin long static reports and there is no capability to interact with thedata by users. Instead, ad hoc data requests are made, resulting ininefficiencies and delays.Use of electronic dashboards for surveillance reporting is notnew. Many public health departments have worked with informationtechnology (IT) contractors to develop such technically sophisticatedproducts requiring IT expertise. The technology and tools now existto equip the public health workforce to develop in-house surveillancedashboards, which allow for unprecedented speed, flexibility, andcost savings while meeting the needs of stakeholders. At AlbertaHealth Services (AHS), in-house, end-to-end dashboard developmentinfrastructure has been established that provides epidemiologists anddata analysts full capabilities for effective and timely reporting ofsurveillance information.MethodsAn internal assessment of the available resources and infrastructurewithin AHS was conducted to iteratively develop a new analyticsmodel that provides a foundation for in-house dashboard developmentcapacity. We acquired SAS® and Tableau® software and conductedinternal training for skills development and to transition staff to thenew model. This model is highlighted below using our respiratoryvirus surveillance (RVS) dashboard as an example.For the RVS dashboard, stakeholder engagements wereconducted to understand the end users’ needs. Next, data access wasimproved, where possible, by securing direct access to source data(e.g. emergency department visits for influenza like illness (ILI),Health Link calls, hospital admissions, etc.) on existing databaseservers. SAS® code was written for routinely connecting withmultiple data sources, data management and analysis, data qualityassurance, and posting summary data on a secure Oracle® server.The Tableau® dashboard development application was then usedto connect to the summary data on the Oracle® server, create theinteractive dashboards and publish the final products to the AHSTableau server environment. Key users were consulted in the iterativedevelopment of the interface to optimize usability and relevantcontent.Finally, the product was promoted to stakeholders with acommitment to use their feedback to drive continuous improvement.ResultsIn-house generated surveillance dashboards provide more timelyaccess to comprehensive surveillance information for a broadaudience of over 108,000 AHS employees; within as little as 3 hoursof all data being available. They facilitate user-directed deep divesinto the data to understand a more complete surveillance picture aswell as stimulating hypothesis generation. Additionally they enhanceproductivity of personnel, by significantly reducing response timesfor ad hoc request and to generate reports, freeing up more time torespond to other emerging public health issues.Looking specifically at the RVS dashboard, its ability to bring allrelevant surveillance information to one place facilitates valuablediscussions during status update meetings throughout the influenzaseason. Among other things it has allowed Medical Officers ofHealth, emergency department staff, epidemiologists and others tomake informed decisions pertaining to public messaging, the needfor reallocating resources, such as staffing and handling the burden ofILI patients, as well as determining the necessity of opening influenzaassessment centers.ConclusionsSurveillance dashboards can facilitate public health action byassembling comprehensive information in one place in a timelymanner so that informed decisions can be made in emerging situations

Facilitating Public Health Action through Surveillance Dashboards

ObjectiveTo address the limitations of traditional static surveillancereporting by developing in-house infrastructure to create and maintaininteractive surveillance dashboards.IntroductionTraditionally, public health surveillance departments collect,analyze, interpret, and package information into static surveillancereports for distribution to stakeholders. This resource-intensiveproduction and dissemination process has major shortcomings thatimpede end users from optimally utilizing this information for publichealth action. Often, by the time traditional reports are ready fordissemination they are outdated. Information can be difficult to findin long static reports and there is no capability to interact with thedata by users. Instead, ad hoc data requests are made, resulting ininefficiencies and delays.Use of electronic dashboards for surveillance reporting is notnew. Many public health departments have worked with informationtechnology (IT) contractors to develop such technically sophisticatedproducts requiring IT expertise. The technology and tools now existto equip the public health workforce to develop in-house surveillancedashboards, which allow for unprecedented speed, flexibility, andcost savings while meeting the needs of stakeholders. At AlbertaHealth Services (AHS), in-house, end-to-end dashboard developmentinfrastructure has been established that provides epidemiologists anddata analysts full capabilities for effective and timely reporting ofsurveillance information.MethodsAn internal assessment of the available resources and infrastructurewithin AHS was conducted to iteratively develop a new analyticsmodel that provides a foundation for in-house dashboard developmentcapacity. We acquired SAS® and Tableau® software and conductedinternal training for skills development and to transition staff to thenew model. This model is highlighted below using our respiratoryvirus surveillance (RVS) dashboard as an example.For the RVS dashboard, stakeholder engagements wereconducted to understand the end users’ needs. Next, data access wasimproved, where possible, by securing direct access to source data(e.g. emergency department visits for influenza like illness (ILI),Health Link calls, hospital admissions, etc.) on existing databaseservers. SAS® code was written for routinely connecting withmultiple data sources, data management and analysis, data qualityassurance, and posting summary data on a secure Oracle® server.The Tableau® dashboard development application was then usedto connect to the summary data on the Oracle® server, create theinteractive dashboards and publish the final products to the AHSTableau server environment. Key users were consulted in the iterativedevelopment of the interface to optimize usability and relevantcontent.Finally, the product was promoted to stakeholders with acommitment to use their feedback to drive continuous improvement.ResultsIn-house generated surveillance dashboards provide more timelyaccess to comprehensive surveillance information for a broadaudience of over 108,000 AHS employees; within as little as 3 hoursof all data being available. They facilitate user-directed deep divesinto the data to understand a more complete surveillance picture aswell as stimulating hypothesis generation. Additionally they enhanceproductivity of personnel, by significantly reducing response timesfor ad hoc request and to generate reports, freeing up more time torespond to other emerging public health issues.Looking specifically at the RVS dashboard, its ability to bring allrelevant surveillance information to one place facilitates valuablediscussions during status update meetings throughout the influenzaseason. Among other things it has allowed Medical Officers ofHealth, emergency department staff, epidemiologists and others tomake informed decisions pertaining to public messaging, the needfor reallocating resources, such as staffing and handling the burden ofILI patients, as well as determining the necessity of opening influenzaassessment centers.ConclusionsSurveillance dashboards can facilitate public health action byassembling comprehensive information in one place in a timelymanner so that informed decisions can be made in emerging situations

Correlation of school absenteeism and laboratory results for Flu A in Alberta, Canada

ObjectiveTo assess the correlations between weekly rates of elementaryschool absenteeism due to illness (SAi) and percent positivity forinfluenza A from laboratory testing (PPFluA) when conducted at acity level from September to December over multiple years.IntroductionRates of student absenteeism in schools have been mainly used todetect outbreaks in schools and prompt public health action to stoplocal transmission1,2. A report by Mogto et al.3stated that aggregatedcounts of school absenteeism (SAi) were correlated with PPFluA, butthe sample may have been biased. The purpose of this study was toassess the correlation between aggregated rates of SAi and PPFluAfor two cities, Calgary and Edmonton, in Alberta. In such situations,SAi could potentially be used as a proxy for PPFluA when there arenot enough samples for stable laboratory estimates.MethodsThe Alberta Real-Time Syndromic Surveillance Net (ARTSSN)4collects elementary SA data from the two major school boards intwo cities in Alberta with populations >800,000. Since reasons forSA are stated, rates of SAi can be calculated. Data were obtained forthree years, 2012 to 2014, for each city. Laboratory data on tests ofrespiratory agents using a standardized protocol were obtained fromAlberta’s Provincial Laboratory for Public Health for the same timeperiod and locations. The dates of the specimens being received bythe laboratory were used in this analysis. For each data source, therelative proportions (SAi and PPFluA) were calculated. Data forthe first week of school in September and for the last two weeks ofDecember were removed for each year due to the SAi rates beingunstable. Linear regression models were constructed, with rates ofSAi predicted by PPFluA. Separate models were run for each cityand for each year, resulting in a total of 6 models. Percent positivityfor entero-rhinoviruses (PPERV) was added to see if it improved themodel. The regression models were created using Excel and checkedin the statistical programs, SAS and R. An analysis to assess theinfluence of a lag period was assessed using R.ResultsFor each city, the provincial lab tested between 4,000 and 6,000specimens each fall and SAi rates were based on denominators ofbetween 20,000 and 36,000 children. The R2, betas, and p-valuesfor all 6 regression models are shown in Table 1. The minimumcorrelation value was 0.693 and the maximum was 0.935. Dueto the strong negative correlations between PPERV and PPFluA,PPERV was not retained in the models. Looking at the lag periods,the maximum correlations occurred at a zero week lag in two years(2012 and 2014) and at a -1 week lag in 2013. The two years with azero lag were both dominated by a H3N2 strain while the year withmainly a H1N1 strain showed a lag of -1. Only one year of H1N1 datawas available for analysis.ConclusionsWe observed strong correlations between the weekly rates ofelementary SAi and PPFluA at the city level over three years, fromSeptember to December. The reasons for the difference in lag timesbetween the H1N1 and H3N2 seasons are being investigated

Correlation of school absenteeism and laboratory results for Flu A in Alberta, Canada

ObjectiveTo assess the correlations between weekly rates of elementaryschool absenteeism due to illness (SAi) and percent positivity forinfluenza A from laboratory testing (PPFluA) when conducted at acity level from September to December over multiple years.IntroductionRates of student absenteeism in schools have been mainly used todetect outbreaks in schools and prompt public health action to stoplocal transmission1,2. A report by Mogto et al.3stated that aggregatedcounts of school absenteeism (SAi) were correlated with PPFluA, butthe sample may have been biased. The purpose of this study was toassess the correlation between aggregated rates of SAi and PPFluAfor two cities, Calgary and Edmonton, in Alberta. In such situations,SAi could potentially be used as a proxy for PPFluA when there arenot enough samples for stable laboratory estimates.MethodsThe Alberta Real-Time Syndromic Surveillance Net (ARTSSN)4collects elementary SA data from the two major school boards intwo cities in Alberta with populations >800,000. Since reasons forSA are stated, rates of SAi can be calculated. Data were obtained forthree years, 2012 to 2014, for each city. Laboratory data on tests ofrespiratory agents using a standardized protocol were obtained fromAlberta’s Provincial Laboratory for Public Health for the same timeperiod and locations. The dates of the specimens being received bythe laboratory were used in this analysis. For each data source, therelative proportions (SAi and PPFluA) were calculated. Data forthe first week of school in September and for the last two weeks ofDecember were removed for each year due to the SAi rates beingunstable. Linear regression models were constructed, with rates ofSAi predicted by PPFluA. Separate models were run for each cityand for each year, resulting in a total of 6 models. Percent positivityfor entero-rhinoviruses (PPERV) was added to see if it improved themodel. The regression models were created using Excel and checkedin the statistical programs, SAS and R. An analysis to assess theinfluence of a lag period was assessed using R.ResultsFor each city, the provincial lab tested between 4,000 and 6,000specimens each fall and SAi rates were based on denominators ofbetween 20,000 and 36,000 children. The R2, betas, and p-valuesfor all 6 regression models are shown in Table 1. The minimumcorrelation value was 0.693 and the maximum was 0.935. Dueto the strong negative correlations between PPERV and PPFluA,PPERV was not retained in the models. Looking at the lag periods,the maximum correlations occurred at a zero week lag in two years(2012 and 2014) and at a -1 week lag in 2013. The two years with azero lag were both dominated by a H3N2 strain while the year withmainly a H1N1 strain showed a lag of -1. Only one year of H1N1 datawas available for analysis.ConclusionsWe observed strong correlations between the weekly rates ofelementary SAi and PPFluA at the city level over three years, fromSeptember to December. The reasons for the difference in lag timesbetween the H1N1 and H3N2 seasons are being investigated

Wastewater surveillance monitoring of SARS-CoV-2 variants of concern and dynamics of transmission and community burden of COVID-19

ABSTRACTWastewater-based surveillance is a valuable approach for monitoring COVID-19 at community level. Monitoring SARS-CoV-2 variants of concern (VOC) in wastewater has become increasingly relevant when clinical testing capacity and case-based surveillance are limited. In this study, we ascertained the turnover of six VOC in Alberta wastewater from May 2020 to May 2022. Wastewater samples from nine wastewater treatment plants across Alberta were analysed using VOC-specific RT-qPCR assays. The performance of the RT-qPCR assays in identifying VOC in wastewater was evaluated against next generation sequencing. The relative abundance of each VOC in wastewater was compared to positivity rate in COVID-19 testing. VOC-specific RT-qPCR assays performed comparatively well against next generation sequencing; concordance rates ranged from 89% to 98% for detection of Alpha, Beta, Gamma, Omicron BA.1 and Omicron BA.2, with a slightly lower rate of 85% for Delta (p < 0.01). Elevated relative abundance of Alpha, Delta, Omicron BA.1 and BA.2 were each associated with increased COVID-19 positivity rate. Alpha, Delta and Omicron BA.2 reached 90% relative abundance in wastewater within 80, 111 and 62 days after their initial detection, respectively. Omicron BA.1 increased more rapidly, reaching a 90% relative abundance in wastewater after 35 days. Our results from VOC surveillance in wastewater correspond with clinical observations that Omicron is the VOC with highest disease burden over the shortest period in Alberta to date. The findings suggest that changes in relative abundance of a VOC in wastewater can be used as a supplementary indicator to track and perhaps predict COVID-19 burden in a population