Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.

Publicly funded research and innovation (R&I) organizations around the world are facing increasing demands to demonstrate the impacts of their investments. In most cases, these demands are shifting from academically based outputs to impacts that benefit society. Funders and other organizations are grappling to understand and demonstrate how their investments and activities are achieving impact. This is compounded with challenges that are inherent to impact assessment, such as having an agreed understanding of impact, the time lag from research to impact, establishing attribution and contribution, and consideration of diverse stakeholder needs and values. In response, many organizations are implementing frameworks and using web-based tools to track and assess academic and societal impact. This conceptual analysis begins with an overview of international research impact frameworks and emerging tools that are used by an increasing number of public R&I funders to demonstrate the value of their investments. From concept to real-world, this paper illustrates how one organization, Alberta Innovates, used the Canadian Academy of Health Sciences (CAHS) impact framework to guide implementation of its fit-for-purpose impact framework with an agnostic international six-block protocol. The implementation of the impact framework at Alberta Innovates is also supported by adopting emerging web-based tools. Drawing on the lessons learned from this continuous organizational endeavor to assess and measure R&I impact, we present preliminary plans for developing an impact strategy for Alberta Innovates that can be applied across sectors, including energy, environment and agriculture, and may possibly be adopted by other international funders.