Lauren Gardner's research while affiliated with Johns Hopkins University and other places

... Here, we focus on reported cases and primarily on the European Forecast Hub but our observations hold, in our view, across COVID-19 Forecast Hubs and to a lesser degree targets. We focus on reported cases as these represent the most common forecast target for COVID-19 forecast models (Nixon et al. 2022), they are often of the most direct interest due to being a leading indicator for other metrics such as hospitalisations (Meakin et al. 2022), and they are generally the most challenging to predict . In general, 5 main classes of forecast models are submitted Cramer et al. 2022), statistical forecasting models such as ARIMA models, mechanistic forecasting models based on the compartmental modelling framework and its generalisations (Srivastava, Xu, and Prasanna 2020;Li et al. 2021), semi-mechanistic approaches that blend both of these approaches (Castro et al. 2021;, agent-based simulation models (Rakowski et al. 2010;Adamik et al. 2020), and human insight based forecast models that may also include elements of other methods (Karlen 2020; . ...

... Therefore a coherent judgment on Covid-19 studies and NPIs is possible only with quite a few diculties. 4 see [193] 136 of 164 ...

... In the 14 th century, the Black Death caused by Yersinia pestis, raged for three centuries across the continent of Europe, killing more than 25 million people [2]. Since 2019, the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly worldwide, infected nearly 700 million people and caused almost 7 million death [3]. Although medical level and epidemic prevention measures have been substantially improved, infectious disease is still a great threat to human health. ...

... Forecasting COVID-19 in the USA has been done as a collective effort, with several teams of infectious disease modelers submitting forecasts for each county and state to the COVID-19 Forecast Hub [2], coordinated by researchers at the University of Massachusetts Amherst, who then generate an ensemble forecast building on the teams' inputs. The US Center for Disease Prevention and Control (CDC) relies on this ensemble forecast to inform how health officials formulate policy, communicate with the public, and devise clinical guidelines. ...

... Merow and Urban pointed out a temporary decline in SARS-CoV-2 infection in the summer, whereas, in the autumn, the COVID-19 cases rose and reached the maximum in the winter. A study conducted in Brazil showed the seasonality of COVID-19 transmission [27]. ...

... 6 However, even within the scientific community, the sheer volume of information obstructs efficient synthesis of the literature to establish best practices. 9 Efforts to address some of these problems exist, such as recruiting researchers to conduct rapid and publicly available reviews of papers. 10 Nevertheless, these disparate efforts (including informal reviews on social media) still leave information scattered and difficult to synthesise. ...

... Both prior to and during the COVID-19 pandemic, forecasts of public health indicators around outbreaks have been used as inputs to decision-making [6]. Hospitalization forecasts can provide useful information for healthcare providers to better allocate medical resources and communicate treatment plans [7]. ...

... Standard SIR-type modelling [27], for instance, is associated with the "who" and the "when", with the "who" being associated with a fixed-number of states-susceptible, infected, etc. that are associated with individuals [28]. It is the "when" and, relatedly, "how many" that has dominated the modelling of the COVID-19 pandemic, with standard SIR models, and variations thereof [29][30][31], playing an important role [32,33]. However, more sophisticated techniques, such as deep learning, have also been used [34]. ...

... In addition, historical data are analyzed to identify and visualize disease-related trends, such as seasonal influenza, which has often been occurring around the same months throughout the preceding decades, and may, therefore, be predicted with a reasonable accuracy [6,48]. In contrast, other researchers report that the influenza virus continuously evolves into slightly different variations each year, which makes forecasting the timing of influenza outbreaks as well as their impacts on the society very difficult [49]. ...

... One way to attempt improving the robustness of forecasts is to combine them into an ensemble forecast, whereby predictions from several different models are combined into a single forecast. This reduces reliance on a single forecasting model and, given a minimum quality of the constituent models, the average performance of ensembles is generally comparable, if not better than, its best constituent models [8,21]. Ensemble methods have been widely used in real-time during the COVID-19 pandemic to leverage the contributions of multiple modelling groups to a single forecasting task [8,22,23], as well as previously during outbreaks of influenza [18,24], Ebola virus disease [25], dengue [26], and Zika [27]. ...