Regional Estimates of Reproduction Numbers with Application to COVID-19

In the last year many public health decisions were based on real-time monitoring the spread of the ongoing COVID-19 pandemic. For this one often considers the reproduction number which measures the amount of secondary cases produced by a single infectious individual. While estimates of this quantity are readily available on the national level, subnational estimates, e.g. on the county level, pose more difficulties since only few incidences occur there. However, as countermeasures to the pandemic are usually enforced on the subnational level, such estimates are of great interest to assess the efficacy of the measures taken, and to guide future policy. We present a novel extension of the well established estimator (Fraser, PloS One 2:8, 2007) of the country level reproduction number to the county level by applying techniques from small-area estimation. This new estimator yields sensible estimates of reproduction numbers both on the country and county level. It can handle low and highly variable case counts on the county level, and may be used to distinguish local outbreaks from more widespread ones. We demonstrate the capabilities of our novel estimator by a simulation study and by applying the estimator to German case data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Testing, social distancing and age specific quarantine for COVID-19: Case studies in Iligan City and Cagayan de Oro City, Philippines

The world today grapples with the health crisis caused by the novel COVID-19. Unfortunately, the Philippines, which was spared from the previous epidemics like Ebola and SARS, is now facing this major obstacle. The local government units of the country responded by issuing guidelines to mitigate the spread of infection. Two neighboring cities in the Philippines, namely, Iligan and Cagayan de Oro have been the focus of this study. Simulations have been undertaken to investigate the prevalence of the disease and the health care systems capacity to accommodate critically ill patients over time. The effects of mitigating strategies like social distancing, age specific quarantine and testing were considered. The model used for the spread of COVID-19 is an individual-based SIR model. The results indicated that social distancing and age specific quarantine can effectively slow down the progression of the disease. Moreover, social distancing combined with an effective testing strategy can keep the epidemic subcritical. © 2020 Author(s).