Purpose Accurately forecasting the occurrence of future covid-19-related cases across relaxed (Sweden) and stringent (USA and Canada) policy contexts has a renewed sense of urgency. Moreover, there is a need for a multidimensional county-level approach to monitor the second wave of covid-19 in the USA. Method We use an artificial intelligence framework based on timeline of policy interventions that triangulated results based on the three approaches-Bayesian susceptible-infected-recovered (SIR), Kalman filter, and machine learning. Results Our findings suggest three important insights. First, the effective growth rate of covid-19 infections dropped in response to the approximate dates of key policy interventions. We find that the change points for spreading rates approximately coincide with the timelines of policy interventions across respective countries. Second, forecasted trend until mid-June in the USA was downward trending, stable, and linear. Sweden is likely to be heading in the other direction. That is, Sweden's forecasted trend until mid-June appears to be non-linear and upward trending. Canada appears to fall somewhere in the middle-the trend for the same period is flat. Third, a Kalman filter based robustness check indicates that by mid-June the USA will likely have close to two million virus cases, while Sweden will likely have over 44,000 covid-19 cases. Conclusion We show that drop in effective growth rate of covid-19 infections was sharper in the case of stringent policies (USA and Canada) but was more gradual in the case of relaxed policy (Sweden). Our study exhorts policy makers to take these results into account as they consider the implications of relaxing lockdown measures.

Detection of target molecules, such as proteins, antibodies, or specific DNA sequences, is critical in medical laboratory science. Commonly used assays rely on tagging the target molecules with fluorescent probes. These are then fed to high-sensitivity detection systems. Such systems typically consist of a photodetector or camera and use time-resolved measurements that require sophisticated and expensive optics. Magnetic modulation biosensing (MMB) is a novel, fast, and sensitive detection technology that has been used successfully to detect viruses such as Zika and SARS-CoV-2. While this powerful tool is known for its high analytical and clinical sensitivity, the current signal-processing method for detecting the target molecule and estimating its dose is based on time-resolved measurements only. To improve the MMB-system performance, we propose here a novel signal processing algorithm that uses both temporally and spatially resolved measurements. We show that this combination significantly improves the sensitivity of the MMB-based assay. To evaluate the new method statistically, we performed multiple dose responses of Human Interleukin 9 (IL -8) on different days. Compared to standard time-resolved methods, the new algorithm provides a 2-3 fold improvement in detection limit and a 25% improvement in quantitative resolution.

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