The role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 to September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.
Performing a complete deep mutational scan with all single point mutations may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In a realistic paradigm of assay context-aware predictive hybrid models that combine minimal experimental data from deep mutational scans with structure, sequence information and computational models, we define and evaluate different strategies for choosing this minimal set. We evaluated the trivial strategy of a systematic reduction in the number of mutational studies from 85% to 15%, along with several others about the choice of the types of mutations such as random versus site-directed with the same 15% data completeness. Interestingly, the predictive capabilities by training on a random set of mutations and using a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH) were comparable. Another strategy we explored, augmenting the training data with measurements of the same mutants at multiple assay conditions, did not improve the prediction quality. For the six proteins we analyzed, the bin-wise error in prediction is optimal when 50-100 mutations per bin are used in training the computational model, suggesting that good prediction quality may be achieved with a library of 500-1000 mutations.
When actively taking measures to control an epidemic, an important indicator of success is crossing the 'peak' of daily new infections. The peak is a positive sign which marks the end of the exponential phase of infection spread and a transition into a phase that is a manageable. Most countries or provinces with similar but independent growth trajectories had taken drastic measures for containing the COVID-19 pandemic and are eagerly waiting to cross the peak. However, the data after many weeks of strict measures suggests that most provinces instead enter a phase where the infections are in a linear growth. While the transition out of an exponential phase is relieving, the roughly constant number of daily new infections differ widely, range from around 50 in Singapore to around 2000 just in Lombardy (Italy), and 7600 in Spain. The daily new infection rate of a region seems to depend heavily on the time point in the exponential evolution when the restrictive measures were adopted, rather than on the population of the region. It is not easy to point the critical source of these persistent infections. We attempt to interpret this data using a simple model of newer infections mediated by asymptomatic patients, which underscores the importance of actively identifying any potential leakages in the quarantine. Given the novelty of the virus, it is hard to predict too far into the future and one needs to be observant to see if a plan B is needed as a second round of interventions. So far, the peak achieved by most countries with the first round of intervention is extremely flat.
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