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.

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (R2training=0.79, R2test=0.76) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are overdesigned. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

How does one interpret the observed increase or decrease in COVID-19 case rates? Did the compliance to the non-pharmaceutical interventions, seasonal changes in the temperature influence the transmission rates or are they purely an artefact of the number of tests? To answer these questions, we estimate the effect-sizes from these different factors on the reproduction ratios (Rt) from the different states of the USA during March 9 to August 9. Ideally Rt should be less than 1 to keep the pandemic under control and our model predicts many of these factors contributed significantly to the Rt’s: Post-lockdown opening of the restaurants and nightclubs contributed 0.04 (CI 0.04-0.04) and 0.11 (CI. 0.11-0.11) to Rt. The mask mandates helped reduce Rt by 0.28 (CI 0.28-0.29)), whereas the testing rates which may have influenced the number of infections observed, did not influence Rt beyond 10,000 daily tests 0.07 (CI -0.57-0.42). In our attempt to understand the role of temperature, the contribution to the Rt was found to increase on both sides of 55 F, which we infer as a reflection of the climatization needs. A further analysis using the cooling and heating needs showed contributions of 0.24 (CI 0.18-0.31) and 0.31 (CI 0.28-0.33) respectively. The work thus illustrates a data-driven approach for estimating the effect-sizes on the graded policies, and the possibility of prioritizing the interventions, if necessary by weighing the economic costs and ease of acceptance with them.

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (R2training=0.79, R2test=0.76) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are ‘overdesigned’ for the success they have. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

Immediate and universal vaccination is a way of controlling the COVID-19 infections and deaths. Shortages of vaccine supplies and practical deployment rates on the field necessitate prioritization. The global strategy has been to prioritize those with a high personal risk due to their age or comorbidities and those who constitute the essential workforce of the society. Rather than a systematic age-based roll-down, assigning the next priority requires a local strategy based on the vaccine availability, the effectiveness of this specific vaccine, the population size as well as its age-demographics, the scenario of how the pandemic is likely to develop. The Adult (ages 20-60) - Senior (ages over 60) duo from a multigenerational home presents a high-risk demographic, with an estimated 'effective age' of an adult to be 40 years more if they live with an unvaccinated grandparent. Our model suggests that strategically vaccinating the Adults from multigenerational homes in India may be effective in saving the lives of around 70,000 to 200,000 of Seniors, under the different epidemiological scenarios possible with or without strict lockdowns.