Estimating Effect-sizes to Infer if COVID-19 transmission rates were low because of Masks, Heat or High because of Air-conditioners, Tests

Abstract

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.

Using high effective risk of Adult-Senior duo in multigenerational homes to prioritize COVID-19 vaccination

“…The rising pattern appears to parallel the rise of infections in the spring and summer of 2020. If this situation continues, either because there is an inherent spread of infection under hot conditions due to climatization [Sruthi et al 2021] or any other reasons, one may expect rising infections until September 2021. Studying the second-wave from the countries [Worldometers, 2020] which had the most infections (Supplementary Table 1), it is clear that all of them had a peak of the second wave that was worse than the first wave of the spring of 2020 (ratio: 4.35±1.95).…”