In this letter, we report a large-scale synchronized replacement pattern of the Alpha strain by the Delta strain. We argue that this phenomenon is associated with the invasion timing and transmissibility advantage of the Delta strain. Alpha strain skipped some countries/regions, e.g. India and neighboring countries/regions, which led to a mild first wave before the invasion of the Delta strain, in term of reported COVID-deaths per capita.

Population-based sero-epidemiological studies are widely used to estimate the proportion of a population infected (infection attack rate, IAR) with SARS-CoV-2. However, the accuracy of the estimates relies on the design of the study (e.g. sample size) and the sensitivity (e.g. decay of sensitivity) of the assay used. This study aims to resolve these issues with the seroprevalence of COVID-19 and infection attack rates in 12 Indian cities as examples. We examine serological data that used Abbott to reconstruct a sensitivity decay function and use it to infer attack rates and seroprevalence based on reported COVID-19 death in these cities. We find that the reconstructed seroprevalence matched with the reported scenario reasonably well in most cities, where Abbott or similar assay was likely used, but failed in two cities, where non-Abbott assay was likely used. We propose an approach to connect the serological data and the reported COVID-19 deaths with the testing sensitive decay function to increase the confidence in estimating the size of the epidemic.

The COVID-19 pandemic poses a serious threat to global health, and one of the key epidemiological factors that shape the transmission of COVID-19 is its serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, slight discrepancies in SI across different transmission generations are observed from the aggregated statistics in recent studies. To explore the change in SI across transmission generations, we develop a likelihood-based statistical inference framework to examine and quantify the change in SI. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that the individual SI of COVID-19 is likely to shrink with a rate of 0.72 per generation and 95%CI: (0.54, 0.96) as the transmission generation increases. We speculate that the shrinkage in SI is an outcome of competition among multiple candidate infectors within a cluster of cases. The shrinkage in SI may speed up the transmission process, and thus the nonpharmaceutical interventive strategies are crucially important to mitigate the COVID-19 epidemic.

The raw case fatality rate (CFR, reported number of COVID-19 deaths divided by the number of cases) is a useful indicator to quantify the severity or treatment efficacy in a locality. In many countries, the pandemic showed a two-wave pattern now, namely the daily reported cases once reached a low level and now went up. To our knowledge, no study has compared the CFR for the two waves. In this work, we report that in 53 countries or regions with the highest deaths, the CFR is reduced in 43 countries or regions in the on-going second wave. We discussed the possible reasons. Also, we compare the two-wave pattern of COVID-19 with the weekly influenza positive tests. The influenza activity in pre-pandemic era provided an indicator for climate in a country, since it is well-known that influenza is driven by weather. The sharp drop in 2020 influenza activity is an indicator of the effects of social distancing.