Seroprevalence and COVID-19 deaths in Indian Cities (preprint)

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.

Seroprevalence and Infection Attack Rate of COVID-19 in Indian Cities (preprint)

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

Attack rate of COVID-19 and the onset of herd immunity in Manaus, Brazil (preprint)

Buss et al. (2021) and Faria et al. (2021) reported that ~76% of the residents of the capital city of Manaus, had been infected by SARS-CoV-2 by October 2020 suggesting that herd immunity had been achieved by the end of the first wave. But the announcement of herd immunity, which would imply reasonable protection from future outbreaks, only provided the Manaus population with a false sense of security. Within two months later, a second wave of COVID-19 was initiated with death rates much larger than the first attributed to the appearance of the new P.1 Variant of Concern. Faria et al. (2021) suggest that large scale reinfections played an important role in enabling the huge second epidemic wave. In this Technical Comment we challenge such interpretations, and provide quantitative arguments that suggest the attack rate of the first wave was well below 76%. We then present alternative interpretations of the data.

Shrinkage in serial intervals across cluster transmission generations of COVID-19 (preprint)

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.

Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19) in Africa (preprint)

Background: Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 15207 including 831 deaths by April 13, 2020. Methods: We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020, by using the simple exponential growth model. Results: We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number, R0, to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020. Conclusion: The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries. Our estimates should be useful in preparedness planning. Trial registration: NA