The year 2019 had a SARS-CoV-2–driven wave of COVID worldwide that soon turned into a pandemic, and to date, this disease has killed about 65 million people [1]. Since the pandemic’s start, much policy talk has been about whether an impending COVID wave can be predicted [2]. Unfortunately, successful prediction of COVID waves has not yet been achieved. A prediction tool that can inform about an upcoming COVID wave well before time and reasonably accurately could minimize the enormous loss of life and other collateral damages.

Multiple waves at a global scale driven by SARS-CoV-2 variants, primarily Alpha, Delta [3], and, most recently, Omicron [4], have followed since the first wave. The successive SARS-CoV-2 variants showed increased transmissibility and virulence compared with the wild-type strain [3]; however, the latest Omicron variant has shown higher transmissibility and immune escape but lesser lethality compared with the Delta variant [4]. The Delta variant–driven wave was characterized by high speed of rising cases, increased oxygen demand, vaccine breakthrough [5], a highly increased proportion of severe cases, and high mortality [6].

More comprehensive coverage of COVID vaccines in the global population is helping to create an immunity barrier against the rise of a new wave. However, an increase in the immune escape potential of emerging variants causes a grave concern for vaccine breakthroughs and reinfections [3,4,7]. With the waning of immunity derived from vaccines and previous infections [8], the risk of the emergence of a more lethal variant capable of creating a global wave remains high and therefore demands continued surveillance [9].

The Delta variant–driven wave showed a rapid peak and fall to the baseline, making it ideal for prediction studies. The Delta strain was first reported from India [10]. Of note, India witnessed a devastating second COVID wave that began toward the end of February 2021 [11]. The unexpected arrival of the second COVID wave, accompanied by an exponential increase in infections, brought the country’s epidemic response system and health infrastructure to a standstill [11], and resulted in massive suffering and loss of life [12].

The Delta variant belongs to the SARS-CoV-2 lineage B.1.617, which appeared as a precursor. The first case of the B.1.617 variant was also reported from India as early as October 2020 [13]. The World Health Organization (WHO) recognized the B.1.617 lineage as a global variant of concern (VOC). The strain evolved into 3 more sublines, namely, B.1.617.1-3, of which B.1.617.1 (the Kappa variant) was declared a variant of interest (VOI) and B.1.617.2 was later declared a VOC by the WHO [14]. B.1.617 contained mutations in key spike protein regions involved in host interactions and the induction of neutralizing antibodies (S: L452R, E484Q, D614G, del681, and del1072) [15]. The sublineages contained lineage-defining spike mutations (L452R and D614G) as well as newly developed mutations as follows: B.1.617.1 (S: T95I, G142D, E154K, L452R, E484Q, D614G, P681R, and Q1071H); B.1.617.2 (S: T19R, G142D, 156del, 157del, R158G, L452R, T478K, D614G, P681R, D614G, P681R, and D950N); and B.1.617.3 (S: T19R, L452R, E484Q, D614G, and P681R) [16]. Contemporary studies suggested that B.1.617 lineage variants were more easily transmissible [13,17-21] and deadlier [18] than the B.1.1.7 lineage (Alpha variant), a globally dominant strain before the second wave [10]. Studies also showed a significant reduction in the neutralization of variants of the B.1.617 lineage by antibodies derived from natural infections and many currently used COVID-19 vaccines, and multiple monoclonal antibodies [18-21]. Notably, B.1.617.2 showed very high transmissibility and immunological escape [10,13,17,22].

Several studies worldwide have shown that predicting an impending COVID-19 wave is possible [23-28]. These studies used mathematical modeling of epidemiological data. Unfortunately, none of them could accurately anticipate a COVID-19 wave. The ability to predict an established wave from epidemiological data alone seems severely limited [12,29].

The analysis of SARS-CoV-2 genomic sequences has emerged as an efficient surveillance tool for understanding the emergence of new variants and their spread. Fortunately, millions of SARS-CoV-2 genomic sequences from regions worldwide are being made publicly available as a collaborative effort to contain the pandemic [30]. The easy availability of high-quality viral sequences with patient metadata has opened a new avenue for potential predictions of the COVID-19 pandemic [31]. However, viral genomic sequences alone may not be sufficient for efficient predictions, and their current uses for this purpose are constrained.

In this study, we propose an integrated approach using viral genome surveillance and epidemiological data for the prediction of an impending COVID-19 wave. We retrospectively analyzed viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct such a model.

Our retrospective analysis of the epidemiological data reflected that the second COVID-19 wave started rising by the end of February 2021 and peaked by the end of the first week of May 2021. Based on the distinct epidemiological trends observed (Multimedia Appendix 1), we divided the study period (December 1, 2020, to July 26, 2021; 34 weeks) into prepeak (weeks 1-23) and postpeak (weeks 24-34) periods. The weekly average of new cases and deaths showed a strong correlation in the study period (R=0.98, P<.001), signifying the high statistical validity of the data for further comparisons. Further, we analyzed the distribution of SARS-CoV-2 variants circulating in the Indian population in correlation with new cases and deaths before and after the peak. For description, based on epidemiological trends, the prepeak period was further divided into the following 3 time series intervals: “very early” (weeks 1-8), “early” (weeks 9-16), and “near peak” (weeks 17-23). New cases and deaths showed a downward trend in the “very early” period and maintained a plateau in the “early” period (except toward the end when cases and deaths started increasing, indicating the start of the second wave). In the “near peak” period, a steep rise in new cases and deaths was observed (Figure 1).

The rise and fall of circulating SARS-CoV-2 variants were studied against the observed epidemiological data trends in the respective time series intervals. Observing the composite data trends of epidemiological and SARS-CoV-2 genomic data provides a glimpse of the formation of the second COVID-19 wave, with clear indications of which SARS-CoV-2 strains may have driven it (Figures 1 and 2). By December 2020, 8 SARS-CoV-2 Pango lineages and their multiple sublineages were circulating in the Indian population, including 4 VOCs (B.1.1.7, B.1.351, P1, and B.1.617.2) and 3 VOIs (B.1.617.1, B.1.127/B.1.429, and B.1.525). However, B.1.1.7 was the most dominant variant in that period. B.1.617 lineage variants collectively (B.1.617+) showed an upward trend since their emergence, and surpassed other VOCs, including B.1.1.7, by the end of January 2021 (weeks 8-9) and subsequently kept rising. In contrast, B.1.1.7 showed a downward trend by the end of March 2021 (weeks 17-18), with B.1.617 lineage variants becoming the dominant variants. By the end of April 2021, B.1.617 lineage variants were detected in 78.5% of SARS-CoV-2 sequences uploaded on the GISAID database, reaching about 83% in the week of the peak.

The phylodynamic analysis of the circulating variants in the study period strongly corroborated the trends present in the graph data, showing an exclusive increase in the cluster density of B.1.617.2 compared with other variants in the “near peak” period (Figure 3).

To know whether the rise in the B.1.617.2 variant was localized to specific geographical regions, which may have influenced the collective data trends, we compared the monthly distribution of genomic sequences of SARS-CoV-2 variants for the states and union territories of India individually. A similar increase in the detection of the B.1.617.2 variant was observed in most states and union territories (Multimedia Appendix 2), except Kerala, where different patterns were visible (Figure S15 in Multimedia Appendix 2). In Kerala, the rise of the B.1.617.2 variant was slower in comparison with the rest of the country (55.5% vs 72% of total cases by the end of April 2021), which was further confirmed in the state-wise serosurvey data from the period of the second wave (44.4% vs 67.7% of the national average) [34]. Notably, a sharp rise in B.1.617.2 cases was observed in Kerala in a later period.